,
Maria Foti [fnd] | Title: | Integrate microRNA and gene expression to decipher pathway complexity |
|---|---|
| Description: | MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization. |
| Authors: | Jacopo Ronchi [aut, cre] ,
Maria Foti [fnd] |
| Maintainer: | Jacopo Ronchi <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1.0 |
| Built: | 2024-07-27 05:55:26 UTC |
| Source: | https://github.com/bioc/MIRit |
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.
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" )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" )
mirnaObj |
A |
mirnaDE |
A |
geneDE |
A |
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 |
mirna.pAdjustment |
The p-value correction method for miRNA multiple
testing. It must be one of: |
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 |
gene.pAdjustment |
The p-value correction method for gene multiple
testing. It must be one of: |
The following paragraphs briefly explain the formats needed for mirnaDE, geneDE, and differential expression parameters.
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.
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.
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.
Jacopo Ronchi, [email protected]
# 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 )# 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 )
This function accesses the pathways slot of a
FunctionalEnrichment object and returns
a list object with the augmented pathways that were considered by the
topologicalAnalysis() function to perform the integrative analysis.
augmentedPathways(object)augmentedPathways(object)
object |
An object of class
|
A list object with the miRNA-augmented biological pathways.
Jacopo Ronchi, [email protected]
# load the example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # extract the pathways ps <- augmentedPathways(obj)# load the example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # extract the pathways ps <- augmentedPathways(obj)
This function allows to remove unwanted batch effects from miRNA and gene expression matrices. In particular, this function fits a linear model to miRNA/gene expression levels, and then removes the variability caused by batch effects. Furthermore, a weighted surrogate variable analysis (WSVA) can also be included to remove the effects due to surrogate variables. If batch effects are present, it is crucial to remove them with this function before moving to correlation analysis.
batchCorrection( mirnaObj, assay, batch = NULL, batch2 = NULL, covariates = NULL, includeWsva = FALSE, n.sv = 1L, weight.by.sd = TRUE )batchCorrection( mirnaObj, assay, batch = NULL, batch2 = NULL, covariates = NULL, includeWsva = FALSE, n.sv = 1L, weight.by.sd = TRUE )
mirnaObj |
A |
assay |
The expression matrix to correct. It must be one of |
batch |
It must be the name of a variable present in the |
batch2 |
It must be the name of a variable present in the |
covariates |
Additional numeric covariates that we want to correct for.
It must be a |
includeWsva |
Logical, whether to correct for surrogate variables or not. Default is FALSE |
n.sv |
The number of surrogate variables to estimate |
weight.by.sd |
Logical, whether to specifically tune the surrogate variables to the more variable genes or not. Default is TRUE |
Batch effects consist in unwanted sources of technical variation that
confound expression variability and limit downstream analyses. Since the
reliability of biological conclusions of integrative miRNA-mRNA analyses
depends on the association between miRNA and gene expression levels, it is
pivotal to ensure that expression measurements are not affected by technical
variations. In this regard, if batch effects are noticed in the data, the
user should run this function before using the mirnaIntegration()
function to perform a correlation analysis.
Usually, given a MirnaExperiment object, the user
should specify:
the assay from which we want to remove batch effects (one between
genes and microRNA);
the batch variable, which is a variable that defines the different
batches;
the batch2 variable, which can be included to correct for a second
series of batches that have additive effects to those specified in batch;
the covariates variables, which allows correction for one or more
continuous numeric effects.
In particular, batch and batch2 could be provided as the names of
covariates included in the colData of a
MirnaExperiment object. Alternatively, they can
be character/factor objects that declare batch memberships.
Similarly, covariates can be supplied as a vector containing the names
of numeric variables listed in the colData of
MirnaExperiment objects, or they can be provided
as a simple matrix.
