pcaExplorer {pcaExplorer} | R Documentation

## 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

### 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.

### Value

A Shiny App is launched for interactive data exploration

### Author(s)

Federico Marini marinif@uni-mainz.de, 2016

Maintainer: Federico Marini marinif@uni-mainz.de

### 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)

[Package pcaExplorer version 2.6.0 Index]