Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Bioinformatics. 2011 Sep 15;27(18):2546-53. doi: 10.1093/bioinformatics/btr422. Epub 2011 Jul 15.
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.
Supplementary data are available at Bioinformatics online.
细胞的发育阶段可以通过细胞形态或其他各种可观察到的指标来确定。这种经典的标记可以用现代的替代物来补充，如全基因组转录谱，它可以编码整个生物体的状态，并提供更高的定量分辨率。最近的研究结果表明，这些图谱提供了足够的信息，可以可靠地预测细胞的发育阶段。
我们使用全基因组转录数据和几种数据投影方法来推断胚胎细胞的分化阶段预测模型。对于一个特征未知的细胞的转录谱，这些模型可以预测其发育阶段。在一系列包含来自基因表达综合数据库的 14 个数据集的实验中，我们证明了该方法在特定细胞系内和不同细胞系之间都具有稳健性和优异的预测能力。
以 Python 脚本形式提供模型推断和计算评估过程，并附有数据集，可在 http://www.biolab.si/supp/stagerank 上获取。
补充数据可在生物信息学在线获取。
