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An alternative to log(CPM+1) transformation of count data is the TF-IDF transform, adopted from text analysis. Similar to finding characteristic words describing a topic in the document, TF-IDF can be used to find stand-out genes ("terms") for each cell ("document").
It should be relatively straightforward to include this approach into Single-cell preprocess.
I read your papers about this method, however I’m new to coding, do you mind sharing the code or telling me where I can find code or tutorial for this method which id like to apply for scRNA-seq Gene clustering?
An alternative to
log(CPM+1)
transformation of count data is the TF-IDF transform, adopted from text analysis. Similar to finding characteristic words describing a topic in the document, TF-IDF can be used to find stand-out genes ("terms") for each cell ("document").It should be relatively straightforward to include this approach into Single-cell preprocess.
See https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4922-4
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