Independent component analysis recovers consistent regulatory signals from disparate datasets

Anand V. Sastry, Alyssa Hu, David Heckmann, Saugat Poudel, Erol Kavvas, Bernhard O. Palsson*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.

Original languageEnglish
Article numbere1008647
JournalPLOS Computational Biology
Volume17
Issue number2
Number of pages23
ISSN1553-734X
DOIs
Publication statusPublished - 2 Feb 2021

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2021 Sastry et al.

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