Additionally, the influence of unknown sources of technical variation can
be removed by including surrogate variables estimated through WSVA. To do
so, we can set includeWsva to TRUE, and then we can specify the number of
surrogate variables to use through the n.sv parameter. Further, the
surrogate variables can be tuned to the more variable genes by setting
weight.by.sd to TRUE.
Please note that we only recommend to remove batch effects directly from expression measurements prior to correlation analysis. This function can't be used to remove batch effects before differential expression analysis, because for that purpose, it is better to include batch variables in the linear model. In this way, we do not underestimate the residual degrees of freedom, so that the calculated standard errors, t-statistics and p-values are not overoptimistic.
A MirnaExperiment object containing batch
effect-corrected expression matrices.
To estimate surrogate variables and to remove batch effects from expression
data, MIRit uses the limma::wsva() and limma::removeBatchEffect()
functions, respectively.
Jacopo Ronchi, [email protected]
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.
# load example MirnaExperiment object obj <- loadExamples() # correct batch effects due to the patient from miRNA expression matrix obj <- batchCorrection(obj, "microRNA", batch = "patient")# load example MirnaExperiment object obj <- loadExamples() # correct batch effects due to the patient from miRNA expression matrix obj <- batchCorrection(obj, "microRNA", batch = "patient")
MirnaExperiment objectThe mirnaDE() and geneDE() are two accessor functions for the mirnaDE
and geneDE slots of MirnaExperiment class,
respectively. Thus, they can be used to explore the results of miRNA and
gene differential expression analysis stored in a
MirnaExperiment object.
mirnaDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE) geneDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)mirnaDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE) geneDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE)
object |
A |
onlySignificant |
Logical, if |
param |
Logical, whether to return the complete |
returnObject |
Logical, if |
A data.frame with miRNA/gene differential expression, or a list object
with the parameters used if param = TRUE.
mirnaDE(): Extract miRNA differential expression results
geneDE(): Extract gene differential expression results
Jacopo Ronchi, [email protected]
# load example MirnaExperiment object obj <- loadExamples() # access miRNA differential expression of a MirnaExperiment object sig <- mirnaDE(obj) all <- mirnaDE(obj, onlySignificant = FALSE) # access gene differential expression of a MirnaExperiment object sig <- geneDE(obj) all <- geneDE(obj, onlySignificant = FALSE)# load example MirnaExperiment object obj <- loadExamples() # access miRNA differential expression of a MirnaExperiment object sig <- mirnaDE(obj) all <- mirnaDE(obj, onlySignificant = FALSE) # access gene differential expression of a MirnaExperiment object sig <- geneDE(obj) all <- geneDE(obj, onlySignificant = FALSE)
performMirnaDE() and performGeneDE() are two functions provided by MIRit
to conduct miRNA and gene differential expression analysis, respectively.
In particular, these functions allow the user to compute differential
expression through different methods, namely edgeR (Quasi-Likelihood
framework), DESeq2, limma-voom and limma. Data deriving from NGS
experiments and microarray technology are all suitable for these functions.
For precise indications about how to use these functions, please refer to
the details section.
