1 - 13 of 13 results


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.


Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. CellMapper is an R package that can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before. It was developed as an approach to obtain the gene expression profiles unique to individual cell types. This method is effective for cell types that have never been isolated before, providing an opportunity to fill gaps in available expression data.


Facilitates expression-based isoform-level analysis of large-scale TCGA (The Cancer Genome Atlas) multi-cancer RNA-seq data. ISOexpresso is a database which provides information regarding isoform existence and expression, which can be grouped by cancer vs. normal conditions, cancer types, and tissue types. It implements two main functions: the Isoform Expression View function creates visualizations for condition-specific RNA/isoform expression patterns upon query of a gene of interest and the User Data Annotation function supports annotation of genomic variants to determine the most plausible consequence of a variation among many possible interpretations.


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.


Computes the causal target parameter defined as the difference between the biomarker expression values under treatment and those same values under no treatment. Biotmle is an R package that facilitates the discovery of biomarkers from biological sequencing data (e.g., microarrays, RNA-seq) based on the associations of potential biomarkers with exposure outcome variables by implementing an estimation procedure that combines a generalization of the moderated t-statistic with asymptotically linear statistical parameters estimated via targeted minimum loss-based estimation (TMLE).

fCI / f-divergence Cutoff Index

Finds differentially expressed genes (DEGs) in the transcriptomic & proteomic data. fCI is a package that can be used to analysis transcriptomics and proteomics. It also identifies DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods. It offers functions to give expression matrix, find targets and their detailed expression changes and many others.


A method for statistical inference of correlation significance. CorSig is based on a biology-informed null hypothesis, i.e., testing whether the true PCC (ρ) between two variables is statistically larger than a user-specified PCC cutoff (τ), as opposed to the simple null hypothesis of ρ = 0 in existing methods, i.e., testing whether an association can be declared without a threshold. CorSig incorporates Fisher's Z transformation of the observed PCC (r), which facilitates use of standard techniques for p-value computation and multiple testing corrections.

iDEP / Integrated Differential Expression and Pathway analysis

Analyzes gene expression data from DNA microarray or RNA-Seq. iDEP integrates many commonly-used R/Bioconductor packages with comprehensive annotation databases. Normalized expression and RNA-Seq read counts are handled by two analysis workflows, and both involve a 4-stage process: pre-processing, Exploratory Data Analysis (EDA), differential expression, and pathway analysis and visualization. It can serve for preliminary analysis as it circumcises the need for many tedious tasks such as converting gene IDs and downloading software packages and annotations.