The 'MirnaExperiment' class

This class extends the MultiAssayExperiment from the homonym package to provide the flexibility of handling genomic data of both microRNAs and their targets, allowing to store information about microRNA and gene expression, differential expression results, microRNA targets and miRNA-gene integration analysis.

This class can be used to manage genomic data deriving from different sources, like RNA-Seq, microarrays and mass spectrometry. Moreover, microRNA and gene expression levels may originate from the same individuals (paired samples) or from different subjects (unpaired samples).

Usage

# S4 method for class 'MirnaExperiment'
mirnaDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)

# S4 method for class 'MirnaExperiment'
geneDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)

# S4 method for class 'MirnaExperiment'
significantMirnas(object)

# S4 method for class 'MirnaExperiment'
significantGenes(object)

# S4 method for class 'MirnaExperiment'
pairedSamples(object)

# S4 method for class 'MirnaExperiment'
mirnaTargets(object)

# S4 method for class 'MirnaExperiment'
integration(object, param = FALSE)

# S4 method for class 'MirnaExperiment'
show(object)

Arguments

object: An object of class MirnaExperiment
onlySignificant: Logical, if TRUE differential expression results will be returned just for statistically significant miRNAs/genes, if FALSE the full table of miRNA/gene differential expression will be provided. Default is TRUE to only report significant miRNAs/genes
param: Logical, whether to return the complete list object with the parameters used, or just the results stored in data. Default is FALSE
returnObject: Logical, if TRUE this function will return the limma/edgeR/DESeq2 object used for differential expression analysis

Value

the mirnaDE() function returns a data.frame object with the results of miRNA differential expression analysis.

the geneDE() function returns a data.frame object with the results of gene differential expression analysis.

the significantMirnas() function gives back a character vector with the names of differentially expressed miRNAs.

the significantGenes() function gives back a character vector with the names of differentially expressed genes.

the pairedSamples() function returns a logical object that specifies if the MirnaExperiment object has paired measurements or not.

the mirnaTargets() function provides a data.frame object with the list of target genes for the differentially expressed miRNAs.

the integration() function gives back a data.frame object with the results of the integrative miRNA-mRNA analysis.

Functions

mirnaDE(MirnaExperiment): Access the results of miRNA differential expression
geneDE(MirnaExperiment): Access the results of gene differential expression
significantMirnas(MirnaExperiment): Access the names of differentially expressed miRNAs
significantGenes(MirnaExperiment): Access the names of differentially expressed genes
pairedSamples(MirnaExperiment): Check if the object derives from sample-matched data
mirnaTargets(MirnaExperiment): Extract the miRNA-targets interactions retrieved for the differentially expressed miRNAs
integration(MirnaExperiment): Access the results of the integrative miRNA-mRNA analysis
show(MirnaExperiment): Show method for objects of class MirnaExperiment

Slots

ExperimentList: An ExperimentList class object for each assay dataset
colData: A DataFrame of all clinical/specimen data available across experiments
sampleMap: A DataFrame of translatable identifiers of samples and participants
metadata: Additional data describing the object
drops: A metadata list of dropped information
mirnaDE: A list object containing the results of miRNA differential expression
geneDE: A list object containing the results of gene differential expression
pairedSamples: A logical parameter that specifies whether miRNA and gene expression measurements derive from the same individuals (TRUE) or from different subjects (FALSE)
targets: A data.frame object containing miRNA-target pairs. This slot is commonly populated by the getTargets() function
integration: A list object containing the results of the integration analysis between miRNA and gene expression values. This slot is commonly populated by the mirnaIntegration() function

Note

To create a MirnaExperiment object, you can use the MirnaExperiment() constructor function, which allows to easily build and verify a valid object starting from miRNA and gene expression matrices.

