Independent component analysis recovers consistent regulatory signals from disparate datasets

PLoS Comput Biol. 2021 Feb 2;17(2):e1008647. doi: 10.1371/journal.pcbi.1008647. eCollection 2021 Feb.

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, we expanded our analysis to over 3,000 E. coli expression profiles and predicted three high-impact regulons that respond to oxidative stress, anaerobiosis, and antibiotic treatment. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Databases, Genetic*
  • Escherichia coli / genetics*
  • Gene Expression Profiling / methods*
  • Linear Models
  • Oligonucleotide Array Sequence Analysis / methods*
  • Principal Component Analysis
  • RNA-Seq
  • Transcriptome*

Grant support

AVS, AH, DH, SP, EK, and BOP were funded by the Novo Nordisk Foundation Center for Biosustainability and the Technical University of Denmark (grant number NNF10CC1016517). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.