Introduction

What is MIRit

MIRit (miRNA integration tool) is an open source R package that aims to facilitate the comprehension of microRNA (miRNA) biology through the integrative analysis of gene and miRNA expression data deriving from different platforms, including microarrays, RNA-Seq, miRNA-Seq, proteomics and single-cell transcriptomics. Given their regulatory importance, a complete characterization of miRNA dysregulations results crucial to explore the molecular networks that may lead to the insurgence of complex diseases. Unfortunately, there are no currently available options for thoroughly interpreting the biological consequences of miRNA dysregulations, thus limiting the ability to identify the affected pathways and reconstruct the compromised molecular networks. To tackle this limitation, we developed MIRit, an all-in-one framework that provides flexible and powerful methods for performing integrative miRNA-mRNA multi-omic analyses from start to finish. In particular, MIRit includes multiple modules that allow to perform:

Differential expression analysis, which identifies miRNAs and genes that vary across biological conditions for both RNA-Seq and microarray experiments (even though other technologies can also be used);
Functional enrichment analysis, which allows to understand the consequences of gene dysregulations through different strategies, including over-representation analysis (ORA), gene-set enrichment analysis (GSEA) and Correlation Adjusted MEan RAnk gene set test (CAMERA);
SNP association, that links miRNA dysregulations to disease-associated SNPs occurring at miRNA gene loci;
Target identification, which retrieves predicted and validated miRNA-target interactions through innovative state-of-the-art approaches that limit false discoveries;
Expression levels integration, which integrates the expression levels of miRNAs and mRNAs for both paired data, through correlation analyses, and unpaired data, through association tests and rotation gene-set tests;
Topological analysis, which implements a novel approach called Topology-Aware Integrative Pathway Analysis (TAIPA) for identifying the impaired molecular networks that affect biological pathways retrieved from KEGG, Reactome and WikiPathways.

How to cite MIRit

If you use MIRit in published research, please cite the following paper:

Ronchi J and Foti M. ‘MIRit: an integrative R framework for the identification of impaired miRNA-mRNA regulatory networks in complex diseases’. bioRxiv (2023). doi:10.1101/2023.11.24.568528

This package internally uses different R/Bioconductor packages, remember to cite the appropriate publications.

Installation

Before starting, MIRit must be installed on your system. You can do this through Bioconductor.

## install MIRit from Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("MIRit")

If needed, you could also install the development version of MIRit directly from GitHub:

## install the development version from GitHub
if (!requireNamespace("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("jacopo-ronchi/MIRit")

When MIRit is installed, we can load it through:

## load MIRit
library(MIRit)

Data preparation

Load example data

To demonstrate the capabilities of MIRit we will use RNA-Seq data from Riesco-Eizaguirre et al. (2015). This experiment collected samples from 8 papillary thyroid carcinoma tumors and contralateral normal thyroid tissue from the same patients. These samples were profiled for gene expression through RNA-Seq, and for miRNA expression through miRNA-Seq. To provide easy access to the user, raw count matrices have been retrieved from GEO and included in this package.

To load example data, we can simply use the data() function:

## load count matrix for genes
data(geneCounts, package = "MIRit")

## load count matrix for miRNAs
data(mirnaCounts, package = "MIRit")

Paired vs unpaired data

When using MIRit, we must specify whether miRNA and gene expression values derive from the same individuals or not. As already mentioned, paired data are those where individuals used to measure gene expression are the same subjects used to measure miRNA expression. On the other hand, unpaired data are those where gene expression and miRNA expression derive from different cohorts of donors. Importantly, MIRit considers as paired samples those data sets where paired measurements are available for at least some samples.

In our case, miRNA and gene expression data originate from the same subjects, and therefore we will conduct a paired samples analysis.

Set up expression matrices

As input data, MIRit requires miRNA and gene expression measurements as matrices with samples as columns, and genes/miRNAs as rows. Further, the row names of miRNA expression matrix should contain miRNA names according to miRBase nomenclature (e.g. hsa-miR-151a-5p, hsa-miR-21-5p), whereas for gene expression matrix, row names must contain gene symbols according to HGNC (e.g. TYK2, BDNF, NTRK2).

These matrices may handle different types of values deriving from multiple technologies, including microarrays, RNA-Seq and proteomics. The only requirement is that, for microarray studies, expression matrices must be normalized and log2 transformed. This can be achieved by applying the RMA algorithm implemented in the oligo (Carvalho and Irizarry 2010) package, or by applying other quantile normalization strategies. On the contrary, for RNA-Seq and miRNA-Seq experiments, the simple count matrix must be supplied.

Eventually, expression matrices required by MIRit should appear as those in mirnaCounts and geneCounts, which are displayed in Tables @ref(tab:geneExpr) and @ref(tab:mirnaExpr).

Gene expression matrix. An expression matrix containing normalized values, with row names according to hgnc.
	PTC 1	PTC 2	PTC 3	PTC 4	PTC 5
A1BG	7	6	9	12	7
A1BG-AS1	20	8	22	6	16
A1CF	0	0	0	1	1
A2M	9	11	9	37	18
A2M-AS1	1486	722	801	968	1787

MiRNA expression matrix. An expression matrix containing normalized values, with row names according to miRBase.
	PTC 1	PTC 2	PTC 3	PTC 4	PTC 5
hsa-let-7a-2-3p	3	0	9	1	4
hsa-let-7a-3p	472	82	228	122	313
hsa-let-7a-5p	141101	45543	105598	45503	159598
hsa-let-7b-3p	412	81	120	147	164
hsa-let-7b-5p	16337	6586	8121	7993	16516

Define sample metadata

Once we have expression matrices in the proper format, we need to inform MIRit about the samples in study and the biological conditions of interest. To do so, we need to create a data.frame that must contain:

a column named primary, specifying a unique identifier for each different subject;
a column named mirnaCol, matching the column name used for each sample in the miRNA expression matrix;
a column named geneCol, matching the column name used for each sample in the gene expression matrix;
a column that defines the biological condition of interest;
other optional columns that store specific sample metadata, such as age, sex and so on…

Firstly, let’s take a look at the column names used for miRNA and gene expression matrices.

