Application of factor analysis on mycobacterium tuberculosis transcriptional responses for drug clustering, drug target, and pathway detections

  • Authors:

  • Jeerayut Chaijaruwanich;Jamlong Khamphachua;Sukon Prasitwattanaseree;Saradee Warit;Prasit Palittapongarnpim

  • Affiliations:

  • Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand;Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand;Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand;National Center for Genetic Engineering and Biotechnology (BIOTEC), Pathumthani, Thailand;National Center for Genetic Engineering and Biotechnology (BIOTEC), Pathumthani, Thailand

  • Venue:
  • ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
  • Year:
  • 2006

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Abstract

Recently, the differential transcriptional responses of Mycobacterium tuberculosis to drug and growth-inhibitory conditions were monitored to generate a data set of 436 microarray profiles. These profiles were valuably used for grouping drugs, identifying drug targets and detecting related pathways, based on various conventional methods; such as Pearson correlation, hierarchical clustering, and statistical tests. These conventional clustering methods used the high dimensionality of gene space to reveal drug groups basing on the similarity of expression levels of all genes. In this study, we applied the factor analysis with these conventional methods for drug clustering, drug target detection and pathway detection. The latent variables or factors of gene expression levels in loading space from factor analysis allowed the hierarchical clustering to discover true drug groups. The t-test method was applied to identify drug targets which most significantly associated with each drug cluster. Then, gene ontology was used to detect pathway associations for each group of drug targets.