diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..962fbb8 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,5 @@ +# Auto detect text files and perform LF normalization +* text=auto + +# Remove the tutorial jupyter notebook from the language calculation of github +tutorial.ipynb linguist-vendored diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml new file mode 100644 index 0000000..7995d65 --- /dev/null +++ b/.github/workflows/test.yaml @@ -0,0 +1,38 @@ +name: test + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + test: + name: test ${{ matrix.py }} on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + py: + - "3.9" + - "3.10" + - "3.11" + os: + - ubuntu-latest + - windows-latest + - macos-latest + steps: + - name: Checkout sources + uses: actions/checkout@v4 + + - name: Setup Python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.py }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install tox tox-gh-actions + + - name: Run test suite + run: tox diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..02cb4e1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,6 @@ +.ipynb_checkpoints +**/__pycache__/ + +dist/ + +.tox/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..50e6ca5 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +# See https://pre-commit.com for more information +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.0.1 + hooks: + - id: check-toml + - id: check-yaml + - id: end-of-file-fixer + - id: mixed-line-ending + - repo: https://github.com/python-poetry/poetry + rev: 1.6.1 + hooks: + - id: poetry-check + - id: poetry-lock + - repo: https://github.com/psf/black + rev: 23.9.0 + hooks: + - id: black + - repo: https://github.com/PyCQA/isort + rev: 5.12.0 + hooks: + - id: isort + args: ["--profile", "black"] diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..f40cc02 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,11 @@ +#### Changelog + +All noteable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), +and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [Unreleased] + +--- +--- diff --git a/README.md b/README.md index 0e2cdae..4c9fcb1 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,70 @@ -# salamander -Salamander is a non-negative matrix factorization framework for signature analysis +# Salamander + +[![Python versions supported][python-image]][python-url] +[![License][license-image]][license-url] +[![Code style][style-image]][style-url] + +[python-image]: https://img.shields.io/badge/python-3.9%20|%203.10%20|%203.11-blue.svg +[python-url]: https://github.com/BeGeiger/CorrNMF +[license-image]: https://img.shields.io/badge/License-MIT-yellow.svg +[license-url]: https://github.com/BeGeiger/CorrNMF/blob/main/LICENSE +[style-image]: https://img.shields.io/badge/code%20style-black-000000.svg +[style-url]: https://github.com/psf/black + +Salamander is a non-negative matrix factorization (NMF) framework for signature analysis. +It implements multiple NMF algorithms, common visualizations, and can be easily customized & expanded. + +--- + +## Installation + +PyPI: +```bash +pip install salamander-learn +``` + +## Usage + +The following example illustrates the basic syntax: + +```python +import pandas as pd +import salamander-learn as sal + +# samples and features have to be named appropriately +data_path = "..." +data = pd.read_csv(data_path, index_col=0) + +# NMF with a Poisson noise model +model = sal.KLNMF(n_signatures=5) +model.fit(data) + +# barplot +model.plot_signatures() + +# stacked barplot +model.plot_exposures() + +# signature correlation +model.plot_correlation() + +# sample_correlation +model.plot_correlation(data="samples") + +# dimensionality reduction of the exposures +# method: umap, pca or tsne +model.plot_embeddings(method="umap") +``` + +For examples of how to customize any NMF algorithm and the plots, check out [the tutorial](). The following algorithms are currently available: +* [NMF with KL-divergence loss](https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf) +* [minimum-volume NMF](https://browse.arxiv.org/pdf/1907.02404.pdf) +* [a variant of correlated NMF](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=87224164eef14589b137547a3fa81f06eef9bbf4) + +## License + +MIT + +## Changelog + +Consult the [CHANGELOG](https://github.com/BeGeiger/CorrNMF/blob/main/CHANGELOG.md) file for enhancements and fixes of each version. diff --git a/data/pcawg_breast_indel.csv b/data/pcawg_breast_indel.csv new file mode 100755 index 0000000..b444a59 --- /dev/null +++ b/data/pcawg_breast_indel.csv @@ -0,0 +1,84 @@ +Indel,SP10084,SP10150,SP10470,SP10563,SP10635,SP10944,SP11045,SP11171,SP11235,SP116331,SP116333,SP116335,SP116339,SP116341,SP116343,SP116345,SP116347,SP116349,SP116351,SP116353,SP116355,SP116357,SP116359,SP116361,SP116363,SP116365,SP116367,SP116369,SP116371,SP116373,SP116375,SP116377,SP116379,SP116946,SP116947,SP116948,SP116963,SP116991,SP117011,SP117032,SP117057,SP117078,SP117079,SP117105,SP117108,SP117113,SP117126,SP117127,SP117136,SP117162,SP117244,SP117245,SP117250,SP117257,SP117312,SP117344,SP117354,SP117363,SP117369,SP117370,SP117372,SP117378,SP117402,SP117409,SP117454,SP117538,SP117552,SP117593,SP117598,SP117614,SP117618,SP117629,SP117639,SP117676,SP117710,SP117724,SP117728,SP117778,SP117785,SP117800,SP117812,SP117834,SP117840,SP117850,SP117882,SP117887,SP117901,SP117919,SP117933,SP117944,SP117983,SP118012,SP118038,SP118073,SP118074,SP118077,SP11808,SP11878,SP11948,SP12049,SP12186,SP124193,SP124195,SP124197,SP124199,SP124207,SP13036,SP2144,SP2145,SP2146,SP2147,SP2148,SP2150,SP2151,SP2152,SP2153,SP2154,SP2155,SP2156,SP2158,SP2159,SP2160,SP2349,SP2357,SP2714,SP2731,SP2766,SP2793,SP2799,SP2801,SP2826,SP2881,SP2997,SP3016,SP3415,SP3631,SP4265,SP4472,SP4523,SP4535,SP4557,SP4593,SP4820,SP4875,SP5017,SP5052,SP5279,SP5381,SP5393,SP5448,SP5473,SP5559,SP5636,SP5784,SP5808,SP5820,SP5844,SP5980,SP6115,SP6223,SP6429,SP6519,SP6673,SP6730,SP6766,SP6813,SP6825,SP7171,SP7291,SP7378,SP7421,SP7692,SP7785,SP8085,SP8157,SP8229,SP8394,SP8564,SP8660,SP8795,SP8831,SP8891,SP8987,SP9251,SP9433,SP9481,SP96147,SP96163,SP96511,SP9816,SP9930,SP9979 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index 0000000..fe1dd97 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,40 @@ +[tool.poetry] +name = "salamander-learn" +version = "0.1.1" +description = "Salamander is a non-negative matrix factorization framework for signature analysis" +license = "MIT" +authors = ["Benedikt Geiger"] +maintainers = [ + "Benedikt Geiger ", +] +packages = [{ include = "salamander", from = "src" }] + + +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.9,<3.12" +fastcluster = "^1.2.6" +matplotlib = "^3.7.1" +numba = "^0.57" +numpy = "^1.24.3" +pandas = "^1.5.3" +scikit-learn = "^1.3.0" +scipy = "^1.10.1" +seaborn = "^0.13.0" +umap-learn = "^0.5.4" + +[tool.poetry.group.dev.dependencies] +pytest = "^7.4.2" +pre-commit = "^3.4.0" +tox = "^4.11.3" + +[tool.pytest.ini_options] +# /site-packages/umap/__init__.py:36: DeprecationWarning: pkg_resources is deprecated as an API. +filterwarnings = [ + "ignore::DeprecationWarning:umap.*:", +] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/src/salamander/__init__.py b/src/salamander/__init__.py new file mode 100644 index 0000000..88173ef --- /dev/null +++ b/src/salamander/__init__.py @@ -0,0 +1,11 @@ +""" +Salamander: a non-negative matrix factorization framework for signature analysis +================================================================================ +""" +from .nmf_framework.corrnmf_det import CorrNMFDet +from .nmf_framework.klnmf import KLNMF +from .nmf_framework.multimodal_corrnmf import MultimodalCorrNMF +from .nmf_framework.mvnmf import MvNMF + +__version__ = "0.1.0" +__all__ = ["CorrNMFDet", "KLNMF", "MvNMF", "MultimodalCorrNMF"] diff --git a/src/salamander/consts.py b/src/salamander/consts.py new file mode 100644 index 0000000..e18dea9 --- /dev/null +++ b/src/salamander/consts.py @@ -0,0 +1,88 @@ +NUCLEOTIDES = ["A", "C", "G", "T"] + +SBS_TYPES_6 = ["C>A", "C>G", "C>T", "T>A", "T>C", "T>G"] +SBS_TYPES_96 = [ + f"{n1}[{sbs_6}]{n2}" + for sbs_6 in SBS_TYPES_6 + for n1 in NUCLEOTIDES + for n2 in NUCLEOTIDES +] + +# fmt: off +INDEL_TYPES_83 = [ + "DEL.C.1.1", "DEL.C.1.2", 'DEL.C.1.3', "DEL.C.1.4", "DEL.C.1.5", "DEL.C.1.6+", + "DEL.T.1.1", "DEL.T.1.2", 'DEL.T.1.3', "DEL.T.1.4", "DEL.T.1.5", "DEL.T.1.6+", + "INS.C.1.0", "INS.C.1.1", 'INS.C.1.2', "INS.C.1.3", "INS.C.1.4", "INS.C.1.5+", + "INS.T.1.0", "INS.T.1.1", 'INS.T.1.2', "INS.T.1.3", "INS.T.1.4", "INS.T.1.5+", + "DEL.repeats.2.1", "DEL.repeats.2.2", "DEL.repeats.2.3", + "DEL.repeats.2.4", "DEL.repeats.2.5", "DEL.repeats.2.6+", + "DEL.repeats.3.1", "DEL.repeats.3.2", "DEL.repeats.3.3", + "DEL.repeats.3.4", "DEL.repeats.3.5", "DEL.repeats.3.6+", + "DEL.repeats.4.1", "DEL.repeats.4.2", "DEL.repeats.4.3", + "DEL.repeats.4.4", "DEL.repeats.4.5", "DEL.repeats.4.6+", + "DEL.repeats.5+.1", "DEL.repeats.5+.2", "DEL.repeats.5+.3", + "DEL.repeats.5+.4", "DEL.repeats.5+.5", "DEL.repeats.5+.6+", + "INS.repeats.2.0", "INS.repeats.2.1", "INS.repeats.2.2", + "INS.repeats.2.3", "INS.repeats.2.4", "INS.repeats.2.5+", + "INS.repeats.3.0", "INS.repeats.3.1", "INS.repeats.3.2", + "INS.repeats.3.3", "INS.repeats.3.4", "INS.repeats.3.5+", + "INS.repeats.4.0", "INS.repeats.4.1", "INS.repeats.4.2", + "INS.repeats.4.3", "INS.repeats.4.4", "INS.repeats.4.5+", + "INS.repeats.5+.0", "INS.repeats.5+.1", "INS.repeats.5+.2", + "INS.repeats.5+.3", "INS.repeats.5+.4", "INS.repeats.5+.5+", + "DEL.MH.2.1", + "DEL.MH.3.1", "DEL.MH.3.2", + "DEL.MH.4.1", "DEL.MH.4.2", "DEL.MH.4.3", + "DEL.MH.5+.1", "DEL.MH.5+.2", "DEL.MH.5+.3", "DEL.MH.5+.4", "DEL.MH.5+.5+" +] +# fmt: on + +# 10 colors +COLORS_MATHEMATICA = [ + (0.368417, 0.506779, 0.709798), + (0.880722, 0.611041, 0.142051), + (0.560181, 0.691569, 0.194885), + (0.922526, 0.385626, 0.209179), + (0.528288, 0.470624, 0.701351), + (0.772079, 0.431554, 0.102387), + (0.363898, 0.618501, 0.782349), + (1.0, 0.75, 0.0), + (0.280264, 0.715, 0.429209), + (0.0, 0.0, 0.0), +] + +# Trinucleotide colors for the 96 dimensional mutation spectrum +COLORS_TRINUCLEOTIDES = [ + (0.33, 0.75, 0.98), + (0.0, 0.0, 0.0), + (0.85, 0.25, 0.22), + (0.78, 0.78, 0.78), + (0.51, 0.79, 0.24), + (0.89, 0.67, 0.72), +] + +COLORS_SBS96 = [COLORS_TRINUCLEOTIDES[i // 16] for i in range(96)] + +COLORS_INDEL = [ + "#FCBD6F", # 1bp Del C + "#FD8001", # 1bp Del T + "#B0DC8B", # 1bp Ins C + "#35A02E", # 1bp Ins T + "#FCC9B4", # 2bp Del Repeats + "#FC896B", # 3bp Del Repeats + "#F04432", # 4bp Del Repeats + "#BC1A1A", # 5+ bp Del Repeats + "#CFE0F0", # 2bp Ins Repeats + "#94C3DF", # 3bp Ins Repeats + "#4A98C8", # 4bp Ins Repeats + "#1665AA", # 5+ bp Ins Repeats + "#E1E0ED", # 2bp Del MH + "#B5B5D8", # 3bp Del MH + "#8683BC", # 4bp Del MH + "#624099", # 5+bp Del MH +] + +# 12 * 6 + 11 = 83 colors +n_times = 12 * [6] + [1, 2, 3, 5] +COLORS_INDEL83 = [n * [col] for n, col in zip(n_times, COLORS_INDEL)] +COLORS_INDEL83 = [col for color_list in COLORS_INDEL83 for col in color_list] diff --git a/src/salamander/nmf_framework/__init__.py b/src/salamander/nmf_framework/__init__.py new file mode 100644 index 0000000..e16c76d --- /dev/null +++ b/src/salamander/nmf_framework/__init__.py @@ -0,0 +1 @@ +"" diff --git a/src/salamander/nmf_framework/corrnmf.py b/src/salamander/nmf_framework/corrnmf.py new file mode 100644 index 0000000..8cbee5e --- /dev/null +++ b/src/salamander/nmf_framework/corrnmf.py @@ -0,0 +1,741 @@ +import multiprocessing +import os +import warnings +from abc import abstractmethod +from copy import deepcopy + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy.spatial.distance import squareform +from scipy.special import gammaln + +from ..plot import pca_2d, scatter_1d, scatter_2d, tsne_2d, umap_2d +from ..utils import ( + kl_divergence, + match_signatures_pair, + poisson_llh, + samplewise_kl_divergence, + shape_checker, + type_checker, + value_checker, +) +from .initialization import ( + init_custom, + init_flat, + init_nndsvd, + init_random, + init_separableNMF, +) +from .signature_nmf import SignatureNMF + +EPSILON = np.finfo(np.float32).eps + + +class CorrNMF(SignatureNMF): + r""" + The abstract class CorrNMF unifies the structure of deterministic and + stochastic correlated NMF (CorrNMF) with and without given signatures. + Both variants of CorrNMF have an identical generative model and objective function. + The model parameters are the sample biases \alpha, variance \sigma^2, + signature matrix W and the auxiliary parameters p. + The latent variables are the signature embeddings L and the sample embeddings U. + + Overview: + + Every child class has to implement the following methods: + + - _update_alpha: + update the sample exposure biases \alpha + + - _update_sigma_sq: + update the embedding distribution variance \sigma^2 + + - _update_W: + update the signature matrix W + + - _update_p: + update the auxiliary parameters p + + - _update_l: + update a single signature embedding l + + - _update_u: + update a single sample embedding u + + - fit: + Run CorrNMF for a given mutation count data. Every + fit method should also implement a "refitting version", where the signatures + W are known in advance and fixed + + + The following attributes are implemented in the abstract class CNMF: + + - signatures: pd.DataFrame + The signature matrix including mutation type names and signature names + + - exposures: pd.DataFrame + The exposure matrix including the signature names and sample names + + - reconstruction_error: float + The reconstruction error between the count matrix + and the reconstructed count matrix. + + - samplewise_reconstruction_error: np.ndarray + The samplewise reconstruction error between the sample counts + and the reconstructed sample counts. + + - _n_parameters: + The number of parameters fitted in CorrNMF + + - objective: str + "minimize" or "maximize". Whether the NMF algorithm maximizes + or minimizes the objective function. Some algorithms maximize a likelihood, + others minimize a distance. The distinction is useful for filtering NMF + runs based on the fitted objective function value. + + - corr_signatures: pd.DataFrame + The signature correlation matrix induced by the signature embeddings + + - corr_samples: pd.DataFrame + The sample correlation matrix induced by the sample embeddings + + + The following methods are implemented in the abstract class CorrNMF: + + - objective_function: + The evidence lower bound (ELBO) of the log-likelihood. + Note: The ELBO is sometimes called the variational lower bound. + + - _surrogate_objective_function: + A surrogate lower bound of the ELBO after introducing the + auxiliary parameters p. In contrast to the original objective_function, + the surrogate is strictly convex in the signature and sample embeddings + + - loglikelihood: + The loglikelihood of the underyling generative model + + - _initialize: + Initialize all model parameters and latent variables depending on the + initialization method chosen + + - _get_embedding_annotations: + A helper function to concatenate signature and sample names + + - plot_embeddings: + Plot signature or sample embeddings in 2D using PCA, tSNE or UMAP. + The respective plotting functions are implemented in the plot.py module + + More specific docstrings are written for the respective attributes and methods. + """ + + def __init__( + self, + n_signatures=1, + dim_embeddings=None, + init_method="nndsvd", + update_W="1999-Lee", + min_iterations=500, + max_iterations=10000, + tol=1e-7, + ): + """ + Input: + ------ + n_signatures: int + The number of underlying signatures that are assumed to + have generated the mutation count data + + dim_embeddings: int + The assumed dimension of the signature and sample embeddings. + Should be smaller or equal to the number of signatures as a dimension + equal to the number of signatures covers the case of independent + signatures. The smaller the embedding dimension, the stronger the + enforced correlation structure on both signatures and samples. + + init_method: str + One of "custom", "flat", "hierarchical_cluster", "nndsvd", + "nndsvda", "nndsvdar" "random" and "separableNMF". + See the initialization module for further details. + + update_W: str, "1999-Lee" or "surrogate" + The signature matrix W can be inferred by either using the Lee and Seung + multiplicative update rules to optimize the objective function or by + maximizing the surrogate objective function. + + min_iterations: int + The minimum number of iterations to perform during inference + + max_iterations: int + The maximum number of iterations to perform during inference + + tol: float + The CorrNMF algorithm is converged when the relative change of the + surrogate objective function of one iteration is smaller + than the tolerance 'tol'. + """ + super().__init__(n_signatures, init_method, min_iterations, max_iterations, tol) + + if dim_embeddings is None: + dim_embeddings = n_signatures + + self.dim_embeddings = dim_embeddings + value_checker("update_W", update_W, ["1999-Lee", "surrogate"]) + self.update_W = update_W + + # initialize data/fitting dependent attributes + self.W = None + self.alpha = None + self.L = None + self.U = None + self.sigma_sq = None + + @property + def signatures(self) -> pd.DataFrame: + signatures = pd.DataFrame( + self.W, index=self.mutation_types, columns=self.signature_names + ) + + return signatures + + @property + def exposures(self) -> pd.DataFrame: + """ + In contrast to the classical NMF framework, the exposure matrix is + restructured and determined by the signature and sample embeddings. + """ + exposures = pd.DataFrame( + np.exp(np.tile(self.alpha, (self.n_signatures, 1)) + self.L.T @ self.U), + index=self.signature_names, + columns=self.sample_names, + ) + + return exposures + + @property + def _n_parameters(self): + """ + There are n_features * n_signatures parameters corresponding to + the signature matrix, each embedding corresponds to dim_embeddings parameters, + and each sample has a bias parameter. + Finally, the model variance is a single positive real number. + + Note: We do not include the number of auxiliary parameters p. + """ + n_parameters_signatures = self.n_features * self.n_signatures + n_parameters_embeddings = self.dim_embeddings * ( + self.n_signatures + self.n_samples + ) + n_parameters_biases = self.n_samples + n_parameters_exposures = n_parameters_embeddings + n_parameters_biases + n_parameters = n_parameters_signatures + n_parameters_exposures + 1 + + return n_parameters + + @property + def reconstruction_error(self): + return kl_divergence(self.X, self.W, self.exposures.values) + + @property + def samplewise_reconstruction_error(self): + return samplewise_kl_divergence(self.X, self.W, self.exposures.values) + + def objective_function(self, penalize_sample_embeddings=True) -> float: + """ + The evidence lower bound (ELBO) + """ + elbo = poisson_llh(self.X, self.signatures.values, self.exposures.values) + elbo -= ( + 0.5 + * self.dim_embeddings + * self.n_signatures + * np.log(2 * np.pi * self.sigma_sq) + ) + elbo -= np.sum(self.L**2) / (2 * self.sigma_sq) + + if penalize_sample_embeddings: + elbo -= ( + 0.5 + * self.dim_embeddings + * self.n_samples + * np.log(2 * np.pi * self.sigma_sq) + ) + elbo -= np.sum(self.U**2) / (2 * self.sigma_sq) + + return elbo + + @property + def objective(self) -> str: + return "maximize" + + def _surrogate_objective_function( + self, p, penalize_sample_embeddings=True + ) -> float: + """ + The surrogate lower bound of the ELBO after + introducing the auxiliary parameters p. + """ + exposures = self.exposures.values + aux = np.log(self.W)[:, :, None] + np.log(exposures)[None, :, :] - np.log(p) + sof_value = np.einsum("VD,VKD,VKD->", self.X, p, aux, optimize="greedy").item() + sof_value -= np.sum(gammaln(1 + self.X)) + sof_value -= np.sum(exposures) + sof_value -= ( + 0.5 + * self.dim_embeddings + * self.n_signatures + * np.log(2 * np.pi * self.sigma_sq) + ) + sof_value -= np.sum(self.L**2) / (2 * self.sigma_sq) + + if penalize_sample_embeddings: + sof_value -= ( + 0.5 + * self.dim_embeddings + * self.n_samples + * np.log(2 * np.pi * self.sigma_sq) + ) + sof_value -= np.sum(self.U**2) / (2 * self.sigma_sq) + + return sof_value + + def loglikelihood(self): + return self.objective_function() + + @abstractmethod + def _update_alpha(self): + pass + + @abstractmethod + def _update_sigma_sq(self): + pass + + @abstractmethod + def _update_W(self, p): + """ + Input: + ------ + p: np.ndarray + The auxiliary parameters of CorrNMF + """ + + @abstractmethod + def _update_p(self): + pass + + @abstractmethod + def _update_l(self, index, aux_row, outer_prods_U): + r""" + Input: + ------ + index: int + The index of the signature whose embedding is updated + + aux_row: nd.ndarray + Row of the following matrix: + aux_kd = \sum_v X_vd * p_vkd. + This auxiliary matrix is used for updating the signatures + and the sample embeddidngs. The aux_row argument + is the k-th