Single-cell RNASeq (scRNASeq) has emerged as a powerful method for quantifying the
transcriptome of individual cells. However, the data from scRNASeq experiments is often
both noisy and high dimensional, making the computational analysis non-trivial. Here we
provide an overview of different experimental protocols and the most popular methods for
facilitating the computational analysis. We focus on approaches for identifying biologically
important genes, projecting data into lower dimensions and clustering data into putative cell …