Unclassified software tools | Gene expression microarray data analysis

Assists users with unsupervised deconvolution of tumor and stromal mixed expression data. UNDO detects cell-specific marker genes (MGs) located on the scatter radii of mixed gene expressions, estimates cellular proportions in each sample and deconvolutes mixed expressions into cell-specific expression profiles. This method also detects differentially expressed genes (DEGs) without deconvolution.

Offers powerful yet simple visualization, normalization and significance testing tools. DetectiV uses simple and well established visualization and statistical techniques to analyze data from pathogen detection microarrays. It enables researchers to quickly and easily identify possible infectious agents. The tool performs better than previously published software on a publicly available microarray dataset.

Enables cross-platform comparisons, and proposes a comprehensive procedure for assessments based on spike-in experiments. spkTools is implemented as a user friendly Bioconductor package. It contains functions that can be used to compare expression measures on different array platforms. Its utility has been demonstrated by presenting a spike-in-based comparison of the three major platforms: Affymetrix, Agilent and Illumina.

Estimates gene and eQTL networks from high-throughput expression and genotyping assays. qpgraph is based in the so-called q-order limited partial correlation graphs, qp-graphs, which is specifically tailored towards molecular network discovery from microarray expression data. qp-graphs yield more stable performance figures than other state-of-the-art methods when the ratio of genes to experiments exceeds one order of magnitude. More importantly, the better performance of the qp-graph method on such a gene-to-sample ratio has a decisive impact on the functional coherence of the reverse-engineered transcriptional regulatory modules and becomes crucial in such a challenging situation in order to enable the discovery of a network of reasonable confidence that includes a substantial number of genes relevant to the essayed conditions.

A compliant library for computing principal component analysis on incomplete data sets. pcaMethods combines an expectation maximization approach with a probabilistic model. It also offers methods for visualization of the results, e.g. for plotting an arbitrary number of scores/loadings side by side. pcaMethods is especially suitable for data from experiments where the studied response is non-linear.