13,421 research outputs found
Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review
A variety of genome-wide profiling techniques are available to probe
complementary aspects of genome structure and function. Integrative analysis of
heterogeneous data sources can reveal higher-level interactions that cannot be
detected based on individual observations. A standard integration task in
cancer studies is to identify altered genomic regions that induce changes in
the expression of the associated genes based on joint analysis of genome-wide
gene expression and copy number profiling measurements. In this review, we
provide a comparison among various modeling procedures for integrating
genome-wide profiling data of gene copy number and transcriptional alterations
and highlight common approaches to genomic data integration. A transparent
benchmarking procedure is introduced to quantitatively compare the cancer gene
prioritization performance of the alternative methods. The benchmarking
algorithms and data sets are available at http://intcomp.r-forge.r-project.orgComment: PDF file including supplementary material. 9 pages. Preprin
Sparse integrative clustering of multiple omics data sets
High resolution microarrays and second-generation sequencing platforms are
powerful tools to investigate genome-wide alterations in DNA copy number,
methylation and gene expression associated with a disease. An integrated
genomic profiling approach measures multiple omics data types simultaneously in
the same set of biological samples. Such approach renders an integrated data
resolution that would not be available with any single data type. In this
study, we use penalized latent variable regression methods for joint modeling
of multiple omics data types to identify common latent variables that can be
used to cluster patient samples into biologically and clinically relevant
disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996)
267-288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005)
301-320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005)
91-108] methods to induce sparsity in the coefficient vectors, revealing
important genomic features that have significant contributions to the latent
variables. An iterative ridge regression is used to compute the sparse
coefficient vectors. In model selection, a uniform design [Monographs on
Statistics and Applied Probability (1994) Chapman & Hall] is used to seek
"experimental" points that scattered uniformly across the search domain for
efficient sampling of tuning parameter combinations. We compared our method to
sparse singular value decomposition (SVD) and penalized Gaussian mixture model
(GMM) using both real and simulated data sets. The proposed method is applied
to integrate genomic, epigenomic and transcriptomic data for subtype analysis
in breast and lung cancer data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS578 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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