#43 Generalized PCA for single-cell data with William Townes

Will Townes proposes a new, simpler way to analyze scRNA-seq data with unique
molecular identifiers (UMIs). Observing that such data is not zero-inflated,
Will has designed a PCA-like procedure inspired by generalized linear models
(GLMs) that, unlike the standard PCA, takes into account statistical
properties of the data and avoids spurious correlations (such as one or more
of the top principal components being correlated with the number of non-zero
gene counts).

Also check out Will’s paper for a feature selection algorithm based on
deviance, which we didn’t get a chance to discuss on the podcast.





Links:


Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (F. William Townes, Stephanie C. Hicks, Martin J. Aryee, Rafael A. Irizarry)
GLM-PCA for R
GLM-PCA for Python
scry: an R package for feature selection by deviance (alternative to highly variable genes)
Droplet scRNA-seq is not zero-inflated (Valentine Svensson)






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