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Validation of noise models for single-cell transcriptomics

GSE54695 Mus musculus Expression profiling by high throughput sequencing 16 samples Submitted 2014/04/20 Platform GPL17021
Summary
Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity amongst 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 and after validation by single-molecule FISH experiments, we apply these models to demonstrate that growing mES cells in 2i instead of serum/LIF globally reduces gene expression variability.
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Also filed as BioProject PRJNA237439 and SRA study SRP036633. Searching any of these in the dataset finder brings you back here.

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