## print sample names in geneCounts
colnames(geneCounts)
#>  [1] "PTC 1" "PTC 2" "PTC 3" "PTC 4" "PTC 5" "PTC 6" "PTC 7" "PTC 8" "NTH 1"
#> [10] "NTH 2" "NTH 3" "NTH 4" "NTH 5" "NTH 6" "NTH 7" "NTH 8"

## print sample names in mirnaCounts
colnames(mirnaCounts)
#>  [1] "PTC 1" "PTC 2" "PTC 3" "PTC 4" "PTC 5" "PTC 6" "PTC 7" "PTC 8" "NTH 1"
#> [10] "NTH 2" "NTH 3" "NTH 4" "NTH 5" "NTH 6" "NTH 7" "NTH 8"

## check if samples in geneCounts are equal to those in mirnaCounts
identical(colnames(geneCounts), colnames(mirnaCounts))
#> [1] TRUE

In our case, we see that both expression matrices have the same column names, and therefore mirnaCol and geneCol will be identical. However, note that is not always the case, especially for unpaired data, where miRNA and gene expression values derive from different subjects. In these cases, mirnaCol and geneCol must map each column of miRNA and gene expression matrices to the relative subjects indicated in the primary column. Notably, for unpaired data, NAs can be used for missing entries in mirnaCol/geneCol.

That said, we can proceed to create the data.frame with sample metadata as follows.

## create a data.frame containing sample metadata
meta <- data.frame(primary = colnames(mirnaCounts),
                   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

At this point, we need to create an object of class MirnaExperiment, which is the main class used in MIRit for integrative miRNA-mRNA analyses. In particular, this class extends the MultiAssayExperiment class from the homonym package (Ramos et al. 2017) to store expression levels of both miRNAs and genes, differential expression results, miRNA-target pairs and integrative miRNA-gene analysis.

The easiest way to create a valid MirnaExperiment object is to use the MirnaExperiment() function, which automatically handles the formatting of input data and the creation of the object.

## create the MirnaExperiment object
experiment <- MirnaExperiment(mirnaExpr = mirnaCounts,
                              geneExpr = geneCounts,
                              samplesMetadata = meta,
                              pairedSamples = TRUE)

Differential expression analysis

Now that the MirnaExperiment object has been created, we can move to differential expression analysis, which aims to define differentially expressed features across biological conditions.

Visualize expression variability

Firstly, before doing anything else, it is useful to explore expression variability through dimensionality reduction techniques. This is useful because it allows us to visualize the main drivers of expression differences. In this regard, MIRit offers the plotDimensions() function, which enables to visualize both miRNA and gene expression in the multidimensional space (MDS plots). Moreover, it is possible to color samples based on specific variables, hence allowing to explore specific patterns between distinct biological groups.

In our example, let’s examine expression variability for both miRNAs and genes, and let’s color the samples based on “disease”, a variable included in the previously defined metadata.

geneMDS <- plotDimensions(experiment,
                          assay = "genes",
                          condition = "disease",
                          title = "MDS plot for genes")

mirnaMDS <- plotDimensions(experiment,
                           assay = "microRNA",
                           condition = "disease",
                           title = "MDS plot for miRNAs")

ggpubr::ggarrange(geneMDS, mirnaMDS,
                  nrow = 1, labels = "AUTO", common.legend = TRUE)

MDS plots for miRNAs and genes. Both plots show that the main source of variability is given by the disease condition.

As we can see from Figures @ref(fig:mds)A and @ref(fig:mds)B, the samples are very well separated on the basis of disease condition, thus suggesting that this is the major factor that influences expression variability. This is exactly what we want, since we aim to evaluate the differences between cancer and normal tissue. If this wasn’t the case, we should identify the confounding variables and include them in the model used for differential expression analysis (see Section @ref(model)).

Perform miRNA and gene differential expression

Available methods for RNA-Seq and microarrays

Now, we are ready to perform differential expression analysis. In this concern, MIRit provides different options based on the technology used. Indeed, when expression measurements derive from microarrays, MIRit calculates differentially expressed features through the pipeline implemented in the limma package. On the other hand, when expression values derive from RNA-Seq experiments, MIRit allows to choose between different approaches, including:

the Quasi-Likelihood framework defined in the edgeR package;
the approach defined in the DESeq2 package;
the limma-voom approach defined in the limma package.

Moreover, MIRit gives the possibility of fully customizing the parameters used for differential expression analysis, thus allowing a finer control that makes it possible to adopt strategies that differ from the standard pipelines proposed in these packages. For additional information, see Section @ref(param).

Model design

After identifying the variable of interest and the confounding factors, we must indicate the experimental model used for fitting expression values. Notably, MIRit will automatically take care of model fitting, so that we only need to indicate a formula with the appropriate variables.

In our case, we want to evaluate the differences between cancer and normal thyroid. Therefore, disease condition is our variable of interest. However, in this experimental design, each individual has been assayed twice, one time for cancer tissue, and one time for healthy contralateral thyroid. Thus, we also need to include the patient ID as a covariate in order to prevent the individual differences between subjects from confounding the differences due to disease.

## design the linear model for both genes and miRNAs
model <- ~ disease + patient

If other variables affecting miRNA and gene expression are observed, they should be included in this formula.

The `performMirnaDE()` and `performGeneDE()` functions

Once the linear model has been defined, we can perform differential expression analysis through the performMirnaDE() and performGeneDE() functions, which take as input a MirnaExperiment object, and compute differential expression for miRNAs and genes, respectively.

