pcaExplorer: Explore a dataset from a PCA perspective

In pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach

Description

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

Usage

pcaExplorer(
  dds = NULL,
  dst = NULL,
  countmatrix = NULL,
  coldata = NULL,
  pca2go = NULL,
  annotation = NULL,
  runLocal = TRUE
)

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
dst	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
runLocal	A logical indicating whether the app is to be run locally or remotely on a server, which determines how documentation will be accessed.

Value

A Shiny App is launched for interactive data exploration

Examples

library(airway)
data(airway)
airway
dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
                                             colData = colData(airway),
                                             design = ~dex+cell)
## Not run: 
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)

## End(Not run)

pcaExplorer documentation built on Nov. 8, 2020, 5:29 p.m.