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Abstract:
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Single cell RNA-seq (scRNA-seq) enables the transcriptomic profiling at individual cell level. This new level of resolution revewls inter-cellular transcriptomic heterogeneity and brings new promises to the understanding of transcriptional regulation mechanism. Similar to data from other high-throughput technologies, scRNA-seq data are affected by substantial technical and biological artifacts, maybe more so due to the low amount of starting materials and more complex sample preparation. With high heterogeneity expected between cells, normalization faces new challenge because typical assumptions made for bulk RNA samples no longer hold. Yet it is still a necessary step to ensure reproducibility between studies. We present a probabilistic model of sequencing counts that well explains the characteristics of single cell RNA-seq data, and discuss different strategies in normalization and compare their impact on reproducibility.
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