MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms

Abstract

BackgroundMolecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integrative analysis, additionally increasing sample size. However, the different protocols and technological platforms across transcriptomic studies produce unwanted systematic variation that strongly confounds the integrative analysis results. When studies aim to discriminate an outcome of interest, the common approach is a sequential two-step procedure; unwanted systematic variation removal techniques are applied prior to classification methods.ResultsTo limit the risk of overfitting and over-optimistic results of a two-step procedure, we developed a novel multivariate integration method, MINT, that simultaneously accounts for unwanted systematic variation and identifies predictive gene signatures with greater reproducibility and accuracy. In two biological examples on the classification of three human cell types and four subtypes of breast cancer, we combined high-dimensional microarray and RNA-seq data sets and MINT identified highly reproducible and relevant gene signatures predictive of a given phenotype. MINT led to superior classification and prediction accuracy compared to the existing sequential two-step procedures.Conclusions MINT is a powerful approach and the first of its kind to solve the integrative classification framework in a single step by combining multiple independent studies. MINT is computationally fast as part of the mixOmics R CRAN package, available at http://www.mixOmics.org/mixMINT/and http://cran.r-project.org/web/packages/mixOmics/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1553-8) contains supplementary material, which is available to authorized users.

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“…Our two novel frameworks and focus on the integration of multiple data sets for different biological questions ( Fig 1 ). enables the integration of the same biological N samples measured on different ‘omics platforms ( N -integration, [ 11 ]), while enables the integration of several independent data sets or studies measured on the same P predictors ( P -integration, [ 12 ]). To date, very few statistical methods can perform N - and P -integration in a supervised context.…”

Eleven grand challenges in single-cell data science

“…Such protocols combine scDNA-seq and scRNA-seq [333][334][335]; methylation data and scRNA-seq [336]; all of scRNA-seq, scDNA-seq, methylation, and chromatin accessibility data [41]; or targeted queries on a cell's genotype, expression (scRNA-seq), and methylation status (sc-GEM [337]). For these single cell-specific approaches, bulk approaches that address the integration of data from different types of experiments have the potential to be adapted to single cellspecific noise characteristics (MOFA [92], DIABLO [338], mixOmics [339], and MINT [340]).…”

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