Current techniques for case-control comparisons in high-throughput transcriptomics and the need for contrastive methods
Gladstone Institutes
High-throughput RNA-sequencing (RNA-seq) technologies are powerful tools for understanding cellular state. Often it is of interest to quantify and summarize changes in cell state that occur between experimental or biological conditions. Typical analysis strategies will begin with encoding samples in some low-dimensional space based on shared variation, followed by identification of cell states or types that change between conditions. With these states identified, the function, identities, and differences between conditions are described using univariate tests of differential expression on individual genes. These approaches ignore changes in transcriptional correlation and gene pathways across conditions. In this talk, we will motivate the need to identify the low-dimensional structure capturing variation exclusive to the case data.