performMirnaDE( mirnaObj, group, contrast, design, method = "edgeR", logFC = 0, pCutoff = 0.05, pAdjustment = "fdr", filterByExpr.args = list(), calcNormFactors.args = list(), estimateDisp.args = list(robust = TRUE), glmQLFit.args = list(), glmQLFTest.args = list(), DESeq.args = list(), useVoomWithQualityWeights = TRUE, voom.args = list(), lmFit.args = list(), eBayes.args = list(), useArrayWeights = TRUE, useWsva = FALSE, wsva.args = list(), arrayWeights.args = list(), useDuplicateCorrelation = FALSE, correlationBlockVariable = NULL, duplicateCorrelation.args = list() ) performGeneDE( mirnaObj, group, contrast, design, method = "edgeR", logFC = 0, pCutoff = 0.05, pAdjustment = "fdr", filterByExpr.args = list(), calcNormFactors.args = list(), estimateDisp.args = list(robust = TRUE), glmQLFit.args = list(), glmQLFTest.args = list(), DESeq.args = list(), useVoomWithQualityWeights = TRUE, voom.args = list(), lmFit.args = list(), eBayes.args = list(), useArrayWeights = TRUE, useWsva = FALSE, wsva.args = list(), arrayWeights.args = list(), useDuplicateCorrelation = FALSE, correlationBlockVariable = NULL, duplicateCorrelation.args = list() )performMirnaDE( mirnaObj, group, contrast, design, method = "edgeR", logFC = 0, pCutoff = 0.05, pAdjustment = "fdr", filterByExpr.args = list(), calcNormFactors.args = list(), estimateDisp.args = list(robust = TRUE), glmQLFit.args = list(), glmQLFTest.args = list(), DESeq.args = list(), useVoomWithQualityWeights = TRUE, voom.args = list(), lmFit.args = list(), eBayes.args = list(), useArrayWeights = TRUE, useWsva = FALSE, wsva.args = list(), arrayWeights.args = list(), useDuplicateCorrelation = FALSE, correlationBlockVariable = NULL, duplicateCorrelation.args = list() ) performGeneDE( mirnaObj, group, contrast, design, method = "edgeR", logFC = 0, pCutoff = 0.05, pAdjustment = "fdr", filterByExpr.args = list(), calcNormFactors.args = list(), estimateDisp.args = list(robust = TRUE), glmQLFit.args = list(), glmQLFTest.args = list(), DESeq.args = list(), useVoomWithQualityWeights = TRUE, voom.args = list(), lmFit.args = list(), eBayes.args = list(), useArrayWeights = TRUE, useWsva = FALSE, wsva.args = list(), arrayWeights.args = list(), useDuplicateCorrelation = FALSE, correlationBlockVariable = NULL, duplicateCorrelation.args = list() )
mirnaObj |
A |
group |
The variable of interest for differential expression analysis.
It must be the column name of a variable present in the metadata (colData)
of a |
contrast |
A |
design |
An R |
method |
The statistical package used to compute differential
expression. For NGS experiments, it must be one of |
logFC |
The minimum log2 fold change required to consider a gene as differentially expressed. Optional, default is 0 |
pCutoff |
The adjusted p-value cutoff to use for statistical
significance. The default value is |
pAdjustment |
The p-value correction method for multiple testing. It
must be one of: |
filterByExpr.args |
A |
calcNormFactors.args |
A |
estimateDisp.args |
A |
glmQLFit.args |
A |
glmQLFTest.args |
A |
DESeq.args |
A |
useVoomWithQualityWeights |
Logical, whether to use the
|
voom.args |
A |
lmFit.args |
A |
eBayes.args |
A |
useArrayWeights |
Logical, whether to use the |
useWsva |
Logical, whether to use the |
wsva.args |
A |
arrayWeights.args |
A |
useDuplicateCorrelation |
Logical, whether to use the
|
correlationBlockVariable |
It is the blocking variable to use for
|
duplicateCorrelation.args |
A |
When performing differential expression for NGS experiments, count matrices
are detected and method parameter must be one of edgeR, DESeq2,
and voom. On the other hand, when dealing with microarray studies, only
limma can be used.
To calculate differential expression, MIRit must be informed about the
variable of interest and the desired contrast. In particular, the group
parameter must be the name of a variable present in the metadata (colData)
of a MirnaExperiment object, which specifies the
variable used to compute differential expression analysis, between the groups
indicated in contrast. Specifically, contrast must be a character vector
that defines the levels to compare separated by a dash. For example, if we
have a variable named 'condition', with two levels, namely 'disease' and
'healthy', we can identify differentially expressed genes in 'disease'
samples compared to 'healthy' subjects by specifying: group = 'condition'
and contrast = 'disease-healthy'. Furthermore, the user needs to specify
the model to fit expression values. To do so, the user has to state the
model formula in the design parameter. Please note that for a correct
inner working of these functions, the group variable of interest must be
the first variable in model formula. Moreover, the user can include in the
design any other sources of variation by specifying covariates that will be
taken into account. For instance, if we want to compare 'disease' subjects
against 'healthy' individuals, without the influence of sex differences,
we may specify design = ~ condition + sex, where 'sex' is also a
variable present in the metadata (colData) of mirnaObj.