row of aux, where k is equal to 'index'. + + outer_prods_U: np.ndarray + All outer products of the sample embeddings. + shape: (n_samples, dim_embeddings, dim_embeddings) + """ + + @abstractmethod + def _update_u(self, index, aux_col, outer_prods_L): + r""" + Input: + ------ + index: int + The index of the sample whose embedding is updated + + aux_col: nd.ndarray + Column of the following matrix: + aux_kd = \sum_v X_vd * p_vkd. + This auxiliary matrix is used for updating the signatures + and the sample embeddidngs. The aux_col argument + is the d-th row of aux, where d is equal to 'index'. + + outer_prods_L: np.ndarray + All outer products of the signature embeddings. + shape: (n_signatures, dim_embeddings, dim_embeddings) + """ + + def _check_given_signature_embeddings(self, given_signature_embeddings: np.ndarray): + type_checker("signature embeddings", given_signature_embeddings, np.ndarray) + shape_checker( + "given_signature_embeddings", + given_signature_embeddings, + (self.dim_embeddings, self.n_signatures), + ) + + def _check_given_sample_embeddings(self, given_sample_embeddings: np.ndarray): + type_checker("sample embeddings", given_sample_embeddings, np.ndarray) + shape_checker( + "given_sample_embeddings", + given_sample_embeddings, + (self.dim_embeddings, self.n_samples), + ) + + def _initialize( + self, + given_signatures=None, + given_signature_embeddings=None, + given_sample_embeddings=True, + init_kwargs=None, + ): + """ + Initialize the signature matrix W, sample biases alpha, the squared variance, + and the signature and sample embeddings. + The signatures or signature embeddings can also be provided by the user. + + Input: + ------ + init_kwargs: dict + Any further arguments to be passed to the initialization method. + This includes, for example, a possible 'seed' keyword argument + for all stochastic methods. + """ + if given_signatures is not None: + self._check_given_signatures(given_signatures) + + if given_signature_embeddings is not None: + self._check_given_signature_embeddings(given_signature_embeddings) + + if given_sample_embeddings is not None: + self._check_given_sample_embeddings(given_sample_embeddings) + + init_kwargs = {} if init_kwargs is None else init_kwargs.copy() + + if self.init_method == "custom": + self.W, _ = init_custom(self.X, self.n_signatures, **init_kwargs) + + elif self.init_method == "flat": + self.W, _ = init_flat(self.X, self.n_signatures) + + elif self.init_method in ["nndsvd", "nndsvda", "nndsvdar"]: + self.W, _ = init_nndsvd( + self.X, self.n_signatures, init=self.init_method, **init_kwargs + ) + + elif self.init_method == "random": + self.W, _ = init_random(self.X, self.n_signatures, **init_kwargs) + + else: + self.W = init_separableNMF(self.X, self.n_signatures) + + if given_signatures is not None: + self.W = given_signatures.copy().values + self.signature_names = given_signatures.columns.to_numpy(dtype=str) + + self.W /= np.sum(self.W, axis=0) + self.W = self.W.clip(EPSILON) + self.alpha = np.zeros(self.n_samples, dtype=float) + self.sigma_sq = 1.0 + self.L = np.random.multivariate_normal( + np.zeros(self.dim_embeddings), + np.identity(self.dim_embeddings), + size=self.n_signatures, + ).T + self.U = np.random.multivariate_normal( + np.zeros(self.dim_embeddings), + np.identity(self.dim_embeddings), + size=self.n_samples, + ).T + + if given_signature_embeddings is not None: + self.L = given_signature_embeddings + + if given_sample_embeddings is not None: + self.U = given_sample_embeddings + + @property + def corr_signatures(self) -> pd.DataFrame: + norms = np.sqrt(np.sum(self.L**2, axis=0)) + + corr_vector = np.array( + [ + np.dot(l1, l2) / (norms[k1] * norms[k1 + k2 + 1]) + for k1, l1 in enumerate(self.L.T) + for k2, l2 in enumerate(self.L[:, (k1 + 1) :].T) + ] + ) + corr_matrix = squareform(corr_vector) + np.identity(self.n_signatures) + corr = pd.DataFrame( + corr_matrix, index=self.signature_names, columns=self.signature_names + ) + + return corr + + @property + def corr_samples(self) -> pd.DataFrame: + norms = np.sqrt(np.sum(self.U**2, axis=0)) + + corr_vector = np.array( + [ + np.dot(u1, u2) / (norms[d1] * norms[d1 + d2 + 1]) + for d1, u1 in enumerate(self.U.T) + for d2, u2 in enumerate(self.U[:, (d1 + 1) :].T) + ] + ) + corr_matrix = squareform(corr_vector) + np.identity(self.n_samples) + corr = pd.DataFrame( + corr_matrix, index=self.sample_names, columns=self.sample_names + ) + + return corr + + def reorder(self, other_signatures, metric="cosine", keep_names=False): + reordered_indices = match_signatures_pair( + other_signatures, self.signatures, metric=metric + ) + self.W = self.W[:, reordered_indices] + self.L = self.L[:, reordered_indices] + + if keep_names: + self.signature_names = self.signature_names[reordered_indices] + + return reordered_indices + + def _get_embedding_annotations(self, annotate_signatures, annotate_samples): + # Only annotate with the first 20 characters of names + annotations = np.empty(self.n_signatures + self.n_samples, dtype="U20") + + if annotate_signatures: + annotations[: self.n_signatures] = self.signature_names + + if annotate_samples: + annotations[-self.n_samples :] = self.sample_names + + return annotations + + def plot_embeddings( + self, + method="umap", + annotate_signatures=True, + annotate_samples=False, + annotation_kwargs=None, + normalize=False, + ax=None, + outfile=None, + **kwargs, + ): + """ + Plot the signature and sample embeddings. If the embedding dimension is two, + the embeddings will be plotted directly, ignoring the chosen method. + See plot.py for the implementation of scatter_2d, tsne_2d, pca_2d, umap_2d. + + Input: + ------ + methdod: str + Either 'tsne', 'pca' or 'umap'. The respective dimensionality reduction + will be applied to plot the signature and sample embeddings in 2D. + + annotate_signatures: bool + + annotate_samples: bool + + normalize: bool + Normalize the embeddings before applying the dimensionality reduction. + + *args, **kwargs: + arguments to be passed to scatter_2d, tsne_2d, pca_2d or umap_2d + """ + value_checker("method", method, ["pca", "tsne", "umap"]) + annotations = self._get_embedding_annotations( + annotate_signatures, annotate_samples + ) + + data = np.concatenate([self.L, self.U], axis=1).T + + if normalize: + data /= np.sum(data, axis=0) + + if self.dim_embeddings in [1, 2]: + warnings.warn( + f"The embedding dimension is {self.dim_embeddings}. " + f"The method argument '{method}' will be ignored " + "and the embeddings are plotted directly.", + UserWarning, + ) + + if self.dim_embeddings == 1: + ax = scatter_1d( + data[:, 0], + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + elif self.dim_embeddings == 2: + ax = scatter_2d( + data, + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + elif method == "tsne": + ax = tsne_2d( + data, + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + elif method == "pca": + ax = pca_2d( + data, + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + else: + ax = umap_2d( + data, + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return ax + + +class CorrNMFHyperparameterSelector: + """ + The embedding dimension of samples and signatures is + the only hyperparameter of correlated NMF. + This class implements methods to select the "optimal" embedding dimension. + The framework of hyperparameter selectors allows to implement + a denovo signature analysis pipeline in an NMF algorithm agnostic manner: + A dictionary can be used to set all hyperparameters, + irrespective of the NMF algorithm and its arbitrary number of hyperparameters. + """ + + def __init__(self, method="unbiased", method_kwargs=None): + value_checker("method", method, ["BIC", "proportional", "unbiased"]) + self.method = method + self.method_kwargs = {} if method_kwargs is None else method_kwargs.copy() + + # initialize selection dependent attributes + self.corrnmf_algorithm = None + self.dims_embeddings = np.empty(0, dtype=int) + self.data = None + self.given_signatures = None + self.init_kwargs = None + self.verbose = 0 + self.models = [] + + def _job_select_bic(self, dim_embeddings): + """ + Apply CorrNMF for a single embedding dimension. + """ + model = deepcopy(self.corrnmf_algorithm) + model.dim_embeddings = dim_embeddings + model.fit( + data=self.data, + given_signatures=self.given_signatures, + init_kwargs=self.init_kwargs, + verbose=0, + ) + + if self.verbose: + print(f"CorrNMF with dim_embeddings = {dim_embeddings} finished.") + + return model + + def select_bic(self, ncpu=None): + """ + Select the best embedding dimension based + on the Bayesian Information Criterion (BIC). + """ + if ncpu is None: + ncpu = os.cpu_count() + + if ncpu > 1: + workers = multiprocessing.Pool(ncpu) + models = workers.map(self._job_select_bic, self.dims_embeddings) + workers.close() + workers.join() + + else: + models = [ + self._job_select_bic(dim_embeddings) + for dim_embeddings in self.dims_embeddings + ] + + self.models = models + bics = np.array([model.bic for model in models]) + best_index = np.argmin(bics) + best_model = models[best_index] + + return best_model.dim_embeddings + + def select_proportional(self, proportion=0.75): + """ + The embedding dimension is set to a proportion of the number of signatures. + """ + n_signatures = self.corrnmf_algorithm.n_signatures + dim_embeddings = int(proportion * n_signatures) if n_signatures > 1 else 1 + + return dim_embeddings + + def select_unbiased(self, normalized=True): + """ + The embedding dimension is set to the number of signatures + if 'normalized' is false. If 'normalized' is true, the embedding + dimension is set to the number of signatures minus one. + + Input: + ------ + normalized: bool + If the input count matrix will be normalized, the number of free + parameters for each sample exposure is 'n_signatures - 1'. + Without the normalization, there are 'n_signatures' many free parameters. + """ + n_signatures = self.corrnmf_algorithm.n_signatures + + if not normalized: + return n_signatures + + return max(1, n_signatures - 1) + + def select( + self, + corrnmf_algorithm, + data: pd.DataFrame, + given_signatures=None, + init_kwargs=None, + ncpu=None, + verbose=0, + ): + self.corrnmf_algorithm = corrnmf_algorithm + self.dims_embeddings = np.arange(1, corrnmf_algorithm.n_signatures + 1) + self.data = data + self.given_signatures = given_signatures + self.init_kwargs = init_kwargs + self.verbose = verbose + + if self.method == "BIC": + dim_embeddings = self.select_bic(ncpu=ncpu, **self.method_kwargs) + + elif self.method == "proportional": + dim_embeddings = self.select_proportional(**self.method_kwargs) + + elif self.method == "unbiased": + dim_embeddings = self.select_unbiased(**self.method_kwargs) + + hyperparameters = {"dim_embeddings": dim_embeddings} + + return hyperparameters diff --git a/src/salamander/nmf_framework/corrnmf_det.py b/src/salamander/nmf_framework/corrnmf_det.py new file mode 100644 index 0000000..8d62db8 --- /dev/null +++ b/src/salamander/nmf_framework/corrnmf_det.py @@ -0,0 +1,292 @@ +import numpy as np +import pandas as pd +from scipy import optimize + +from .corrnmf import CorrNMF + +EPSILON = np.finfo(np.float32).eps + + +class CorrNMFDet(CorrNMF): + r""" + The CorrNMFDet class implements the deterministic batch version of + a variant of the correlated NMF (CorrNMF) algorithm devolped in + "Bayesian Nonnegative Matrix Factorization with Stochastic Variational + Inference" by Paisley et al. + + The following methods are implemented to match the structure + of the abstract class CorrNMF: + + - _update_alpha: + update the sample exposure biases \alpha + + - _update_sigma_sq: + update the variance \sigma^2 assumed in the generative model + + - _update_W: + update the signature matrix W + + - _update_p: + update the auxiliary parameters p + + - _update_l: + update a single signature embedding l + + - _update_u: + update a single sample embedding u + + The following method is implemented to match the structure of SignatureNMF: + + - fit: + Perform CorrNMF for the given mutation count data or + for given signatures and mutation count data + """ + + def _update_alpha(self): + exp_LTU = np.exp(self.L.T @ self.U) + self.alpha = np.log(np.sum(self.X, axis=0)) - np.log(np.sum(exp_LTU, axis=0)) + + def _update_sigma_sq(self): + sum_norm_sigs = np.sum(self.L**2) + sum_norm_samples = np.sum(self.U**2) + + self.sigma_sq = (sum_norm_sigs + sum_norm_samples) / ( + self.dim_embeddings * (self.n_signatures + self.n_samples) + ) + self.sigma_sq = np.clip(self.sigma_sq, EPSILON, None) + + def _update_W(self, p): + if self.update_W == "1999-Lee": + self.W = self.W * ( + (self.X / (self.W @ self.exposures.values)) @ self.exposures.values.T + ) + + else: + self.W = np.einsum("vd,vkd->vk", self.X, p) + + self.W /= np.sum(self.W, axis=0) + self.W = self.W.clip(EPSILON) + + def _update_p(self): + p = np.einsum("vk,kd->vkd", self.W, self.exposures.values) + p /= np.sum(p, axis=1, keepdims=True) + p = p.clip(EPSILON) + + return p + + def _objective_fun_l(self, l, aux_row): + UTl = self.U.T.dot(l) + s = np.dot(aux_row, UTl) + s -= np.sum(np.exp(self.alpha + UTl)) + s -= np.dot(l, l) / (2 * self.sigma_sq) + + return -s + + def _gradient_l(self, l, s_grad): + s = -np.sum(np.exp(self.alpha + self.U.T.dot(l)) * self.U, axis=1) + s -= l / self.sigma_sq + + return -(s_grad + s) + + def _hessian_l(self, l, outer_prods_U): + scalings = np.exp(self.alpha + self.U.T.dot(l)) + s = -np.einsum("D,Dmn->mn", scalings, outer_prods_U) + s -= np.diag(np.full(self.dim_embeddings, 1 / self.sigma_sq)) + + return -s + + def _update_l(self, index, aux_row, outer_prods_U): + def objective_fun(l): + return self._objective_fun_l(l, aux_row) + + s_grad = np.sum(aux_row * self.U, axis=1) + + def gradient(l): + return self._gradient_l(l, s_grad) + + def hessian(l): + return self._hessian_l(l, outer_prods_U) + + self.L[:, index] = optimize.minimize( + fun=objective_fun, + x0=self.L[:, index], + method="Newton-CG", + jac=gradient, + hess=hessian, + ).x + + def _update_L(self, aux, outer_prods_U=None): + r""" + Update all signature embeddings by optimizing + the surrogate objective function using scipy.optimize.minimize + with the 'Newton-CG' method (strictly convex for each embedding). + + aux: np.ndarray + aux_kd = \sum_v X_vd * p_vkd + is used for updating the signatures and the sample embeddidngs. + """ + if outer_prods_U is None: + outer_prods_U = np.einsum("mD,nD->Dmn", self.U, self.U) + + for k, aux_row in enumerate(aux): + self._update_l(k, aux_row, outer_prods_U) + + self.L[(0 < self.L) & (self.L < EPSILON)] = EPSILON + self.L[(-EPSILON < self.L) & (self.L < 0)] = -EPSILON + + def _objective_fun_u(self, u, index, aux_col, add_penalty_u=True): + LTu = self.L.T.dot(u) + s = np.dot(aux_col, LTu) + s -= np.sum(np.exp(self.alpha[index] + LTu)) + + if add_penalty_u: + s -= np.dot(u, u) / (2 * self.sigma_sq) + + return -s + + def _gradient_u(self, u, index, s_grad, add_penalty_u=True): + s = -np.exp(self.alpha[index]) * np.sum( + np.exp(self.L.T.dot(u)) * self.L, axis=1 + ) + + if add_penalty_u: + s -= u / self.sigma_sq + + return -(s_grad + s) + + def _hessian_u(self, u, index, outer_prods_L, add_penalty_u=True): + scalings = np.exp(self.alpha[index] + self.L.T.dot(u)) + s = -np.einsum("K,Kmn->mn", scalings, outer_prods_L) + + if add_penalty_u: + s -= np.diag(np.full(self.dim_embeddings, 1 / self.sigma_sq)) + + return -s + + def _update_u(self, index, aux_col, outer_prods_L): + def objective_fun(u): + return self._objective_fun_u(u, index, aux_col) + + s_grad = np.sum(aux_col * self.L, axis=1) + + def gradient(u): + return self._gradient_u(u, index, s_grad) + + def hessian(u): + return self._hessian_u(u, index, outer_prods_L) + + u = optimize.minimize( + fun=objective_fun, + x0=self.U[:, index], + method="Newton-CG", + jac=gradient, + hess=hessian, + options={"maxiter": 3}, + ).x + u[(0 < u) & (u < EPSILON)] = EPSILON + u[(-EPSILON < u) & (u < 0)] = -EPSILON + self.U[:, index] = u + + def _update_U(self, aux): + r""" + Update all sample embeddings by optimizing + the surrogate objective function using scipy.optimize.minimize + with the 'Newton-CG' method (strictly convex for each embedding). + + aux: np.ndarray + aux_kd = \sum_v X_vd * p_vkd + is used for updating the signatures and the sample embeddidngs. + """ + outer_prods_L = np.einsum("mK,nK->Kmn", self.L, self.L) + + for d, aux_col in enumerate(aux.T): + self._update_u(d, aux_col, outer_prods_L) + + def _update_LU(self, p, given_signature_embeddings, given_sample_embeddings): + aux = np.einsum("vd,vkd->kd", self.X, p) + + if given_signature_embeddings is None: + self._update_L(aux) + + if given_sample_embeddings is None: + self._update_U(aux) + + def fit( + self, + data: pd.DataFrame, + given_signatures=None, + given_signature_embeddings=None, + given_sample_embeddings=None, + init_kwargs=None, + history=False, + verbose=0, + ): + """ + Maximize the surrogate objective function of correlated NMF (CNMF). + + Input: + ------ + data: pd.DataFrame + The mutation count data + + given_signatures: pd.DataFrame, default=None + Known signatures which will be fixed during model fitting. + + given_signature_embeddings: np.ndarray, default=None + Known signature embeddings which will be fixed during model fitting. + + given_sample_embeddings: np.ndarray, default=None + Known sample embeddings which will be fixed during model fitting. + + init_kwargs: dict + Any further keywords arguments to be passed to the initialization method. + This includes, for example, a possible 'seed' keyword argument + for all stochastic methods. + + history: bool + When set to true, the history of the objective function and + surrogate objective function will be stored in a dictionary. + + verbose: int + Every 100th iteration number will be printed when set unequal to zero. + """ + self._setup_data_parameters(data) + self._initialize( + given_signatures=given_signatures, + given_signature_embeddings=given_signature_embeddings, + given_sample_embeddings=given_sample_embeddings, + init_kwargs=init_kwargs, + ) + of_values = [self.objective_function()] + sof_values = [self.objective_function()] + + n_iteration = 0 + converged = False + + while not converged: + n_iteration += 1 + + if verbose and n_iteration % 100 == 0: + print("iteration ", n_iteration) + + self._update_alpha() + p = self._update_p() + self._update_LU(p, given_signature_embeddings, given_sample_embeddings) + self._update_sigma_sq() + + if given_signatures is None: + self._update_W(p) + + of_values.append(self.objective_function()) + prev_sof_value = sof_values[-1] + sof_values.append(self._surrogate_objective_function(p)) + rel_change = (sof_values[-1] - prev_sof_value) / np.abs(prev_sof_value) + converged = ( + rel_change < self.tol and n_iteration >= self.min_iterations + ) or (n_iteration >= self.max_iterations) + + if history: + self.history["objective_function"] = of_values[1:] + self.history["surrogate_objective_function"] = sof_values[1:] + + return self diff --git a/src/salamander/nmf_framework/initialization.py b/src/salamander/nmf_framework/initialization.py new file mode 100644 index 0000000..b46c983 --- /dev/null +++ b/src/salamander/nmf_framework/initialization.py @@ -0,0 +1,99 @@ +""" +Initialization methods for non-negative matrix factorization (NMF) +""" +import numpy as np +from sklearn.decomposition import _nmf as sknmf + +from ..utils import shape_checker, type_checker + + +def init_custom( + X: np.ndarray, n_signatures: int, W_custom: np.ndarray, H_custom: np.ndarray +): + """ + Perform type and shape checks on custom signature and + exposure matrix initializations. + """ + type_checker("W_custom", W_custom, np.ndarray) + type_checker("H_custom", H_custom, np.ndarray) + + n_features, n_samples = X.shape + shape_checker("W_custom", W_custom, (n_features, n_signatures)) + shape_checker("H_custom", H_custom, (n_signatures, n_samples)) + + return W_custom, H_custom + + +def init_flat(X: np.ndarray, n_signatures: int): + """ + Initialize the signature and exposure matrices with one float, respectively. + """ + n_features, n_samples = X.shape + scaling = np.mean(np.sum(X, axis=0)) + + W = np.full((n_features, n_signatures), 1 / n_features) + H = np.full((n_signatures, n_samples), scaling / n_signatures) + + return W, H + + +def init_nndsvd(X: np.ndarray, n_signatures: int, init: str, seed=None): + """ + A wrapper around the non-negative double singular value decomposition (NNDSVD) + initialization methods "nndsvd", "nndsvda" and "nndsvdar" from scikit-learn. + + Inputs: + ------ + init: str + One of "nndsvd", "nndsvda" and "nndsvdar" + """ + if seed is not None: + np.random.seed(seed) + + # pylint: disable-next=W0212 + W, H = sknmf._initialize_nmf(X, n_signatures, init=init) + + return W, H + + +def init_random(X: np.ndarray, n_signatures: int, seed=None): + """ + Initialize each signature by drawing from the uniform + distribution on the simplex. + Initialize the exposures of each sample as a scaled sample + from