A major challenge of microarray analysis is to detect genes whose expression in a single or small number of tissues is significantly different than in other tissues. Accurate identification of such tissue-specific genes can allow researchers to deduce the function of their tissues and organs at the molecular level.
Detects genes with tissue-specific expression patterns. ROKU ranks genes according to overall tissue-specificity and identifies specific tissues whose observations are regarded as outliers. It is able to analyze any type of tissue-specific genes. The tool consists in calculation of an entropy and assignment of specific tissues to each gene. It was applied on synthetic expression data and appears to be a valuable tool for detection of various types of specific expression patterns.
Uses gene expression signature to infer the fraction of stromal and immune cells in tumor tissues. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies.
Enables a range of flexible query mechanisms for Allen Brain Atlas (ABA). ALLENMINER allows users to define custom regions of interest, search for genes that are graded or patterned in regions of interest, and view 3D ABA data on platforms where the BrainExplorer is not available. It can serve for identification of genes or combinations of genes that express in a specific region or cell type of the mouse brain.
Performs analyzes of high-throughput data from complex, heterogeneous tissues. TOAST is an R package based on linear model for characterizing the high-throughput data from mixed samples. The software accounts for sample mixing and detects cell type-specific differential features. It provides flexibility for detecting cell-type specific differential expression/ cell-type specific differential methylation (csDE/csDM). TOAST can be applied to several high-throughput experiments, including but not limited to gene expression microarray data, DNA methylation data, or proteomics data.
Predicts tissue/cell type marker genes using microarray gene expression data. The main advantages of MGFM are i) a short running time of some seconds per analysis. This is achieved by sorting the gene expression values instead of using gene differential expression. ii) MGFM offers the user the possibility to modify the set of samples by easily removing or adding new samples. iii) MGFM is available as a standalone version (R-package) as well as a web application integrated into the CellFinder platform.
Proposes a method for the determination of edging genes (EGs) for different classes. The algorithm aims to assists researchers in discovery of EG sets to improve the classification of normal and disease tissues. The application also allows users to detect those which are biologically co-regulated or repressed by some siRNA or miRNA genes. It was tested on five microarray datasets.