Manually add differential expression results to a MirnaExperiment object

This function allows to add miRNA and gene differential expression results to a MirnaExperiment object. Instead of running performMirnaDE() and performGeneDE() functions, this one allows to use differential expression analyses carried out in other ways. Note that it is possible to manually add differential expression results just for miRNAs or just for genes. This is particularly useful in order to use the pipeline implemented in MIRit for proteomic data and for expression data deriving from different technologies.

Usage

addDifferentialExpression(
  mirnaObj,
  mirnaDE = NULL,
  geneDE = NULL,
  mirna.logFC = 0,
  mirna.pCutoff = 0.05,
  mirna.pAdjustment = "fdr",
  gene.logFC = 0,
  gene.pCutoff = 0.05,
  gene.pAdjustment = "fdr"
)

Arguments

mirnaObj

A MirnaExperiment object containing miRNA and gene data

mirnaDE

A data.frame containing the output of miRNA differential expression analysis. Check the details section to see the required format. Default is NULL not to add miRNA differential expression results

geneDE

A data.frame containing the output of gene differential expression analysis. Check the details section to see the required format. Default is NULL not to add gene differential expression results

mirna.logFC

The minimum log2 fold change required to consider a miRNA as differentially expressed. Optional, default is 0

mirna.pCutoff

The adjusted p-value cutoff to use for miRNA statistical significance. The default value is 0.05

mirna.pAdjustment

The p-value correction method for miRNA multiple testing. It must be one of: fdr (default), BH, none, holm, hochberg, hommel, bonferroni, BY

gene.logFC

The minimum log2 fold change required to consider a gene as differentially expressed. Optional, default is 0

gene.pCutoff

The adjusted p-value cutoff to use for gene statistical significance. The default value is 0.05

gene.pAdjustment

The p-value correction method for gene multiple testing. It must be one of: fdr (default), BH, none, holm, hochberg, hommel, bonferroni, BY

Value

A MirnaExperiment object containing differential expression results. To access these results, the user may run the mirnaDE() and geneDE() functions for miRNAs and genes, respectively.

Details

The following paragraphs briefly explain the formats needed for mirnaDE, geneDE, and differential expression parameters.

mirnaDE and geneDE

mirnaDE and geneDE are two objects of class data.frame containing the results of miRNA and gene differential expression analysis respectively. These tables should contain the differential expression results for all miRNAs/genes analyzed, not just for statistically significant species.

Note that you can individually add differential expression results for miRNAs and genes. For instance, it is possible to manually add gene differential expression through this function, while performing miRNA differential expression through the performMirnaDE() function, and vice versa. In order to only add miRNA or gene differential expression results, you must leave mirnaDE or geneDE slots to NULL.

All data.frame objects can be used, as long as they have:

• One column containing miRNA/gene names (according to miRBase/hgnc nomenclature). Accepted column names are: ID, Symbol, Gene_Symbol, Mirna, mir, Gene, gene.symbol, Gene.symbol;

• One column with log2 fold changes. Accepted column names are: logFC, log2FoldChange, FC, lFC;

• One column with average expression. Accepted column names are: AveExpr, baseMean, logCPM;

• One column with the p-values resulting from the differential expression analysis. Accepted column names are: P.Value, pvalue, PValue, Pvalue;

• One column containing p-values adjusted for multiple testing. Accepted column names are: adj.P.Val, padj, FDR, fdr, adj, adj.p, adjp.

Differential expression cutoffs

mirna.logFC, mirna.pCutoff, mirna.pAdjustment, and gene.logFC, gene.pCutoff, gene.pAdjustment represent the parameters used to define the significance of differential expression results. These are needed in order to inform MIRit about the features that are considered as differentially expressed.

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
)

# load example tables with differential expression results
de_m <- mirnaDE(object = loadExamples(), onlySignificant = FALSE)
de_g <- geneDE(object = loadExamples(), onlySignificant = FALSE)

# add DE results to MirnaExperiment object
obj <- addDifferentialExpression(obj, de_m, de_g,
    mirna.pCutoff = 0.05,
    gene.pCutoff = 0.05
)