the uniform distribution on a simplex. + The scaling is chosen such that the expected total exposure is equal to + the column sum of that sample in the count matrix X. + """ + if seed is not None: + np.random.seed(seed) + + n_features, n_samples = X.shape + W = np.random.dirichlet(np.ones(n_features), size=n_signatures).T + scaling = np.sum(X, axis=0) + H = scaling * np.random.dirichlet(np.ones(n_signatures), size=n_samples).T + + return W, H + + +def init_separableNMF(X: np.ndarray, n_signatures: int): + r""" + This code is following Algorithm 1 from "Fast and Robust Recursive + Algorithms for Separable Nonnegative Matrix Factorization" + (Gillis and Vavasis, 2013), with the canonical choice of + f(x) = \| x \|_2^2 as the strongly convex function f satisfying + Assumption 2 from the paper. + """ + signature_indices = np.empty(n_signatures, dtype=int) + R = X / np.sum(X, axis=0) + + for k in range(n_signatures): + column_norms = np.sum(R**2, axis=0) + kstar = np.argmax(column_norms) + u = R[:, kstar] + R = (np.identity(X.shape[0]) - np.outer(u, u) / column_norms[kstar]) @ R + signature_indices[k] = kstar + + W = X[:, signature_indices].astype(float) + + return W diff --git a/src/salamander/nmf_framework/klnmf.py b/src/salamander/nmf_framework/klnmf.py new file mode 100644 index 0000000..5aa377e --- /dev/null +++ b/src/salamander/nmf_framework/klnmf.py @@ -0,0 +1,128 @@ +import numpy as np +import pandas as pd + +from ..utils import kl_divergence, normalize_WH, poisson_llh, samplewise_kl_divergence +from .nmf import NMF + +EPSILON = np.finfo(np.float32).eps + + +class KLNMF(NMF): + """ + Decompose a mutation count matrix X into the product of a signature + matrix W and an exposure matrix H by using the generalized Kullback-Leibler (KL) + loss induced multiplicative update rules derived by Lee and Seung + in "Algorithms for non-negative matrix factorization". + + The class KLNMF is implemented as a child class of NMF to inherit + its unified mutational signature analysis structure. + """ + + @property + def reconstruction_error(self) -> float: + return kl_divergence(self.X, self.W, self.H) + + @property + def samplewise_reconstruction_error(self) -> np.ndarray: + return samplewise_kl_divergence(self.X, self.W, self.H) + + def objective_function(self) -> float: + return self.reconstruction_error + + @property + def objective(self) -> str: + return "minimize" + + def loglikelihood(self) -> float: + return poisson_llh(self.X, self.W, self.H) + + def _update_W(self): + """ + The multiplicative update rule of the signature matrix W + derived by Lee and Seung. See Theorem 2 in + "Algorithms for non-negative matrix factorization". + + Clipping the matrix avoids floating point errors. + """ + self.W *= (self.X / (self.W @ self.H)) @ self.H.T + self.W /= np.sum(self.H, axis=1) + self.W = self.W.clip(EPSILON) + + def _update_H(self): + """ + The multiplicative update rule of the exposure matrix H + derived by Lee and Seung. See Theorem 2 in + "Algorithms for non-negative matrix factorization". + + Clipping the matrix avoids floating point errors. + """ + self.H *= self.W.T @ (self.X / (self.W @ self.H)) + self.H /= np.sum(self.W, axis=0)[:, np.newaxis] + self.H = self.H.clip(EPSILON) + + def fit( + self, + data: pd.DataFrame, + given_signatures=None, + init_kwargs=None, + history=False, + verbose=0, + ): + """ + Minimize the generalized Kullback-Leibler divergence D_KL(X || WH) between + the mutation count matrix X and product of the signature matrix W and + exposure matrix H by altering the multiplicative update steps for W and H. + + Input: + ------ + data: pd.DataFrame + The mutation count data + + given_signatures: pd.DataFrame, default=None + In the case of refitting, a priori known signatures have to be provided. The + number of signatures has to match to the NMF object and the mutation type + names have to match to the mutation count matrix + + init_kwargs: dict + Any further keyword arguments to be passed to the initialization method. + This includes, for example, a possible 'seed' keyword argument + for all stochastic methods. + + history: bool + When set to true, the history of the objective function + will be stored in a dictionary. + + verbose: int + Every 100th iteration number will be printed when set unequal to zero. + """ + self._setup_data_parameters(data) + self._initialize(given_signatures, init_kwargs) + of_values = [self.objective_function()] + n_iteration = 0 + converged = False + + while not converged: + n_iteration += 1 + + if verbose and n_iteration % 100 == 0: + print(f"iteration {n_iteration}") + + self._update_H() + + if given_signatures is None: + self._update_W() + + self.W, self.H = normalize_WH(self.W, self.H) + self.W, self.H = self.W.clip(EPSILON), self.H.clip(EPSILON) + + prev_of_value = of_values[-1] + of_values.append(self.objective_function()) + rel_change = (prev_of_value - of_values[-1]) / prev_of_value + converged = ( + rel_change < self.tol and n_iteration >= self.min_iterations + ) or (n_iteration >= self.max_iterations) + + if history: + self.history["objective_function"] = of_values[1:] + + return self diff --git a/src/salamander/nmf_framework/multimodal_corrnmf.py b/src/salamander/nmf_framework/multimodal_corrnmf.py new file mode 100755 index 0000000..72ed331 --- /dev/null +++ b/src/salamander/nmf_framework/multimodal_corrnmf.py @@ -0,0 +1,701 @@ +""" +Multimodal correlated NMF (MultiCorrNMF) fits multiple correlated NMF (CorrNMF) +models jointly in the following manner: +Assuming that the input data for each modality originates from the identical samples, +MultiCorrNMF fixes the sample embeddings accross modalities and learns signature +embeddings for all modalities in the same embedding space. +""" +# This implementation heavily relies on the implementaion of CorrNMF in +# corrnmf_det.py. In particular, CorrNMFDet methods with a leading '_' +# are accessed. +# pylint: disable=protected-access + +import warnings + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy import optimize +from scipy.spatial.distance import squareform + +from ..plot import ( + corr_plot, + paper_style, + pca_2d, + scatter_1d, + scatter_2d, + signatures_plot, + tsne_2d, + umap_2d, +) +from ..utils import type_checker, value_checker +from .corrnmf_det import CorrNMFDet + +EPSILON = np.finfo(np.float32).eps + + +class MultimodalCorrNMF: + def __init__( + self, + n_modalities, + ns_signatures=None, + dim_embeddings=None, + init_method="nndsvd", + update_W="1999-Lee", + min_iterations=500, + max_iterations=10000, + tol=1e-7, + ): + self.n_modalities = n_modalities + + if ns_signatures is None: + ns_signatures = np.ones(n_modalities, dtype=int) + + self.ns_signatures = ns_signatures + + if dim_embeddings is None: + dim_embeddings = np.max(ns_signatures) + + self.dim_embeddings = dim_embeddings + self.init_method = init_method + self.min_iterations = min_iterations + self.max_iterations = max_iterations + self.tol = tol + self.models = [ + CorrNMFDet(n_signatures, dim_embeddings, init_method, update_W) + for n_signatures in ns_signatures + ] + + # initialize data/fitting dependent attributes + self.modality_names = np.empty(n_modalities, dtype=str) + self.n_samples = 0 + self.history = {} + + @property + def signatures(self) -> dict: + return { + name: model.signatures + for name, model in zip(self.modality_names, self.models) + } + + @property + def exposures(self) -> dict: + return { + name: model.exposures + for name, model in zip(self.modality_names, self.models) + } + + @property + def data_reconstructed(self) -> dict: + return { + name: model.data_reconstructred + for name, model in zip(self.modality_names, self.models) + } + + @property + def Xs_reconstructed(self) -> np.ndarray: + return { + name: model.X_reconstructed + for name, model in zip(self.modality_names, self.models) + } + + @property + def reconstruction_errors(self) -> float: + return { + name: model.reconstruction_error + for name, model in zip(self.modality_names, self.models) + } + + @property + def samplewise_reconstruction_errors(self) -> np.ndarray: + return { + name: model.samplewise_reconstruction_error + for name, model in zip(self.modality_names, self.models) + } + + def objective_function(self) -> float: + """ + The objective function to be optimized during fitting. + """ + elbo = np.sum( + [ + model.objective_function(penalize_sample_embeddings=False) + for model in self.models + ] + ) + elbo -= ( + 0.5 + * self.dim_embeddings + * self.n_samples + * np.log(2 * np.pi * self.models[0].sigma_sq) + ) + elbo -= np.sum(self.models[0].U ** 2) / (2 * self.models[0].sigma_sq) + + return elbo + + @property + def objective(self) -> str: + return "maximize" + + def _surrogate_objective_function(self, ps) -> float: + """ + The surrogate lower bound of the ELBO. + """ + sof_value = np.sum( + [ + model._surrogate_objective_function(p, penalize_sample_embeddings=False) + for model, p in zip(self.models, ps) + ] + ) + sof_value -= ( + 0.5 + * self.dim_embeddings + * self.n_samples + * np.log(2 * np.pi * self.models[0].sigma_sq) + ) + sof_value -= np.sum(self.models[0].U ** 2) / (2 * self.models[0].sigma_sq) + + return sof_value + + def loglikelihood(self) -> float: + """ + The log-likelihood of the underlying generative model. + """ + return self.objective_function() + + @property + def _n_parameters(self) -> int: + n_parameters_signatures = np.sum( + [model.n_features * model.n_signatures for model in self.models] + ) + n_parameters_embeddings = self.dim_embeddings * ( + np.sum(self.ns_signatures) + self.n_samples + ) + n_parameters_biases = self.n_modalities * self.n_samples + n_parameters_exposures = n_parameters_embeddings + n_parameters_biases + n_parameters = n_parameters_signatures + n_parameters_exposures + 1 + + return n_parameters + + @property + def bic(self) -> float: + return self._n_parameters * np.log(self.n_samples) - 2 * self.loglikelihood() + + def _update_alphas(self): + for model in self.models: + model._update_alpha() + + def _update_sigma_sq(self): + sum_norm_sigs = np.sum([np.sum(model.L**2) for model in self.models]) + sum_norm_samples = np.sum(self.models[0].U ** 2) + + sigma_sq = (sum_norm_sigs + sum_norm_samples) / ( + self.dim_embeddings * (np.sum(self.ns_signatures) + self.n_samples) + ) + sigma_sq = np.clip(sigma_sq, EPSILON, None) + + for model in self.models: + model.sigma_sq = sigma_sq + + def _update_Ws(self, ps, given_signatures): + for model, p, given_sigs in zip(self.models, ps, given_signatures): + if given_sigs is None: + model._update_W(p) + + def _update_ps(self): + return [model._update_p() for model in self.models] + + def _objective_fun_u(self, u, index, aux_cols): + s = -np.sum( + [ + model._objective_fun_u(u, index, aux_col, add_penalty_u=False) + for model, aux_col in zip(self.models, aux_cols) + ] + ) + s -= np.dot(u, u) / (2 * self.models[0].sigma_sq) + + return -s + + def _gradient_u(self, u, index, s_grads): + s = -np.sum( + [ + model._gradient_u(u, index, s_grad, add_penalty_u=False) + for model, s_grad in zip(self.models, s_grads) + ], + axis=0, + ) + s -= u / self.models[0].sigma_sq + + return -s + + def _hessian_u(self, u, index, outer_prods_Ls): + s = -np.sum( + [ + model._hessian_u(u, index, outer_prods_L, add_penalty_u=False) + for model, outer_prods_L in zip(self.models, outer_prods_Ls) + ], + axis=0, + ) + s -= np.diag(np.full(self.dim_embeddings, 1 / self.models[0].sigma_sq)) + + return -s + + def _update_u(self, index, aux_cols, outer_prods_Ls): + def objective_fun(u): + return self._objective_fun_u(u, index, aux_cols) + + s_grads = np.array( + [ + np.sum(aux_col * model.L, axis=1) + for model, aux_col in zip(self.models, aux_cols) + ] + ) + + def gradient(u): + return self._gradient_u(u, index, s_grads) + + def hessian(u): + return self._hessian_u(u, index, outer_prods_Ls) + + u = optimize.minimize( + fun=objective_fun, + x0=self.models[0].U[:, index], + method="Newton-CG", + jac=gradient, + hess=hessian, + options={"maxiter": 3}, + ).x + u[(0 < u) & (u < EPSILON)] = EPSILON + u[(-EPSILON < u) & (u < 0)] = -EPSILON + + for model in self.models: + model.U[:, index] = u + + def _update_U(self, auxs): + outer_prods_Ls = [ + np.einsum("mK,nK->Kmn", model.L, model.L) for model in self.models + ] + + for d in range(self.n_samples): + aux_cols = [aux[:, d] for aux in auxs] + self._update_u(d, aux_cols, outer_prods_Ls) + + def _update_Ls(self, auxs, outer_prods_U, given_signature_embeddings): + for model, aux, given_sig_embs in zip( + self.models, auxs, given_signature_embeddings + ): + if given_sig_embs is None: + model._update_L(aux, outer_prods_U) + + def _update_LsU(self, ps, given_signature_embeddings, given_sample_embeddings): + auxs = [ + np.einsum("vd,vkd->kd", model.X, p) for model, p in zip(self.models, ps) + ] + outer_prods_U = np.einsum("mD,nD->Dmn", self.models[0].U, self.models[0].U) + self._update_Ls(auxs, outer_prods_U, given_signature_embeddings) + + if given_sample_embeddings is None: + self._update_U(auxs) + + def _setup_data_parameters(self, data: list): + type_checker("data", data, list) + + if len(data) != self.n_modalities: + raise ValueError( + f"The input data has to be {self.n_modalities} " + "many named pandas dataframes." + ) + + for df in data: + type_checker("input dataframe", df, pd.DataFrame) + + if df.index.name is None: + raise ValueError( + "You have to set 'df.index.name' to a " + "meaningful name for every input dataframe." + ) + + self.modality_names = np.array([df.index.name for df in data]) + self.n_samples = data[0].shape[1] + + for model, df in zip(self.models, data): + model._setup_data_parameters(df) + + def _initialize( + self, + given_signatures=None, + given_signature_embeddings=None, + given_sample_embeddings=None, + init_kwargs=None, + ): + if given_sample_embeddings is None: + U = np.random.multivariate_normal( + np.zeros(self.dim_embeddings), + np.identity(self.dim_embeddings), + size=self.n_samples, + ).T + else: + U = given_sample_embeddings + + for model, modality_name, given_sigs, given_sig_embs in zip( + self.models, + self.modality_names, + given_signatures, + given_signature_embeddings, + ): + if given_sigs is None: + model.signature_names = np.char.add( + modality_name + " ", model.signature_names + ) + + model._initialize( + given_signatures=given_sigs, + given_signature_embeddings=given_sig_embs, + given_sample_embeddings=U, + init_kwargs=init_kwargs, + ) + + def fit( + self, + data: list, + given_signatures=None, + given_signature_embeddings=None, + given_sample_embeddings=None, + init_kwargs=None, + history=False, + verbose=0, + ): + if given_signatures is None: + given_signatures = [None for _ in range(self.n_modalities)] + + if given_signature_embeddings is None: + given_signature_embeddings = [None for _ in range(self.n_modalities)] + + self._setup_data_parameters(data) + self._initialize( + given_signatures=given_signatures, + given_signature_embeddings=given_signature_embeddings, + given_sample_embeddings=given_sample_embeddings, + init_kwargs=init_kwargs, + ) + of_values = [self.objective_function()] + sof_values = [self.objective_function()] + + n_iteration = 0 + converged = False + + while not converged: + n_iteration += 1 + + if verbose and n_iteration % 100 == 0: + print("iteration ", n_iteration) + + self._update_alphas() + ps = self._update_ps() + self._update_LsU(ps, given_signature_embeddings, given_sample_embeddings) + self._update_sigma_sq() + self._update_Ws(ps, given_signatures) + + of_values.append(self.objective_function()) + prev_sof_value = sof_values[-1] + sof_values.append(self._surrogate_objective_function(ps)) + rel_change = (sof_values[-1] - prev_sof_value) / np.abs(prev_sof_value) + converged = ( + rel_change < self.tol and n_iteration >= self.min_iterations + ) or (n_iteration >= self.max_iterations) + + if history: + self.history["objective_function"] = of_values[1:] + self.history["surrogate_objective_function"] = sof_values[1:] + + return self + + @paper_style + def plot_signatures( + self, + colors=None, + annotate_mutation_types=False, + figsize=None, + outfile=None, + **kwargs, + ): + if colors is None: + colors = [None for _ in range(self.n_modalities)] + + max_n_signatures = np.max(self.ns_signatures) + + if figsize is None: + figsize = (8 * self.n_modalities, 2 * max_n_signatures) + + fig, axes = plt.subplots(max_n_signatures, self.n_modalities, figsize=figsize) + + for axs, model, cols in zip(axes.T, self.models, colors): + model.plot_signatures( + colors=cols, + annotate_mutation_types=annotate_mutation_types, + axes=axs[: model.n_signatures], + **kwargs, + ) + + for ax in axs[model.n_signatures :]: + fig.delaxes(ax) + + plt.tight_layout() + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return axes + + @paper_style + def plot_exposures( + self, + reorder_signatures=True, + annotate_samples=True, + colors=None, + ncol_legend=1, + axes=None, + outfile=None, + **kwargs, + ): + """ + Visualize the exposures as a stacked bar chart, + see plot.py for the implementation. + + Input: + ------ + **kwargs: + arguments to be passed to exposure_plot + """ + if axes is None: + _, axes = plt.subplots( + self.n_modalities, figsize=(20, 3 * self.n_modalities) + ) + + if colors is None: + colors = [None for _ in range(self.n_modalities)] + + for n, (ax, model, cols) in enumerate(zip(axes, self.models, colors)): + ax = model.plot_exposures( + reorder_signatures=reorder_signatures, + annotate_samples=annotate_samples, + colors=cols, + ncol_legend=ncol_legend, + ax=ax, + **kwargs, + ) + ax.set_title(f"{self.modality_names[n]} signature exposures") + + plt.tight_layout() + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return axes + + @property + def corr_signatures(self) -> pd.DataFrame: + Ls = np.concatenate([model.L for model in self.models], axis=1) + signature_names = np.concatenate( + [model.signature_names for model in self.models] + ) + norms = np.sqrt(np.sum(Ls**2, axis=0)) + + corr_vector = np.array( + [ + np.dot(l1, l2) / (norms[k1] * norms[k1 + k2 + 1]) + for k1, l1 in enumerate(Ls.T) + for k2, l2 in enumerate(Ls[:, k1 + 1 :].T) + ] + ) + corr_matrix = squareform(corr_vector) + np.identity(np.sum(self.ns_signatures)) + corr = pd.DataFrame(corr_matrix, index=signature_names, columns=signature_names) + + return corr + + @property + def corr_samples(self) -> pd.DataFrame: + return self.models[0].corr_samples + + @paper_style + def plot_correlation(self, data="signatures", annot=False, outfile=None, **kwargs): + """ + Plot the correlation matrix of the signatures or samples. + See plot.py for the implementation of corr_plot. + + Input: + ------ + *args, **kwargs: + arguments to be passed to corr_plot + """ + value_checker("data", data, ["signatures", "samples"]) + + if data == "signatures": + corr = self.corr_signatures + + else: + corr = self.corr_samples + + clustergrid = corr_plot(corr, annot=annot, **kwargs) + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return clustergrid + + def _get_embedding_annotations(self, annotate_signatures, annotate_samples): + # Only annotate with the first 20 characters of names + annotations = np.empty(np.sum(self.ns_signatures) + self.n_samples, dtype="U20") + + if annotate_signatures: + signature_names = np.concatenate( + [model.signature_names for model in self.models] + ) + annotations[: len(signature_names)] = signature_names + + if annotate_samples: + annotations[-self.n_samples :] = self.models[0].sample_names + + return annotations + + @paper_style + def plot_embeddings( + self, + method="umap", + annotate_signatures=True, + annotate_samples=False, + normalize=False, + ax=None, + outfile=None, + **kwargs, + ): + """ + Plot the signature and sample embeddings. If the embedding dimension + is two, the embeddings will be plotted directly, ignoring the chosen method. + See plot.py for the implementation of scatter_2d, tsne_2d, pca_2d, umap_2d. + + Input: + ------ + methdod: str + Either 'tsne', 'pca' or 'umap'. The respective dimensionality reduction + will be applied to plot the signature and sample embeddings in 2D space. + + annotate_signatures: bool + + annotate_samples: bool + + normalize: bool + Normalize the embeddings before applying the dimensionality reduction. + + *args, **kwargs: + arguments to be passed to scatter_2d, tsne_2d, pca_2d or umap_2d + """ + value_checker("method", method, ["pca", "tsne", "umap"]) + annotations = self._get_embedding_annotations( + annotate_signatures, annotate_samples + ) + + Ls = np.concatenate([model.L for model in self.models], axis=1) + data = np.concatenate([Ls, self.models[0].U], axis=1).T + + if normalize: + data /= np.sum(data, axis=0) + + if self.dim_embeddings in [1, 2]: + warnings.warn( + f"The embedding dimension is {self.dim_embeddings}. " + f"The method argument '{method}' will be ignored " + "and the embeddings are plotted directly.", + UserWarning, + ) + + if self.dim_embeddings == 1: + ax = scatter_1d(data[:, 0], annotations=annotations, ax=ax, **kwargs) + + elif