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Abstract:
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Transcriptomic studies such as in bulk RNA-sequencing, one can examine transcript abundance measurements averaged over bulk populations of thousands (or even millions) of cells. While these measurements have been valuable in countless studies, they often conceal cell-specific heterogeneity in expression signals that may be paramount to new biological findings. Fortunately, with single cell RNA-sequencing (scRNA-Seq), transcriptome data from individual cells are now accessible, providing opportunities to investigate functional states of cells, identify rare cell populations and uncover diverse gene expression patterns in cell populations that seemed homogeneous. Most importantly it provides an unprecedented resolution to the characterization of cellular clinical isolates. However, there are challenges analyzing such scRNA-Seq data. Amongst many challenges the most significant are the bimodal or multimodal distribution, sparsity and tremendous heterogeneity in the data. Consequently, we will describe potential ways of statistical modeling of such data, finding differentially expressed genes and methods for constructing gene-gene interaction network using this data.
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