Multi‐Omics Factor Analysis: model overview and downstream analyses

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Multi‐Omics Factor Analysis: model overview and downstream analyses Multi‐Omics Factor Analysis: model overview and downstream analyses Model overview: MOFA takes M data matrices as input (Y1,…, YM), one or more from each data modality, with co‐occurrent samples but features that are not necessarily related and that can differ in numbers. MOFA decomposes these matrices into a matrix of factors (Z) for each sample and M weight matrices, one for each data modality (W1,.., WM). White cells in the weight matrices correspond to zeros, i.e. inactive features, whereas the cross symbol in the data matrices denotes missing values.The fitted MOFA model can be queried for different downstream analyses, including (i) variance decomposition, assessing the proportion of variance explained by each factor in each data modality, (ii) semi‐automated factor annotation based on the inspection of loadings and gene set enrichment analysis, (iii) visualization of the samples in the factor space and (iv) imputation of missing values, including missing assays. Ricard Argelaguet et al. Mol Syst Biol 2018;14:e8124 © as stated in the article, figure or figure legend