We also collected genes based on expression dynamics across the day and mapped these communities back to snRNAseq, finding enrichment in specific cell states and types.

This allows retention of genes that are expressed at a low level, but with consistent measurements. Many transcription factors are among the genes that are recovered using this method, and they map to specific cell populations in single-nucleus RNAseq data.

This is the most generalizable work I think I have every done. RNAseq is everywhere these days, and most analysis pipelines have a step for filtering out genes that are expected to be too noisy to interpret. We came up with a strategy to filter based on variance, rather than expression level.

Precision-Based Filtering Facilitates Integration of Conventional and Single-Nucleus Transcriptomes to Identify Time- and Temperature-Sensitive Cell Populations https://www.biorxiv.org/content/10.64898/2026.01.28.702137v1