self.dim_embeddings == 2: + ax = scatter_2d(data, annotations=annotations, ax=ax, **kwargs) + + elif method == "tsne": + ax = tsne_2d(data, annotations=annotations, ax=ax, **kwargs) + + elif method == "pca": + ax = pca_2d(data, annotations=annotations, ax=ax, **kwargs) + + else: + ax = umap_2d(data, annotations=annotations, ax=ax, **kwargs) + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return ax + + def feature_change(self, in_modality=None, out_modalities="all", normalize=True): + if in_modality is None: + in_modality = self.modality_names[0] + + in_model = self.models[list(self.modality_names).index(in_modality)] + + if out_modalities == "all": + out_modalities = self.modality_names + + if type(out_modalities) is str: + out_modalities = [out_modalities] + + out_modalities = [name for name in out_modalities if name != in_modality] + out_modalities_indices = [ + n for n, name in enumerate(self.modality_names) if name in out_modalities + ] + results = [in_model.signatures] + + for n in out_modalities_indices: + result = self.models[n].signatures @ np.exp(self.models[n].L.T @ in_model.L) + result.columns = in_model.signature_names + + if normalize: + result = result / result.sum(axis=0) + + results.append(result) + + return results + + @paper_style + def plot_feature_change( + self, + in_modality=None, + out_modalities="all", + normalize=True, + colors=None, + annotate_mutation_types=False, + figsize=None, + outfile=None, + **kwargs, + ): + # result[0] are the 'in_modality' signatures + results = self.feature_change(in_modality, out_modalities, normalize) + n_signatures = results[0].shape[1] + n_feature_spaces = len(results) + + if colors is None: + colors = [None for _ in range(n_feature_spaces)] + + if figsize is None: + figsize = (8 * n_feature_spaces, 2 * n_signatures) + + fig, axes = plt.subplots(n_signatures, n_feature_spaces, figsize=figsize) + fig.suptitle("Signature feature change") + + for axs, result, cols in zip(axes.T, results, colors): + signatures_plot( + result, + colors=cols, + annotate_mutation_types=annotate_mutation_types, + axes=axs, + **kwargs, + ) + + plt.tight_layout() + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return axes diff --git a/src/salamander/nmf_framework/mvnmf.py b/src/salamander/nmf_framework/mvnmf.py new file mode 100644 index 0000000..b96f7e2 --- /dev/null +++ b/src/salamander/nmf_framework/mvnmf.py @@ -0,0 +1,406 @@ +import multiprocessing +import os +import warnings +from copy import deepcopy + +import numpy as np +import pandas as pd +from scipy import stats + +from ..utils import ( + differential_tail_test, + kl_divergence, + normalize_WH, + poisson_llh, + samplewise_kl_divergence, +) +from .nmf import NMF + +EPSILON = np.finfo(np.float32).eps + + +class MvNMF(NMF): + """ + Min-volume non-negative matrix factorization. Based on Algorithm 1 of + + Leplat, V., Gillis, N. and Ang, A.M., 2020. + Blind audio source separation with minimum-volume beta-divergence NMF. + IEEE Transactions on Signal Processing, 68, pp.3400-3410. + + Input: + ------ + n_signatures: int + Number of signatures to decipher. + + init_method: str + One of "custom", "flat", "hierarchical_cluster", "nndsvd", + "nndsvda", "nndsvdar" "random" and "separableNMF". Please see the initialization + module for further details on each method. + + lambda_tilde : float + Objective function hyperparameter. + + delta : float + Objective function hyperparameter. + + min_iterations : int, default=200 + Minimum number of iterations. + + max_iterations : int, default=400 + Maximum number of iterations. + + tol : float, default=1e-7 + Tolerance of the stopping condition. + + Note: + ----- + The algorithm should work better when the initial guesses are better. + One reason lies in lambda and lambda_tilde. + Lambda is calculated in a way such that the two terms + in the objective function are comparable. Ideally, lambda should be set to + kl_divergence(X, W_true @ H_true)/abs(volume(W_true)) * lambda_tilde. + In our code, the true W and H are replaced by the initial guesses. + So if the initial guesses are good, then indeed the two terms will be + comparable. If the initial guesses are far off, then the kl_divergence + part will be far over-estimated. As a result, the two terms are + not comparable anymore. One potential improvement is to first run + a small number of NMF iterations, and then use the NMF results + as hot starts for the mvNMF algorithm. + """ + + def __init__( + self, + n_signatures=1, + init_method="nndsvd", + lambda_tilde=1e-5, + delta=1.0, + min_iterations=500, + max_iterations=10000, + tol=1e-7, + ): + super().__init__(n_signatures, init_method, min_iterations, max_iterations, tol) + self.lambda_tilde = lambda_tilde + self.lam = lambda_tilde + self.delta = delta + self.gamma = 1.0 + + @property + def reconstruction_error(self): + return kl_divergence(self.X, self.W, self.H) + + @property + def samplewise_reconstruction_error(self): + return samplewise_kl_divergence(self.X, self.W, self.H) + + @staticmethod + def _volume_logdet(W, delta) -> float: + n_signatures = W.shape[1] + diag = delta * np.identity(n_signatures) + volume = np.log(np.linalg.det(W.T @ W + diag)) + + return volume + + @staticmethod + def _objective_function( + X: np.ndarray, W: np.ndarray, H: np.ndarray, lam: float, delta: float + ) -> float: + reconstruction_error = kl_divergence(X, W, H) + volume = MvNMF._volume_logdet(W, delta) + loss = reconstruction_error + lam * volume + + return loss + + def objective_function(self): + return self._objective_function(self.X, self.W, self.H, self.lam, self.delta) + + @property + def objective(self) -> str: + return "minimize" + + def loglikelihood(self) -> float: + return poisson_llh(self.X, self.W, self.H) + + def _update_H(self): + self.H *= self.W.T @ (self.X / (self.W @ self.H)) + self.H /= np.sum(self.W, axis=0)[:, np.newaxis] + self.H = self.H.clip(EPSILON) + + def _update_W_unconstrained(self): + diag = np.diag(np.full(self.n_signatures, self.delta)) + Y = np.linalg.inv(self.W.T @ self.W + diag) + + Y_minus = np.maximum(0, -Y) + Y_abs = np.abs(Y) + + WY_minus = self.W @ Y_minus + WY_abs = self.W @ Y_abs + + rowsums_H = np.sum(self.H, axis=1) + + discriminant_s1 = (rowsums_H - 4 * self.lam * WY_minus) ** 2 + discriminant_s2 = ( + 8 * self.lam * WY_abs * ((self.X / (self.W @ self.H)) @ self.H.T) + ) + + numerator_s1 = np.sqrt(discriminant_s1 + discriminant_s2) + numerator_s2 = -rowsums_H + 4 * self.lam * WY_minus + numerator = numerator_s1 + numerator_s2 + + denominator = 4 * self.lam * WY_abs + + W_uc = self.W * numerator / denominator + W_uc = W_uc.clip(EPSILON) + + return W_uc + + def _line_search(self, W_uc, loss_prev): + W_new = self.W + self.gamma * (W_uc - self.W) + W_new, H_new = normalize_WH(W_new, self.H) + W_new, H_new = W_new.clip(EPSILON), H_new.clip(EPSILON) + + loss = self._objective_function(self.X, W_new, H_new, self.lam, self.delta) + + while loss > loss_prev and self.gamma > 1e-16: + self.gamma *= 0.8 + + W_new = self.W + self.gamma * (W_uc - self.W) + W_new, H_new = normalize_WH(W_new, self.H) + W_new, H_new = W_new.clip(EPSILON), H_new.clip(EPSILON) + + loss = self._objective_function(self.X, W_new, H_new, self.lam, self.delta) + + self.gamma = min(1.0, 2 * self.gamma) + self.W, self.H = W_new, H_new + + # pylint: disable-next=W0221 + def _update_W(self, loss_prev): + W_uc = self._update_W_unconstrained() + self._line_search(W_uc, loss_prev) + + def _initialize_mvnmf_parameters(self): + # lambda is chosen s.t. both loss summands + # approximately contribute equally for lambda_tilde = 1 + init_reconstruction_error = self.reconstruction_error + init_volume = self._volume_logdet(self.W, self.delta) + self.lam = self.lambda_tilde * init_reconstruction_error / abs(init_volume) + self.gamma = 1.0 + + def fit( + self, + data: pd.DataFrame, + given_signatures=None, + init_kwargs=None, + history=False, + verbose=0, + ): + """ + Input: + ------ + data : array-like of shape (n_features, n_samples) + The mutation count data. + + init_kwargs: dict + Any further keyword arguments to be passed to the initialization method. + This includes, for example, a possible 'seed' keyword argument + for all stochastic methods. + + verbose : int, default=0 + Verbosity level. + """ + self._setup_data_parameters(data) + self._initialize(given_signatures, init_kwargs) + self._initialize_mvnmf_parameters() + + of_values = [self.objective_function()] + n_iteration = 0 + converged = False + + while not converged: + n_iteration += 1 + + if verbose and n_iteration % 100 == 0: + print(f"iteration {n_iteration}") + + self._update_H() + prev_of_value = of_values[-1] + + if given_signatures is None: + self._update_W(prev_of_value) + + of_values.append(self.objective_function()) + rel_change = (prev_of_value - of_values[-1]) / prev_of_value + converged = ( + rel_change < self.tol and n_iteration >= self.min_iterations + ) or (n_iteration >= self.max_iterations) + + if history: + self.history["objective_function"] = of_values[1:] + + return self + + +class MvNMFHyperparameterSelector: + """ + The volume-regularization weight is the only hyperparameter of mvNMF. + This class implements methods to select the "optimal" volume-regularization weight. + The framework of hyperparameter selectors allow to implement + a denovo signature analysis in an NMF algorithm agnostic manner: A dictionary can + be used to set all hyperparameters, irrespective of the NMF algorithm + and its arbitrary number of hyperparameters. + + The best model is defined as the model with the strongest volume regularization + such that the samplewise reconstruction errors are still (approximately) identically + distributed to the model with the lowest volume regularization. + The distributions are compared with the Mann-Whitney-U test. + """ + + # fmt: off + default_lambda_tildes = ( + 1e-10, 2e-10, 5e-10, 1e-9, 2e-9, 5e-9, + 1e-8, 2e-8, 5e-8, 1e-7, 2e-7, 5e-7, + 1e-6, 2e-6, 5e-6, 1e-5, 2e-5, 5e-5, + 1e-4, 2e-4, 5e-4, 1e-3, 2e-3, 5e-3, + 1e-2, 2e-2, 5e-2, 1e-1, 2e-1, 5e-1, + 1.0, 2.0, + ) + # fmt: on + + def __init__(self, lambda_tildes=default_lambda_tildes, pthresh=0.05): + """ + Inputs: + ------ + lambda_tildes: tuple + An ordered list of possible volume-regularization parameters. + + pthresh: float + The distribution of samplewise reconstruction errors between two + mvNMF fitted models is considered different if the Mann-Whitney-U + test pvalue of comparing their, and their tail, distribution is lower + than pthresh. + """ + self.lambda_tildes = lambda_tildes + self.pthresh = pthresh + + # initialize selection dependent attributes + self.mvnmf_algorithm = None + self.data = None + self.given_signatures = None + self.init_kwargs = None + self.verbose = 0 + + def _job(self, lambda_tilde): + """ + Apply mvNMF for a single lambda_tilde volume regularization. + """ + model = deepcopy(self.mvnmf_algorithm) + model.lambda_tilde = lambda_tilde + model.fit( + data=self.data, + given_signatures=self.given_signatures, + init_kwargs=self.init_kwargs, + verbose=0, + ) + + if self.verbose: + print(f"mvNMF with lambda_tilde = {lambda_tilde:.2E} finished.") + + return model + + def _indicate_good_models(self, rerrors_base, rerrors_rest): + """ + Compare the distributions of the samplewise baseline reconstruction errors + and the samplewise model reconstruction errors. + + Output: + ------- + indicators: np.ndarray + One-dimensional boolean array indicating all models having + samplewise reconstruction errors similar to the baseline errors. + """ + n_models_rest = len(rerrors_rest) + + pvalue_indicators = np.empty(n_models_rest, dtype=bool) + pvalue_tail_indicators = np.empty(n_models_rest, dtype=bool) + + for i, rerrors in enumerate(rerrors_rest): + # Turn everything non-negative for the differential tail test. + # Note: The Mann-Whitney U test statistic is shift-invariant + shift = np.min([rerrors_base, rerrors]) + re_base = rerrors_base - shift + re = rerrors - shift + + pvalue = stats.mannwhitneyu(re_base, re, alternative="less")[1] + pvalue_indicators[i] = pvalue > self.pthresh + + pvalue_tail = differential_tail_test( + re_base, re, percentile=90, alternative="less" + )[1] + pvalue_tail_indicators[i] = pvalue_tail > self.pthresh + + indicators = pvalue_indicators & pvalue_tail_indicators + + return indicators + + def _get_best_lambda_tilde(self, indicators): + # np.argmin returns the first "bad" model index + # Note: self.lambda_tildes[index] will be a "good" model because the number of + # possible volume regularizations and the length of indicators differs by one. + index = np.argmin(indicators) + + if all(indicators): + index = len(indicators) + warnings.warn( + "For all lambda_tilde, the sample-wise reconstruction errors are " + "comparable to the reconstruction errors with no regularization. " + "The model with the strongest volume regularization is selected.", + UserWarning, + ) + + if index == 0: + warnings.warn( + "The smallest lambda_tilde is selected. The optimal lambda_tilde " + "might be smaller. We suggest to extend the grid to smaller " + "lambda_tilde values to validate.", + UserWarning, + ) + + best_lambda_tilde = self.lambda_tildes[index] + + return best_lambda_tilde + + def select( + self, + mvnmf_algorithm, + data: pd.DataFrame, + given_signatures=None, + init_kwargs=None, + ncpu=1, + verbose=0, + ): + self.mvnmf_algorithm = mvnmf_algorithm + self.data = data + self.given_signatures = given_signatures + self.init_kwargs = init_kwargs + self.verbose = verbose + + if ncpu is None: + ncpu = os.cpu_count() + + workers = multiprocessing.Pool(ncpu) + models = workers.map(self._job, self.lambda_tildes) + workers.close() + workers.join() + + samplewise_rerrors_all = np.array( + [model.samplewise_reconstruction_error for model in models] + ) + rerrors_base, rerrors_rest = ( + samplewise_rerrors_all[0], + samplewise_rerrors_all[1:], + ) + + indicators = self._indicate_good_models(rerrors_base, rerrors_rest) + best_lambda_tilde = self._get_best_lambda_tilde(indicators) + hyperparameters = {"lambda_tilde": best_lambda_tilde} + + return hyperparameters diff --git a/src/salamander/nmf_framework/nmf.py b/src/salamander/nmf_framework/nmf.py new file mode 100644 index 0000000..4f916e9 --- /dev/null +++ b/src/salamander/nmf_framework/nmf.py @@ -0,0 +1,346 @@ +import warnings +from abc import abstractmethod + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from ..plot import pca_2d, scatter_1d, scatter_2d, tsne_2d, umap_2d +from ..utils import match_signatures_pair, normalize_WH, value_checker +from .initialization import ( + init_custom, + init_flat, + init_nndsvd, + init_random, + init_separableNMF, +) +from .signature_nmf import SignatureNMF + +EPSILON = np.finfo(np.float32).eps + + +class NMF(SignatureNMF): + """ + The abstract class NMF unifies the structure of NMF algorithms + with a single signature matrix W and exposure matrix H. + Examples of these algorithms include the standard NMF algorithm + (Lee and Seung, 1999), minimum volume NMF (mvNMF) or NMF variants + with regularizations on the entries of W and H. + All of these NMF algorithms have the same parameters. Therefore, + many properties of interest such as the signature correlation martrix + or the sample embeddings are computed in the same manner. + + Overview: + + Every child class has to implement the following attributes: + + - reconstruction_error: float + The reconstruction error between the count matrix and + the reconstructed count matrix. + + - samplewise_reconstruction_error: np.ndarray + The samplewise reconstruction error between the sample counts + and the reconstructed sample counts. + + - objective: str + "minimize" or "maximize". Whether the NMF algorithm maximizes or + minimizes the objective function. + Some algorithms maximize a likelihood, others minimize a distance. + The distinction is useful for filtering NMF runs based on + the fitted objective function value downstream. + + + Every child class has to implement the following methods: + + - objective_function: + The algorithm-specific objective function + + - loglikelihood: + The loglikelihood of the underyling generative model + + - _update_W: + update the signature matrix W + + - _update_H: + update the exposure matrix H + + - fit: + Apply the NMF algorithm for a given mutation count data or + for given signatures and mutation count data + + + The following attributes are implemented in the abstract class NMF: + + - signatures: pd.DataFrame + The signature matrix including mutation type names and signature names + + - exposures: pd.DataFrame + The exposure matrix including the signature names and sample names + + - _n_parameters: + The number of parameters fitted + + - corr_signatures: pd.DataFrame + The signature correlation matrix induced by their sample exposures + + - corr_samples: pd.DataFrame + The sample correlation matrix induced by their signature exposures + + + The following methods are implemented in the abstract class NMF: + + - initialize: + Initialize all model parameters and latent variables depending on the + initialization method chosen + + - _get_embedding_annotations: + A helper function to get the sample names for the embedding plots + + - plot_embeddings: + Plot signature or sample embeddings in 2D using PCA, tSNE or UMAP. + The respective plotting functions are implemented in the plot.py module. + + More details are explained in the respective attributes and methods. + """ + + def __init__( + self, + n_signatures=1, + init_method="nndsvd", + min_iterations=500, + max_iterations=10000, + tol=1e-7, + ): + """ + Input: + ------ + n_signatures: int + The number of underlying signatures that are assumed to + have generated the mutation count data. + + init_method: str + One of "custom", "flat", "hierarchical_cluster", "nndsvd", + "nndsvda", "nndsvdar" "random" and "separableNMF". + See the initialization module for further details on each method. + + min_iterations: int + The minimum number of iterations to perform during inference + + max_iterations: int + The maximum number of iterations to perform during inference + + tol: float + The NMF algorithm is converged when the relative change + of the objective function of one iteration is smaller + than the tolerance 'tol'. + """ + super().