Abstract
Drugs have multiple, not single, effects. Decomposition of drug effects into basic components helps us to understand the pharmacological properties of a drug and contributes to drug discovery. We have extended factor analysis and developed a novel profile data analysis method: orthogonal linear separation analysis (OLSA). OLSA contracted 11,911 genes to 118 factors from transcriptome data of MCF7 cells treated with 318 compounds in a Connectivity Map. Ontology of the main genes constituting the factors detected significant enrichment of the ontology in 65 of 118 factors and similar results were obtained in two other data sets. In further analysis of the Connectivity Map data set, one factor discriminated two Hsp90 inhibitors, geldanamycin and radicicol, while clustering analysis could not. Doxorubicin and other topoisomerase inhibitors were estimated to inhibit Na+/K+ ATPase, one of the suggested mechanisms of doxorubicin-induced cardiotoxicity. Based on the factor including PI3K/AKT/mTORC1 inhibition activity, 5 compounds were predicted to be novel inducers of autophagy, and other analyses including western blotting revealed that 4 of the 5 actually induced autophagy. These findings indicate the potential of OLSA to decompose the effects of a drug and identify its basic components.
Highlights
The Connectivity Map (CMap) project initiated by the Broad Institute greatly contributed to the field[4,5]
A concern of using factor analysis (FA) with the principal component method in profiling is that the centroid in the novel co-ordinate space has no biological meaning and varies among data sets, which means that the obtained factors in such a situation may not correspond to consistent biological meanings
By employing orthogonal linear separation analysis (OLSA), a response-profile matrix is described by the product of a response-vector matrix, a response-score matrix, and a total strength matrix, corresponding to the eigenvector matrix, the loading matrix, and a diagonal matrix of the L2-norm used for intensity correction (Supplementary Fig. S1)
Summary
Considering the complex effect of a drug, we began to investigate whether it is possible to decompose it into basic components described by variable patterns using profile data analysis, in an unsupervised way, and focused on factor analysis (FA). To our knowledge, there are no studies that employ FA to separate the effects of a drug and extract the more basic components. Linear separation enables us to approach the molecular mechanism behind the composition using an omics data matrix in which the new indicators generated are easier to comprehend than those obtained by non-linear separation or machine learning[16]. We report the performance and possibility for OLSA to separate a perturbagen effect into basic components by analysing transcriptome profiles
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