CgCorrect: a method to correct for confounding cell–cell variation due to cell growth in single-cell transcriptomics. (11th May 2017)

• Record Type: Journal Article
• Authors: Blasi, Thomas; Buettner, Florian; Strasser, Michael K; Marr, Carsten; Theis, Fabian J
• Is Part Of: Physical biology. Volume 14:Number 3(2017:Jun.)
• Publication Date: 2017-05-11
• Subjects: single cell gene expression -- cell growth -- computational biology; Biophysics -- Periodicals; Biochemistry -- Periodicals; Biology -- Data processing -- Periodicals; 570.5
• DOI: 10.1088/1478-3975/aa609a
• Languages: English
• ISSNs: 1478-3967

Abstract

Abstract: Accessing gene expression at a single-cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remains obscured when using traditional population-based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and modeling the data. One such issue is differences in cell size, which introduce additional variability into the data and for which appropriate normalization techniques are needed. Otherwise, these differences in cell size may obscure genuine heterogeneities among cell populations and lead to overdispersed steady-state distributions of mRNA transcript numbers. We present cgCorrect, a statistical framework to correct for differences in cell size that are due to cell growth in single-cell transcriptomics data. We derive the probability for the cell-growth-corrected mRNA transcript number given the measured, cell size-dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportionally to the cell's volume during the cell cycle. cgCorrect can be used for both data normalization and to analyze the steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time polymerase chain reaction (PCR) data from mouse blood stem and progenitor cells (and to quantitative single-cell RNA-sequencing data obtained from mouse embryonic stem cells). We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.