dictyExpress is an interactive, web-based exploratory data analytics application providing access to over 1,000 Dictyostelium gene expression experiments from Baylor College of Medicine. The applications consists of components for data retrieval, selection of individual genes or groups of genes, graphic display of gene expression time courses, Gene Ontology term enrichment analysis, display of gene co-expression networks, hierarchical clustering, and expression profile visualization of selected genes in different experiments. The components are connected such that a change in any one of the components (e.g., selection of a gene subset from the hierarchical clustering dendrogram) can propagate to other components and their associated visualizations.
EDGE is a software package for the significance analysis of DNA microarray experiments for both standard and time course experiments based on our new Optimal Discovery Procedure and Time Course Methodology
DNA-Chip Analyzer (dChip) is a Windows software package for probe-level (e.g. Affymetrix platform) and high-level analysis of gene expression microarrays and SNP microarrays.
Gene expression or SNP data from various microarray platforms can also be analyzed by importing as external dataset. At the probe level, dChip can display and normalize the CEL files, and the model-based approach allows pooling information across multiple arrays and automatic probe selection to handle cross-hybridization and image contamination. High-level analysis in dChip includes comparing samples, hierarchical clustering, view expression and SNP data along chromosome, LOH and copy number analysis of SNP arrays, and linkage analysis. In these functions the gene information and sample information are correlated with the analysis results.
Started in Wing Wong Lab , Developed & Maintained by Cheng Li Lab.
consensusOV implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype.
Genefu contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis.
KGGSEE is a standalone Java tool for knowledge-based secondary analyses of genomic and genetic association summary statistics of complex phenotypes by integrating gene expression and related data. It has four major integrative analyses, 1) unconditional gene-based association guided by expression quantitative trait loci (eQTLs), 2) conditional gene-based association guided by selective expression in tissues or cell types, 3) estimation of phenotype-associated tissues or cell-type based on gene expression in single-cell or bulk cells of different tissues, and 4) causal gene inference for complex diseases and/or traits based-on multiple eQTL.
1. Xue C., et al. A global overview of single-cell type selectivity and pleiotropy in complex diseases and traits. In Submission (For estimation of phenotype-associated tissues or cell-type based on gene expression in single-cell or bulk cells of different tissues)
2. Jiang L., et al. Systematic comparative analysis of Mendelian randomization methods for inferring causal genes of complex phenotypes and the application to psychiatric diseases. In Submission (For causal gene inference for complex diseases and/or traits based-on multiple eQTL)
3. Li X.Y., et al. Gene-based association guided by eQTL. In Submission (For unconditional and condition gene-based association guided by eQTL)
Tricluster is the first tri-clustering algorithm for microarray expression clustering. It builds upon the new microCluster bi-clustering approach. Tricluster first mines all the bi-clusters across the gene-sample slices, and then it extends these into tri-clusters across time or space (depending on the third dimension). It can find both scaling and shifting patterns
MicroCluster is a deterministic biclustering algorithm that can mine arbitrarily positioned and overlapping clusters of gene expression data to find interesting patterns
ECLAIR (Ensemble Clustering for Lineage Analysis, Inference and Robustness) achieves a higher level of confidence in the estimated lineages through the use of approximation algorithms for consensus clustering and by combining the information from an ensemble of minimum spanning trees so as to come up with an improved, aggregated lineage tree.