Skip to contents

The constructor function for MirnaExperiment

Source: R/AllClasses.R
MirnaExperiment.Rd

This is the constructor function that allows to easily create objects of class MirnaExperiment. This function requires as inputs miRNA and gene expression matrices, as well as sample metadata.

Usage

MirnaExperiment(mirnaExpr, geneExpr, samplesMetadata, pairedSamples = TRUE)

Arguments

mirnaExpr

A matrix object containing microRNA expression levels. Other objects coercible to matrix are also accepted (e.g. data.frame). This object must be structured as specified in the details section

geneExpr

A matrix object containing gene expression levels. Other objects coercible to matrix are also accepted (e.g. data.frame). This object must be structured as specified in the details section

samplesMetadata

A data.frame object containing information about samples used for microRNA and gene expression profiling. For further information see the details section

pairedSamples

Logical, whether miRNA and gene expression levels derive from the same subjects or not. Check the details section for additional instructions. Default is TRUE

Value

A valid MirnaExperiment object containing information about miRNA and gene expression.

Details

This function requires data to be prepared as described below.

mirnaExpr and geneExpr

mirnaExpr and geneExpr must be matrix objects (or objects coercible to one) that contain miRNA and gene expression values, respectively. Rows must represent the different miRNAs/genes analyzed while columns must represent the different samples in study. For mirnaExpr, row names must contain miRNA names according to miRBase nomenclature, whereas for geneExpr, row names must contain gene symbols according to hgnc nomenclature. The values contained in these objects can derive from both microarray and RNA-Seq experiments.

For NGS experiments, mirnaExpr and geneExpr should just be un-normalized count matrices. Instead, for microarray experiments, data should be normalized and log2 transformed, for example with the RMA algorithm.

samplesMetadata

samplesMetadata must be a data.frame object containing information about samples used for miRNA profiling and for gene expression analysis. Specifically, this data.frame must contain:

  • A column named primary, specifying an identifier for each sample;

  • A column named mirnaCol, containing the column names used for each sample in the mirnaExpr object;

  • A column named geneCol, containing the column names used for each sample in the geneExpr object;

  • Other eventual columns that define specific sample metadata, such as disease condition, age, sex and so on...

For unpaired samples, NAs can be used for missing entries in mirnaCol/geneCol.

pairedSamples

MicroRNA and gene expression measurements may derive from the same subjects (i.e. samples used to generate both miRNA and gene expression data) or from different individuals (i.e. miRNA expression assayed on a group of samples and gene expression retrieved from a different group of samples). pairedSamples is a logical parameter that defines the relationship between miRNA and gene expression measurements. It must be TRUE if data derive from the same individuals, while it must be FALSE when data derive from different subjects.

Author

Jacopo Ronchi, jacopo.ronchi@unimib.it

Examples

# load example data
data(geneCounts, package = "MIRit")
data(mirnaCounts, package = "MIRit")

# create samples metadata
meta <- data.frame(
    "primary" = colnames(geneCounts),
    "mirnaCol" = colnames(mirnaCounts), "geneCol" = colnames(geneCounts),
    "disease" = c(rep("PTC", 8), rep("NTH", 8)),
    "patient" = c(rep(paste("Sample_", seq(8), sep = ""), 2))
)

# create a 'MirnaExperiment' object
obj <- MirnaExperiment(
    mirnaExpr = mirnaCounts, geneExpr = geneCounts,
    samplesMetadata = meta, pairedSamples = TRUE
)