Background: Bacteria employ a variety of adaptation strategies during the course of chronic infections.
Understanding bacterial adaptation can facilitate the identification of novel drug targets for better treatment of
infectious diseases. Transcriptome profiling is a comprehensive and high-throughput approach for characterization
of bacterial clinical isolates from infections. However, exploitation of the complex, noisy and high-dimensional
transcriptomic dataset is difficult and often hindered by low statistical power.
Results: In this study, we have applied two kinds of unsupervised analysis methods, principle component analysis
(PCA) and independent component analysis (ICA), to extract and characterize the most informative features from
transcriptomic dataset generated from cystic fibrosis (CF) Pseudomonas aeruginosa isolates. ICA was shown to be
able to efficiently extract biological meaningful features from the transcriptomic dataset and improve clustering
patterns of CF isolates. Decomposition of the transcriptomic dataset by ICA also facilitates gene identification and
gene ontology enrichment.
Conclusions: Our results show that P. aeruginosa employs multiple patient-specific adaption strategies during the
early stage infections while certain essential adaptations are evolved in parallel during the chronic infections.
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