Independent component analysis recovers consistent regulatory signals from disparate datasets.

ABSTRACT: The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. With this understanding, w

SUBMITTER: Sastry AV 

PROVIDER: S-EPMC7888660 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature