Quantifying expression variability in single-cell RNA sequencing data
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Transcriptional noise is an intrinsic feature of cell populations and plays a driving role in mammalian development, tissue homoeostasis and immune function. While expression heterogeneity, a phenotypic readout of transcriptional noise, has been broadly studied in prokaryotic model systems or by profiling individual genes, few whole-transcriptome studies in mammalian systems have been reported. The development of single-cell RNA sequencing technologies introduced powerful tools to investigate transcriptional differences between individual cells, therefore allowing the in-depth characterisation of expression variability. In this thesis, I computationally analysed single-cell RNA sequencing data to understand transcriptional variability and expanded a statistical model to avoid confounding effects when quantifying such variability. First, I profiled individual transcriptomes of CD4