cgCorrect: A method to correct for confounding cell-cell variation due to cell growth in single-cell transcriptomics
| Autor: | Carsten Marr, Fabian J. Theis, Thomas Blasi, Florian Buettner, Michael Strasser |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Normalization (statistics) Cell Population Biophysics Gene Expression Computational biology Biology Cell Enlargement Bioinformatics Transcriptome Database normalization 03 medical and health sciences Structural Biology Gene expression medicine RNA Messenger Progenitor cell education Molecular Biology Cell Size education.field_of_study Models Genetic Cell growth Gene Expression Profiling Computational Biology Cell Biology Cell cycle Embryonic stem cell 030104 developmental biology medicine.anatomical_structure |
| DOI: | 10.1101/057463 |
| Popis: | Motivation: Accessing gene expression at the single cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remained obscured in 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 are differences in cell size, which introduce additional variability into the data, 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.Results: 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 proportional to the cell’s volume during cell cycle. cgCorrect can be used for both data normalization, and to analyze steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time PCR data from mouse blood stem and progenitor cells. We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.Availability: A Matlab implementation of cgCorrect is available at http://icb.helmholtz-muenchen.de/cgCorrectSupplementary information: Supplementary information are available online. The simulated data set is available at http://icb.helmholtz-muenchen.de/cgCorrect |
| Databáze: | OpenAIRE |
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