ORSA 2021.owl

<?xml version="1.0"?>
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     ontologyIRI="http://purl.obolibrary.org/obo/ORSA.owl"
     versionIRI="http://purl.obolibrary.org/obo/ornaseq/releases/2021-02-10/ORSA.owl">
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        <Literal>Lucas Serra</Literal>
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        <Literal>an algorithm that describes the dynamics of gene expression throughout differentiation directly, including pseudo-time and cell fate of individual cells. SCOUP is based on the Ornstein–Uhlenbeck process, which represents a variable moving toward an attractor with Brownian motion</Literal>
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        <Literal>an algorithm and an analytic framework which learns a similarity measure from single-cell RNA-seq data in order to perform dimension reduction, clustering and visualization</Literal>
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        <Literal>an algorithm that provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions. PAGA maps provide interpretable discrete and continuous latent coordinates for both disconnected and continuous structure in data, preserve the global topology of data, allow analyzing data at different resolutions and result in much higher computational efficiency of the typical exploratory data analysis workflow</Literal>
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        <Literal>an algorithm that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts in order to deal with &quot;drop out&quot; found in single-cell RNA sequencing technologies</Literal>
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        <Literal>an algorithm for mapping the quantitative energy landscape of single-cell dynamical processes using statistical physics modeling, such that we can obtain transition probabilities between cell states, reconstructed lineages and pseudotemporal ordering of cells.</Literal>
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        <Literal>a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.</Literal>
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        <Literal>a numerically efficient and highly scalable toolbox for an end-to-end analysis of single-cell transcriptomic profiles. CALISTA includes four essential single-cell analyses for cell differentiation studies, including single-cell clustering, reconstruction of cell lineage specification, transition gene identification, and pseudotemporal cell ordering.</Literal>
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        <Literal>a two-part, generalized linear model for single-cell transcriptomics bimodal data that parameterizes both of these features: stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. Our model provides gene set enrichment analysis tailored to single-cell data.</Literal>
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        <Literal>web server application that leverages a powerful enrichment analysis and underlying data environment for comparative analyses of multiple gene lists. It generates heatmaps or connectivity networks that reveal functional features shared or specific to multiple gene lists. ToppCluster uses hypergeometric tests to obtain list-specific feature enrichment P-values for currently 17 categories of annotations of humanortholog genes, and provides user-selectable cutoffs and multiple testing correction methods to control false discovery.</Literal>
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        <Literal>an algorithm to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings.</Literal>
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