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.
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.
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.
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.
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