Additionally, we must define multiple arguments, namely:

group, which corresponds to the name of the variable of interest, as specified in Section @ref(meta). In our case, we are interested in studying the differences between cancer tissue and normal tissue. Therefore our variable of interest is “disease”.
contrast, which indicates the levels of the variable of interest to be compared. In particular, this parameter takes as input a string where the levels are separated by a dash, and where the second level corresponds to the reference group. In our example, we want to compare samples from papillary thyroid cancer (PTC) against normal thyroid tissue (NTH), thus we set contrast to “PTC-NTH”.
design, which specifies the linear model with the variable of interest and eventual covariates. To do so, we pass to this parameter the R formula that we defined in Section @ref(model).
method, which tells MIRit which pipeline we want to use for computing differentially expressed features. As stated in Section @ref(deMethods), for microarray studies the only option available is limma, while for RNA-Seq experiments, the user can choose between edgeR, DESeq2, and voom (for limma-voom). In our case we are going to perform differential expression analysis through the Quasi-Likelihood pipeline implemented in the edgeR package.

Following our example, let’s calculate differentially expressed genes and differentially expressed miRNAs in thyroid cancer.

## perform differential expression for genes
experiment <- performGeneDE(experiment,
                            group = "disease",
                            contrast = "PTC-NTH",
                            design = model,
                            pCutoff = 0.01)
#> Performing differential expression analysis with edgeR...
#> Differential expression analysis reported 188 significant genes with p < 0.01 (correction: fdr). You can use the 'geneDE()' function to access results.

## perform differential expression for miRNAs
experiment <- performMirnaDE(experiment,
                             group = "disease",
                             contrast = "PTC-NTH",
                             design = model,
                             pCutoff = 0.01)
#> Performing differential expression analysis with edgeR...
#> Differential expression analysis reported 18 significant miRNAs with p < 0.01 (correction: fdr). You can use the 'mirnaDE()' function to access results.

If not specified, the performMirnaDE() and performGeneDE() functions will define differentially expressed genes/miRNAs as those having an adjusted p-value lower than 0.05. However, this behavior can be changed by tweaking the pCutoff parameter, that specifies the statistical significance threshold; and the pAdjustment option, which specifies the approach used for multiple testing correction (default is fdr to use the Benjamini-Hochberg method). Optionally, it is possible to set the logFC parameter, which indicates the minimum log2 fold change that features must display for being considered as differentially expressed. Please note that the simultaneous use of adjusted p-value and logFC cutoffs is discouraged and not recommended.

Advanced parameters

In addition to the above mentioned settings, other options can be passed to the performMirnaDE() and performGeneDE() functions. Specifically, depending on the method adopted for differential expression analysis, the user can finely control the arguments passed to each function involved in the pipeline. In particular, the following advanced parameters can be set:

filterByExpr.args,
calcNormFactors.args,
estimateDisp.args,
glmQLFit.args,
glmQLFTest.args,
DESeq.args,
useVoomWithQualityWeights,
voom.args,
lmFit.args,
eBayes.args,
useArrayWeights,
useWsva,
arrayWeights.args,
wsva.args.
useDuplicateCorrelation
correlationBlockVariable
duplicateCorrelation.args

In detail, when using limma-voom strategy, the useVoomWithQualityWeights parameter tells MIRit whether to use voomWithWualityWeights() instead of the standard voom() function. In the same way, for microarray studies, the useArrayWeights specifies whether to consider array quality weights during the limma pipeline. Similarly, useWsva can be set to TRUE to include a weighted surrogate variable analysis for batch effect correction. Moreover, useDuplicateCorrelation can be set to TRUE if you want to consider the effect of correlated samples through the duplicateCorrelation() function in limma. In this concern, the correlationBlockVariable specifies the blocking variable to use. All the other parameters ending with “.args”, accept a list object with additional parameters to be passed to the relative functions. In this way, the user has full control over the strategy used.

For a complete reference on the usage of these parameters, check the help page of these functions. Instead, for further instructions on how to use these tools, please refer to their original manuals, which represent exceptional resources for learning the basics of differential expression analysis:

limma User’s Guide,
edgeR User’s Guide,
Analysing RNA-Seq data with DESeq2.

Add differential expression results from other technologies

Even though MIRit implements all the most commonly used strategies for differential expression analyses, these methods may not be suitable for all kind of experiments. For instance, expression data deriving from technologies different from microarrays and RNA-Seq can’t be processed through performGeneDE() and performMirnaDE() functions. Therefore, MIRit grants the possibility to perform differential expression analysis with every method of choice, and then add the results to an existing MirnaExperiment object. This is particularly valuable for proteomic studies, where different normalization strategies are used. In this way, MIRit fully supports the use of proteomic data for conducting miRNA integrative analyses.

To do so, we can make use of the addDifferentialExpression() function, which takes as input a MirnaExperiment object, and a table containing the differential expression results for all miRNAs/genes analyzed (not just for statistically significant species). If we want to manually set differential expression results for both miRNAs and genes, two different tables must be supplied. These tables must include:

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.

Further, we must specify the cutoff levels used to consider miRNAs/genes as significantly differentially expressed.

Visualize differentially expressed features

Access differential expression tables

Once differential expression analysis has been performed, we can use the mirnaDE() and geneDE() functions to access a table with differentially expressed features.

## access DE results for genes
deGenes <- geneDE(experiment)

## access DE results for miRNAs
deMirnas <- mirnaDE(experiment)

Create a volcano plot for miRNAs and genes

In addition to tables, we can also generate a graphical overview of differential expression through volcano plots, which are extremely useful for visualizing features changing across biological conditions. To produce volcano plots, MIRit offers the plotVolcano() function.

## create a volcano plot for genes
geneVolcano <- plotVolcano(experiment,
                           assay = "genes",
                           title = "Gene differential expression")

## create a volcano plot for miRNAs
mirnaVolcano <- plotVolcano(experiment,
                            assay = "microRNA",
                            title = "miRNA differential expression")

## plot graphs side by side
ggpubr::ggarrange(geneVolcano, mirnaVolcano,
                  nrow = 1, labels = "AUTO", common.legend = TRUE)

Volcano plots of gene and miRNA differential expression. (A) shows the differentially expressed genes, while (B) displays differentially expressed miRNAs.

