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Updated: Oct 10, 2025
Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
Published on: December 1, 2023
On the relation between input and output distributions of scRNA-seq experiments
Daniel Schwabe1, Martin Falcke1,2
1Mathematical Cell Physiology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany.
Motivation:
Single-cell RNA sequencing determines RNA copy numbers per cell for a given gene. However, technical noise poses the question how observed distributions (output) are connected to their cellular distributions (input).
Results:
We model a single-cell RNA sequencing setup consisting of PCR amplification and sequencing, and derive probability distribution functions for the output distribution given an input distribution. We provide copy number distributions arising from single transcripts during PCR amplification with exact expressions for mean and variance. We prove that the coefficient of variation of the output of sequencing is always larger than that of the input distribution. Experimental data reveals the variance and mean of the input distribution to obey characteristic relations, which we specifically determine for a HeLa dataset. We can calculate as many moments of the input distribution as are known of the output distribution (up to all). This, in principle, completely determines the input from the output distribution.
Availability And Implementation:
Source code freely available at https://github.com/danielschw188/InputOutputSCRNASeq.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
