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Fig. 1 | BioData Mining

Fig. 1

From: Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer

Fig. 1

JAMMIT analysis of global mRNA, microRNA, and methylation data from 291 ovarian tumors from TCGA. This workflow focuses on iteration #1 of a JAMMIT analyses of a MMDS composed of three large data matrices that was reduced in a step-wise fashion to a 12-gene signature (see Results and discussion for more details). This mRNA signature was found to be predictive of overall survival and enriched for biology associated with immunological response in the tumor microenvironment. Step 1) Heat maps of mRNA, microRNA and DNA methylation data matrices assembled and pre-processed for input to JAMMIT algorithm. Step 2) JAMMIT analysis with minus-one cross-validation. Step 3) Scatter plots of sparse eigen-arrays generated by JAMMIT for each data type. Note that most of the variables for each data type have zero weighting. Step 4). 2-way hierarchically clustered heatmaps of each type-specific signature selected by the non-zeros coefficients of the corresponding sparse eigen-array. Note each heatmap enables the visual identification and extraction of coherent “metavariables” composed of type-specific variables that exhibit coordinated patterns of variation. Step 5) The mRNA meta-variable signature is further reduced using IPA and the SVD to arrive at a 12-gene expression signature that was regulated upstream by IL4. Subsequent eigene-survival and pathway analysis of the 12-gene signature established a connection between overall survival of patients with stage 3 disease being treated with platinum-based chemotherapy plus taxane and the distribution of the M1 and M2 macrophage phenotypes in the tumor micro-environment

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