Produce differential expression bar plots

Finally, if we are interested in specific genes/miRNAs, MIRit implements the plotDE() function that allows to represent expression changes of specific features as box plots, bar plots, or violin plots. In our example, we can use this function to visualize expression changes of different genes involved in the normal functioning of thyroid gland. Note that we use the linear = FALSE option to plot data in log scale (useful when multiple genes have very different expression levels).

## create a bar plot for all thyroid features
plotDE(experiment,
       features = c("TG", "TPO", "DIO2", "PAX8"),
       graph = "barplot", linear = FALSE)

Differential expression bar plots for different thyroid genes. Differential expression analysis demonstrated how TG, TPO, DIO2 and PAX8 result downregulated in thyroid cancer.

Functional enrichment analysis

After finding differentially expressed genes, we usually end up having long lists of features whose expression changes between conditions. However, this is usually not very informative, and we seek to understand which functions result impaired in our experiments. In this regard, different methods exist for determining which cellular processes are dysregulated in our samples.

Available approaches: ORA, GSEA and CAMERA

MIRit supports different strategies for functional enrichment analysis of genes, including over-representation analysis (ORA), gene-set enrichment analysis (GSEA), and Correlation Adjusted MEan RAnk gene set test (CAMERA). In this way, the user can infer compromised biological functions according to the approach of choice.

Among these methods, the first one that has been developed is over-representation analysis (Boyle et al. 2004), often abbreviated as ORA. This analysis aims to define whether genes annotated to specific gene-sets (such as ontological terms or biological pathways) are differentially expressed more than would be expected by chance. To do this, a p-value is calculated by the hypergeometric distribution as in Equation @ref(eq:hyper).

Here, N is the total number of genes tested, M is the number of genes that are annotated to a particular gene-set, n is the number of differentially expressed genes, and k is the number of differentially expressed genes that are annotated to the gene set.

Additionally, another available approach is the gene set enrichment analysis (Subramanian et al. 2005), often refereed to with the acronym GSEA, which is suitable to find categories whose genes change in a small but coordinated way. The GSEA algorithm takes as input a list of genes ranked with a particular criterion, and then walks down the list to evaluate whether members of a given gene set are normally distributed or are mainly present at the top or at the bottom of the list. To check this out, the algorithm uses a running-sum that increases when finding a gene belonging to a given category, and decreases when a gene not contained in that specific set is found. The maximum distance from zero occurred in the running score is defined as the enrichment score (ES). To estimate the statistical significance of enrichment scores, a permutation test is performed by swapping gene labels annotated to a gene set.

Even though GSEA is arguably the most commonly used approach for functional enrichment, Wu and Smyth (2012) demonstrated that inter-gene correlations might affect its reliability. To overcome this issue, they developed the Correlation Adjusted MEan RAnk gene set test (CAMERA). The main advantage of this method is that it adjusts the gene set test statistic according to inter-gene correlations.

Available databases and categories

As described above, functional enrichment analysis relies on gene-sets, which consist in collections of genes that are annotated to specific functions or terms. Independently from the strategy used for the analysis, functional enrichment methods need access to these properly curated collections of genes. In the effort of providing access to a vast number of these resources, MIRit uses the geneset package to support multiple databases, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (DO), Network of Cancer Genes (NCG), DisGeNET, and COVID-19. However, the majority of these databases contain multiple categories. To see the complete list of available gene-sets for each database refer to the documentation of the enrichGenes() function.

Supported species

The above-mentioned collections have their own lists of supported species. To check the available species for a given database, MIRit provides a practical helper function named supportedOrganisms(). For example, to retrieve the species supported by Reactome database, we can simply run the following piece of code.

## list available species for Reactome database
supportedOrganisms("Reactome")
#>  [1] "Bos taurus"               "Caenorhabditis elegans"  
#>  [3] "Danio rerio"              "Drosophila melanogaster" 
#>  [5] "Gallus gallus"            "Homo sapiens"            
#>  [7] "Mus musculus"             "Rattus norvegicus"       
#>  [9] "Saccharomyces cerevisiae" "Sus scrofa"              
#> [11] "Xenopus tropicalis"

Perform functional enrichment with the `enrichGenes()` function

The main function in MIRit for the functional enrichment analysis of genes is enrichGenes(), which requires as input:

the MirnaExperiment object that we get after running differential expression analysis;
method, which specifies the desired approach among ORA, GSEA, and CAMERA;
database and category, which define the gene-set that you want to use (see Section @ref(categories) for a complete reference of available databases and categories);
organism, which indicates the specie under investigation (defaults to “Homo sapiens”);
pCutoff and pAdjustment, which specify the cutoff for statistical significance and the multiple testing correction, respectively.

In our example, we are going to perform the enrichment analysis by using ORA with GO database (biological processes).

## perform over-representation analysis with GO
ora <- enrichGenes(experiment,
                   method = "ORA",
                   database = "GO",
                   organism = "Homo sapiens")
#> Since not specified, 'category' for GO database is set to bp (default).
#> Preparing the appropriate gene set...
#> Performing the enrichment of upregulated genes...
#> Performing the enrichment of downregulated genes...
#> The enrichment of genes reported 9 significantly enriched terms for downregulated genes and 0 for upregulated genes.

MIRit performs ORA separately for upregulated and downregulated genes, as it has been shown to be more powerful compared to enriching all DE genes (Hong et al. 2014). Therefore, when we use ORA, enrichGenes() returns a list containing two objects of class FunctionalEnrichment, one storing enrichment results for upregulated genes, and one for downregulated genes.

Before exploring the results of the analysis, we will also demonstrate the use of GSEA with the gene-sets provided by the KEGG pathway database.

