aRrayLasso implements a set of five open-source R functions that allow the user to acquire data from public sources such as GEO, train a set of Lasso models on that data, and directly map one microarray platform to another. aRrayLasso significantly predicts expression levels with higher fidelity than technical replicates of the same RNA pool, demonstrating its utility in the integration of data sets from different platforms.
BRB-ArrayTools is an integrated software package for the analysis of DNA microarray data.
BRB-ArrayTools contains utilities for processing expression data from multiple experiments, visualization of data, multidimensional scaling, clustering of genes and samples, and classification and prediction of samples. BRB-ArrayTools features drill-down linkage to NCBI databases using clone, GenBank, or UniGene identifiers, and drill-down linkage to the NetAffx database using Probeset ids.
OOMPA is an object-oriented microarray and proteomics analysis library implemented in R using S4 classes and compatible with BioConductor.
OOMPA includes experimental versions of two new packages:
ArrayCube: builds on fundamental classes from BioConductor to define a structure that generalizes the MINiML format used at the Gene Expression Omnibus. The main enhancement over MINiML format is the inclusion of an annotated data frame containing sample characteristics. The package provides routines to convert an ArrayCube into either an AffyBatch or an RGList, as appropriate.
MINiML: reads files in the MINiML format, as downloaded from the Gene Expression Omnibus, and stores them in R as ArrayCubes.
BATS is a new user friendly GUI software for Bayesian Analysis of Time Series microarray experiments. It implements a truly functional fully Bayesian approach which allows an user to automatically identify and estimate differentially expressed genes.
KegArray is a Java application that provides an environment for analyzing both transcriptome data (gene expression profiles) and metabolome data (compound profiles). Tightly integrated with the KEGG database, KegArray enables you to easily map those data to KEGG resources including PATHWAY, BRITE and genome maps.
Luís M. O. Matos, António J. R. Neves, Armando J. Pinho,
“Lossy-to-lossless compression of biomedical images based on image decomposition”,
in Digital Signal Processing, (chapter proposal accepted on December 4, 2014).