subhadeep1024/OMICsPCA: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples

OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals.

Getting started

Package details

AuthorSubhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb]
Bioconductor views BiologicalQuestion BiomedicalInformatics Clustering DataRepresentation DimensionReduction Epigenetics EpigeneticsWorkflow FunctionalGenomics GUI GeneticVariability ImmunoOncology MultipleComparison PrincipalComponent SingleCell Transcription Visualization Workflow
MaintainerSubhadeep Das <>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
subhadeep1024/OMICsPCA documentation built on March 17, 2020, 5:23 p.m.