## set seed for reproducible results
set.seed(1234)

## perform gene-set enrichment analysis with KEGG
gse <- enrichGenes(experiment,
                   method = "GSEA",
                   database = "KEGG",
                   organism = "Homo sapiens")
#> Since not specified, 'category' for KEGG database is set to pathway (default).
#> Preparing the appropriate gene set...
#> 
#> Some ID occurs one-to-many match, like "79154, 7920, 79143"...
#> 99.96% genes are mapped to symbol
#> Ranking genes based on signed.pval...
#> Performing gene-set enrichment analysis (GSEA)...
#> GSEA reported 2 significantly enriched terms.

In this case, the enrichGenes() function returns a single object of class FunctionalEnrichment, containing GSEA results.

Visualize enriched sets

Access results table

After running the enrichGenes() function, we get FunctionalEnrichment objects holding the results of enrichment analyses. To access the full table containing significantly affected functions, we can use the enrichmentResults() function. In our case, we can check the results of GSEA (Table @ref(tab:gseaTab)).

GSEA results. A table listing the affected KEGG pathways in thyroid cancer.
pathway	pval	padj	log2err	ES	NES	size	leadingEdge
Thyroid hormone synthesis	0	0.01	0.56	-0.67	-2.01	27	SLC26A4,….
Protein processing in endoplasmic reticulum	0	0.03	0.50	-0.54	-1.89	55	BCL2, HY….

Enrichment dot plots and bar plots

Further, MIRit offers several options for the visualization of enrichment analyses, including dot plots and bar plots. These plots are available for every FunctionalEnrichment object independently from the method used.

Following our example, we can visualize ORA results that we obtained in Section @ref(enrichment) through a simple dot plot.

## create a dot plot for ORA
enrichmentDotplot(ora$downregulated, title = "Depleted functions")

ORA results for downregulated genes. The enrichment of downregulated genes through the gene sets provided by GO database.

Other plots for GSEA

Additionally, MIRit provides specific visualization methods that are exclusive for GSEA, including ridge plots and GSEA plots. For instance, after running GSEA through enrichGenes(), we can produce a GSEA-style plot through the gseaPlot() function. In our case, we are going to produce this plot for the “Thyroid hormone synthesis” pathway.

## create a GSEA plot
gseaPlot(gse, "Thyroid hormone synthesis", rankingMetric = TRUE)

GSEA-style plot for Thyroid hormone synthesis. This type of plot shows the running sum that GSEA uses to determinate the enrichment score for each pathway.

Retrieve miRNA targets

Before performing integrative miRNA-mRNA analyses, we need to identify the targets of differentially expressed miRNAs, so that we can test whether they really affect the levels of their targets or not.

Databases with miRNA-mRNA interactions

Different resources have been developed over the years to predict and collect miRNA-target interactions, and we can categorize them in two main types:

Prediction databases, that contain information about computationally determined miRNA-target interactions; and
Validated databases, which only contain interactions that have been proven through biomolecular experiments.

The choice of which type of resources to use for identifying miRNA targets drastically influences the outcome of the analysis. In this regard, some researchers tend to give the priority to validated interactions, even though they are usually fewer than predicted ones. On the other hand, predicted pairs are much more numerous, but they exhibit a high number of false positive hits.

The mirDIP approach

The downside of miRNA target prediction algorithms is also the scarce extend of overlap existing between different tools. To address this issue, several ensemble methods have been developed, trying to aggregate the predictions obtained by different algorithms. Initially, several researchers determined as significant miRNA-target pairs those predicted by more than one tool (intersection method). However, this method is not able to capture an important number of meaningful interactions. Alternatively, other strategies used to merge predictions from several algorithms (union method). Despite identifying more true relationships, the union method leads to a higher proportion of false discoveries. Therefore, other ensemble methods started using other statistics to rank miRNA-target predictions obtained by multiple algorithms. Among these newly developed ensemble methods, one of the best performing one is the microRNA Data Integration Portal (mirDIP) database, which aggregates miRNA target predictions from 24 different resources by using an integrated score inferred from different prediction metrics. In this way, mirDIP reports more accurate predictions compared to those of individual tools. For additional information on mirDIP database and its ranking metric refer to Tokar et al. (2018) and Hauschild et al. (2023).

Download predicted and validated interactions with `getTargets()`

Given the above, MIRit allows the prediction of miRNA-target interactions via the mirDIP database (version 5.2). In addition, in order to raise the number of true interactions, MIRit combines the miRNA-target pairs returned by mirDIP with the experimentally validated interactions contained in miRTarBase (version 9) (Huang et al. 2022). In practice, to identify miRNA targets, MIRit implements the getTargets() function. Specifically, this function includes a parameter called score that determines the degree of confidence required for the targets predicted by mirDIP. The value of this parameter must be one of Very High, High (default), Medium, and Low, which correspond to ranks among top 1%, top 5%, top 1/3, and remaining predictions, respectively. Moreover, the includeValidated parameter tells MIRit whether to include experimentally validated interactions deriving from miRTarBase. It is also possible (with the evidence parameter) to consider all interactions in miRTarBase, or just limiting the retrieval to those interactions with strong experimental evidence. Please note that mirDIP database is only available for human miRNAs; thus, for species other than Homo sapiens, only validated interactions contained in miRTarBase are used.

In our example, we are going to retrieve both predicted and validated interactions by using default settings.

## retrieve miRNA target genes
experiment <- getTargets(experiment)

After running this function, we obtain a MirnaExperiment object containing miRNA-target interactions in its targets slot. The user can access a data.frame detailing these interactions through the mirnaTargets() function.

Assess the effects of miRNAs on target genes

Now that we have defined the targets of differentially expressed miRNAs, we can continue with the integrative analysis of miRNA and gene expression levels. The purpose of this analysis is to only consider miNA-target pairs where an inverse relationship is observed.

