Differential co-expression analysis software tools | Transcription data analysis
In the transcriptome analysis domain, differential co-expression analysis (DCEA) is emerging as a unique complement to traditional differential expression analysis. Rather than calculating expression level changes of individual genes, DCEA investigates differences in gene interconnection by calculating the expression correlation changes of gene pairs between two conditions.
Provides a wide range of tests for comparisons of independent and dependent correlations with either overlapping or nonoverlapping variables. cocor offers two convenient graphical user interfaces (GUIs): a plug-in for RKWard and a web interface. It enables users of all knowledge levels to access a large variety of tests for comparing correlations in a convenient and user-friendly way.
Implements valuable differential co-expression analysis and differential regulation analysis methodologies. DCGL has universal applicability and is suitable for both microarray data and RNA-seq data. DCGL can be used to systematically identify novel transcription factors (TFs) contributing to phenotypic change that have not yet been documented as critical, thereby significantly increasing the biological knowledge that could be derived from expression data.
A simple method for identifying pattern changes between 2 experimental conditions in correlation networks, which builds on a commonly used association measure, such as Pearson's correlation coefficient. DiffCorr calculates correlation matrices for each dataset, identifies the first principal component-based "eigen-molecules" in the correlation networks, and tests differential correlation between the 2 groups based on Fisher's z-test. DiffCorr can explore differential correlations between 2 conditions in the context of post-genomics data types, namely transcriptomics and metabolomics. DiffCorr is simple to use in calculating differential correlations and is suitable for the first step towards inferring causal relationships and detecting biomarker candidates.
Allows to visualize complex gene expression analysis results coming from biclustering algorithms. BicOverlapper visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering. The tool permits to have an overall view of several expression aspects, from raw data to analysis results and functional annotations.
Detects condition-specificity from a dataset of gene expression measurements. SpeCond is based on a normal mixture model to the expression profile of each gene, and identifies outlier conditions. It is able to determine whether a gene is specifically expressed by computing a P-value for every expression value. The tool can be used in many datasets measuring gene expression, including the detection of tissue-specific alternative splicing, in any species.
Identifies microRNA (miRNA) that are differentially expressed between two different groups of samples. miRtest combines high-throughput miRNA and mRNA expression data to ameliorate the power of testing either data type individually. This method decreases the number of false positives found in gene set testing. It also finds miRNAs that show an effect either in their own expression.
Permits to test framework for identifying variations in correlation patterns across a ranking of samples. DCARS allows to conduct deep interrogation of complex relationships observed in gene expression systems. This application has been tested to the gene expression data in the Cancer Genome Atlas (TCGA) but can be used to many other biological data platforms diseases such as DNA methylation patterns.