It will be interesting to investigate whether these genes are functionally related with the annotated genes ON-01910 price identified in the same ICs. Since ICA can reveal patient-specific adaptations of P. aeruginsoa isolates, it is possible to design patient-specific therapies based on these adaptations. For example, combination of iron chelators and efflux pump inhibitors might be used to inhibit the growth of B12-4 and B12-7, which have high expression levels of genes involved in efflux pump and iron uptake systems [33]. Ligands with high affinity to pili can be used to inhibit adhesion Mocetinostat and biofilm formation of the CF114-1973 isolate [34]. Conclusions In conclusion, the ICA is shown to be able to extract the most essential

features from the complex multiple variant microarray dataset and identify significant genes contribute BMS202 purchase to these features. Our results show that P. aeruginosa employ a diverse set of patient-specific adaption strategies during the early stage infections while certain essential evolutionary events occurred in parallel

during the chronic infections in CF infections. The ICA has a great potential in studying large-scale datasets acquired from omics research from different areas. Methods P. aeruginosa clinical isolates The P. aeruginosa strains were isolated from 6 CF patients with long-term chronic infection and 3 CF patients who were intermittently colonized or recently chronically infected and who were attending the Danish CF Center, Rigshospitalet, Copenhagen. P. aeruginosa PAO1 [35] was used as a reference strain. DNA microarray Transcriptomic profiles of clinical isolates were obtained using the Affymetrix P. aeruginosa gene chip (Santa Clara, CA) [5, 8]. Triplicate experiments were performed for each strain. The microarray raw datasets are accessible at NCBI’s Gene Expression Omnibus (GEO) with series accession number GSE31227. Mathematical model of gene regulation by ICA The FastICA package (http://​research.​ics.​tkk.​fi/​ica/​fastica/​) was used to analyze the microarray dataset. The microarray gene expression data is considered a linear combination of some independent components which have specific biological interpretations

(-)-p-Bromotetramisole Oxalate [11]. A n × m matrix X is used to represent the microarray gene expression data with m gene expressions from n clinical isolates. x ij in X is the expression level of the j-th gene in the i-th isolate. After data have been preprocessed and normalized, the ICA model for gene expression data can be expressed as: (1) or in matrix notation as: (2) In this ICA model, the columns of A = [a 1 , a 2 ,..., a n ] are the n × n latent vectors of the gene microarray data. Each column of A is associated with a specific gene expression mode. S contains the n × m gene signatures where the rows of S are statistically independent to each other. The gene profiles in X are considered to be a linear mixture of statistically independent components S combined by an unknown mixing matrix A.