Showing 8 of total 8 results (show query)
bioc
EGSEA:Ensemble of Gene Set Enrichment Analyses
This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set.
Maintained by Monther Alhamdoosh. Last updated 5 months ago.
immunooncologydifferentialexpressiongogeneexpressiongenesetenrichmentgeneticsmicroarraymultiplecomparisononechannelpathwaysrnaseqsequencingsoftwaresystemsbiologytwochannelmetabolomicsproteomicskegggraphandnetworkgenesignalinggenetargetnetworkenrichmentnetworkclassification
5.81 score 64 scriptsbioc
PADOG:Pathway Analysis with Down-weighting of Overlapping Genes (PADOG)
This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package.
Maintained by Adi L. Tarca. Last updated 2 months ago.
microarrayonechanneltwochannel
5.46 score 12 scripts 2 dependents
sistm
TcGSA:Time-Course Gene Set Analysis
Implementation of Time-course Gene Set Analysis (TcGSA), a method for analyzing longitudinal gene-expression data at the gene set level. Method is detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.
Maintained by Boris P Hejblum. Last updated 3 years ago.
6 stars 5.38 score
tibshirani
samr:SAM: Significance Analysis of Microarrays
Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems.
Maintained by Rob Tibshirani. Last updated 6 years ago.
3 stars 4.97 score 208 scripts 1 dependentsbioc
nethet:A bioconductor package for high-dimensional exploration of biological network heterogeneity
Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013).
Maintained by Nicolas Staedler. Last updated 5 months ago.
4.30 score 7 scriptsbioc
BLMA:BLMA: A package for bi-level meta-analysis
Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis.
Maintained by Van-Dung Pham. Last updated 5 months ago.
genesetenrichmentpathwaysdifferentialexpressionmicroarray
4.18 score 51 scripts
hanjunwei-lab
SMDIC:Identification of Somatic Mutation-Driven Immune Cells
A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..
Maintained by Junwei Han. Last updated 5 months ago.
2 stars 4.00 score 5 scripts