Parallel ICA with multiple references: A semi-blind multivariate approach

High data dimensionality poses a major challenge for imaging genomic studies. To address this issue, a semi-blind multivariate approach, parallel independent component analysis with multiple references (pICA-MR), is proposed. pICA-MR extracts imaging and genetic components in parallel and enhances inter-modality correlations. Prior knowledge is incorporated to emphasize genetic factors with specific attributes. Particularly, pICA-MR can investigate multiple genetic references to explore functional interactions among genes. Simulations demonstrate robust performances with Euclidean distance employed as a metric for reference similarity, where components pointed by the same references are reliably identified and the detection power is significantly improved compared to blind methods.