A natural experimental design question arises (Fig. More reads can significantly reduce the effect of the technical noise in estimating the true transcriptional state of a given cell, whereas more cells can provide us with a broader view of the biological variability in the cell population. From a sequencing budget perspective, however, this seemingly unconstrained increase in the number of cells available for scRNA-seq introduces a practical limitation in the total number of reads that can be sequenced per cell. Indeed, this unprecedented look into population-level heterogeneity has been vital in the success of scRNA-seq, leading up to new biological discoveries 1, 2.Īlthough early single-cell RNA-seq assays were labor intensive and initially constrained by the small number of cells that could be processed in a single experiment, recent technological advances have allowed hundreds of thousands of cells to be assayed in parallel 3, eliminating the otherwise prohibitive per cell cost overhead. In contrast to bulk RNA-Seq, which probes the average gene expression in a cell population, single-cell RNA-seq has unlocked the potential of extracting higher-order information, granting us access to the underlying gene expression distribution. Single-cell RNA sequencing (scRNA-seq) technologies have revolutionized biological research over the past few years by providing the tools to simultaneously interrogate the transcriptional states of thousands of cells in a single experiment.
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