ExperimentList

The ExperimentList slot is designed to contain results from each experiment/assay. In this case, it holds miRNA and gene expression matrices. It contains a SimpleList-class.

colData

The colData slot is a collection of primary specimen data valid across all experiments. This slot is strictly of class DataFrame but arguments for the constructor function allow arguments to be of class data.frame and subsequently coerced.

sampleMap

The sampleMap contains a DataFrame of translatable identifiers of samples and participants or biological units. The standard column names of the sampleMap are "assay", "primary", and "colname". Note that the "assay" column is a factor corresponding to the names of each experiment in the ExperimentList. In the case where these names do not match between the sampleMap and the experiments, the documented experiments in the sampleMap take precedence and experiments are dropped by the harmonization procedure. The constructor function will generate a sampleMap in the case where it is not provided and this method may be a 'safer' alternative for creating the MultiAssayExperiment (so long as the rownames are identical in the colData, if provided). An empty sampleMap may produce empty experiments if the levels of the "assay" factor in the sampleMap do not match the names in the ExperimentList.

mirnaDE and geneDE

mirnaDE and geneDE consist of two list objects storing information regarding miRNA and gene differential expression, including:

data, which contains differential expression results in a data.frame with five columns:
- ID: indicates the name of the miRNA/gene;
- logFC: indicates the fold change of each feature in logarithmic scale;
- AveExpr: represents the average expression of each miRNA/gene;
- P.Value: indicates the resulting p-value;
- adj.P.Val: contains the p-values adjusted for multiple testing.
significant, which is a character vector containing the names of significantly differentially expressed miRNAs/genes that passed the thresholds;
method, which specifies the procedure used to determine differentially expressed miRNAs/gens (eg. "limma-voom", "edgeR", "DESeq2", "limma");
group, which is the column name of the variable (in colData) used for differential expression analysis;
contrast, which represents the groups that are compared during differential expression analysis (e.g. 'disease-healthy');
design, which outlines the R formula used for fitting the model. It includes the variable of interest (group) together with eventual covariates (e.g. '~ 0 + disease + sex');
pCutoff, which indicates the p-value cutoff used for DE analysis;
pAdjustment, the approach used for multiple testing correction;
logFC, which states the log2 Fold Change cutoff used for DE analysis;
deObject, an object deriving from limma/edgeR/DESeq2, that holds additional information regarding data processing.

MiRNA differential expression results can be accessed through the mirnaDE() function, for additional details see ?mirnaDE. Similarly, gene differential expression results can be accessed through the geneDE() function, for additional details see ?geneDE.

pairedSamples

As already mentioned, pairedSamples must be TRUE when miRNA and gene expression derive from the same subjects, while it must FALSE if this is not the case.

targets

targets is a data.frame with miRNA-target interactions, as retrieved by getTargets() function.

integration

Lastly, integration slot contains a list object that stores the results and the options used for performing the integrative miRNA-gene analysis. In particular, integration contains:

data, which is a data.frame object with the results of the integrative analysis;
method, which specifies the procedure used to perform the integrative analysis;
pCutoff, which indicates the p-value cutoff used for the analysis;
pAdjustment, the approach used for multiple testing correction.

Moreover, data differs on the basis of the integration strategy used. For the one-sided association test integration, and for integration based on rotation gene set tests, this data.frame has seven columns:

microRNA: the miRNA ID;
mirna.direction: the fold change direction of the DE-miRNA (Up or Down);
gene.direction: the fold change direction of target genes (Up or Down);
DE: represents the number of differentially expressed targets;
targets: represents the total number of targets for this miRNA;
P.Val: indicates the resulting p-value;
adj.P.Val: contains test p-values corrected for multiple testing;
DE.targets: contains the list of differentially expressed targets whose expression is negatively associated with miRNA expression.

Instead, when a correlation analysis is performed, data has six columns:

microRNA: the miRNA ID;
Target: the correlated target gene;
microRNA.Direction: the fold change direction of the DE-miRNA;
Corr.Coeff: the value of the correlation coefficient used;
Corr.P.Value: the p-value resulting from the correlation analysis;
Corr.Adjusted.P.Val: contains the correlation p-values corrected for multiple testing.

To access the results of the integrative analysis, the data slot can be accessed through the integration() function.

References

Marcel Ramos et al. Software For The Integration Of Multiomics Experiments In Bioconductor. Cancer Research, 2017 November 1; 77(21); e39-42. DOI: 10.1158/0008-5472.CAN-17-0344

Author

Jacopo Ronchi, jacopo.ronchi@unimib.it