![]() Testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2, airway, org.Hs.eg.db, magrittr, rmarkdown Lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng PCAtools: everything Principal Component AnalysisĪTACSeq, GeneExpression, PrincipalComponent, RNASeq, SingleCell, Software, Transcription To view documentation for the version of this package installed If (!require("BiocManager", quietly = TRUE))įor older versions of R, please refer to the appropriate To install this package, start R (version PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.Īuthor: Kevin Blighe, Anna-Leigh Brown, Vincent Carey, Guido Hooiveld, Aaron Lun PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. the 'principal components'), while at the same time being capable of easy interpretation on the original data. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. ![]() It extracts the fundamental structure of the data without the need to build any model to represent it. ![]() It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. ![]() Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. ![]() DOI: 10.18129/B9.bioc.PCAtools PCAtools: Everything Principal Components Analysis ![]()
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