DaMiR.makeSE {DaMiRseq} | R Documentation

## Import RNA-Seq count data and variables

### Description

This is an helper function that allows the user to simultaneously import counts, class (mandatory) and variables (optional) data, and creates a `SummarizedExperiment` object.

### Usage

    DaMiR.makeSE(x, y)

### Arguments

`x` | A tab-delimited file which contains RNA-Seq count data. Each row is a feature (i.e. gene, transcript, exon etc.) and each column is a sample
`y` | A tab-delimited file which contains experiment information. Each row is a sample and each column is a variable. This file must contain at least one column which represent 'class' information for data adjustment and classification; the class column must be labeled as 'class'

### Details

Before creating a `SummarizedExperiment` object, the function performs some checks on input data to ensure that only a matrix of raw counts is accordingly loaded. Other checks allows the identification of missing data (NA) in the data frame of the variables of interest.

### Value

A `SummarizedExperiment` object containing raw counts, class and (optionally) variables of interest.

### Author(s)

Mattia Chiesa, Luca Piacentini

### References

Morgan M, Obenchain V, Hester J and Pag\'es H (2016). SummarizedExperiment: SummarizedExperiment container. R package version 1.4.0.

### See Also

`SummarizedExperiment`

### Examples

    rawdata.path <- system.file(package = "DaMiRseq","extdata")
    # import tab-delimited files:
    # sample data are a small subset of Genotype-Tissue Expression (GTEx)
    # RNA-Seq database (dbGap Study Accession: phs000424.v6.p1):
    count_data <- read.delim(file.path(rawdata.path, "counts_import.txt"))
    variables_data <- read.delim(file.path(rawdata.path, "annotation_import.txt"))
    # create a SummarizedExperiment object:
    SE <- DaMiR.makeSE(count_data, variables_data)
    print(SE)

[Package DaMiRseq version 2.0.0 Index]