Notably, for all the methods available, the user can supply additional
arguments to the functions implemented in edgeR, DESeq2 and limma.
Therefore, the user has finer control over how the differential expression
analysis is performed. In this regard, for microarray studies, the user
may opt to include weighted surrogate variable analysis (WSVA) to correct
for unknown sources of variation (useWsva = TRUE). Moreover, for
microarray data, the arrayWeights() function in limma can be used to
assess differential expression with respect to array qualities. Also, the
duplicateCorrelation() function in limma may be included in the pipeline
in order to block the effect of correlated samples. To do this, the user
must set useDuplicateCorrelation = TRUE, and must specify the blocking
variable through the correlationBlockVariable parameter. Additionally,
when using limma-voom, the user may estimate voom transformation with or
without quality weights (by specifying useVoomWithQualityWeights = TRUE).
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.
performMirnaDE(): Perform differential expression analysis for miRNAs
performGeneDE(): Perform differential expression analysis for genes
Jacopo Ronchi, [email protected]
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.
Law, CW, Chen, Y, Shi, W, and Smyth, GK (2014). "Voom: precision weights unlock linear model analysis tools for RNA-seq read counts". Genome Biology 15, R29
Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26(1), 139-140. doi:10.1093/bioinformatics/btp616.
Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi:10.1186/s13059-014-0550-8.
# load example MirnaExperiment object obj <- loadExamples() # perform miRNA DE with edgeR obj <- performMirnaDE(obj, group = "disease", contrast = "PTC-NTH", design = ~ 0 + disease + patient, method = "edgeR" ) # perform miRNA DE with DESeq2 obj <- performMirnaDE(obj, group = "disease", contrast = "PTC-NTH", design = ~ 0 + disease + patient, method = "DESeq2" ) # perform miRNA DE with limma-voom obj <- performMirnaDE(obj, group = "disease", contrast = "PTC-NTH", design = ~ 0 + disease + patient, method = "voom" )# load example MirnaExperiment object obj <- loadExamples() # perform miRNA DE with edgeR obj <- performMirnaDE(obj, group = "disease", contrast = "PTC-NTH", design = ~ 0 + disease + patient, method = "edgeR" ) # perform miRNA DE with DESeq2 obj <- performMirnaDE(obj, group = "disease", contrast = "PTC-NTH", design = ~ 0 + disease + patient, method = "DESeq2" ) # perform miRNA DE with limma-voom obj <- performMirnaDE(obj, group = "disease", contrast = "PTC-NTH", design = ~ 0 + disease + patient, method = "voom" )
This function accesses the features slot of a
FunctionalEnrichment object and returns
a character vector with the names of the features considered in GSEA in
the same order as the ranking metric.
enrichedFeatures(object)enrichedFeatures(object)
object |
An object of class
|
A character vector with the names of the genes ordered based on the
ranking metric.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # extract the ranking metric rmet <- enrichmentMetric(obj) ## extract the corresponding names rnames <- enrichedFeatures(obj)# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # extract the ranking metric rmet <- enrichmentMetric(obj) ## extract the corresponding names rnames <- enrichedFeatures(obj)
This function allows to investigate the biological functions and pathways that result dysregulated across biological conditions. In particular, different enrichment approaches can be used, including over-representation analysis (ORA), gene-set enrichment analysis (GSEA), and Correlation Adjusted MEan RAnk gene set test (CAMERA). Moreover, for all these analyses, the enrichment can be carried out using different databases, namely Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (DO), Network of Cancer Genes (NCG), DisGeNET, and COVID19. For exhaustive information on how to use this function, please refer to the details section.
