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. We introduce 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. We reanalyzed single-cell data from several developmental and disease studies, and demonstrate that Census enabled robust analysis at multiple layers of gene regulation. Census is freely available through our updated single-cell analysis toolkit, Monocle 2.
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- Supplementary Text and Figures (18,419 KB)
Supplementary Figures 1–15, Supplementary Table 2 and Supplementary Note 1
- Supplementary Tables (170 KB)
Supplementary Table 1
- Supplementary Data (1140 KB)
Text file storing the result (p-value) from the permutation test used in benchmarking differential gene expression based on spike-in transcript counts. Each row corresponds to a gene.
- Supplementary Software (6550 KB)
A tarball includes a version of monocle 2 (version: 1.99) used to produce all the figures, supplementary data is provided along with this submission and a helper package including helper functions are included as well as all analysis code which can reproduce all figures in this study are provided.