Package: OMICsPCA 1.30.0

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_1.30.0.tar.gz
OMICsPCA_1.30.0.zip(r-4.7-any)OMICsPCA_1.30.0.zip(r-4.6-any)OMICsPCA_1.30.0.zip(r-4.5-any)
OMICsPCA_1.30.0.tgz(r-4.6-any)OMICsPCA_1.30.0.tgz(r-4.5-any)
OMICsPCA_1.30.0.tar.gz(r-4.7-any)OMICsPCA_1.30.0.tar.gz(r-4.6-any)
OMICsPCA_1.30.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
OMICsPCA/json (API)

# Install 'OMICsPCA' in R:

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

On BioConductor:OMICsPCA-1.31.0(bioc 3.24)OMICsPCA-1.30.0(bioc 3.23)

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

immunooncology multiplecomparison principalcomponent datarepresentation workflow visualization dimensionreduction clustering biologicalquestion epigeneticsworkflow transcription geneticvariability gui biomedicalinformatics epigenetics functionalgenomics singlecell

4.00 score 3 scripts 551 downloads 1 mentions 21 exports 204 dependencies

Last updated from:bfbf204327 (on RELEASE_3_23). Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: no.

Target	Result	Time
bioc-checks	ERROR	297
linux-devel	NOTE	629
source / vignettes	OK	422
linux-release	NOTE	566
macos-release	NOTE	321
macos-oldrel	NOTE	323
windows-devel	NOTE	427
windows-release	NOTE	529
windows-oldrel	NOTE	429
wasm-release	OK	234

Exports:analyse_individuals analyse_integrated_individuals analyse_integrated_variables analyse_variables chart_correlation cluster cluster_boxplot cluster_parameters create_group descriptor extract extract_assay integrate_pca integrate_variables intersect merge_cells plot_density plot_density_3D plot_integrated_density plot_integrated_density_3D prepare_dataset

Dependencies:abind AnnotationDbi askpass backports base64enc BH Biobase BiocBaseUtils BiocGenerics BiocIO BiocParallel Biostrings bit bit64 bitops blob boot broom BSgenome bslib cachem car carData cigarillo class cli cluster clValid codetools colorspace corrplot cowplot cpp11 crayon crosstalk curl data.table DBI DelayedArray dendextend DEoptimR Deriv digest diptest doBy docopt dplyr DT ellipse emmeans estimability evaluate factoextra FactoMineR farver fastmap flashClust flexmix fontawesome forecast formatR Formula fpc fracdiff fs futile.logger futile.options generics GenomicAlignments GenomicFeatures GenomicRanges ggplot2 ggpubr ggrepel ggsci ggsignif glue gridExtra gtable HelloRanges highr htmltools htmlwidgets httr IRanges irlba isoband jquerylib jsonlite kableExtra KEGGREST kernlab knitr labeling lambda.r later lattice lazyeval leaps lifecycle lme4 lmtest magick magrittr MASS Matrix MatrixGenerics MatrixModels matrixStats mclust memoise mgcv mime minqa modelr modeltools multcompView MultiAssayExperiment mvtnorm NbClust nlme nloptr nnet numDeriv OMICsPCAdata openssl otel pbkrtest pdftools PerformanceAnalytics pillar pkgconfig plyr png polynom prabclus promises purrr qpdf quadprog quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen RCurl Rdpack reformulas reshape2 restfulr rgl Rhtslib rjson rlang rmarkdown robustbase Rsamtools RSQLite rstatix rstudioapi rtracklayer S4Arrays S4Vectors S7 sass scales scatterplot3d Seqinfo showtext showtextdb snow SparseArray SparseM stringi stringr SummarizedExperiment survival svglite sys sysfonts systemfonts textshaping tibble tidyr tidyselect timeDate tinytex urca utf8 VariantAnnotation vctrs viridis viridisLite withr xfun XML xml2 xts XVector yaml zoo

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

Rendered fromvignettes.Rmdusingknitr::rmarkdown

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

Help page	Topics
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 points	cluster
Comparison of clusters by boxplot	cluster_boxplot
Detection of algorithm and number of clusters	cluster_parameters
Subsets an Assay dataframe into smaller groups	create_group
Distribution of Assays as percentage of cell lines	descriptor
extraction of projected cooordinates from "PCA" object.	extract
Data extraction from "MultiAssayExperiment" object	extract_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 prepareDataset	intersect
Storage of intersected factors from multiple cell types as columns into a dataframe	merge_cells
Visualization of Density of various groups on Principal components	plot_density
Visualization of 3D Density of various groups on Principal components	plot_density_3D
Visualization of Density of various groups on Principal components of integrated assays	plot_integrated_density
Visualization of 3D Density of various groups on Principal components of multiple Assays	plot_integrated_density_3D
Intersects and merge data from multiple experiments into a dataframe	prepare_dataset

Package: OMICsPCA 1.30.0

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

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