Stage prediction of embryonic stem cell differentiation from genome-wide expression data

The developmental stage of a cell can be determined by cellular morphology or various other observable indicators. Such classical markers could be complemented with modern surrogates, like whole-genome transcription profiles, that can encode the state of the entire organism and provide increased quantitative resolution. Recent findings suggest that such profiles provide sufficient information to reliably predict the cell's developmental stage.

We use whole-genome transcription data and several data projection methods to infer differentiation stage prediction models for embryonic cells. Given a transcription profile of an uncharacterized cell, these models can then predict its developmental stage. In a series of experiments comprising 14 datasets from the Gene Expression Omnibus, we demonstrate that the approach is robust and has excellent prediction ability both within a specific cell line and across different cell lines.

Model inference and computational evaluation procedures in the form of Python scripts and accompanying datasets are available at http://www.biolab.si/supp/stagerank.

blaz.zupan@fri.uni-lj.si

Supplementary data are available at Bioinformatics online.