Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this, which we validate using single-molecule FISH. We demonstrate that gene expression variability in mouse embryonic stem cells depends on the culture condition.
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- Supplementary Text and Figures (11,966 KB)
Supplementary Figures 1–15, Supplementary Table 1 and Supplementary Notes 1–4.
- Supplementary Table 2 (82 KB)
GO terms enriched among genes with increased expression variability in serum versus 2i culture condition. Enriched biological processes and enriched molecular functions are given as separate lists. Only significantly enriched GO-terms (P < 0.05) were included. The lists indicate the GO-term ID, the hypergeometric P-value, the odds ratio, the expected number of genes associated with each GO-term, the observed number of genes for each GO-term, the size of the GO-term (total number of genes associated) and a short description. For the inference of over-represented GO terms, the set of differentially variable genes was compared to the universe of all genes expressed in the two conditions. The GOstats package was used to compute GO enrichment in R.
- Supplementary Table 3 (56 KB)
Probe set composition of smFISH probes used. Each column represents a probe set for the gene specified in the column header. All probes were labeled on the 3' end with TMR, Alexa594 or Cy5.