Abstract
Recent experimental advances in single-cell RNA sequencing (scRNA-seq) have enabled the quantification of transcriptomes with single-molecule resolution. However, thus far, the stochastic modeling of transcription has been separate from the discussion of the statistics of the sequencing process, leading to simplifications that may obfuscate transcriptional dynamics, and technical artifacts in the assays. For example, imputation, normalization, and smoothing, used to correct for stochastic sequencing phenomena, make experimental molecule count data incompatible with a discrete representation, thus rendering the data uninterpretable in the context of conventional Chemical Master Equation (CME) models.
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