Bacterial adaptation during chronic infection revealed by independent component analysis of transcriptomic data
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Bacterial adaptation during chronic infection revealed by independent component analysis of transcriptomic data. / Yang, Lei; Rau, Martin Holm; Yang, Liang; Høiby, Niels; Molin, Søren; Jelsbak, Lars.
In: B M C Microbiology, Vol. 11, 01.01.2011, p. 184.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Bacterial adaptation during chronic infection revealed by independent component analysis of transcriptomic data
AU - Yang, Lei
AU - Rau, Martin Holm
AU - Yang, Liang
AU - Høiby, Niels
AU - Molin, Søren
AU - Jelsbak, Lars
PY - 2011/1/1
Y1 - 2011/1/1
N2 - BackgroundBacteria 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. ResultsIn 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. ConclusionsOur 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.
AB - BackgroundBacteria 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. ResultsIn 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. ConclusionsOur 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.
U2 - 10.1186/1471-2180-11-184
DO - 10.1186/1471-2180-11-184
M3 - Journal article
C2 - 21851621
VL - 11
SP - 184
JO - BMC Microbiology
JF - BMC Microbiology
SN - 1471-2180
ER -
ID: 40217024