A subpopulation model to analyze heterogeneous cell differentiation dynamics

We present statistical methodology that can be used to quantify the effect of heterogeneity and to infer the subpopulation specific molecular interactions. After a proof of principle study with simulated data, we apply our methodology to analyze the differentiation of human Th17 cells using time-course RNA sequencing data. We construct putative molecular networks driving the T cell activation and Th17 differentiation and allow the cell populations to be split into two subpopulations in the case of heterogeneous samples. Our analysis shows that the heterogeneity indeed has a statistically significant effect on observed dynamics and, furthermore, our statistical methodology can infer both the subpopulation specific molecular mechanisms and the effect of heterogeneity.
Availability and Implementation: An implementation of the method is available at http://research.ics.aalto.fi/csb/software/subpop/.
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Supplementary information: Supplementary data are available at Bioinformatics online.

Source: Bioinformatics - October 24, 2016 Category: Bioinformatics Authors: Chan, Y. H., Intosalmi, J., Rautio, S., Lähdesmäki, H. Tags: SYSTEMS BIOLOGY Source Type: research