As already mentioned, MIRit can work with both paired and unpaired data by using different statistical approaches, including:

Correlation analysis, which is the recommended method when samples are paired;
Association tests, like Fisher’s exact test and Boschloo’s exact test;
Rotation gene-set tests, as implemented in the fry function from limma package.

For unpaired data, only association tests and rotation gene-set tests are available, whereas correlation analysis is the best performing strategy for paired data. The integrative analysis, either performed through correlation, association tests, or rotation gene-set tests, is implemented in the mirnaIntegration() function. When using the default option test = "auto", MIRit automatically performs the appropriate test for paired and unpaired samples. If only some samples of the dataset have paired measurements, a correlation analysis will be carried out only for those subjects.

Correlation analysis for paired data

When both miRNA and gene expression measurements are available for the same samples, a correlation analysis is the recommended procedure. In statistics, correlation is a measure that expresses the extent to which two random variables are dependent. In our case, we want to assess whether a statistical relationship is present between the expression of a miRNA and the expression of its targets.

Statistical correlation coefficients

Several statistical coefficients can be used to weigh the degree of a correlation. Among them, the most commonly used are Pearson’s correlation coefficient r, Spearman’s correlation coefficient ρ, and Kendall’s Tau-b correlation coefficient τ_b. Pearson’s r is probably the most diffused for determining the association between miRNA and gene expression. However, it assumes that the relationship between miRNA and gene expression values is linear. This is typically not true for miRNAs, whose interactions with their targets are characterized by imperfect complementarity. Additionally, miRNAs can target multiple genes with different binding sites, and this implies that a simple linear relationship may not be sufficient to properly model the complexity of these interactions. In contrast, Spearman’s and Kendall’s Tau-b correlation coefficients result more suitable for representing the interplay between miRNAs and targets, because they are robust to non-linear relationships and outliers. However, Kendall’s correlation just relies on the number of concordant and discordant pairs, and is less sensitive then Spearman’s correlation; so, when many ties are present or when the sample size is small, it may have a lower detection power. This is the reason why Spearman’s correlation coefficient is the default used in the mirnaIntegration() function. Moreover, since miRNAs mainly act as negative regulators, only negatively correlated miRNA-target pairs are considered, and statistical significance is estimated through a one-tailed t-test.

Perform a correlation analysis in MIRit

To sum up the steps that MIRit follows when evaluating the correlation between miRNAs and genes, what the mirnaIntegration() function does during a correlation analysis is to:

consider the miRNA-target interactions retrieved with the getTargets() function;
calculate the correlation coefficient for each miRNA-target pair based on their expression values;
compute the statistical significance of all miRNA-target pairs;
adjust p-values for multiple testing before reporting significant results.

In our thyroid cancer example, we want to find the miRNA-target pairs that exhibit a negative correlation with a Spearman’s coefficient lower than -0.5 and with an adjusted p-value less than 0.05.

## perform a correlation analysis
experiment <- mirnaIntegration(experiment, test = "correlation")
#> As specified by the user, a correlation will be used.
#> Performing Spearman's correlation analysis...
#> A statistically significant correlation between 215 miRNA-target pairs was found!

Please note that all the parameters used for the correlation analysis are customizable. For instance, the user can change the significance threshold and the multiple testing correction method by setting the pCutoff and pAdjustment parameters, respectively. Further, it is also possible to change the correlation coefficient used, by editing the corMethod option, and the minimum required value of the correlation coefficient, by changing the corCutoff setting.

Account for batch effects prior to correlation analysis

Sometimes, when exploring expression variability through MDS plots, as we do with the plotDimensions() function, we notice the presence of batch effects that prevent a clear separation of our biological groups. Batch effects consist in unwanted sources of technical variation that confound expression variability and limit downstream analyses. Since the reliability of biological conclusions depends on the correlation between miRNAs and genes, it is pivotal to ensure that expression measurements are scarcely affected by technical artifacts. In this regard, if strong batch effects are noticed in the data, MIRit provides the batchCorrection() function, which removes batch effects prior to correlation analysis. Please note that this procedure cannot be used before differential expression testing, because for that purpose it is more appropriate to include batch variables in the linear model, as specified in Section @ref(model). For additional information, please refer to the manual of the batchCorrection() function.

Explore the succesfully integrated miRNA-target pairs

Before moving to the identification of the altered miRNAs regulatory networks, we can explore correlated miRNA-target pairs thanks to the integration() function, which returns a data.frame object with comprehensive details about the computed interactions.

## extract correlation results
integrationResults <- integration(experiment)

## take a look at correlation results
head(integrationResults)
#>                     microRNA Target microRNA.Direction Corr.Coefficient
#> hsa.miR.1179.7  hsa-miR-1179  ITGA6      downregulated       -0.8617647
#> hsa.miR.1179.10 hsa-miR-1179  RBMS2      downregulated       -0.7764706
#> hsa.miR.1179.12 hsa-miR-1179 SPIRE1      downregulated       -0.7117647
#> hsa.miR.1179.14 hsa-miR-1179   TAF5      downregulated       -0.5970588
#> hsa.miR.1275.1  hsa-miR-1275  CCND2      downregulated       -0.7794118
#> hsa.miR.1275.2  hsa-miR-1275   CD44      downregulated       -0.8382353
#>                 Corr.P.Value Corr.Adjusted.P.Val
#> hsa.miR.1179.7  8.902592e-06        0.0006579303
#> hsa.miR.1179.10 2.021754e-04        0.0034840545
#> hsa.miR.1179.12 9.920216e-04        0.0072485216
#> hsa.miR.1179.14 7.304866e-03        0.0266876638
#> hsa.miR.1275.1  1.858401e-04        0.0034840545
#> hsa.miR.1275.2  2.505171e-05        0.0013423542

Visualize the correlation between miRNAs and genes

Additionally, MIRit allows to graphically represent inverse correlations through a scatter plot. To do so, we can use the plotCorrelation() function to display the correlation between specific miRNA-target pairs. For example, we can plot the existing correlation between miR-146b-5p and DIO2, which is crucial for thyroid hormone functioning. Furthermore, we can also show how the upregulation of miR-146b-3p is associated with the downregulation of PAX8, which directly induces TG transcription.

