Independent component analysis recovers consistent regulatory signals from disparate datasets

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result detailed inference underlying regulatory networks, but the diversity experimental platforms and protocols introduces critical biases that hinder scalable analysis existing data. Here, we show structure E . coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq microarray datasets. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles discovered its ICA-based was still comparable to individual With this understanding, expanded our 3,000 predicted three high-impact regulons respond oxidative stress, anaerobiosis, antibiotic treatment. ICA thus enables deep disparate uncover new insights were not visible