__init__(n_signatures, init_method, min_iterations, max_iterations, tol) + + # initialize data/fitting dependent attributes + self.W, self.H = None, None + + @property + def signatures(self) -> pd.DataFrame: + signatures = pd.DataFrame( + self.W, index=self.mutation_types, columns=self.signature_names + ) + + return signatures + + @property + def exposures(self) -> pd.DataFrame: + exposures = pd.DataFrame( + self.H, index=self.signature_names, columns=self.sample_names + ) + + return exposures + + @property + def _n_parameters(self) -> int: + """ + There are n_features * n_signatures parameters corresponding to + the signature matrix and n_signatures * n_samples parameters + corresponding to the exposure matrix. + """ + return self.n_signatures * (self.n_features + self.n_samples) + + @abstractmethod + def _update_W(self): + pass + + @abstractmethod + def _update_H(self): + pass + + def _initialize(self, given_signatures=None, init_kwargs=None): + """ + Initialize the signature matrix W and exposure matrix H. + When the signatures are given, the initialization + of W is overwritten by the given signatures. + + Input: + ------ + init_kwargs: dict + Any further keywords arguments to be passed to the initialization method. + This includes, for example, a possible 'seed' keyword argument + for all stochastic methods. + """ + if given_signatures is not None: + self._check_given_signatures(given_signatures) + + init_kwargs = {} if init_kwargs is None else init_kwargs.copy() + + if self.init_method == "custom": + self.W, self.H = init_custom(self.X, self.n_signatures, **init_kwargs) + + elif self.init_method == "flat": + self.W, self.H = init_flat(self.X, self.n_signatures) + + elif self.init_method in ["nndsvd", "nndsvda", "nndsvdar"]: + self.W, self.H = init_nndsvd( + self.X, self.n_signatures, init=self.init_method, **init_kwargs + ) + + elif self.init_method == "random": + self.W, self.H = init_random(self.X, self.n_signatures, **init_kwargs) + + else: + self.W = init_separableNMF(self.X, self.n_signatures) + + if given_signatures is not None: + self.W = given_signatures.copy().values + self.signature_names = given_signatures.columns.to_numpy(dtype=str) + + if not hasattr(self, "H"): + _, self.H = init_random(self.X, self.n_signatures) + + self.W, self.H = normalize_WH(self.W, self.H) + self.W, self.H = self.W.clip(EPSILON), self.H.clip(EPSILON) + + @property + def corr_signatures(self) -> pd.DataFrame: + """ + The correlation of two signatures is given by the pearson correlation of + the respective rows of the exposure matrix H. + + The pandas dataframe method 'corr' computes the pairwise correlation of columns. + """ + return self.exposures.T.corr(method="pearson") + + @property + def corr_samples(self) -> pd.DataFrame: + """ + The correlation of two samples is given by the pearson correlation of + the respective columns of the exposure matrix H. + + The pandas dataframe method 'corr' computes the pairwise correlation of columns. + """ + return self.exposures.corr(method="pearson") + + def reorder(self, other_signatures, metric="cosine", keep_names=False): + reordered_indices = match_signatures_pair( + other_signatures, self.signatures, metric=metric + ) + self.W = self.W[:, reordered_indices] + self.H = self.H[reordered_indices, :] + + if keep_names: + self.signature_names = self.signature_names[reordered_indices] + + return reordered_indices + + def _get_embedding_annotations(self, annotate_samples) -> np.ndarray: + # Only annotate with the first 20 characters of names + annotations = np.empty(self.n_samples, dtype="U20") + + if annotate_samples: + annotations[:] = self.sample_names + + return annotations + + def plot_embeddings( + self, + method="umap", + annotate_samples=False, + annotation_kwargs=None, + ax=None, + outfile=None, + **kwargs, + ): + """ + Plot the sample embeddings using the exposure matrix H. + If the embedding dimension is set to two, the embeddings will + be plotted directly, ignoring the method chosen. + See plot.py for the implementation of scatter_2d, tsne_2d, pca_2d, umap_2d. + + Input: + ------ + methdod: str + Either 'tsne', 'pca' or 'umap'. The respective dimensionality reduction + will be applied to plot signature and sample embeddings in 2D. + + **kwargs: + Arguments to be passed to scatter_2d, tsne_2d, pca_2d or umap_2d + """ + value_checker("method", method, ["pca", "tsne", "umap"]) + + data = self.H.T + annotations = self._get_embedding_annotations(annotate_samples) + + if self.n_signatures in [1, 2]: + warnings.warn( + f"The number of signatures is {self.n_signatures}. " + f"The method argument '{method}' will be ignored " + "and the embeddings are plotted directly.", + UserWarning, + ) + + if self.n_signatures == 1: + ax = scatter_1d( + data[:, 0], + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + elif self.n_signatures == 2: + ax = scatter_2d( + data, + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + elif method == "tsne": + ax = tsne_2d( + data, + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + elif method == "pca": + ax = pca_2d( + data, + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + else: + ax = umap_2d( + data, + annotations=annotations, + annotation_kwargs=annotation_kwargs, + ax=ax, + **kwargs, + ) + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return ax diff --git a/src/salamander/nmf_framework/signature_nmf.py b/src/salamander/nmf_framework/signature_nmf.py new file mode 100644 index 0000000..6c54594 --- /dev/null +++ b/src/salamander/nmf_framework/signature_nmf.py @@ -0,0 +1,421 @@ +from abc import ABC, abstractmethod + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from ..plot import corr_plot, exposures_plot, paper_style, signatures_plot +from ..utils import type_checker, value_checker + + +class SignatureNMF(ABC): + """ + The abstract class SignatureNMF unifies the structure of + multiple NMF algorithms used for signature analysis. + + Common properties and methods of all algorithms are indicated, + i.e. have to be implemented by child classes, or implemented. Overview: + + Every child class has to implement the following attributes: + + - signatures: pd.DataFrame + The signature matrix including mutation type names and signature names + + - exposures: pd.DataFrames + The exposure matrix including the signature names and sample names + + - _n_parameters: int + The number of parameters fitted by the NMF algorithm. + This is needed to compute the Bayesian Information Criterion (BIC) + + - reconstruction_error: float + The reconstruction error between the count matrix and + the reconstructed count matrix. + + - samplewise_reconstruction_error: np.ndarray + The samplewise reconstruction error between the sample counts and + the reconstructed sample counts. + + - objective: str + "minimize" or "maximize". Whether the NMF algorithm maximizes or + minimizes the objective function. Some algorithms maximize a likelihood, + others minimize a distance. The distinction is useful for filtering NMF runs + based on the fitted objective function value downstream. + + - corr_signatures: pd.DataFrame + The signature correlation matrix + + - corr_samples: pd.DataFrame + The sample correlation matrix + + + Every child class has to implement the following methods: + + - objective_fuction: + The objective function to optimize when running the algorithm + + - loglikelihood: + The loglikelihood of the underyling generative model + + - _initialize: + A method to initialize all model parameters before fitting + + - fit: + Run the NMF algorithm for a given mutation count data. Every + fit method should also implement a "refitting version", where the signatures + W are known in advance and fixed. + + - plot_embeddings: + Plot the sample (and potentially the signature) embeddings in 2D. + + + The following attributes and methods are implemented in SignatureNMF: + + - data_reconstructed: pd.DataFrame + The recovered mutation count data given + the current signatures and exposures. + + - X_reconstructed: np.ndarray + The recovered mutation count matrix given + the current signatures and exposures + + - bic: float + The value of the Bayesian Information Criterion (BIC) + + - _setup_parameters_fitting: + Perform parameter checks and add the input mutation counts matrix + as an attributes + + - plot_signatures: + Plot the signatures using the signatures_plot function implemented in + the plot module + + - plot_correlation: + Plot the correlation of either the signatures or exposures + using the corr_plot function implemented in the plot module + + More specific docstrings are written for the respective attributes and methods. + """ + + def __init__( + self, + n_signatures=1, + init_method="nndsvd", + min_iterations=500, + max_iterations=10000, + tol=1e-7, + ): + """ + Input: + ------ + n_signatures: int + The number of underlying signatures that are assumed to + have generated the mutation count data + + init_method: str + The initialization method for the NMF algorithm + + min_iterations: int + The minimum number of iterations to perform by the NMF algorithm + + max_iterations: int + The maximum number of iterations to perform by the NMF algorithm + + tol: float + The NMF algorithm is converged when the relative change of + the objective function of one iteration is smaller + than the tolerance 'tol'. + """ + init_methods = [ + "custom", + "flat", + "hierarchical_cluster", + "nndsvd", + "nndsvda", + "nndsvdar", + "random", + "separableNMF", + ] + value_checker("init_method", init_method, init_methods) + + self.n_signatures = n_signatures + self.signature_names = np.array([f"Sig{k+1}" for k in range(n_signatures)]) + self.init_method = init_method + self.min_iterations = min_iterations + self.max_iterations = max_iterations + self.tol = tol + + # initialize data/fitting dependent attributes + self.X = None + self.n_features = 0 + self.n_samples = 0 + self.mutation_types = np.empty(0, dtype=str) + self.sample_names = np.empty(0, dtype=str) + self.history = {} + + @property + @abstractmethod + def signatures(self) -> pd.DataFrame: + """ + Extract the mutational signatures as a pandas dataframe. + """ + pass + + @property + @abstractmethod + def exposures(self) -> pd.DataFrame: + """ + Extract the signature exposures of samples as a pandas dataframe. + """ + pass + + @property + def data_reconstructed(self) -> pd.DataFrame: + return (self.signatures @ self.exposures).astype(int) + + @property + def X_reconstructed(self) -> np.ndarray: + return self.data_reconstructed.values + + @property + @abstractmethod + def reconstruction_error(self) -> float: + """ + The reconstruction error between the count matrix and + the reconstructed count matrix. + """ + pass + + @property + @abstractmethod + def samplewise_reconstruction_error(self) -> np.ndarray: + """ + The samplewise reconstruction error between the sample counts and + the reconstructed sample counts. + """ + pass + + @abstractmethod + def objective_function(self) -> float: + """ + The objective function to be optimized during fitting. + """ + pass + + @abstractmethod + def loglikelihood(self) -> float: + """ + The log-likelihood of the underlying generative model. + """ + pass + + @property + @abstractmethod + def _n_parameters(self) -> int: + """ + Every child class has to implement a function returning + the number of parameters estimated by the respective model. + This is allows to, for example, implement the BIC + (Bayesian information criterion). The BIC can be used to + estimate the optimal number of signatures. + """ + pass + + @property + def bic(self) -> float: + """ + Bayesian information criterion (BIC). + Can only be called after the _setup_parameters_fitting function as it + requires the number of samples be an attribute. + """ + return self._n_parameters * np.log(self.n_samples) - 2 * self.loglikelihood() + + def _check_given_signatures(self, given_signatures: pd.DataFrame): + """ + Check if the given signatures are compatible with the + number of signatures of the algorithm and the + mutation types of the input data and. + + given_signatures: pd.DataFrame + Known signatures that should be fixed by the algorithm. + """ + type_checker("given_signatures", given_signatures, pd.DataFrame) + given_mutation_types = given_signatures.index.to_numpy(dtype=str) + compatible = ( + np.array_equal(given_mutation_types, self.mutation_types) + and given_signatures.shape[1] == self.n_signatures + ) + + if not compatible: + raise ValueError( + f"You have to provide {self.n_signatures} signatures with " + f"mutation types matching to your data." + ) + + @abstractmethod + def _initialize(self): + """ + Initialize model parameters and attributes before fitting. + Enforcing the existence of _initialize unifies the implementation of + the NMF algorithms. + + Example: + + Before running the Lee & Seung NMF multiplicative update rules to + decompose the mutation count matrix X into a signature matrix W and + an exposure matrix H, both W and H have to be initialized. + """ + pass + + def _setup_data_parameters(self, data: pd.DataFrame): + """ + Perform parameter checks before running the fit method. + + Input: + ------ + data: pd.DataFrame + The mutation count pandas dataframe with indices and column names. + Samples are expected to corresponding to columns. + """ + type_checker("data", data, pd.DataFrame) + self.X = data.values + self.n_features, self.n_samples = data.shape + self.mutation_types = data.index.values.astype(str) + self.sample_names = data.columns.values.astype(str) + + @abstractmethod + def fit(self, data: pd.DataFrame, given_signatures=None): + """ + Fit the model parameters. Child classes are expected to handle + 'given_signatures' appropriately. + + Input: + ------ + data: pd.DataFrame + The named mutation count data of shape (n_features, n_samples). + + given_signatures: pd.DataFrame, by default None + In the case of refitting, 'given_signatures' + are the a priori known signatures. + The number of signatures has to match to the NMF algorithm + instance and the mutation type names have to match to the names + of the mutation count data. + """ + pass + + @paper_style + def plot_signatures( + self, + catalog=None, + colors=None, + annotate_mutation_types=False, + axes=None, + outfile=None, + **kwargs, + ): + """ + Plot the signatures, see plot.py for the implementation of signatures_plot. + + Input: + ------ + **kwargs: + arguments to be passed to signatures_plot + """ + axes = signatures_plot( + self.signatures, + catalog=catalog, + colors=colors, + annotate_mutation_types=annotate_mutation_types, + axes=axes, + **kwargs, + ) + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return axes + + @paper_style + def plot_exposures( + self, + reorder_signatures=True, + annotate_samples=True, + colors=None, + ncol_legend=1, + ax=None, + outfile=None, + **kwargs, + ): + """ + Visualize the exposures as a stacked bar chart, + see plot.py for the implementation. + + Input: + ------ + **kwargs: + arguments to be passed to exposure_plot + """ + ax = exposures_plot( + exposures=self.exposures, + reorder_signatures=reorder_signatures, + annotate_samples=annotate_samples, + colors=colors, + ncol_legend=ncol_legend, + ax=ax, + **kwargs, + ) + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return ax + + @property + @abstractmethod + def corr_signatures(self) -> pd.DataFrame: + """ + Every child class of SignatureNMF has to implement a function that + returns the signature correlation matrix as a pandas dataframe. + """ + pass + + @property + @abstractmethod + def corr_samples(self) -> pd.DataFrame: + """ + Every child class of SignatureNMF has to implement a function that + returns the sample correlation matrix as a pandas dataframe. + """ + pass + + def plot_correlation(self, data="signatures", annot=False, outfile=None, **kwargs): + """ + Plot the correlation matrix of the signatures or samples. + See plot.py for the implementation of corr_plot. + + Input: + ------ + *args, **kwargs: + arguments to be passed to corr_plot + """ + value_checker("data", data, ["signatures", "samples"]) + + if data == "signatures": + corr = self.corr_signatures + + else: + corr = self.corr_samples + + clustergrid = corr_plot(corr, annot=annot, **kwargs) + + if outfile is not None: + plt.savefig(outfile, bbox_inches="tight") + + return clustergrid + + @abstractmethod + def plot_embeddings(self): + """ + Plot the sample (and potentially the signature) embeddings in 2D. + """ + pass diff --git a/src/salamander/plot.py b/src/salamander/plot.py new file mode 100644 index 0000000..1221713 --- /dev/null +++ b/src/salamander/plot.py @@ -0,0 +1,515 @@ +import warnings +from functools import wraps + +import fastcluster +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import umap +from scipy.cluster import hierarchy +from scipy.spatial.distance import pdist +from sklearn.decomposition import PCA +from sklearn.manifold import TSNE + +from .consts import COLORS_INDEL83, COLORS_SBS96, INDEL_TYPES_83, SBS_TYPES_96 +from .utils import match_to_catalog + + +def paper_style(func): + @wraps(func) + def rc_wrapper(*args, **kwargs): + sns.set_context("notebook") + sns.set_style("ticks") + + params = { + "axes.edgecolor": "black", + "axes.labelsize": 14, + "axes.spines.top": False, + "axes.spines.right": False, + "axes.titlesize": 16, + "errorbar.capsize": 3, + "font.family": "DejaVu Sans", + "legend.fontsize": 12, + "lines.markersize": 8, + "pdf.fonttype": 42, + "xtick.labelsize": 12, + "ytick.labelsize": 12, + } + + mpl.rcParams.update(params) + + return func(*args, **kwargs) + + return rc_wrapper + + +def _annotate_plot( + ax, data, annotations, ha="left", fontsize="medium", color="black", **kwargs +): + for data_point, annotation in zip(data, annotations): + ax.text( + data_point[0] + 0.01, + data_point[1] + 0.01, + annotation, + ha=ha, + fontsize=fontsize, + color=color, + **kwargs, + ) + + +@paper_style +def scatter_1d( + data: np.ndarray, annotations=None, annotation_kwargs=None, ax=None, **kwargs +): + if data.ndim != 1: + raise ValueError(f"The datapoints of {data} (rows) have to be one-dimensional.") + + annotation_kwargs = {} if annotation_kwargs is None else annotation_kwargs.copy() + + if ax is None: + _, ax = plt.subplots(figsize=(6, 1)) + + y_coordinates = np.zeros_like(data) + + ax.spines[["left", "bottom"]].set_visible(False) + ax.get_yaxis().set_visible(False) + ax.axhline(y=0, color="black", zorder=1) + sns.scatterplot(x=data, y=y_coordinates, ax=ax, zorder=2, **kwargs) + + if annotations is not None: + annotation_data = np.vstack([data, y_coordinates]).T + _annotate_plot(ax, annotation_data, annotations, **annotation_kwargs) + + return ax + + +@paper_style +def scatter_2d(data, annotations=None, annotation_kwargs=None, ax=None, **kwargs): + """ + The rows (!) of 'data' are assumed to be the data points. + """ + if data.shape[1] != 2: + raise ValueError(f"The datapoints of {data} (rows) have to be two-dimensional.") + + annotation_kwargs = {} if annotation_kwargs is None else annotation_kwargs.copy() + + if ax is None: + _, ax = plt.subplots(figsize=(6, 6)) + + ax.set(xlabel="x", ylabel="y") + sns.scatterplot(x=data[:, 0], y=data[:, 1], ax=ax, **kwargs) + + if annotations is not None: + _annotate_plot(ax, data, annotations, **annotation_kwargs) + + return ax + + +@paper_style +def pca_2d(data, annotations=None, annotation_kwargs=None, ax=None, **kwargs): + """ + The rows (!) of 'data' are assumed to be the data points. + """ + data_projected = PCA(n_components=2).fit_transform(data) + annotation_kwargs = {} if annotation_kwargs is None else annotation_kwargs.copy() + + if ax is None: + _, ax = plt.subplots(figsize=(6, 6)) + + ax.set(xlabel="PC1", ylabel="PC2") + sns.scatterplot(x=data_projected[:, 0], y=data_projected[:, 1], ax=ax, **kwargs) + + if annotations is not None: + _annotate_plot(ax, data_projected, annotations, **annotation_kwargs) + + return ax + + +@paper_style +def tsne_2d( + data, perplexity=30, annotations=None, annotation_kwargs=None, ax=None, **kwargs +): + """ + The rows (!) of 'data' are assumed to be the single data points. + """ + annotation_kwargs = {} if annotation_kwargs is None else annotation_kwargs.copy() + + if ax is None: + _, ax = plt.subplots(figsize=(6, 6)) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + data_projected = TSNE(perplexity=perplexity).fit_transform(data) + + ax.set(xlabel="t-SNE1", xticks=[], ylabel="t-SNE2", yticks=[]) + sns.scatterplot(x=data_projected[:, 0], y=data_projected[:, 1], ax=ax, **kwargs) + + if annotations is not None: + _annotate_plot(ax, data_projected, annotations, **annotation_kwargs) + + return ax + + +@paper_style +def umap_2d( + data, + n_neighbors=15, + min_dist=0.1, + annotations=None, + annotation_kwargs=None, + ax=None, + **kwargs, +): + """ + The rows (!) of 'data' are assumed to be the single data points. + """ + annotation_kwargs = {} if annotation_kwargs is None else annotation_kwargs.copy() + + if ax is None: + _, ax = plt.subplots(figsize=(6, 6)) + + n_neighbors = min(n_neighbors, len(data) - 1) + data_projected = umap.UMAP( + n_neighbors=n_neighbors, min_dist=min_dist + ).fit_transform(data) + + ax.set(xlabel="UMAP1", xticks=[], ylabel="UMAP2", yticks=[]) + sns.scatterplot(x=data_projected[:, 0], y=data_projected[:, 1], ax=ax, **kwargs) + + if annotations is not None: + _annotate_plot(ax, data_projected, annotations, **annotation_kwargs) + + return ax + + +@paper_style +def plot_history(function_values, figtitle="", ax=None, **kwargs): + if ax is None: + ax = plt.gca() + + ax.set(title=figtitle, xlabel="training step", ylabel="objective function") + ax.plot(range(len(function_values)), function_values, **kwargs) + + return ax + + +@paper_style +def corr_plot( + corr: pd.DataFrame, figsize=(6, 6), cmap="vlag", annot=True, fmt=".2f", **kwargs +): + linkage = hierarchy.linkage(corr) + clustergrid = sns.clustermap( + corr, + row_linkage=linkage, + figsize=figsize, + vmin=-1, + vmax=1, + cmap=cmap, + annot=annot, + fmt=fmt, + **kwargs, + ) + + return clustergrid + + +def _get_colors_signature_plot(colors, mutation_types): + """ + Given the colors argument of sigplot_bar and the mutation types, return the + colors used in the signature bar chart. + """ + n_features = len(mutation_types) + + if colors == "SBS96" or (n_features == 96 and all(mutation_types == SBS_TYPES_96)): + if n_features != 96: + raise ValueError( + "The standard SBS colors can only be used " + "when the signatures have 96 features." + ) + + colors = COLORS_SBS96 + + elif colors == "Indel83" or ( + n_features == 83 and all(mutation_types == INDEL_TYPES_83) + ): + if n_features != 83: + raise ValueError( + "The standard Indel colors can only be used " + "when the signatures have 83 features." + ) + + colors = COLORS_INDEL83 + + elif type(colors) in [str, tuple]: + colors = n_features * [colors] + + elif type(colors) is list: + if len(colors) != n_features: + raise ValueError( + f"The list of colors must be of length n_features={n_features}." + ) + + else: + colors = n_features * ["gray"] + + return colors + + +@paper_style +def _signature_plot( + signature, colors=None, annotate_mutation_types=False, ax=None, **kwargs +): + """ + Inputs: + ------- + signature: pd.Signature + Signature with mutation types and name. + + colors: str, tuple or list + Can be set to 'SBS96' or 'Indel83' to use the standard bar colors + for these mutation types. + Otherwise, when a single string or tuple is provided, + all bars will have the same color. Alternatively, + a list can be used to specifiy the color of each bar individually. + + ax: + A single matplotlib Axes in which to draw the plot. + + kwargs: dict + Any keyword arguments to be