## plot the correlation between miR-146b-5p and DIO2
cor1 <- plotCorrelation(experiment,
                        mirna = "hsa-miR-146b-5p",
                        gene = "DIO2",
                        condition = "disease")

## plot the correlation between miR-146b-3p and PAX8
cor2 <- plotCorrelation(experiment,
                        mirna = "hsa-miR-146b-3p",
                        gene = "PAX8",
                        condition = "disease")

## plot graphs side by side
ggpubr::ggarrange(cor1, cor2, nrow = 1,
                  labels = "AUTO", common.legend = TRUE)

Correlation between miRNAs and key thyroid genes. These plots suggest that the upregulation of miR-146b-5p and miR-146b-3p may be responsible for the downregulation of DIO2 and PAX8, respectively.

Association tests for unpaired data

For unpaired data, we cannot directly quantify the influence of miRNA expression on the levels of their targets, because we do not have any sample correspondence between miRNA and gene measurements. However, one-sided association tests can be applied in these cases to evaluate if targets of downregulated miRNAs are statistically enriched in upregulated genes, and, conversely, if targets of upregulated miRNAs are statistically enriched in downregulated genes. In this regard, to estimate the effects of differentially expressed miRNAs on their targets, MIRit can use two different one-sided association tests, namely:

Fisher’s exact test,
Boschloo’s exact test (default).

Both these tests consist in a statistical procedure that estimates the association between two dichotomous categorical variables. In our case, for each miRNA, we want to evaluate whether the proportion of targets within the differentially expressed genes significantly differs from the proportion of targets in non-differentially expressed genes. To do this, a 2x2 contingency table is built as shown in Table @ref(tab:contingency).

(#tab:contingency) The 2x2 contingency table that MIRit uses for one-sided association tests. This is the table that the `mirnaIntegration()` function creates to determine if differentially expressed genes are enriched in miRNA targets.
	Target genes	Non target genes	Row total
Differentially expressed	a	b	a + b
Non differentially expressed	c	d	c + d
Column total	a + c	b + d	a + b + c + d = n

Fisher’s exact test

When the contingency table has been defined, Fisher’s exact test p-value can be calculated through Equation @ref(eq:fisher).

Additionally, it is also possible to compute Fisher’s p-values with Lancaster’s mid-p adjustment, since it has been proven that it increases statistical power while retaining Type I error rates.

Boschloo’s exact test

The major drawback of the Fisher’s exact test is that it consists in a conditional test that requires the sum of both rows and columns of a contingency table to be fixed. Notably, this is not true for genomic data because it is likely that different datasets may lead to a different number of DEGs. Therefore, the default behavior in MIRit is to use a variant of Barnard’s exact test, named Boschloo’s exact test, that is suitable when group sizes of contingency tables are variable. Moreover, it is possible to demonstrate that Boschloo’s exact test is uniformly more powerful compared to Fisher’s one. However, keep in mind that Boschloo’s test is much more computational intensive compared to Fisher’s exact test, and it may require some time, even though parallel computing is employed.

Perform one-sideded association tests in MIRit

In MIRit, the mirnaIntegration() function automatically performs association tests for unpaired data when test = "auto". Moreover, the type of association test to use can be specified through the associationMethod parameter, which can be set to:

fisher, to perform a simple one-sided Fisher’s exact test;
fisher-midp, to perform a one-sided Fisher’s exact test with Lancaster’s mid-p correction; and
boschloo, to perform a one-sided Boschloo’s exact test (default option).

For example, we could use Fisher’s exact test with mid-p correction to evaluate the inverse association between miRNA and gene expression.

## perform a one-sided inverse association
exp.association <- mirnaIntegration(experiment,
                                    test = "association",
                                    associationMethod = "fisher-midp",
                                    pCutoff = 0.2,
                                    pAdjustment = "none")
#> As specified by the user, a association will be used.
#> Performing One-sided Fisher's exact test with Lancaster's mid-p correction...
#> A statistically significant association between 3 DE-miRNAs and 33 genes was found!

In the end, results can be accessed through the integration() function in the same way as we can do for correlation analyses.

Rotation gene-set tests for unpaired data

Lastly, for unpaired data, the effect of DE-miRNAs on the expression of target genes can be estimated through rotation gene-set tests. In this approach, we want to evaluate for each miRNA whether its target genes tend to be differentially expressed in the opposite direction. In particular, a fast approximation to rotation gene-set testing called fry, implemented in the limma package, can be used to statistically quantify the impact of miRNAs on expression changes of their targets.

To perform the integrative analysis through rotation gene-set tests, we must simply set test = "fry" when calling the mirnaIntegration() function.

## perform the integrative analysis through 'fry' method
exp.fry <- mirnaIntegration(experiment,
                            test = "fry",
                            pAdjustment = "none")
#> As specified by the user, a fry will be used.
#> Performing miRNA-gene integrative analysis using 'fry' method...
#> A statistically significant association between 3 DE-miRNAs and 31 genes was found!

