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 statisticalproperties of the data and avoids spurious correlations (such as one or moreof the top principal components being correlated with the number of non-zerogene 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)