Single-cell gene expression studies promise to reveal rare cell types and cryptic states, but the high variability of single-cell RNA-seq measurements frustrates efforts to assay transcriptional differences between cells. University of Washington researchers have developed the Census algorithm to convert relative RNA-seq expression levels into relative transcript counts without the need for experimental spike-in controls. Analyzing changes in relative transcript counts led to dramatic improvements in accuracy compared to normalized read counts and enabled new statistical tests for identifying developmentally regulated genes. Census counts can be analyzed with widely used regression techniques to reveal changes in cell-fate-dependent gene expression, splicing patterns and allelic imbalances. The researchers reanalyzed single-cell data from several developmental and disease studies, and demonstrate that Census enabled robust analysis at multiple layers of gene regulation.
Census approximation of relative transcript counts
in single cells without external RNA standards
(a) Typical single-cell RNA-seq procedure for estimating mRNA abundances via spike-in standards. Losses alter the distribution of relative gene expression levels in a single cell. RT, reverse transcription. (b) Distribution of transcript counts corresponding to each cell’s most frequently observed relative abundance (i.e., TPM) in cDNA or lysate RNA from lung epithelial data25. (c) Total transcripts per lung epithelial cell estimated using Census counts versus using spike-in controls. Blue line indicates linear regression. The shading around the blue line indicates the 95% confidence interval of the regression. Black line indicates perfect concordance. (d) MA plot for expressed genes based on contrasts between cells from embryonic day (E)14.5 and cells from all other time points. Census transcript counts (top); transcript counts derived by spike-in regression (bottom). (e) Fold changes in gene expression based on Census counts or spike-in regression of spike-ins, contrasting cells from E14.5 and all other time points.
Availability – Census is freely available through our updated single-cell analysis toolkit, Monocle 2. https://github.com/cole-trapnell-lab/monocle-release