Package: OMICsPCA 1.25.0

Subhadeep Das

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.

Authors:Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb]

OMICsPCA_1.25.0.tar.gz
OMICsPCA_1.25.0.zip(r-4.5)OMICsPCA_1.25.0.zip(r-4.4)OMICsPCA_1.25.0.zip(r-4.3)
OMICsPCA_1.25.0.tgz(r-4.4-any)OMICsPCA_1.25.0.tgz(r-4.3-any)
OMICsPCA_1.25.0.tar.gz(r-4.5-noble)OMICsPCA_1.25.0.tar.gz(r-4.4-noble)
OMICsPCA_1.25.0.tgz(r-4.4-emscripten)OMICsPCA_1.25.0.tgz(r-4.3-emscripten)
OMICsPCA.pdf |OMICsPCA.html✨
OMICsPCA/json (API)
NEWS

# Install 'OMICsPCA' in R:
install.packages('OMICsPCA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On BioConductor:OMICsPCA-1.25.0(bioc 3.21)OMICsPCA-1.24.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncologymultiplecomparisonprincipalcomponentdatarepresentationworkflowvisualizationdimensionreductionclusteringbiologicalquestionepigeneticsworkflowtranscriptiongeneticvariabilityguibiomedicalinformaticsepigeneticsfunctionalgenomicssinglecell

4.00 score 1 scripts 195 downloads 1 mentions 21 exports 195 dependencies

Last updated 2 months agofrom:1659da8929. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 29 2024
R-4.5-winNOTENov 29 2024
R-4.5-linuxNOTENov 29 2024
R-4.4-winNOTENov 29 2024
R-4.4-macNOTENov 29 2024
R-4.3-winOKNov 29 2024
R-4.3-macOKNov 29 2024

Exports:analyse_individualsanalyse_integrated_individualsanalyse_integrated_variablesanalyse_variableschart_correlationclustercluster_boxplotcluster_parameterscreate_groupdescriptorextractextract_assayintegrate_pcaintegrate_variablesintersectmerge_cellsplot_densityplot_density_3Dplot_integrated_densityplot_integrated_density_3Dprepare_dataset

Dependencies:abindAnnotationDbiaskpassbackportsbase64encBHBiobaseBiocBaseUtilsBiocGenericsBiocIOBiocParallelBiostringsbitbit64bitopsblobbootbroomBSgenomebslibcachemcarcarDataclasscliclusterclValidcodetoolscolorspacecorrplotcowplotcpp11crayoncrosstalkcurldata.tableDBIDelayedArraydendextendDEoptimRDerivdigestdiptestdoBydocoptdplyrDTellipseemmeansestimabilityevaluatefactoextraFactoMineRfansifarverfastmapflashClustflexmixfontawesomeformatRFormulafpcfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicFeaturesGenomicRangesggplot2ggpubrggrepelggsciggsignifgluegridExtragtableHelloRangeshighrhtmltoolshtmlwidgetshttpuvhttrIRangesisobandjquerylibjsonlitekableExtraKEGGRESTkernlabknitrlabelinglambda.rlaterlatticelazyevalleapslifecyclelme4magickmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmclustmemoisemgcvmicrobenchmarkmimeminqamodelrmodeltoolsmultcompViewMultiAssayExperimentmunsellmvtnormNbClustnlmenloptrnnetnumDerivOMICsPCAdataopensslpbkrtestpdftoolsPerformanceAnalyticspillarpkgconfigplogrplyrpngpolynomprabcluspromisespurrrqpdfquadprogquantregR6rappdirsRColorBrewerRcppRcppEigenRCurlreshape2restfulrrglRhtslibrjsonrlangrmarkdownrobustbaseRsamtoolsRSQLiterstatixrstudioapirtracklayerS4ArraysS4Vectorssassscalesscatterplot3dsnowSparseArraySparseMstringistringrSummarizedExperimentsurvivalsvglitesyssystemfontstibbletidyrtidyselecttinytexUCSC.utilsutf8VariantAnnotationvctrsviridisviridisLitewithrxfunXMLxml2xtsXVectoryamlzlibbioczoo

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

Subhadeep Das

Rendered fromvignettes.Rmdusingknitr::rmarkdownon Nov 29 2024.

Last update: 2020-03-17
Started: 2018-08-08

Readme and manuals

Help Manual

Help pageTopics
Quick analysis and visualization of the individuals/annotations/rows (e.g. Tss, gene) from integrated Assay.analyse_individuals
Quick analysis and visualization of the individuals/annotations/rows (e.g. Tss, gene)analyse_integrated_individuals
Quick analysis and visualization of the integrated Assays by integrate() function.analyse_integrated_variables
Quick analysis and visualization of the integrated Assays by integrate() function.analyse_variables
Pairwise correlation, scatter plot and histogram on selected groups.chart_correlation
Cluster data pointscluster
Comparison of clusters by boxplotcluster_boxplot
Detection of algorithm and number of clusterscluster_parameters
Subsets an Assay dataframe into smaller groupscreate_group
Distribution of Assays as percentage of cell linesdescriptor
extraction of projected cooordinates from "PCA" object.extract
Data extraction from "MultiAssayExperiment" objectextract_assay
Integration of multiple Assays into linear combinations by PCA.integrate_pca
Integration of an experiment/ Assay done on many Cell lines/ time points into linear combinations by PCA.integrate_variables
support function of prepareDatasetintersect
Storage of intersected factors from multiple cell types as columns into a dataframemerge_cells
Visualization of Density of various groups on Principal componentsplot_density
Visualization of 3D Density of various groups on Principal componentsplot_density_3D
Visualization of Density of various groups on Principal components of integrated assaysplot_integrated_density
Visualization of 3D Density of various groups on Principal components of multiple Assaysplot_integrated_density_3D
Intersects and merge data from multiple experiments into a dataframeprepare_dataset