Improving Differential Expression and Survival Analyses with Sample Specific Compartment Deconvolution

Motivation Studies on bulk RNA-seq of tumor biopsies can yield incorrect results because varying proportions of non-tumor tissues in the samples obscure the true signal and impact the accuracy of survival and differential expression analyses. Single-cell sequencing avoids these problems, but is still too expensive in clinical settings. Other deconvolution algorithms extract tissue-specific gene expression profiles from bulk sequencing, but cannot do this on a per-sample basis. Results We introduce SSCD - sample specific compartment deconvolution. SSCD extends non-negative matrix factorization with per-sample, per-gene constraint optimization. On simulated data, SSCD shows improvements in accuracy over existing methods. Using several real cancer datasets, we show that SSCD refines Differential Expression and survival analyses. Availability Code and data are available at https://github.com/ayurovsky/SSCD .