Copy number and gene expression data integration software tools | Genomic array data analysis
Over the last decade, multiple functional genomic datasets studying chromosomal aberrations and their downstream effects on gene expression have accumulated for several cancer types. A vast majority of them are in the form of paired gene expression profiles and somatic copy number alterations (CNA) information on the same patients identified using microarray platforms. In response, many algorithms and software packages are available for integrating these paired data.
Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. These latter represent batch of arrays of Comparative Genomic Hybridization data. In addition to that, there are slots for representing phenotype and various genomic events associated with aCGH experiments, such as transitions, amplifications, aberrations, and whole chromosomal gains and losses.
Performs integrative analyses of two types of 'omics' variables that are measured on the same samples. integrOmics includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets.
An open-source, web-based, suite for the analysis of gene expression and aCGH data. Asterias implements validated statistical methods, and most of the applications use parallel computing, which permits taking advantage of multicore CPUs and computing clusters. Access to, and further analysis of, additional biological information and annotations (PubMed references, Gene Ontology terms, KEGG and Reactome pathways) are available either for individual genes (from clickable links in tables and figures) or sets of genes. These applications cover from array normalization to imputation and preprocessing, differential gene expression analysis, class and survival prediction and aCGH analysis.
Integrates matched copy number (amplifications and deletions) and gene expression data from tumor samples to identify driving mutations and the processes they influence. CONEXIC is inspired by Module Networks (Segal et al, 2003), but has been augmented by a number of critical modifications that make it suitable for identifying drivers. CONEXIC uses a score-guided search to identify the combination of modulators that best explains the behavior of a gene expression module across tumor samples and searches for those with the highest score within the amplified or deleted region.
Provides an integrative clustering method for multi-type genomic data analysis. iCluster is a R package that offers an approach based on a joint latent variable model and uses a lasso regression to pinpoint the subset of genomic features that have significant weights on the latent variables. The application can be used for cancer gene identification as well as patterns discovery into binary, categorical, and continuous values.
Provides a method to represent the consensus across multiple runs of a clustering algorithm. Consensus Clustering is a methodology that determines the number of clusters in the data and assess the stability of the discovered clusters. This method can be used to represent the consensus over multiple runs of a clustering algorithm with random restart to account for its sensitivity to the initial conditions. It also provides for a visualization tool to inspect cluster number, membership, and boundaries.
Helps to detect genes whose expression is affected by gene dosage within a series of samples. ACE-it is a statistical tool that allows a user-defined cut-off for contaminating samples within the groupings. This application assumes that expression increases with increased gene dosage. It was tested using array expression and array Comparative Genomic Hybridization (CGH) datasets from various institutes and platforms, including a breast tumor series.