passed to matplotlibs ax.bar + """ + if ax is None: + _, ax = plt.subplots(figsize=(4, 1)) + + signature_normalized = signature / signature.sum(axis=0) + mutation_types = signature.index + colors = _get_colors_signature_plot(colors, mutation_types) + + ax.set_title(signature_normalized.columns[0]) + ax.spines["left"].set_visible(False) + ax.get_yaxis().set_visible(False) + ax.set_xlim((-1, len(mutation_types))) + + ax.bar( + mutation_types, + signature_normalized.iloc[:, 0], + linewidth=0, + color=colors, + **kwargs, + ) + + if annotate_mutation_types: + ax.set_xticks(mutation_types) + ax.set_xticklabels( + mutation_types, family="monospace", fontsize=4, ha="center", rotation=90 + ) + + else: + ax.set_xticks([]) + + return ax + + +@paper_style +def signature_plot( + signature, + catalog=None, + colors=None, + annotate_mutation_types=False, + ax=None, + **kwargs, +): + """ + Inputs: + ------- + signature: pd.Signature + Signature with mutation types and name. + + catalog: pd.DataFrame + If a catalog is provided, the single best matching catalog signature + will also be plotted. + + colors: str, tuple or list + Can be set to 'SBS96' or 'Indel83' to use the standard bar colors + for these mutation types. + Otherwise, when a single string or tuple is provided, + all bars will have the same color. Alternatively, + a list can be used to specifiy the color of each bar individually. + + ax: + Axes in which to draw the plot. A single Axes if catalog is None; + two Axes if a catalog is given. + + kwargs: dict + Any keyword arguments to be passed to matplotlibs ax.bar + """ + if catalog is None: + if ax is None: + _, ax = plt.subplots(figsize=(4, 1)) + + signatures = [signature] + axes = [ax] + + else: + if ax is None: + _, ax = plt.subplots(1, 2, figsize=(8, 1)) + + matched_signature = match_to_catalog(signature, catalog, metric="cosine") + signatures = [signature, matched_signature] + axes = ax + + for sig, axis in zip(signatures, axes): + _signature_plot( + sig, + colors=colors, + annotate_mutation_types=annotate_mutation_types, + ax=axis, + **kwargs, + ) + + if catalog is None: + return axes[0] + + return axes + + +@paper_style +def signatures_plot( + signatures, + catalog=None, + colors=None, + annotate_mutation_types=False, + axes=None, + **kwargs, +): + """ + Inputs: + ------- + signatures : pd.DataFrame + Named signatures of shape (n_features, n_signatures) + + catalog: pd.DataFrame + If a catalog is provided, the best matching catalog signatures + will also be plotted. + + axes : list + Axes in which to draw the plot. Multiple Axes if more than one signature + is provided or a catalog is given. Otherwise a single axis. + When a catalog is provided, axes is expected to be of shape (n_signatures, 2). + """ + n_signatures = signatures.shape[1] + + if n_signatures == 1: + ax = signature_plot( + signatures, + catalog=catalog, + colors=colors, + annotate_mutation_types=annotate_mutation_types, + ax=axes, + **kwargs, + ) + return ax + + if axes is None: + if catalog is None: + _, axes = plt.subplots(n_signatures, 1, figsize=(4, n_signatures)) + + else: + _, axes = plt.subplots(n_signatures, 2, figsize=(8, n_signatures)) + + for ax, signature in zip(axes.flatten(), signatures): + signature_plot( + signatures[[signature]], + catalog=catalog, + colors=colors, + annotate_mutation_types=annotate_mutation_types, + ax=ax, + **kwargs, + ) + plt.tight_layout() + + return axes + + +def _reorder_exposures(exposures: pd.DataFrame, reorder_signatures=True): + """ + Reorder the samples using hierarchical clustering and + reorder the signatures by their total relative exposure. + """ + exposures_normalized = exposures / exposures.sum(axis=0) + + d = pdist(exposures_normalized.T) + linkage = fastcluster.linkage(d) + # get the optimal sample order that is consistent + # with the hierarchical clustering linkage + sample_order = hierarchy.leaves_list(hierarchy.optimal_leaf_ordering(linkage, d)) + samples_reordered = exposures_normalized.columns[sample_order] + exposures_reordered = exposures_normalized[samples_reordered] + + # order the signatures by their total exposure + if reorder_signatures: + signatures_reordered = ( + exposures_reordered.sum(axis=1).sort_values(ascending=False).index + ) + exposures_reordered = exposures_reordered.reindex(signatures_reordered) + + return exposures_reordered + + +@paper_style +def exposures_plot( + exposures: pd.DataFrame, + reorder_signatures=True, + annotate_samples=True, + colors=None, + ncol_legend=1, + ax=None, + **kwargs, +): + """ + Visualize the exposures using a stacked bar chart. + """ + n_signatures, n_samples = exposures.shape + exposures_reordered = _reorder_exposures( + exposures, reorder_signatures=reorder_signatures + ) + samples = exposures_reordered.columns + + if ax is None: + _, ax = plt.subplots(figsize=(0.3 * n_samples, 4)) + + if colors is None: + colors = list(sns.color_palette("deep")) * (1 + n_signatures // 10) + + bottom = np.zeros(n_samples) + + for signature, color in zip(exposures_reordered.T, colors): + signature_exposures = exposures_reordered.T[signature].to_numpy() + ax.bar( + np.arange(n_samples), + signature_exposures, + color=color, + width=1, + label=signature, + linewidth=0, + bottom=bottom, + **kwargs, + ) + bottom += signature_exposures + + if annotate_samples: + ax.set_xticks(np.arange(n_samples)) + ax.set_xticklabels(samples, rotation=90, ha="center", fontsize=10) + + else: + ax.get_xaxis().set_visible(False) + + ax.set_title("Sample exposures") + ax.spines[["left", "bottom"]].set_visible(False) + ax.get_yaxis().set_visible(False) + ax.legend(loc="center left", bbox_to_anchor=(0.975, 0.5), ncol=ncol_legend) + + return ax diff --git a/src/salamander/utils.py b/src/salamander/utils.py new file mode 100644 index 0000000..bccd751 --- /dev/null +++ b/src/salamander/utils.py @@ -0,0 +1,213 @@ +import warnings + +import numpy as np +import pandas as pd +from scipy.optimize import linear_sum_assignment +from scipy.special import gammaln +from scipy.stats import mannwhitneyu +from sklearn.metrics import pairwise_distances + +EPSILON = np.finfo(np.float32).eps + + +def shape_checker(arg_name: str, arg, allowed_shape): + """ + A helper function to test the shape of a numpy ndarray or pandas dataframe. + + Input: + ------ + arg_name: str + The name of the argument + arg: + The actual value of the argument + allowed_shape: + The expected shape of 'arg' + """ + type_checker(arg_name, arg, [np.ndarray, pd.DataFrame]) + + if arg.shape != allowed_shape: + raise ValueError(f"The shape of '{arg_name}' has to be {allowed_shape}.") + + +def type_checker(arg_name: str, arg, allowed_types): + """ + A helper function to test the type of an argument. + + Input: + ------ + arg_name: str + The name of the argument + arg: + The actual value of the argument + allowed_types: a type or list of types + The type or list of types allowed for 'arg' + """ + if isinstance(allowed_types, type): + allowed_types = [allowed_types] + + if type(arg) not in allowed_types: + raise TypeError(f"The type of '{arg_name}' has to be one of {allowed_types}.") + + +def value_checker(arg_name: str, arg, allowed_values): + """ + A helper function to test the value of an argument. + + Input: + ------ + arg_name: str + The name of the argument + arg: + The actual value of the argument + allowed_values: + A value or list of values allowed for 'arg' + """ + if not isinstance(allowed_values, list): + allowed_values = [allowed_values] + + if arg not in allowed_values: + raise ValueError( + f"The value of '{arg_name}' has to be one of {allowed_values}." + ) + + +def kl_divergence(X: np.ndarray, W: np.ndarray, H: np.ndarray) -> float: + r""" + The generalized Kullback-Leibler divergence D(X || WH). + + \sum_vd X_vd * ln(X_vd / (WH)_vd) - \sum_vd X_vd + \sum_vd (WH)_vd. + + Summands with X_vd = 0 are neglected and WH is clipped to avoid division by zero. + """ + indices = X.nonzero() + X_data = X[indices] + WH_data = (W @ H)[indices] + WH_data = WH_data.clip(EPSILON) + + s1 = np.dot(X_data, np.log(X_data / WH_data)) + s2 = -np.sum(X_data) + # fast np.sum(W @ H) + s3 = np.dot(np.sum(W, axis=0), np.sum(H, axis=1)) + + return s1 + s2 + s3 + + +def samplewise_kl_divergence(X, W, H): + """ + A fast vectorized samplewise KL divergence. + """ + X_data = np.copy(X).astype(float) + indices = X == 0 + X_data[indices] = EPSILON + WH_data = W @ H + WH_data[indices] = EPSILON + + s1 = np.einsum("vd,vd->d", X_data, np.log(X_data / WH_data)) + s2 = -np.sum(X, axis=0) + s3 = np.dot(H.T, np.sum(W, axis=0)) + + errors = s1 + s2 + s3 + + return errors + + +def poisson_llh(X: np.ndarray, W: np.ndarray, H: np.ndarray) -> float: + """ + The Poisson log-likelihood generalized to X, W and H having + non-negative real numbers. + """ + WH_data = W @ H + indices = WH_data.nonzero() + WH_data = WH_data[indices] + X_data = X[indices] + + s1 = np.dot(X_data, np.log(WH_data)) + # fast np.sum(W @ H) + s2 = -np.dot(np.sum(W, axis=0), np.sum(H, axis=1)) + s3 = -np.sum(gammaln(1 + X)) + + llh = s1 + s2 + s3 + + return llh + + +def normalize_WH(W, H): + normalization_factor = np.sum(W, axis=0) + return W / normalization_factor, H * normalization_factor[:, None] + + +def match_to_catalog(signatures: pd.DataFrame, catalog: pd.DataFrame, metric="cosine"): + """ + Find the best matching signatures in catalog for all signatures. + """ + cosine_sim = 1 - pairwise_distances(signatures.T, catalog.T, metric=metric) + matches_indices = [np.argmax(row) for row in cosine_sim] + matches = catalog.iloc[:, matches_indices] + + return matches + + +def match_signatures_pair( + signatures1: pd.DataFrame, signatures2: pd.DataFrame, metric="cosine" +): + """ + Match a pair of signature catalogs using their pairwise column distances, + see https://en.wikipedia.org/wiki/Assignment_problem. + + Output: + ------ + reordered_indices: np.ndarray + The list of column indices such that reordering signatures2 using this list + minimizes the sum of the pairwise column distances between + signatures1 and signatures2. + """ + if signatures1.shape != signatures2.shape: + raise ValueError("The signatures must be of the same shape.") + + pdist = pairwise_distances(signatures1.T, signatures2.T, metric=metric) + reordered_indices = linear_sum_assignment(pdist)[1] + + return reordered_indices + + +def differential_tail_test(a, b, percentile=90, alternative="two-sided"): + """ + Test if distribution tails are different (pubmed: 18655712) + + Input + ------ + a, b : array-like + must be positive. + + percentile : float + Percentile threshold above which data points are considered tails. + + alternative : {'two-sided', 'less', 'greater'} + Defines the alternative hypothesis. For example, when set to 'greater', + the alternative hypothesis is that the tail of a is greater than the tail + of b. + """ + a, b = np.array(a), np.array(b) + + if len(a) != len(b): + warnings.warn( + "Lengths of a and b are different. " + "The differential tail test could lose power.", + UserWarning, + ) + + both = np.concatenate([a, b]) + thresh = np.percentile(both, percentile) + za, zb = a * (a > thresh), b * (b > thresh) + + # If za and zb contain identical values, e.g., both za and zb are all zeros. + if len(set(np.concatenate((za, zb)))) == 1: + if alternative == "two-sided": + return np.nan, 1.0 + + else: + return np.nan, 0.5 + + statistic, pvalue = mannwhitneyu(za, zb, alternative=alternative) + + return statistic, pvalue diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_corrnmf.py b/tests/test_corrnmf.py new file mode 100644 index 0000000..1e8446f --- /dev/null +++ b/tests/test_corrnmf.py @@ -0,0 +1,201 @@ +import numpy as np +import pandas as pd +import pytest + +from salamander.nmf_framework import corrnmf_det + +PATH = "tests/test_data" +PATH_TEST_DATA = f"{PATH}/nmf_framework/corrnmf" + + +@pytest.fixture +def counts(): + return pd.read_csv(f"{PATH}/nmf_framework/counts.csv", index_col=0) + + +@pytest.fixture(params=[(1, 1), (2, 2)]) +def model(request): + param = request.param + return corrnmf_det.CorrNMFDet(n_signatures=param[0], dim_embeddings=param[1]) + + +@pytest.fixture +def path(model): + return ( + f"{PATH_TEST_DATA}/" + f"corrnmf_nsigs{model.n_signatures}_dim{model.dim_embeddings}" + ) + + +@pytest.fixture +def W_init(path): + return np.load(f"{path}_W_init.npy") + + +@pytest.fixture +def alpha_init(path): + return np.load(f"{path}_alpha_init.npy") + + +@pytest.fixture +def _p(path): + return np.load(f"{path}_p.npy") + + +@pytest.fixture +def _aux(counts, _p): + return np.einsum("vd,vkd->kd", counts.values, _p) + + +@pytest.fixture +def L_init(path): + return np.load(f"{path}_L_init.npy") + + +@pytest.fixture +def U_init(path): + return np.load(f"{path}_U_init.npy") + + +@pytest.fixture +def sigma_sq_init(path): + return np.load(f"{path}_sigma_sq_init.npy") + + +@pytest.fixture +def model_init(model, counts, W_init, alpha_init, L_init, U_init, sigma_sq_init): + model.X = counts.values + model.W = W_init + model.alpha = alpha_init + model.L = L_init + model.U = U_init + model.sigma_sq = sigma_sq_init + model.mutation_types = counts.index + model.signature_names = ["_" for _ in range(model.n_signatures)] + model.sample_names = counts.columns + model.n_samples = len(counts.columns) + model.given_signature_embeddings = None + return model + + +@pytest.fixture +def objective_init(path): + return np.load(f"{path}_objective_init.npy") + + +@pytest.fixture +def surrogate_objective_init(path): + return np.load(f"{path}_surrogate_objective_init.npy") + + +@pytest.fixture +def W_updated_Lee(path): + return np.load(f"{path}_W_Lee_updated.npy") + + +@pytest.fixture +def W_updated_surrogate(path): + return np.load(f"{path}_W_surrogate_updated.npy") + + +@pytest.fixture +def alpha_updated(path): + return np.load(f"{path}_alpha_updated.npy") + + +@pytest.fixture +def L_updated(path): + return np.load(f"{path}_L_updated.npy") + + +@pytest.fixture +def U_updated(path): + return np.load(f"{path}_U_updated.npy") + + +@pytest.fixture +def sigma_sq_updated(path): + return np.load(f"{path}_sigma_sq_updated.npy") + + +class TestCorrNMFDet: + def test_objective_function(self, model_init, objective_init): + assert np.allclose(model_init.objective_function(), objective_init) + + def test_surrogate_objective_function( + self, model_init, _p, surrogate_objective_init + ): + assert np.allclose( + model_init._surrogate_objective_function(_p), surrogate_objective_init + ) + + def test_update_W_Lee(self, model_init, _p, W_updated_Lee): + model_init.update_W = "1999-Lee" + model_init._update_W(_p) + assert np.allclose(model_init.W, W_updated_Lee) + + def test_update_W_surrogate(self, model_init, _p, W_updated_surrogate): + model_init.update_W = "surrogate" + model_init._update_W(_p) + assert np.allclose(model_init.W, W_updated_surrogate) + + def test_update_alpha(self, model_init, alpha_updated): + model_init._update_alpha() + assert np.allclose(model_init.alpha, alpha_updated) + + def test_p(self, model_init, _p): + p_computed = model_init._update_p() + assert np.allclose(p_computed, _p) + + def test_update_L(self, model_init, _aux, L_updated): + model_init._update_L(_aux) + assert np.allclose(model_init.L, L_updated) + + def test_update_U(self, model_init, _aux, U_updated): + model_init._update_U(_aux) + assert np.allclose(model_init.U, U_updated) + + def test_update_sigma_sq(self, model_init, sigma_sq_updated): + model_init._update_sigma_sq() + assert np.allclose(model_init.sigma_sq, sigma_sq_updated) + + +@pytest.mark.parametrize("n_signatures", [1, 2]) +def test_given_signatures(counts, n_signatures): + given_signatures = counts.iloc[:, :n_signatures].astype(float).copy() + given_signatures /= given_signatures.sum(axis=0) + model = corrnmf_det.CorrNMFDet( + n_signatures=n_signatures, + dim_embeddings=n_signatures, + min_iterations=3, + max_iterations=3, + ) 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b/tests/test_data/utils/samplewise_kl_divergence_nsigs1_result.npy new file mode 100644 index 0000000..653cb12 Binary files /dev/null and b/tests/test_data/utils/samplewise_kl_divergence_nsigs1_result.npy differ diff --git a/tests/test_data/utils/samplewise_kl_divergence_nsigs2_result.npy b/tests/test_data/utils/samplewise_kl_divergence_nsigs2_result.npy new file mode 100644 index 0000000..83f1127 Binary files /dev/null and b/tests/test_data/utils/samplewise_kl_divergence_nsigs2_result.npy differ diff --git a/tests/test_klnmf.py b/tests/test_klnmf.py new file mode 100644 index 0000000..4d4c13f --- /dev/null +++ b/tests/test_klnmf.py @@ -0,0 +1,78 @@ +import numpy as np +import pandas as pd +import pytest + +from salamander.nmf_framework import klnmf + +PATH = "tests/test_data" +PATH_TEST_DATA = f"{PATH}/nmf_framework/klnmf" + + +@pytest.fixture +def counts(): + return pd.read_csv(f"{PATH}/nmf_framework/counts.csv", index_col=0) + + +@pytest.fixture(params=[1, 2]) +def model(request): + return klnmf.KLNMF(n_signatures=request.param) + + +@pytest.fixture +def path(model): + return f"{PATH_TEST_DATA}/klnmf_nsigs{model.n_signatures}" + + +@pytest.fixture +def W_init(path): + return np.load(f"{path}_W_init.npy") + + +@pytest.fixture +def H_init(path): + return np.load(f"{path}_H_init.npy") + + +@pytest.fixture +def model_init(model, counts, W_init, H_init): + model.X = counts.values + model.W = W_init + model.H = H_init + return model + + +@pytest.fixture +def objective_init(path): + return np.load(f"{path}_objective_init.npy") + + +@pytest.fixture +def W_updated(path): + return np.load(f"{path}_W_updated.npy") + + +@pytest.fixture +def H_updated(path): + return np.load(f"{path}_H_updated.npy") + + +class TestKLNMF: + def test_objective_function(self, model_init, objective_init): + assert np.allclose(model_init.objective_function(), objective_init) + + def test_update_W(self, model_init, W_updated): + model_init._update_W() + assert np.allclose(model_init.W, W_updated) + + def test_update_H(self, model_init, H_updated): + model_init._update_H() + assert np.allclose(model_init.H, H_updated) + + +@pytest.mark.parametrize("n_signatures", [1, 2]) +def test_given_signatures(counts, n_signatures): + given_signatures = counts.iloc[:, :n_signatures].astype(float).copy() + given_signatures /= given_signatures.sum(axis=0) + model = klnmf.KLNMF(n_signatures=n_signatures, min_iterations=3, max_iterations=3) + model.fit(counts, given_signatures=given_signatures) + assert np.allclose(given_signatures, model.signatures) diff --git a/tests/test_multimodal_corrnmf.py b/tests/test_multimodal_corrnmf.py new file mode 100644 index 0000000..8b6f8d5 --- /dev/null +++ b/tests/test_multimodal_corrnmf.py @@ -0,0 +1,273 @@ +import numpy as np +import pandas as pd +import pytest + +from salamander.nmf_framework import corrnmf_det, multimodal_corrnmf + +PATH = "tests/test_data" +PATH_TEST_DATA = f"{PATH}/nmf_framework/multimodal_corrnmf" +N_MODALITIES = 2 +NS_SIGNATURES = [2, 3] +DIM_EMBEDDINGS = 2 + + +@pytest.fixture +def U_init(): + """ + Initial joint sample embeddings. + """ + return np.load(f"{PATH_TEST_DATA}/U_init.npy") + + +@pytest.fixture +def sigma_sq_init(): + """ + Initial joint variance. + """ + return np.load(f"{PATH_TEST_DATA}/sigma_sq_init.npy") + + +@pytest.fixture +def counts(): + """ + Input count data. + """ + return [ + pd.read_csv(f"{PATH_TEST_DATA}/model{n}_counts.csv", index_col=0) + for n in range(N_MODALITIES) + ] + + +@pytest.fixture +def Ws_init(): + return [ + np.load(f"{PATH_TEST_DATA}/model{n}_W_init.npy") for n in range(N_MODALITIES) + ] + + +@pytest.fixture +def alphas_init(): + return [ + np.load(f"{PATH_TEST_DATA}/model{n}_alpha_init.npy") + for n in range(N_MODALITIES) + ] + + +@pytest.fixture +def Ls_init(): + return [ + np.load(f"{PATH_TEST_DATA}/model{n}_L_init.npy") for n in range(N_MODALITIES) + ] + + +@pytest.fixture +def multi_model_init(counts, Ws_init, alphas_init, Ls_init, U_init, sigma_sq_init): + models = [] + + for n, n_signatures in enumerate(NS_SIGNATURES): + model = corrnmf_det.CorrNMFDet( + n_signatures=n_signatures, dim_embeddings=DIM_EMBEDDINGS + ) + model.X = counts[n].values + model.W = Ws_init[n] + model.alpha = alphas_init[n] + model.L = Ls_init[n] + model.U = U_init + model.sigma_sq = sigma_sq_init + model.mutation_types = counts[n].index + model.signature_names = [f"Sig {k}" for k in range(n_signatures)] + model.sample_names = counts[n].columns + models.append(model) + + multi_model = multimodal_corrnmf.MultimodalCorrNMF( + n_modalities=N_MODALITIES, + ns_signatures=NS_SIGNATURES, + dim_embeddings=DIM_EMBEDDINGS, + ) + multi_model.models = models + multi_model.n_samples = len(counts[0].columns) + return multi_model + + +@pytest.fixture +def _ps(): + return [np.load(f"{PATH_TEST_DATA}/model{n}_p.npy") for n in range(N_MODALITIES)] + + +@pytest.fixture +def _auxs(counts, _ps): + return [np.einsum("vd,vkd->kd", data.values, p) for data, p in zip(counts, _ps)] + + +@pytest.fixture +def objective_init(): + return np.load(f"{PATH_TEST_DATA}/objective_init.npy") + + +@pytest.fixture +def surrogate_objective_init(): + return np.load(f"{PATH_TEST_DATA}/surrogate_objective_init.npy") + + +@pytest.fixture +def Ws_updated_Lee(): + return [ + np.load(f"{PATH_TEST_DATA}/model{n}_W_Lee_updated.npy") + for n in range(N_MODALITIES) + ] + + +@pytest.fixture +def Ws_updated_surrogate(): + return [ + np.load(f"{PATH_TEST_DATA}/model{n}_W_surrogate_updated.npy") + for n in range(N_MODALITIES) + ] + + +@pytest.fixture +def alphas_updated(): + return [ + np.load(f"{PATH_TEST_DATA}/model{n}_alpha_updated.npy") + for n in range(N_MODALITIES) + ] + + +@pytest.fixture +def Ls_updated(): + return [ + np.load(f"{PATH_TEST_DATA}/model{n}_L_updated.npy") for n in range(N_MODALITIES) + ] + + +@pytest.fixture +def U_updated(): + return np.load(f"{PATH_TEST_DATA}/U_updated.npy") + + +@pytest.fixture +def sigma_sq_updated(): + return np.load(f"{PATH_TEST_DATA}/sigma_sq_updated.npy") + + +class TestMultimodalCorrNMFDet: + def test_objective_function(self, multi_model_init, objective_init): + assert np.allclose(multi_model_init.objective_function(), objective_init) + + def test_surrogate_objective_function( + self, multi_model_init, _ps, surrogate_objective_init + ): + assert np.allclose( + multi_model_init._surrogate_objective_function(_ps), + surrogate_objective_init, + ) + + def test_update_W_Lee(self, multi_model_init, _ps, Ws_updated_Lee): + for model in multi_model_init.models: + model.update_W = "1999-Lee" + + given_signatures = [None for _ in range(N_MODALITIES)] + multi_model_init._update_Ws(_ps, given_signatures) + + for model, W_updated_Lee in zip(multi_model_init.models, Ws_updated_Lee): + assert np.allclose(model.W, W_updated_Lee) + + def test_update_W_surrogate(self, multi_model_init, _ps, Ws_updated_surrogate): + for model in multi_model_init.models: + model.update_W = "surrogate" + + given_signatures = [None for _ in range(N_MODALITIES)] + multi_model_init._update_Ws(_ps, given_signatures) + + for model, W_updated_surrogate in zip( + multi_model_init.models, Ws_updated_surrogate + ): + assert np.allclose(model.W, W_updated_surrogate) + + def test_update_alpha(self, multi_model_init, alphas_updated): + multi_model_init._update_alphas() + + for model, alpha_updated in zip(multi_model_init.models, alphas_updated): + assert np.allclose(model.alpha, alpha_updated) + + def test_p(self, multi_model_init, _ps): + ps_computed = multi_model_init._update_ps() + + for p1, p2 in zip(ps_computed, _ps): + assert np.allclose(p1, p2) + + def test_update_L(self, multi_model_init, _auxs, U_init, Ls_updated): + outer_prods_U = np.einsum("mD,nD->Dmn", U_init, U_init) + given_signature_embeddings = [None for _ in range(N_MODALITIES)] + multi_model_init._update_Ls(_auxs, outer_prods_U, given_signature_embeddings) + + for model, L_updated in zip(multi_model_init.models, Ls_updated): + assert np.allclose(model.L, L_updated) + + def test_update_U(self, multi_model_init, _auxs, U_updated): + multi_model_init._update_U(_auxs) + + for model in multi_model_init.models: + assert np.allclose(model.U, U_updated) + + def test_update_sigma_sq(self, multi_model_init, sigma_sq_updated): + multi_model_init._update_sigma_sq() + + for model in multi_model_init.models: + assert np.allclose(model.sigma_sq, sigma_sq_updated) + + +@pytest.mark.parametrize("ns_signatures", [[1, 2], [2, 2]]) +def test_given_signatures(counts, ns_signatures): + given_signatures0 = counts[0].iloc[:, : ns_signatures[0]].astype(float).copy() + given_signatures0 /= given_signatures0.sum(axis=0) + given_signatures = [given_signatures0, None] + multi_model = multimodal_corrnmf.MultimodalCorrNMF( + n_modalities=2, + ns_signatures=ns_signatures, + dim_embeddings=2, + min_iterations=3, + max_iterations=3, + ) + multi_model.fit(counts, given_signatures=given_signatures) + assert np.allclose(given_signatures0, multi_model.models[0].W) + assert not np.allclose(given_signatures0, multi_model.models[1].W) + + +@pytest.mark.parametrize( + "ns_signatures,dim_embeddings", [([1, 2], 1), ([2, 2], 1), ([2, 2], 2)] +) +def test_given_signature_embeddings(counts, ns_signatures, dim_embeddings): + given_signature_embeddings0 = np.random.uniform( + size=(dim_embeddings, ns_signatures[0]) + ) + given_signature_embeddings = [given_signature_embeddings0, None] + multi_model = multimodal_corrnmf.MultimodalCorrNMF( + n_modalities=2, + ns_signatures=ns_signatures, + dim_embeddings=dim_embeddings, + min_iterations=3, + max_iterations=3, + ) + multi_model.fit(counts, given_signature_embeddings=given_signature_embeddings) + assert np.allclose(given_signature_embeddings0, multi_model.models[0].L) + assert not np.allclose(given_signature_embeddings0, multi_model.models[1].L) + + +@pytest.mark.parametrize( + "ns_signatures,dim_embeddings", [([1, 2], 1), ([2, 2], 1), ([2, 2], 2)] +) +def test_given_sample_embeddings(counts, ns_signatures, dim_embeddings): + n_samples = len(counts[0].columns) + given_sample_embeddings = np.random.uniform(size=(dim_embeddings, n_samples)) + multi_model = multimodal_corrnmf.MultimodalCorrNMF( + n_modalities=2, + ns_signatures=ns_signatures, + dim_embeddings=dim_embeddings, + min_iterations=3, + max_iterations=3, + ) + multi_model.fit(counts, given_sample_embeddings=given_sample_embeddings) + + for model in multi_model.models: + assert np.allclose(given_sample_embeddings, model.U) diff --git a/tests/test_mvnmf.py b/tests/test_mvnmf.py new file mode 100644 index 0000000..1787b0f --- /dev/null +++ b/tests/test_mvnmf.py @@ -0,0 +1,81 @@ +import numpy as np +import pandas as pd +import pytest + +from salamander.nmf_framework import mvnmf + +PATH = "tests/test_data" +PATH_TEST_DATA = f"{PATH}/nmf_framework/mvnmf" + + +@pytest.fixture +def counts(): + return pd.read_csv(f"{PATH}/nmf_framework/counts.csv", index_col=0) + + +@pytest.fixture(params=[1, 2]) +def model(request): + return mvnmf.MvNMF(n_signatures=request.param) + + +@pytest.fixture +def path(model): + return f"{PATH_TEST_DATA}/mvnmf_nsigs{model.n_signatures}" + + +@pytest.fixture +def W_init(path): + return np.load(f"{path}_W_init.npy") + + +@pytest.fixture +def H_init(path): + return np.load(f"{path}_H_init.npy") + + +@pytest.fixture +def model_init(model, counts, W_init, H_init): + model.X = counts.values + model.W = W_init + model.H = H_init + model.lam = 1.0 + model.delta = 1.0 + model.gamma = 1.0 + return model + + +@pytest.fixture +def objective_init(path): + return np.load(f"{path}_objective_init.npy") + + +@pytest.fixture +def W_updated(path): + return np.load(f"{path}_W_updated.npy") + + +@pytest.fixture +def H_updated(path): + return np.load(f"{path}_H_updated.npy") + + +class TestMVNMF: + def test_objective_function(self, model_init, objective_init): + assert np.allclose(model_init.objective_function(), objective_init) + + def test_update_W(self, model_init, objective_init, W_updated): + model_init._update_W(objective_init) + assert np.allclose(model_init.W, W_updated) + + def test_update_H(self, model_init, H_updated): + model_init._update_H() + assert np.allclose(model_init.H, H_updated) + + +@pytest.mark.parametrize("n_signatures", [1, 2]) +def test_given_signatures(counts, n_signatures): + given_signatures = counts.iloc[:, :n_signatures].astype(float).copy() + given_signatures /= given_signatures.sum(axis=0) + model = mvnmf.MvNMF(n_signatures=n_signatures, min_iterations=3, max_iterations=3) + model.fit(counts, given_signatures=given_signatures) + assert np.allclose(given_signatures, model.signatures) diff --git a/tests/test_utils.py b/tests/test_utils.py new file mode 100644 index 0000000..c5f7f03 --- /dev/null +++ b/tests/test_utils.py @@ -0,0 +1,62 @@ +import numpy as np +import pandas as pd +import pytest + +from salamander.utils import kl_divergence, poisson_llh, samplewise_kl_divergence + +PATH_TEST_DATA = "tests/test_data" +PATH_TEST_DATA_UTILS = f"{PATH_TEST_DATA}/utils" + + +@pytest.fixture +def counts(): + return pd.read_csv(f"{PATH_TEST_DATA_UTILS}/counts.csv", index_col=0) + + +@pytest.fixture(params=[1, 2]) +def n_signatures(request): + return request.param + + +@pytest.fixture +def objective_inputs(counts, n_signatures): + path = f"{PATH_TEST_DATA_UTILS}/objective_input_nsigs{n_signatures}" + W = np.load(f"{path}_W.npy") + H = np.load(f"{path}_H.npy") + + return (counts.values, W, H) + + +@pytest.fixture +def kl_divergence_output(n_signatures): + path = f"{PATH_TEST_DATA_UTILS}/kl_divergence_nsigs{n_signatures}_result.npy" + return np.load(path) + + +def test_kl_divergence(objective_inputs, kl_divergence_output): + assert np.allclose(kl_divergence(*objective_inputs), kl_divergence_output) + + +@pytest.fixture +def samplewise_kl_divergence_output(n_signatures): + path = ( + f"{PATH_TEST_DATA_UTILS}/" + f"samplewise_kl_divergence_nsigs{n_signatures}_result.npy" + ) + return np.load(path) + + +def test_samplewise_kl_divergence(objective_inputs, samplewise_kl_divergence_output): + assert np.allclose( + samplewise_kl_divergence(*objective_inputs), samplewise_kl_divergence_output + ) + + +@pytest.fixture +def poisson_llh_output(n_signatures): + path = f"{PATH_TEST_DATA_UTILS}/poisson_llh_nsigs{n_signatures}_result.npy" + return np.load(path) + + +def test_poisson_llh(objective_inputs, poisson_llh_output): + assert np.allclose(poisson_llh(*objective_inputs), poisson_llh_output) diff --git a/tox.ini b/tox.ini new file mode 100644 index 0000000..59a983c --- /dev/null +++ b/tox.ini @@ -0,0 +1,38 @@ +[tox] +envlist = py{39,310,311}, flake8, pylint + +[testenv] +deps = + pytest>=7.4 +commands = + pytest tests/ + +[testenv:flake8] +skip_install = true +deps = + flake8 +commands = + flake8 src/ --max-line-length 88 + flake8 tests/ --max-line-length 88 + +[flake8] +extend-ignore = + # see https://github.com/psf/black/issues/315 + E203 +per-file-ignores = + # ignore ambiguous variable name 'l' + src/salamander/nmf_framework/corrnmf_det.py: E741 + +[testenv:pylint] +deps = + pylint +commands = + # extension-pkg-witelist: see https://github.com/pylint-dev/pylint/issues/3703 + # W0107: unnecessary pass statement. This is (mostly) a style issue: see https://github.com/pylint-dev/pylint/issues/2208 + pylint src/ --disable=C,R --extension-pkg-whitelist=scipy.special --disable=W0107 + +[gh-actions] +python = + 3.9: py39 + 3.10: py310 + 3.11: py311, flake8, pylint diff --git a/tutorial.ipynb b/tutorial.ipynb new file mode 100644 index 0000000..e55b963 --- /dev/null +++ b/tutorial.ipynb @@ -0,0 +1,1065 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "9b814016-3b61-491f-8cad-9a525a5f7610", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import salamander" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "58c84d9e-10a6-4a6f-8a4a-5c0da922ddcd", + "metadata": {}, + "outputs": [], + "source": [ + "counts_sbs = pd.read_csv(\"data/pcawg_breast_sbs.csv\", index_col=0)" + ] + }, + { + "cell_type": "markdown", + "id": "85d2262e-a7ca-4e33-b2da-e191f6ff8e6d", + "metadata": {}, + "source": [ + "## NMF with KL-divergence loss" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "532e15ad-94cf-4829-afd2-ee94e68d204e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n_signatures = 6\n", + "\n", + "model = salamander.KLNMF(\n", + " n_signatures=n_signatures,\n", + " max_iterations=500\n", + ")\n", + "model.fit(counts_sbs)" + ] + }, + { + "cell_type": "markdown", + "id": "282cd3d9-134a-44c4-87f0-e2c55b91ecf2", + "metadata": {}, + "source": [ + "The fitted signatures and exposures of all NMF models can be accessed via $\\texttt{model.signatures}$ and $\\texttt{model.exposures}$ respecively:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "05138f71-21bc-4b2e-9175-068a479edf3f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"Sig2 904.555590 878.972603 6.580696e+02 7.865527e+03 5295.373652 \n", + "Sig3 20.306668 62.275359 1.192798e-07 3.471296e+02 9.433533 \n", + "Sig4 13.746514 33.259206 5.853251e+01 7.355592e+01 456.234954 \n", + "Sig5 1550.381589 1128.290912 1.230288e+03 2.090756e-07 1609.928333 \n", + "\n", + " SP117800 SP117724 SP124193 SP2766 SP6115 \n", + "Sig1 380.508304 59.515518 1.004260e+03 3.205625e+02 74.013757 \n", + "Sig2 1792.441593 685.949956 4.221822e+02 6.579297e+03 917.240322 \n", + "Sig3 153.838696 138.294633 1.192798e-07 2.102208e+02 121.391860 \n", + "Sig4 20.731055 27.067187 6.108872e-05 1.192354e-07 0.002848 \n", + "Sig5 1819.383548 1712.581352 1.055936e+03 5.774884e-04 880.310583 \n", + "\n", + "[5 rows x 198 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.exposures.head()" + ] + }, + { + "cell_type": "markdown", + "id": "529023a2-a894-4131-8eb8-36d76e972324", + "metadata": {}, + "source": [ + "All implemented NMF models also come with methods to visualize the signatures, the exposures and the signature and sample correlations:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a0a6a963-28f6-487c-9d71-321ca64d2a73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([, ,\n", + " , ,\n", + " , ],\n", + " dtype=object)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model.plot_signatures()" + ] + }, + { + "cell_type": "markdown", + "id": "251d04c0-8d1f-48fb-bb04-7eff37705e46", + "metadata": {}, + "source": [ + "Like all other plotting methods, $\\texttt{plot\\_signatures()}$ just wraps around matplotlib and returns the matplotlib axes instances. This makes it effortless to apply custom modifications to the plot. For example, it is possible to rearrange the signature plots to our liking, widen all bars, and change the fontsize:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "78253dc3-df04-432d-9d3b-0d8df0a02eea", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 3, figsize=(15, 3))\n", + "axes = model.plot_signatures(axes=axes, width=1)\n", + "\n", + "for ax in axes.flatten():\n", + " ax.set_title(ax.get_title(), fontsize=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "24fb7af3-1028-476b-93d6-9c9c96baf3d8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# stacked barplot of the exposures\n", + "_ = model.plot_exposures()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e61c5ddb-40c9-433a-9e6d-2862cd89ce03", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# PCA, t-SNE or UMAP of the sample exposures\n", + "fig, axes = plt.subplots(1, 3, figsize=(12, 4))\n", + "\n", + "model.plot_embeddings(method=\"pca\", ax=axes[0])\n", + "model.plot_embeddings(method=\"tsne\", ax=axes[1])\n", + "model.plot_embeddings(method=\"umap\", ax=axes[2])" + ] + }, + { + "cell_type": "markdown", + "id": "69018fe4-85fd-4b2f-9309-afdc96413b84", + "metadata": {}, + "source": [ + "Let's say we want to color all samples with a high relative exposure to a certain signature. Again, the UMAP, t-SNE and PCA implementations just wrap around seaborns scatterplot and customizations can be made." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "207a3fc8-9108-4136-a841-289e0713e0d2", + "metadata": {}, + "outputs": [], + "source": [ + "special_signature = \"Sig1\"\n", + "threshold = 0.2\n", + "\n", + "relative_exposures = model.exposures / model.exposures.sum(axis=0)\n", + "relative_exposures = relative_exposures.T # signatures as columns\n", + "special_samples = relative_exposures.loc[relative_exposures[special_signature] >= threshold].index.to_numpy()\n", + "\n", + "group_labels = [\n", + " \"special group\" if sample in special_samples else \"other\"\n", + " for sample in model.sample_names\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6fd4a617-b0ae-40d5-b8af-83d28a8c4ee5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# colored UMAP\n", + "model.plot_embeddings(method=\"umap\", hue=group_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "df3edc20-9e5b-4711-a61e-b487f49a00bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# signature correlations\n", + "model.plot_correlation(annot=True, figsize=(4,4))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "757e652e-baa3-42a9-bbb4-ab5313183a0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# sample correlations\n", + "model.plot_correlation(data=\"samples\")" + ] + }, + { + "cell_type": "markdown", + "id": "376d464d-9724-4a6f-b6a1-d2341f7983c1", + "metadata": {}, + "source": [ + "## Other NMF models" + ] + }, + { + "cell_type": "markdown", + "id": "c72fdfa4-00a7-4492-8d92-14dc7550328f", + "metadata": {}, + "source": [ + "The syntax for minimum volume NMF and correlated NMF is identical. In this tutorial, we only run these models for 3 iterations because of their longer runtime." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5205a6eb-1905-4213-8bfd-d1a71207eb99", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lambda_tilde: volume penalty parameter in the loss function of mvNMF\n", + "model_mvnmf = salamander.MvNMF(\n", + " n_signatures=n_signatures,\n", + " lambda_tilde=1,\n", + " max_iterations=3\n", + ")\n", + "model_mvnmf.fit(counts_sbs)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "eb47f642-7c6c-440d-9846-e3a9a602b25a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# dim_embeddings: common embedding dimension of the signatures and samples\n", + "model_corrnmf = salamander.CorrNMFDet(\n", + " n_signatures=n_signatures,\n", + " dim_embeddings=n_signatures,\n", + " max_iterations=3\n", + ")\n", + "model_corrnmf.fit(counts_sbs)" + ] + }, + { + "cell_type": "markdown", + "id": "85805199-5357-4048-b4b0-9bf528f2c69f", + "metadata": {}, + "source": [ + "The only difference to the above visualizations with these models is that the embedding plots of CorrNMF show the signature embeddings as well." + ] + }, + { + "cell_type": "markdown", + "id": "24c0ad36-43d3-426c-8775-1cebf4e9ff59", + "metadata": {}, + "source": [ + "## Multimodal correlated NMF" + ] + }, + { + "cell_type": "markdown", + "id": "e3752982-b592-468b-8ddf-d3e27406d513", + "metadata": {}, + "source": [ + "Multimodal correlated NMF can process multiple data modalities at once assuming the input data for each modality originates from the identical samples." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "66315278-628f-4f51-8154-276e53ed86a5", + "metadata": {}, + "outputs": [], + "source": [ + "counts_indel = pd.read_csv(\"data/pcawg_breast_indel.csv\", index_col=0)\n", + "n_features_indel = len(counts_indel.index)\n", + "\n", + "# 196 samples with single base substitution data, a subset of 192 samples have indel data\n", + "counts_sbs = counts_sbs[counts_indel.columns]\n", + "n_features_sbs = len(counts_sbs.index)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a8d89385-82ee-47ab-be6e-6739a88d8441", + "metadata": {}, + "outputs": [], + "source": [ + "counts_sbs = 10 * n_features_sbs * counts_sbs / counts_sbs.sum(axis=0)\n", + "counts_indel = 10 * n_features_indel * counts_indel / counts_indel.sum(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f4daad93-54f4-4115-ba00-7dbd9de683e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "multi_model = salamander.MultimodalCorrNMF(\n", + " n_modalities=2,\n", + " ns_signatures=[7, 5],\n", + " dim_embeddings=5,\n", + " min_iterations=50,\n", + " max_iterations=50\n", + ")\n", + "multi_model.fit(data=[counts_sbs, counts_indel], history=True)" + ] + }, + { + "cell_type": "markdown", + "id": "5ea5bd50-202d-4628-b189-a9c24ff4df4d", + "metadata": {}, + "source": [ + "The above cell should take about ten seconds to execute. We can examine the convergence of the algorithm by checking the history of the objective function.\n", + "\n", + "**Important note**: The number of iterations specified above is insufficient and the obtained signatures are nonsensical. This tutorial only focuses on the usage of the package, not on any results." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9889858a-53a0-4ef4-b151-a5126636c01a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "history = multi_model.history[\"objective_function\"]\n", + "\n", + "fig, ax = plt.subplots(figsize=(4, 4))\n", + "ax.plot(np.arange(len(history[10:])), history[10:])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "9138bc3b-75c7-4e7e-9ee1-24e109ef252b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "axes = multi_model.plot_signatures(annotate_mutation_types=True)\n", + "\n", + "# remove mutation type annotations for the SBS signatures\n", + "for ax in axes[:,0]:\n", + " ax.set_xticks([])\n", + "\n", + "for ax in axes.flatten():\n", + " ax.tick_params(axis=\"x\", which=\"major\", labelsize=6)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "50efc2b6-b5a5-4699-9362-c1c399258402", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([,\n", + " ],\n", + " dtype=object)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# exposures for each data modalities. Note: the samples are ordered per modality\n", + "multi_model.plot_exposures()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "682a99d6-5a7b-4772-b392-bf939edc0ab6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "palette = [(0, 0, 0), (0.92, 0.39, 0.21)]\n", + "hue = np.sum(multi_model.ns_signatures) * [\"signature\"] + multi_model.n_samples * [\"sample\"]\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n", + "multi_model.plot_embeddings(\n", + " method=\"umap\",\n", + " hue=hue,\n", + " palette=palette,\n", + " s=30,\n", + " annotation_kwargs={\"fontsize\": 10},\n", + " ax=ax\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "c0d7270d-03e6-4f3d-b26e-8258023e5cac", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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gUMb5b58reeuc2TgA72HBCjiHRbjQcaUEAABwCgtQiDh6RqGqYYEIsKcyLyix8AMAACrK6+cTLEAh6rBghXCqSj2dWPiCHecv+lSWBR8gUrz+y0G0YD8CQPRhAQoAHMKCD9xQGa8KCvU5VcZ9EG1YDAAAAMG4sgB112dFfmPvJNZw46HhIVy5BAD2Ob3IwpVOQNXCoiEAwG2V4gooFrgih+bkAABUDSxYAOXHJ2kBoeE14zwv/ByvFAtQAACgfLjSKfp44YTRCfyyAQCoLCrrz+pwYwGqkuNtbwAAt5i9TZAeTVUXi0wAAJgzW7iqrD8/WYCCZ7F4BgCANf7iGjlO/HLA8YSbKusvuIBTKvNrxq2FMBagAAAAEDWsFmns1sIt3I/llV96vJLDitsZ7RzraNiPXhENxxMIhnn1ixijHJeUdO/eXfv37y8zdji/7G0a1vIfi/bxhrX8b1daOPZBee+75PZ79+71G7/kkkscG7/kkksk+T+u1bjbGUvnCSTU25spOnigzPc1mjT1GysZL+99BLsfp+8/VIH2QahZQr19QUGB33jNmjUdG69Zs6YkhTTudkanx0ue6/GCg2XG69VsYjp+/li0jFs9J7NxpzMW/5znNx574QWOjcdeeIEkhTTudkanx0uea4kWLVro888/99s2FIHOnY4dOxbwtvHx8QFr8fHxptsFq4X6WNGQIxIZrXglYzTksMvN42mXm3PfzYxu80pGr8z9cPNKxmjYj9H+/22kfkaW99zJ9gIUAABAZeTUAlSJgwfPLT42adKk3DU727hdIwcZK3OOaMjolRxkrFw5yFi5cjiRUQrzAhQAAADC44YbbpAkffLJJ+Wu2dnG7Ro5yFiZc0RDRq/kIGPlykHGypXDiYyhqFahrQEAAAAAAIAgWIACAAAAAACAo1iAAgAAAAAAgKNYgAIAAAAAAICjWIACAAAAAACAo1iAAgAAAAAAgKNiDMMwIh0CAAAAAAAAlRdXQAEAAAAAAMBRLEABAAAAAADAUSxAAQAAAAAAwFEsQAEAAAAAAMBRLEABAAAAAADAUSxAAQAAAAAAwFEsQAEAALggJydHH330kd555x199NFHys7Otrz9qlWrdPToUZ0+fbrM+O7duyVJX3/9tXJyclRcXKwVK1bo448/1pkzZwLeV2pqasDx1157zXbGVatWSZKnM5Ynp1cyurkfvX6cw32Mq0pGp18rTmQMNWc0/L/DfIyejIHmo1UWs5pVRqv7C9dzK899OvG6sKN6he8BAAAAliZNmqSdO3fqmmuu0YUXXqhDhw4pPT1dl112mW677Ta/2xuGoRdeeEEtW7ZUbGysmjdvrjFjxuiCCy5QSkqKmjVrptjYWOXn5ys3N1dNmjRR3bp1tXz5cu3fv18xMTG++5Gkf//73/rggw+UlpZW5nG++uqroBnr16+vQYMG+eWbNWuWdu/erTVr1kQ8o9V+tMo5aNAgJSYmRjyjm8fa68c53Me4KmV08rXiREarnF6Zj9GQ0avHOhoynj8fs7KyTLO0bNkyYM3q/+9nn3027M9txIgRId+nEz/r7IoxSh4FAAAAjhg4cKAWLlwoSXrhhRf0zDPP+MZ37NihG2+80W+b9PR0ZWZmSpI2bNigqVOnKiUlRc8884wMw9D8+fNlGIZuvfVWLV26VJKUlJSkvn37atOmTerZs6cSExMlScOGDdOsWbN8952UlCRJ2rFjh6644gpJUnFxccCMbdq0UZ8+ffzyrV69Wi1atPBtE8mMVvvRKuedd96pjRs3Rjyjm8d669at2rhxo18+rxxns3x2j3FVyOjGa8WJjKmpqab/N3plPlrtS69k9Np8jIaMZvOxU6dOpllyc3ND/v9769atYX9u27Zt88TPOrOrpIJhAQoAAMBhzz33nM6ePavOnTurdu3ays3N9Z0M7ty5U1OnTlX9+vXLbJOQkKAvv/xS1aufu2D9hx9+0NNPP63du3erUaNGeuuttySdO8ns3LmzJGnAgAH65z//KcMwtHz5cq1atUpdu3bV0qVLNWfOHL9cSUlJmj9/vmXGlStXaunSpX75Ro0apZycHC1evDjiGa32o1XOdevWlfnFIVIZ3TzWXj/O4T7GVSmjk68VJzJa5fTKfIyGjF491tGQ8fz5OGDAANMshw4dCvn/78aNG4f9uUnyxM862wwAAAA4bvPmzcaCBQuMadOmGQsWLDC++eYbX62goMDv9uvWrTMOHz5cplZQUGAsWrTI2Lt3r/Hzzz+XuX1eXp7x+eef+93fihUrjGnTpgV8rEmTJlU4Y05OTpkxr2W0yjllyhSjqKiozHaR2o9uHmuvH+dwHuOqlNHJ14pTGe3kjIb/d5iP0ZEx0Hy0Eur/32bblGwXjudWnvt06medHSxAAQAARFhSUlLINTvblK7t2rXLWL58ufHdd99VKGNFc7iR0W7NKxnd3I9eP85O5ojmjJF+rTiR0eo+vXKsoyEjr5nAtXDOR6tapPZ/KDUnXhdm+BQ8AACACDMsOiKY1exsM3z4cBmGoXnz5mncuHE6cOCAZs6cqVdeecV2Rjs53M5ot+aVjG7uR68f53DniOaMw4cPlyQdPHgw4q8VJzJaPZ5XjnU0ZOQ1UzafFP75aFVzc//brTnxujDDp+ABAABEWMkn0IRSs7NNfn6+YmJitGLFCqWmpvr6RNxzzz22M9rJ4XZGuzWvZHRzP3r9OIc7RzRnzM/PlyQdO3ZMy5cvj+hrxYmMVo/nlWMdDRl5zZTNJ4V/PlrV3Nz/dmtOvC7MsAAFAABQRezatUuSVKNGDRUXF/tOJvPy8iIZqwwyhofXM3o9n+T9jLt27dLIkSOVn5/vyXxSdGSUvH+sJTKGQ7TMR69w4njyFjwAAIAIc+uy/rS0NLVo0UKLFy9WtWrnTgNPnz6tkSNHSpIKCwtdyeF2Rrs1r2R0cz96/Ti7+bYUr2dMS0vTE088oTZt2kT8teJERqvH88qxjoaMvGbK5nNiPlrVvPI2O6uaE68LqxAAAACIgJUrV4Zcs7NNsFqJQI1I3cwR7ox2a17J6NR+PHLkiHHvvfeWGcvKyjKOHDlinDp1ym+8ZJtw18xs2rTJ6N+/v3HmzBnj448/Nj766COjqKjIV8vOznatZqX0cX7zzTdNb+d2rUTpfK+++qrp7dyulZaUlGQ6R7wyHw3DMG6//faAc4T5GLxWWknGaJyPhmF+bEI5ZuHex3aedzj2lWFYNy83w1vwAAAAHJaVleU3ZhiGZs2apZYtWwasTZkyxa8WbJtgtV69ellmzM3NLZPVqRzhzmj38ew+t3BndPNYT58+XWvWrNGuXbs0duxYjRkzRhdccIGGDx+uFi1aKDY2Vs2bN/eNp6Sk6LrrrtOaNWvCWktNTQ2Y76mnnlJsbKz279+vhx9+WE2aNFHdunW1fPlyXXDBBYqNjVV+fr5yc3Mdr7366qsBM/bt21cxMTHas2eP+vbtK0n697//rQ8++ECGYfj6qRj//1UFTtbS0tICZix9W0n66quvTG/ndq20nJwc/fnPf/abI16Zj9K5Ofnjjz9q0qRJZebISy+9pC5dujAfVb75WPr20TYfU1JS1KxZs4DHxmoe7N+/3/F9bOd5h2Nflc4WihjDzlYAAAAot06dOunGG2/0G1+9erVyc3MD1t577z3dcccdIW0TrPavf/3LMmOtWrXUrVs3x3OEO6Pdx7P73MKd0c1jPXDgQC1cuFDJycl6+OGHNXXqVKWkpOjOO+/Uxo0bJUkbNmzwjT/zzDMqLi7WwoULw1oz+4U/KSlJ8+fPV1JSko4dO6alS5f6xiVp/vz5MgxDt956q+O1+fPnB8z4/vvva9OmTfr666+Vnp4uSRo2bJhmzZrlq/Xs2VOJiYmO16wkJyf7fkHcsWOHrrjiCkny7fuS5+9WLZBOnToFnHdemY8lzykmJkZvvvlmmTlSkp35WL75WLLPWrduHXXz8ZlnnpFhGAGPjdU86Nu3r2P72M7zDue+ks79HxPsNn5CvmYKAAAAIbn77ruNI0eO+I2PHDnStJaQkBDyNsFqwTIOGDDAlRzhzmj38ew+t3BndPNY9+vXzygqKvK9deLQoUPG0KFDjbZt25Z5y0jJePfu3X3bhLNmpn///oZhnHtrx/r1633jd999t69mGIYrNStnz541evfubTz66KPGu+++awwZMqRMbdmyZa7VzJR+e8x9991neju3a6X9/ve/DzhHvDIfDePcnCzZl6XnSIcOHXxfMx+Dz0fD+GVORtt87N69u+mxsZoHhuH8PrbzvMOxrwzD3lvwWIACAACIsIKCgpBrdrYJVrM68XQzR7gz2q15JWM4n9e6deuMw4cPl8lYUFBgjB8/3jh8+HCZbQoKCoxFixYZ69atM3JycvzuuyI1s3x79+41ioqKyuTLy8szPv/8c2Pv3r3Gzz//XOb2TtbMMpYoybhixQpj2rRpZZ5jCbdrgfIZhmFMmjTJ9Hm4XSvttttuMw4fPlxmzEvz0TDOzcmBAweWGcvLyzPefvtto6ioqMx2zMfyZYy2+bho0SLTY2M1D0q2L+HEPrbzvMOxrwwjtMWqEixAAQAARJjVXxHNana2MavZbawd7hxWtYpktFvzSkY396Obx3nAgAEhN4R2a76VMGs+7aWMwZpP79q1y1i+fLnx3XffRbwWSoNjw/D+fLS6T+bjOefPyco4H61qTu5jO8/b7r4yjND3VyA0IQcAAIgww6WPdrZqkG3VWDvcOdzOaLfmlYxu7ke3jvP06dOVlZWlkSNHhtQQOtw5rGpmzaetGkK7mdGq+XT9+vU1Y8YMzZs3T59//rm6deumjz/+WC1atND27dtdrf35z3/2ZQ6lwbHVPvHKfHQzo9fno3RuTu7du1f9+vUr0zx78uTJ2rhxY6Wdj1a1cO/j4cOHyzCMkJ53ZmamVq5cWaF9JYW+vwJhAQoAACDCSj7tJpSanW0GDBigWrVqafbs2WXGDxw4YDujnRxuZ7Rb80pGN/ejW8f5X//6l1q3bq3Zs2drw4YNevDBB5WSkmKZzYkcVrXs7Gxdeumlmjhxom699VZNnTpV0i/NeiOdMSkpSZs2bVJBQYHeffddSb80Kk5OTpYkrVixQqmpqape/dyvfffcc49q1Kjhaq0kq3SuwXFJtvI0L/b6fHQzo9fnY0mWv//97xoxYkSZ5tmFhYWSKu98tKqFex/n5+crJiYmpOd99dVXBxwvz76S7O+vQFiAAgAAqCJ++9vfKiYmRi+++GKZ8VGjRkUmUABkDA+vZywsLFStWrUkSZ07d9bf/vY3Pf300/r+++8jnOwXhYWFqlmzpmJiYjR27FjfeFFRUQRT/eL2229Xnz59dMstt+jPf/6zunbtqrNnz0qSdu3apZEjRyo7O1vFxcW+Xyrz8vK0b98+V2uSfJ/glpSUZPsXVycxH8Pj9ttv17vvvqvc3Nwyc5L5GD67du2SJNWoUSOk5213X0nh3V8sQAEAAESYW5f1//Of/wz41/KSt28UFhYqLi7O8RxuZ7Rb80pGN/ejW8f58ccf19///nff940aNdK0adN8V/KEms+J2ksvvaRnnnlG0rlFCenc1QcPPvigZzLGxMSofv36euWVV7Ry5UolJCRIktLS0lRUVKQaNWqoWrVqkqTTp09r5MiRatWqlau10vuq5EqM8vL6fHQzYzTMxxK9e/dW7969fXPyr3/9q6Rzi2WVcT5a1cK9j9PS0vToo49q0qRJ5X7ezz//vK699tqQ979Udl6Fur8CiTGCzR4AAABUyNq1a9WmTRtddNFFvrETJ05o69atiomJCVh799131a9fv5C2CVa79tprTTM+/fTTys7O9vvr5tNPP63x48ebbhPumpW1a9dq8uTJWrRokW+sIvvE7j62sx+tnrfb+1GSkpOTlZqaqrNnz2rbtm1q1aqV760YkkzHnaqFmo+MwTOW5PNK7dlnn1VWVpZ+85vfqE2bNgFv5/X5GA0ZvTofS2cs77iTtUDzceHChZb5A9mwYYNvQdANAwcOtKzb2Sfl2SYrKyvo67c8qtneEgAAAOUyadKkMgsZklS3bl298sorprUpU6aEvE2wmpXs7OyAf3HNzs623CbcNSuTJk1SbGxsmbGK7BO7+9iK2X4sqYUyXpFaMFu3btXZs2c1btw4zZkzx/fWvOTk5IDjTtVCzUfG8md0+8qXQLXhw4dLkg4ePKhx48bpwIEDmjlzpt/ryOy5eWE/ljw3r2f0+nws2Y+hjIe7Fmw+1q5d2/dv4cKFZb43q/3qV78KeZuK1IIJ51VapRueW71+Q8Fb8AAAABxWvXp1nTlzxtdbQSp7WXugWrVq1ULepjw1M5mZmZKkfv36+cYMw9DWrVvLjJWMl3z6VrhraWlpphlLP6dAz83NfWzGbD9aPW8n9rHVfixRrVo1nThxQn//+981YMAA3/aBxp2qhZqPjOXP6GbzabNafn6+JOnYsWNavnx5wAbHkvlz88J+LHluXs8oeXs+luzHUMbDXQs2H++44w7fbZcsWVLm+9JK1+xsE65aIOFslG7V8NwuFqAAAAAcNmzYMA0ePFgJCQmqW7eujh8/ro0bN+r+++9XTExMwFpycnLI2wSrWWnbtq1iYmL8LsNPSkryNSA9nxM1K8OGDdPjjz+uyZMnh2Wf2N3HVsz2o9Xzdns/SlJcXJwGDhyoPn36qLi42Hdl2a9//euA407VQs1HxtAyRlpJ8+n8/HzTBseS+XPz0n70ekbmY3DB5uPnn38u6dzi2vHjx33fl3Z+7dtvv1W7du1C2qYite7du9t45vZYNTy3ix5QAAAALsjNzdXmzZt19OhR1a9fX+3bt9eFF14oSTp+/Li2bdvmV8vNzdXGjRt16tSpcm8TrGZ2Fc+zzz6rvXv3+i1oPPvss3r++ecDbvfss8/qmWeeMb0/OzWrjJJ077336uGHHw75eYd7H4e6H62etxP7ONh+TEpK0uTJkxUfH68zZ87o+PHjuvjiiyWduzog0LhTtVDzkbF8Gd1ePA5Uy8nJkSQ9+uijWrhwoWrUqKHTp09rw4YN6tGjR5k5avbcIr0fSz83r2f08nwsvR/LOx7uWrD5+Nprr4W8kLZu3Tpdc801IW1TESNGjLCs29lfZuM5OTm+hucNGza0fP2WFwtQAAAAEeZU09DzWTXxtmqsHe4cVjWrJt7BhDuLm/vRTg6rWrD9uH37dl1xxRW+7/fs2aNLL73UdNxqm4rUSpzfyNjtHJUho5eaT4faKN3r89FqO69k9Np8NNuPVuORqJXWqVMnX4Pt0sskJW/BLHmrWunatm3bQt6mIrWbb77ZNL8Zu43SK9Lw3AxNyAEAACLMrY92tmribTdjuJvHWjXxDibcWdzcj3ZyWNWC7cfzPz1v2rRpluNO1cwaGbudI5ozut1g2olG6V6fj9GQ0Svz0Sqj1+fjr371KzVt2lSdO3fW448/runTp2v69On6xz/+oQ4dOgSs9evXL+RtKlJzs1F6MHZ+VtMDCgAAIMLC2TTUqhasibcVt5rHWjXxDtZYO9xZ3NyPdnJY1cz24+nTp9WzZ0/t379fEyZMkCQVFRVpx44deumll/zGDx06pNWrVysjIyOstdKZSjcy7t27t6s5KkNGtxtMh7Mx9cmTJwPuS6/MxwEDBphu55WMXpuPVvPA6/PxN7/5jcaNG6f09HSNGjVK//3f/6277rpLkvTYY4+puLjYrzZmzJiA41bbVKQWqUbpgVj9bDLDAhQAAEAVYdXE2yusmnh7RTTvx5ycHOXk5GjNmjVKTEyUdK7BbP/+/XXs2DG/8VGjRun48eOqUaNGWGslzm9kXLt2bfXo0cO1HJUho9sNpsPZmLpWrVoB96VX5mNsbKxatmwZcDuvZPTafLSaB16fj1lZWfrTn/6k3//+95o4caLq1q2rrKwsSeeu8Dp27JhfbeLEiSoqKgppm4rUSvpYudEo3YmG5/SAAgAAiLBwNg0NVrNq4h2sabUbzWOtmnjbzWi35uZ+tJvD7n58//33dfvtt/uNL126VLfcckvAbZyoSYEbGbudI9ozut1gOtyN0s2em1eOtdV2XsnopfloltFqPBK181133XW6/vrrTeuBFrE2btyoTp06mS5wWS182ak1a9Ys5E8dtNsovSINz82wAAUAABAhc+bM0dChQ0Oq2dkmWK1EoIaibuYIlvHAgQN64okntGDBAp0+fVrvvPOOiouL1bdvX+Xl5al+/fqqVatWuWvXXXedLrnkkpC26du3r+Lj48Oa0W4Ouxmlc784jBw5Uj///LNvrHv37iouLtbXX3/tNy7JkZpZI2O3c0RzRi81n7bbKN3suXnlWEdDRq/MR6uM0TIfzbj1IRJWtaSkJG3fvl2tW7d2vFG6VcPzYA3KzfAWPAAAgAjJyMgwXXAxq9nZJlitRKC/S7qZI1jG0aNH+06Un3jiCbVv316NGzfW6NGjlZeXp3nz5oVUS05O1urVq0O+v9mzZ4c1o90cdjNK0u7du5Wenq6MjAx17NhRp06dUvfu3fXkk0/qwgsv9BuX5Eht/PjxZX7JmjZtmiZOnOh6jmjOaJbPKrvbteTkZM2bN0/jxo3T8ePHlZ+frzfeeMN3O7Pn5pVjHQ0ZvTIfrTJGy3w049aHSFjVOnTooD179qhz587q3r27fvvb3/pq06ZN048//qhmzZqVqU2ePFnHjx/3G7faRpI+/vhj39czZ87Un/70J9O85cWn4AEAAECSvYaiboqNjVVsbKyOHz+uvXv36oEHHtDtt9+uwsJCxcbGKi4uLqSaYRghb1NYWBj2jHZz2M0onWsanJKSoqZNm/p+WZOkH3/8MeB4uGurV68u0zh5woQJGjdunK+RsVs5ojnj0aNHTfNZZXe7Jvk3gz5y5Ei5nrdXjnU0ZIz0fLTKeH7Ddq/PRzNufYiEVe2xxx7TpZdeqhYtWmjUqFFatmyZ71PrHnvsMY0bN86vNmbMmIDjVtvUrl1bd9xxh+9fgwYNynxvF1dAAQAAOOyRRx7xO5k0DEM7d+40rW3YsEEjR44MaZtgtWAZd+3aVeYxncphN2PTpk21fv16jRkzxncCnJubq7y8PF122WV65ZVXtHPnznLXYmNjQ94mLy8v7Bnt5rCbUTr31pPCwkLVqVNHixcv1oEDBySd618VaDzcNbPGziWNjN3KEc0ZT5065Znm0xVplG71vL1yrKMhY6Tno1VGtxu2V3Q+etljjz2m7du3a//+/Y43SrdqeG63QTk9oAAAABxWchIXikOHDqlx48ZhzdGsWTPTWk5Ojh599FFNmjTJ8RxWrDKePXtWffr00VNPPaUuXbpIko4ePaoffvhBl19+uTIyMlS9evVy1w4ePKgjR46EtM0PP/yg1q1bhzWj3Rx2M0rSgAEDlJqaqhMnTmjp0qW69tprdcUVV+jIkSOqU6eO37gkR2pmjYzdzhHNGb3UfNpuo3Sz5+aVYx0NGb0yH60yRst8NOPmB4aY1Z566in961//Mm2WHs5G6VYNz4M1KDfDAhQAAIDDSn8Cze7du3XZZZdJklatWqU6deoErE2dOlUPPvhgSNsEq/Xq1Stgvjlz5qht27au5bCb8ccffzR9W0LpRqrlra1fv14JCQkh3999991n2vx7xowZruWwmzE+Pl6FhYVKT0/X8ePHlZSUpMzMTHXs2NF0XJIjNbNGxm7niOaMXmo+badRutVz88qxjoaMXpmPVhmjZT6ezysfGGL3w0SC1ew0PDd7jGDoAQUAAOCw0s1NU1JSfF+npqaa1ubOnRvyNsFqZjIyMlzNYTdjjx49lJiYqMTERHXr1k2StGLFCmVnZ9uq1apVy9b9jR49WtWqnTuNfuKJJ1RQUKCLL75Yo0ePdjWH3YzSuWbpZ8+e1cqVKxUXF6fXXnvNctyp2pNPPqn09HQ9//zzWrp0qRYvXhyRHNGc0Syfl2rjx49XadOmTSvzvdfnYzRk9Mp8tMoYLfPxfBkZGSHX7GxTkVqJcDZK79Chg+Li4tS5c2c9/vjjmj59uqZPn65//OMfQXOYYQEKAAAAUSEhIUEXXHCBVq1apcWLF6tly5ZasmSJJk+ebKu2YMECW/dn1fzbzRx2M0rSiRMndNdddykuLk7SL7+AmI07VTNrcux2jmjO6IXm02Y1q0bppXl9PkZDRq/MR6uM0TIfo1k4G6VbNTy3iybkAAAADvv+++81YcIEGYZR5uv9+/fLMIyAtZ9//jnkbYLVrJp/V69e3bUcdjP+53/+p/Ly8tS7d29dc801KioqUnp6uiRp/vz5IdfeeOMN1ahRI+T7a9q0qWnzbzsZ7eawm1GSWrZsqZdfflnHjh3TjBkz1LJlS8txp2pmTY7dzhHNGb3QfNqsZtUovTSvz8doyOiV+WiV0evz0SsfGGL3gzrCzarh+W9/+1tb90kPKAAAAIetX79eR48eVfXq1X2NT4uKitSgQQNJClg7fPiwGjVqFNI2wWrBmpC7lcNuxoyMDN9f20srKiqSYRgh17788ktf8+5Q7q9fv36mzb+3bNniWg67GUsalH/yySe+3ic9e/b0/cJjNu5EzaqRsZs5ojmjl5pP22mUHux5e+VYR0NGL8xHq4xen49e+cAQK1Y/I6XwNkq3anj+4osvqrCwMODPICtcAQUAAOCwI0eO6O2331bXrl110UUXad++fcrIyFD//v0VExMTsNaqVSt99tlnIW0TrGYYhmnz76KiItdy2M24bNky0waqUuAeUla18ePH+71lozz3t3fvXt8vWuf35MjMzHQth92MrVu3VmFhofLz8yVJ+fn5KioqUlxcnOm4JEdqderU8Wtk7NRjVdaMZvmssrtd++Mf/6iNGzcGbBRt9dy8cqyjIaNX5qNVRq/Px+zs7JA/PON///d/Xf2gDrMFqJIG5YEWksxqwbZ58cUXAz5WiWHDhoXcjJwroAAAAByWlJSkuXPnqnr1X/72V1hYqKFDhyomJiZgrUuXLlq7dm1I2wSrVatWzXeyWPpTb5KTk2UYhms57GbMzMz0+6QiwzC0b98+GYYRci0zM1Pt2rUL+f5KN6wtLi7WF198oRUrVqhNmzbKyMhwLYfdjJMnT9YDDzygjh076pJLLtHevXu1ceNGzZgxw3RckiO1Rx55RN26ddO7776rf/7znxo0aJDefPNN13NEc0azfFbZ3a6NHj1aF154oTIyMtSxY0edOnWqTCNjr8/HaMjolfloldHr8/H8nzmlv5YUsNa5c2dt2LAhpG0qUjNb8Bk6dKjmzJkTUs3ONqVZXW1lhiugAAAAHHb8+HGtXr3ab/zkyZMyDCNgraCgIORtgtXq1asXckYnctjN2LZtW9O3D0gKudapUyelpaWFfH8JCQn69ttvlZ6erkOHDqlr165asmSJLrroIsu3OIQ7h92M0rm36Q0fPty3zZo1ayzHnaqVNDIu6V1V8rdxt3NEc0azfF6q/fjjj3r55ZeVlJSkiRMn6oEHHlBpXp+P0ZDRK/PRKmO0zEeUj1XDc9NtuAIKAADAWa+//nrI26xbt853CX64pKWlqXfv3jIMQ8uXL/d9/dFHH6lv376u5bCbcdGiRWrSpInfNgcPHpSkkGubN2/WVVddFfL9DRkyxNf8u3nz5mXqPXv2dC2H3YwDBw5UUlKSatasqUsvvVR79uxRfn6+2rdvr2XLlul3v/ud37gkffvttwG3qUjt559/Vp06dfT555/r1ltvVXZ2tp5//nnTfE7liOaMq1evVrdu3fzySdLYsWMDZne7ds899+jNN9/UqFGj1K1bNy1evFgffPCBb056fT5GQ0avzEerjF6fj4mJiaY/fwzDCFh78803NXjw4JC2qUitXbt2ARuUb9q0SR07dgxY+/TTT9WzZ8+Qttm0aZPf27fPZ3VFlhkWoAAAAKoIq2boCQkJkY4nKToyvvXWW6bNv/v37x+BRP6CZVy/fn3A7bZt2+ZrUu6GhISEgI2MzfJFQjRkPHXqlCeaT5vVgjX39vp8jIaMXpqPVvvRy/PRKx8YYlUL1oQ8ELuN0ivS8NxMtZBTAAAAICodOXJEb731lv79739Lkvbt26e33npLP/30U4ST/SIaMi5btkx33HGH37+lS5dGOppPsIwdOnTQ/v379e2336pDhw6qXr26EhISdM899wQcT0hIMN2mIrVAjYyt8jmVI5ozdujQIWA+KXCj6EjUSppBf/DBBxo4cKByc3PLzFevz8doyOiV+WiV0evz0ernj1ktJycn5G0qUsvOzlazZs3UrFkz5efn+77etm2baW3dunUhb7Nt2zaZKekNFerik8QCFAAAQJWxaNEizZw5U/fff7/uuusuDRs2TDNmzNCiRYsiHc0nGjJmZmaqX79+Zf717dtX3333XaSj+QTLOHr0aJ09e1YrV65UXFycXnvtNctxp2qPPPKIcnJy1LJlS+Xk5GjEiBERyRHNGc3yealm9bys6l451tGQ0Svz0Sqj1+ej1c8fs9qSJUtC3qYitTfeeMP3/FJSUnxfp6ammtbmzp0b8jZWb60L9tY8KzQhBwAAqCKsmqF7RTRkDNYM3QuCZfRKk+BoaKzt9Yxeaj5tZ19Z1b1yrKMho1fmo1VGr89Hr3xgiN0P6ogGLEABAABUETfffLO+/fZbv/GbbropAmkCi4aMEyZMCGk8EoJlbNmypV5++WUdO3ZMM2bMUMuWLS3HnaoVFhZq2LBhvkbGRUVFmjBhgo4fP+5qjmjOaJbPSzWrfWX13LxyrKMho1fmo1XGrKwsT8/HYD9/AtU6dOgQ8jYVqaWlpWnChAkyDEPff/+97+v9+/fLMIyAtZ9//jnkbfbv369HHnkkYIPynTt3+mUrL5qQAwAAAFVQSXPeSy65RDfccINfk+Dzx52oWTVOLmms7UaOaM7opebTVqwapUven4+lG2t7NaMX5qNVRrcbtlsJNh+9ys1G6VZNyO00Q5ckGQAAAACqlDFjxhjFxcXlHneqVlBQYKSlpRmzZs0yCgoKjK+++ioiOaI5o1k+L9UKCgqMpUuXGjNmzDCWLl1qFBQUlOu5eeVYR0NGr8xHq4zRMh+97MMPPzQGDx5szJo1y3j77beNmTNnGoMGDTKWLVtmWhs3blzI2yxbtsxYu3at73GzsrJ8X69cudJ2fpqQAwAAAFVMTk6OqlXz/1XAbNypmlkzYLdzRHNGLzWfttMo3eq5eeVYR0NGr8xHq4zRMh+9zM1G6XYalAdDDygAAACgimnVqpX+9re/qXPnzqpe/dyvBN27dzcdt9qmIjWzZsBu54jmjF5qPm2nUbrVc/PKsY6GjF6Zj1YZo2U+epmbjdKdaHjOAhQAAABQxZT8YrFt2zbfWPfu3U3HrbapSM2skbHbOaI5o9sNpsPdKP2JJ57w/Hy02s4rGb0yH60yRst89DI3G6VbNTy3iybkAAAAACSd+8UsLi6u3OPhqFk1OXYzRzRn/OKLLzzRfNqsFqy5txmvz8doyOi110w0z8eqxqrhue39ZLt7FAAAAIBKJSkpKaTxitasGhm7mcOq5vWM1113nWeaT9tplG7F6/PR6j69ktFLr5lon49VjVWDcrtoQg4AAABAUtn+KeUZr2jNqpGxmzmsal7PmJ+f75nm03YapVvx+ny0uk+vZPTSayba52NVY9Wg3C56QAEAAACQJNO38li9xaciNatGxm7miOaMtWvX9kzzaTuN0q14fT5GQ0YvvWaifT5WNVYNz+1iAQoAAABARFg1MvYKr2esXr26fvWrX3mi+bSdRule4vVjLUV3RuZjdAnW8NwOFqAAAAAASHL/7UQjRowIWDNrchyJtxN5PWOzZs0CZiwsLLTM7mbtL3/5i7744gvVrVtXl1xyif70pz8FvN35vD4foyGjl14zw4cPN23YHg3zsaox238VYrt7FAAAAIBKYfbs2SGNO1UrcX6TY7dzVIaMkWg+bcasUXpOTo6Rl5dnGIZhnDp1ypgzZ44xc+ZMY+vWrQHHjx49arpNRWpWcnJyjIEDB3o2Y8k2SUlJruSwux8Nw/2G7WasGvfDWTQhBwAAAKq4jIyMkMadqpUwzrvCwu0clSHj+fkiWTNrlD569Gjf+BNPPKGCggJdfPHFSk5ODjg+evRo020qUrMyevRo33PzYsaSbQzDcCWH3f0ouX+1mBmrxv1wFm/BAwAAAOApVk2OvcLrGSPRfNqMWaP02NhYxcXF6fjx49q7d6+mTp0qSXrhhRcCjr/77rum21SkZiU2NlYxMTGezViyTXFxsSs57O5Hyf2G7WasGvfDWSxAAQAAAFXEI4884veLmWEY2rBhg0aOHOk3vnPnTtNtKlILlm/Xrl2+PE7mqKwZvcasUXrTpk31yiuvaOfOnbrjjjskSbm5uYqNjQ04npeXp8suuyzsNStNmzbV+vXrNWbMGE9mLNmHu3fv1v333+94Drv70UusGvfDWTGG1bVpAAAAACqNnJycgOOHDh1S48aNXcvRrFmzgOMl+R599FFNmjTJtTyBRHNGSUpKStL8+fM9XcvPz9f69etVvXp1denSRZJ09OhRHTx4UEeOHPEb/+GHH3T55ZcrIyMjrLXWrVsHzC1JZ8+eVZ8+ffTUU095MuPZs2eVkZGhl19+WR988IHjOezuR8l8HnhlPlo1m0d4sAAFAAAAVBHr1q3TNddcI0navXu3LrvsMknS1KlT9eCDD/qNr1q1SnXq1Am4TUVqvXr1Cpjvueee03PPPedajsqYcc6cORo6dGjA7F6qSVJiYqJ69+7tN75+/XolJCQE3MYwDNO3V9mt3Xfffapfv75q1aql06dP65133lFxcbH69u2rGTNmBNzOKxkPHDigWrVquZbDTsbrrrtOl1xyScD9m5eXZ7rvnajFx8cHzC5JycnJSk1NNa2j4ui8BQAAAFQRb7zxhu/rlJQU39dz584NOJ6ammq6TUVqZj788ENXc1TGjF5qhh6sUXrdunWVmJioxMREdevWTZK0YsUK1apVK+B4dna2evToEfaaVWNts+28knHLli2u5rCT0e2G7XYbpXNtjvPoAQUAAAAAcF3dunV1wQUXKD09XYcOHVLXrl21ZMkSXXTRRfr2228DjksKey0pKcm0sXZCQoLpdl7JaLYPnchhJ6PbDdvtNkr3+gcLVAa8BQ8AAACoIkre8mQYhpYvX+77+s0339TgwYP9xj/66CMZhhFwm4rU2rVrF7Cx9sqVKzVkyBDXckRzxkWLFikxMdEv36ZNm9SxY8eA2SNRs7oK6qqrrlK9evXUu3dvNW/e3Df+xhtvqEaNGn7jkjR//nzl5eWFtbZlyxY1atRIO3fuVMeOHTV8+HDl5uYqOTlZp0+fDridVzL+4Q9/UHx8vGs57GTs0aOH7r777oD797LLLjPd907U0tLSZIa34DmPBSgAAACgili/fr2OHj2q6tWr69ixY4qPj1dRUZEOHz6sRo0a+Y03aNBAkgJuU5GaWfPszZs3q1q1aq7liOaMhmHoqquuqsBscIdVo/Sbb75ZDzzwgN/4l19+6WtwXVrJ8w7UKLoitX79+pk21t6yZUvA7bySMTU1Vf/xH//hWg47Gd1u2G63UbpV43KEBwtQAAAAQBWxfPlyvf322+ratasuuuginThxQhkZGWrVqpV27drlN96/f3/FxMQE3KYitQYNGgRsrD1hwgRt27bNtRzRnLFjx4565JFH/PJFohm63UbpZlecdO7cWRs2bPAbT05OlhS4/1VFam3btjV9+1VmZqanM3744Yf67LPPXMthJ6PbDdvNaqdPn9bTTz8dcnNyhA8LUAAAAEAVkZSUpLlz56p69V9awRYWFqpLly5au3at3/jQoUMVExMTcJuK1KpVq+b7Jbb0IkSnTp1czRHNGbt06aKNGzf65Tt/kSDStUCLFUOHDtWcOXPUsWNHXXrppWVqhmEoMzNT7dq18xvft2+fDMMIuE1FatOmTfONFRcX64svvtCKFSvUpk0bZWRkeDrjgQMH1KZNG9dy2MnYuHFj32Lp+fv3vvvuC7iNE7VTp07piy++UFxcnB588EG1b99ejRs3Vnp6umbPni04jybkAAAAQBVx/PhxrV692m+8oKAg4PjJkydlGEbYa/Xq1QuY78yZM67miOaMxcXFAfNFk7Zt2wZ8y1OnTp0C9upJSkqSpIDbVKRm1Wjc7G1ZXsn40EMPhf2xwp3R7YbtZrWHHnrIVnNyhA9XQAEAAABVxOuvvx5wfN26db63ULkhLS0tYGPtd955R4MHD3YthxWvZ5w7d67uvvvuiDdDt9soPSMjQwcPHlSTJk38ntvmzZsD9rc6ePCgJAXcpiK1IUOGmDbW7tmzp6cznjhxQg8++KBrOexkdLthu1nt/fff17XXXhtyc3KEDwtQAAAAAFxl1gy9QYMGpr1i3Ob1jFb5JHcbtpvVrBqlWzUnd9tbb71l2li7f//+EUjkL5ozut2w3axWUFCgpk2bhtycHOHDW/AAAAAAuOrIkSNlGmvv27fP13TbK7ye0Srf+Y3LI1Xr2LGjevfuLcm/ObmXFqCWLVtm2ljbK8c7mjOOHz9eEydO9Bt3u1F6YmKibz5mZGSUqbEA5Q6ugAIAAADgKrNm6EOHDtWCBQsimOwXXs9olc/thu12GqUHWiCIFLNm6Pv27dNXX30VoVRlRXNGtxu2m9V2796tGTNmSPJvXD558uQKPXeUD1dAAQAAAHCVWTP0kydPRiBNYF7PaJXP7Ybt0d4o3awZeknTbS+I5oxuN2y3ql1wwQWmjcvhPK6AAgAAAOAqs2bokjRixAgXk5jzekarfF5h1Sj9008/jXQ8H7Nm6GbjkRDNGd1u2G5W69Wrl4qKigI2Lh84cKDf7RF+LEABAAAAAMLO643cUbVEQyP3yo634AEAAAAAws7rjdxRtURDI/fKjiugAAAAAABh5/VG7qhaoqGRe2XHFVAAAAAAgLDzeiN3VC3R0Mi9suMKKAAAAABA2Hm9kTuqlmho5F7ZsQAFAAAAAAAAR1WLdAAAAAAAAABUbixAAQAAAAAAwFEsQAEAAAAAAMBRLEABAAAAAADAUSxAAQAAAAAAwFEsQAEAAAAAAMBRLEABAAAAAADAUSxAAQAAAAAAwFEsQAEAAAAAAMBRLEABAAAAAADAUSxAAQAAAAAAwFEsQAEAAAAAAMBRLEABAAAAAADAUSxAAQAAAAAAwFEsQAEAAAAAAMBRLEABAAAAAADAUdUjHQCA8/bt26fU1FStXbtWBw4cUHFxseLj4/XrX/9aV111la655hrdfPPNZbZJSkrS+vXr/e6rVq1aatq0qbp06aL7779fTZs2DdtjBnPq1CnNnz9fn376qfbs2aP8/HzVrVtXDRo00BVXXKGrr75at912m2rXru3bZsqUKXr99dc1YsQIPfzwwyE9XnkVFxfr3nvv1TfffCNJWrhwoTp16uTIYwEAAOdx7sS5E4DwYwEKqORWrFihxx57TIWFhapXr546duyo+Ph4nTx5Utu2bdPChQu1bNky0xOaK664Qq1bt5YkGYahn376SVu2bNGCBQv0/vvva+7cuWrfvn1YHzOQPXv2aMiQITp06JDi4uJ01VVXqWHDhiooKNDu3bv1wQcf6IMPPlDHjh3VqlUr+zvMhtmzZ+ubb75RTEyMDMNw9bEBAEB4ce7kPM6dgKqJBSigEvvpp5/05JNPqrCwUEOHDtWoUaNUs2bNMrfJzMzUxx9/bHofvXr18vvr16lTp/TAAw9o48aNeu6557RkyZKwPmYgjz/+uA4dOqRrrrlGr776qurXr1+mfuDAAb333ntl/oInSQMHDlTv3r0VHx8f0uOV165duzRlyhT16NFDO3fuVE5OjiOPAwAAnMe5E+dOAJzDAhRQiX366afKzc1Vw4YN9eSTTwa8zZVXXqkrr7wypPutU6eOHnroIQ0ZMkTfffedTp06pTp16jj2mN9//70yMzMlSSkpKX4nUJLUtGlTPfTQQ37j9evXD3j7cDhz5oyefPJJ1apVSykpKbrnnnsceRwAAOAOzp04dwLgHJqQA5XYkSNHJMmRk4iLL77Y9/WZM2ccfcyffvrJ93WDBg1C2nbKlCm6/PLLNWXKFL+aYRhKS0vTnXfe6eutMGzYMG3atEnr1q3T5ZdfrqSkJNP7/sc//qHvvvtOY8aMUaNGjULKBQAAvIdzJ86dADiHBSigEmvSpImkc5c6r1mzJqz3vWXLFknnTqZKX6LtxGOWbtaZmpoalvuUzv1F8C9/+Yu2bdum9u3b67rrrtOhQ4d033336bPPPrPcdtu2bfrHP/6hrl27qm/fvmHLBAAAIodzJ2ucOwGoCN6CB1RivXr1UqNGjfTDDz9oyJAh6ty5s6699lq1bdtW7dq1C/kvbYZh6MiRI1q9erUmTpwoSX6Xbof7MSWpcePGuuGGG/TJJ59oypQpWr58ua6//nq1a9dOV155pX7zm9+EfJ+ffPKJFi9erNq1a2v27Nnq2LGjrzZ37lz97W9/M922sLBQTz75pOLi4vTXv/415McGAADexLmTOc6dAFQUC1BAJXbhhRdq3rx5GjNmjDZv3qz169eX+Xjg1q1b6+6771b//v0VGxsb8D5ef/11vf76637jLVu21EsvvaTExMSwP2YgEyZMUEpKitLT05WVlaWsrCxfrXHjxurTp4/uv/9+1a1bt1z3V/LXwKSkpDInUJI0ZMgQLVu2TN9++23Abd944w3t2LFDKSkpph+lDAAAog/nTuY4dwJQUSxAAZXcpZdeqrfffltbtmzRZ599ps2bN2vr1q06evSotm3bpueee04rVqzQ9OnTFRcX57d96Y8SlqQTJ05oz5492rdvn1588UXVr1/f76OEK/qYgfzqV7/SxIkT9cgjj+iTTz7Rpk2btHXrVu3fv1+HDh3S9OnTlZ6ervnz56t58+aW93XmzBl9/fXXkqRbb7014G1uueWWgCdRW7Zs0cyZM/WHP/xBd999d7myAwCA6MG5kz/OnQCEAwtQQBXRvn1738mOYRjaunWrZs+erWXLlunLL79Uamqqhg0b5rddoI8SNgxDixYt0vPPP6/k5GR9+OGHAf+aZfcxrbRo0UKDBw/W4MGDJUk5OTlKS0vTrFmzdODAAT3//POaMWOG5X0cO3ZMBQUFkqRmzZoFvE2gE7GCggKNGTNGNWvW1AsvvKCYmJiQsgMAgOjBudMvOHcCEA40IQeqoJiYGLVt21avvPKKevbsKUlatWpVSNsPHDhQbdu2VV5enubPn+/4Y5pp1qyZRo4cqccee0yStHr1auXn51f4fgPZs2ePdu/erZo1a+rpp59WUlJSmX8//vijJOmFF15QUlJS0JM5AAAQHTh3sodzJwClcQUUUMV17dpV//d//6djx46FvG3z5s313Xffac+ePa49ppnrrrtO0rlLxE+ePKlatWqZ3rZevXqKi4tTYWGhDhw4oN/+9rd+t8nJyTHd/tixY2V6M5xv27Ztksz/QggAAKIX506cOwGwhwUooBIzDCPopc4HDhyQdK4ZZaj2798vSapdu7ajjxnKfcbFxZX5aONAatSooQ4dOmj9+vVKT0/Xo48+6nebZcuW+Y21bt1aO3bsML3fnj17KicnRwsXLlSnTp0sMwAAAO/h3Ckwzp0AhANvwQMqsUWLFunJJ5/Upk2b/GqGYWjFihVauHChJOm//uu/yn2/JX0Mtm7dKkm64YYbHH3MHTt2KCkpSStXrlRhYaFfffv27Ro/frwk6aabblKNGjWC3mdycrIkaf78+frmm2/K1N58801t3ry5XNkAAEDlwbmTOc6dAFQUV0ABlVhRUZHef/99vf/++6pfv77atGmjevXq6dSpU8rKyvJdKn3bbbepX79+Ae9j1apVZS6pPnnypHbv3q19+/ZJkvr06VPmZCgcj3k+wzB8H0lcu3ZttW7dWo0aNVJRUZGys7N9l223bt1af/nLX8p1nzfeeKP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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# co-occuring Indel spectrum for all SBS signatures\n", + "axes = multi_model.plot_feature_change(in_modality=\"SBS\", figsize=(12, 15), annotate_mutation_types=True)\n", + "\n", + "for ax in axes[:,0]:\n", + " ax.set_xticks([])\n", + "\n", + "for ax in axes.flatten():\n", + " ax.tick_params(axis=\"x\", which=\"major\", labelsize=6)\n", + "\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "salamander_test", + "language": "python", + "name": "salamander_test" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}