Pseudotime estimation: deconfounding single cell time series.

ABSTRACT:

Motivation

Repeated cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell-to-cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements are not averaged over populations of cells. When several genes are assayed in parallel these effects can be estimated and corrected for under certain smoothness assumptions on cell progression.

Results

We present a principled probabilistic model with a Bayesian inference scheme to analyse such data. We demonstrate our method's utility on public microarray, nCounter and RNA-seq datasets from three organisms. Our method almost perfectly recovers withheld captur

SUBMITTER: Reid JE 

PROVIDER: S-EPMC5039927 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature