DaMiR.makeSE: Import RNA-Seq count data and variables
In DaMiRseq: Data Mining for RNA-seq data: normalization, feature selection and classification

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1	DaMiR.makeSE(x, y)

`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'

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.

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

Mattia Chiesa, Luca Piacentini

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

SummarizedExperiment

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)