Biology subjects

Florian Rohart

Publications and source records attributed to Florian Rohart.

3 recordsLinked to original sources

bioRxiv2016

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

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.\n\nResultsTo 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.\n\nConclusionsMINT 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/.

Bioinformatics↗

bioRxiv2016

DIABLO - an integrative, multi-omics, multivariate method for multi-group classification

Systems biology approaches, leveraging multi-omics measurements, are needed to capture the complexity of biological networks while identifying the key molecular drivers of disease mechanisms. We present DIABLO, a novel integrative method to identify multi-omics biomarker panels that can discriminate between multiple phenotypic groups. In the multi-omics analyses of simulated and real-world datasets, DIABLO resulted in superior biological enrichment compared to other integrative methods, and achieved comparable predictive performance with existing multi-step classification schemes. DIABLO is a versatile approach that will benefit a diverse range of research areas, where multiple high dimensional datasets are available for the same set of specimens. DIABLO is implemented along with tools for model selection, and validation, as well as graphical outputs to assist in the interpretation of these integrative analyses (http://mixomics.org/).

Bioinformatics↗

bioRxiv2015

A Molecular Classification of Human Mesenchymal Stromal Cells

Mesenchymal stromal cells (MSC) are widely used, isolated from a variety of tissues and increasingly adopted for cell therapy, but the identity of these cells is poorly defined and commonalities between MSC from different tissues sources is controversial. Here we undertook a comprehensive review of all public MSC expression studies to assess whether cells derived from different sources shared any common molecular attributes. In doing so, we discovered an over-arching transcriptional phenotype shared by a wide variety of MSC, freshly isolated or cultured cells, and under a variety of growth conditions. We developed a modified variable selection protocol that included cross platform normalisation, and assessment of the selected gene stability and informativeness. A 16-gene signature classified MSC with >97% accuracy, discriminating these from fibroblasts, other adult stem/progenitor cell types and differentiated cells. The genes form part of a protein-interaction network, and mutations in more than 65% of this network were associated with Mendelian disorders of skeletal growth or metabolism. The signature and accompanying datasets are provided as a community resource at www.stemformatics.org resource, and the method is available from the CRAN repository.

Systems Biology↗