Session info

#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
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#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] fstcore_0.9.18              MIRit_1.3.0                
#>  [3] MultiAssayExperiment_1.33.1 SummarizedExperiment_1.37.0
#>  [5] Biobase_2.67.0              GenomicRanges_1.59.1       
#>  [7] GenomeInfoDb_1.43.2         IRanges_2.41.2             
#>  [9] S4Vectors_0.45.2            BiocGenerics_0.53.3        
#> [11] generics_0.1.3              MatrixGenerics_1.19.0      
#> [13] matrixStats_1.4.1           BiocStyle_2.35.0           
#> 
#> loaded via a namespace (and not attached):
#>   [1] splines_4.4.2           genekitr_1.2.8          bitops_1.0-9           
#>   [4] ggplotify_0.1.2         urltools_1.7.3          filelock_1.0.3         
#>   [7] tibble_3.2.1            R.oo_1.27.0             polyclip_1.10-7        
#>  [10] triebeard_0.4.1         lifecycle_1.0.4         rstatix_0.7.2          
#>  [13] edgeR_4.5.1             lattice_0.22-6          MASS_7.3-61            
#>  [16] backports_1.5.0         magrittr_2.0.3          openxlsx_4.2.7.1       
#>  [19] limma_3.63.2            sass_0.4.9              rmarkdown_2.29         
#>  [22] jquerylib_0.1.4         yaml_2.3.10             ggtangle_0.0.5         
#>  [25] zip_2.3.1               ggvenn_0.1.10           cowplot_1.1.3          
#>  [28] DBI_1.2.3               buildtools_1.0.0        RColorBrewer_1.1-3     
#>  [31] abind_1.4-8             zlibbioc_1.52.0         quadprog_1.5-8         
#>  [34] purrr_1.0.2             R.utils_2.12.3          ggraph_2.2.1           
#>  [37] RCurl_1.98-1.16         yulab.utils_0.1.8       tweenr_2.0.3           
#>  [40] GenomeInfoDbData_1.2.13 enrichplot_1.27.3       ggrepel_0.9.6          
#>  [43] tidytree_0.4.6          maketools_1.3.1         codetools_0.2-20       
#>  [46] DelayedArray_0.33.3     ggforce_0.4.2           DOSE_4.1.0             
#>  [49] xml2_1.3.6              tidyselect_1.2.1        aplot_0.2.3            
#>  [52] UCSC.utils_1.3.0        farver_2.1.2            viridis_0.6.5          
#>  [55] BiocFileCache_2.15.0    jsonlite_1.8.9          fst_0.9.8              
#>  [58] tidygraph_1.3.1         Formula_1.2-5           tools_4.4.2            
#>  [61] progress_1.2.3          treeio_1.31.0           Rcpp_1.0.13-1          
#>  [64] glue_1.8.0              gridExtra_2.3           SparseArray_1.7.2      
#>  [67] BiocBaseUtils_1.9.0     xfun_0.49               qvalue_2.39.0          
#>  [70] dplyr_1.1.4             withr_3.0.2             BiocManager_1.30.25    
#>  [73] fastmap_1.2.0           fansi_1.0.6             digest_0.6.37          
#>  [76] R6_2.5.1                gridGraphics_0.5-1      colorspace_2.1-1       
#>  [79] GO.db_3.20.0            RSQLite_2.3.9           R.methodsS3_1.8.2      
#>  [82] utf8_1.2.4              tidyr_1.3.1             data.table_1.16.4      
#>  [85] graphlayouts_1.2.1      prettyunits_1.2.0       httr_1.4.7             
#>  [88] S4Arrays_1.7.1          pkgconfig_2.0.3         gtable_0.3.6           
#>  [91] blob_1.2.4              XVector_0.47.0          sys_3.4.3              
#>  [94] clusterProfiler_4.15.1  htmltools_0.5.8.1       carData_3.0-5          
#>  [97] geneset_0.2.7           fgsea_1.33.0            scales_1.3.0           
#> [100] png_0.1-8               ggfun_0.1.8             knitr_1.49             
#> [103] reshape2_1.4.4          nlme_3.1-166            curl_6.0.1             
#> [106] cachem_1.1.0            stringr_1.5.1           parallel_4.4.2         
#> [109] AnnotationDbi_1.69.0    pillar_1.9.0            grid_4.4.2             
#> [112] vctrs_0.6.5             ggpubr_0.6.0            car_3.1-3              
#> [115] dbplyr_2.5.0            evaluate_1.0.1          cli_3.6.3              
#> [118] locfit_1.5-9.10         compiler_4.4.2          rlang_1.1.4            
#> [121] crayon_1.5.3            ggsignif_0.6.4          labeling_0.4.3         
#> [124] plyr_1.8.9              fs_1.6.5                stringi_1.8.4          
#> [127] viridisLite_0.4.2       BiocParallel_1.41.0     munsell_0.5.1          
#> [130] Biostrings_2.75.3       lazyeval_0.2.2          GOSemSim_2.33.0        
#> [133] Matrix_1.7-1            europepmc_0.4.3         hms_1.1.3              
#> [136] patchwork_1.3.0         bit64_4.5.2             ggplot2_3.5.1          
#> [139] KEGGREST_1.47.0         statmod_1.5.0           igraph_2.1.2           
#> [142] broom_1.0.7             memoise_2.0.1           bslib_0.8.0            
#> [145] ggtree_3.15.0           fastmatch_1.1-4         bit_4.5.0.1            
#> [148] MonoPoly_0.3-10         ape_5.8-1               gson_0.1.0

References

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Riesco-Eizaguirre, Garcilaso, León Wert-Lamas, Javier Perales-Patón, Ana Sastre-Perona, Lara P. Fernández, and Pilar Santisteban. 2015. “The miR-146b-3p/PAX8/NIS Regulatory Circuit Modulates the Differentiation Phenotype and Function of Thyroid Cells During Carcinogenesis.” Cancer Research 75 (19): 4119–30. https://doi.org/10.1158/0008-5472.CAN-14-3547.

Subramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.

Tokar, Tomas, Chiara Pastrello, Andrea E M Rossos, Mark Abovsky, Anne-Christin Hauschild, Mike Tsay, Richard Lu, and Igor Jurisica. 2018. “mirDIP 4.1—Integrative Database of Human microRNA Target Predictions.” Nucleic Acids Research 46 (January): D360–70. https://doi.org/10.1093/nar/gkx1144.

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Integrate miRNA and gene expression data with MIRit