Showing 4 of total 4 results (show query)
bioc
pcaExplorer:Interactive Visualization of RNA-seq Data Using a Principal Components Approach
This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis.
Maintained by Federico Marini. Last updated 3 months ago.
immunooncologyvisualizationrnaseqdimensionreductionprincipalcomponentqualitycontrolguireportwritingshinyappsbioconductorprincipal-componentsreproducible-researchrna-seq-analysisrna-seq-datashinytranscriptomeuser-friendly
56 stars 9.63 score 180 scriptsbioc
ASSIGN:Adaptive Signature Selection and InteGratioN (ASSIGN)
ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature.
Maintained by Ying Shen. Last updated 5 months ago.
softwaregeneexpressionpathwaysbayesian
2 stars 7.37 score 65 scripts 1 dependents
january3
tmod:Feature Set Enrichment Analysis for Metabolomics and Transcriptomics
Methods and feature set definitions for feature or gene set enrichment analysis in transcriptional and metabolic profiling data. Package includes tests for enrichment based on ranked lists of features, functions for visualisation and multivariate functional analysis. See Zyla et al (2019) <doi:10.1093/bioinformatics/btz447>.
Maintained by January Weiner. Last updated 2 months ago.
3 stars 6.88 score 168 scripts 1 dependents
wanchanglin
mt:Metabolomics Data Analysis Toolbox
Functions for metabolomics data analysis: data preprocessing, orthogonal signal correction, PCA analysis, PCA-DA analysis, PLS-DA analysis, classification, feature selection, correlation analysis, data visualisation and re-sampling strategies.
Maintained by Wanchang Lin. Last updated 1 years ago.
3 stars 4.57 score 50 scripts