Biology subjects

Kim-Anh Le Cao

Publications and source records attributed to Kim-Anh Le Cao.

4 recordsLinked to original sources

bioRxiv2016

DynOmics to identify delays and co-expression patterns across time course experiments

Dynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in omics data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across omics data within an organism and for comparative gene expression analysis across organisms.

Bioinformatics↗

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↗

bioRxiv2016

mixMC: a multivariate statistical framework to gain insight into Microbial Communities

Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting.\n\nWe present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner.\n\nWe applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.

Bioinformatics↗