pcaExplorer (version 1.0.2)

pcaExplorer: pcaExplorer: analyzing time-lapse microscopy imaging, from detection to tracking

Description

pcaExplorer 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.

Launch a Shiny App for interactive exploration of a dataset from the perspective of Principal Components Analysis

Usage

pcaExplorer(dds = NULL, rlt = NULL, countmatrix = NULL, coldata = NULL,
  pca2go = NULL, annotation = NULL)

Arguments

dds

A DESeqDataSet object. If not provided, then a countmatrix and a coldata need to be provided. If none of the above is provided, it is possible to upload the data during the execution of the Shiny App

rlt

A DESeqTransform object. Can be computed from the dds object if left NULL. If none is provided, then a countmatrix and a coldata need to be provided. If none of the above is provided, it is possible to upload the data during the execution of the Shiny App

countmatrix

A count matrix, with genes as rows and samples as columns. If not provided, it is possible to upload the data during the execution of the Shiny App

coldata

A data.frame containing the info on the covariates of each sample. If not provided, it is possible to upload the data during the execution of the Shiny App

pca2go

An object generated by the pca2go function, which contains the information on enriched functional categories in the genes that show the top or bottom loadings in each principal component of interest. If not provided, it is possible to compute live during the execution of the Shiny App

annotation

A data.frame object, with row.names as gene identifiers (e.g. ENSEMBL ids) and a column, gene_name, containing e.g. HGNC-based gene symbols

Value

• A Shiny App is launched for interactive data exploration

Details

pcaExplorer 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.

Examples

library(airway)
data(airway)
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
                                             colData = colData(airway),
                                             design=~dex+cell)
rld_airway <- DESeq2::rlogTransformation(dds_airway)

pcaExplorer(dds_airway,rld_airway)

pcaExplorer(countmatrix = counts(dds_airway), coldata = colData(dds_airway))

pcaExplorer() # and then upload count matrix, covariate matrix (and eventual annotation)