enrichGenes( mirnaObj, method = "GSEA", database = "GO", category = NULL, organism = "Homo sapiens", pCutoff = 0.05, pAdjustment = "fdr", minSize = 10L, maxSize = 500L, rankMetric = "signed.pval", eps = 1e-50 )enrichGenes( mirnaObj, method = "GSEA", database = "GO", category = NULL, organism = "Homo sapiens", pCutoff = 0.05, pAdjustment = "fdr", minSize = 10L, maxSize = 500L, rankMetric = "signed.pval", eps = 1e-50 )
mirnaObj |
A |
method |
The functional enrichment analysis to perform. It must be one
of |
database |
The name of the database used for the enrichment analysis.
It must be one of: |
category |
The desired subcategory of gene sets present in |
organism |
The name of the organism under consideration. The different
databases have different supported organisms. To see the list of supported
organisms for a given database, use the |
pCutoff |
The adjusted p-value cutoff to use for statistical
significance. The default value is |
pAdjustment |
The p-value correction method for multiple testing. It
must be one of: |
minSize |
The minimum size for a gene set. All gene sets containing less than this number of genes will not be considered. Default is 10 |
maxSize |
The maximum size for a gene set. All gene sets containing more than this number of genes will not be considered. Default is 500 |
rankMetric |
The ranking statistic used to order genes before performing
GSEA. It must be one of |
eps |
The lower boundary for p-value calculation (default is 1e-50). To compute exact p-values, this parameter can be set to 0, even though the analysis will be slower |
The method used for functional enrichment analysis will drastically
influence the biological results, and thus, it must be carefully chosen.
ORA (Boyle et al., 2004) takes differentially expressed genes (separately
considering upregulated and downregulated features) and uses the
hypergeometric test to infer the biological processes that are regulated by
these genes more than would be expected by chance. The downside of this
approach is that we only consider genes that passed a pre-defined threshold,
thus losing all the slight changes in gene expression that may have important
biological consequences.
To address this limit, GSEA was introduced (Subramanian, 2005). This
analysis starts by ranking genes according to a specific criterion, and then
uses a running statistic that is able to identify even slight but coordinated
expression changes of genes belonging to a specific pathway. Therefore,
GSEA is the default method used in MIRit to perform the functional
enrichment analysis of genes.
Moreover, in addition to ORA and GSEA, this function allows to perform the
enrichment analysis through CAMERA (Wu and Smyth, 2012), which is another
competitive test used for functional enrichment of genes. The main advantage
of this method is that it adjusts the gene set test statistic according to
inter-gene correlations. This is particularly interesting since it was
demonstrated that inter-gene correlations may affect the reliability of
functional enrichment analyses.
Regarding gene sets, multiple databases can be used to investigate the
consequences of gene expression alterations. However, different databases
also includes several subcategories with different annotations. To
specifically query desired categories, the category parameter is used. As
a reference, here are listed the available categories for the different
databases supported:
Gene Ontology (GO):
bp, for GO - Biological Processes;
mf, for GO - Molecular Function;
cc, for GO - Cellular Component;
Kyoto Encyclopedia of Genes and Genomes (KEGG):
pathway, for KEGG biological pathways;
module, for KEGG reaction modules;
enzyme, for KEGG enzyme nomenclature;
disease, for KEGG diseases (only Homo sapiens supported);
drug, for KEGG drug targets (only Homo sapiens supported);
network, for KEGG disease/drug perturbation netowrks (only Homo
sapiens supported);
MsigDB:
H, for MsigDB hallmark genes of specific biological states/processes;
C1, for gene sets of human chromosome cytogenetic bands;
C2-CGP, for expression signatures of genetic and chemical
perturbations;
C2-CP-BIOCARTA, for canonical pathways gene sets derived from the
BioCarta pathway database;
C2-CP-KEGG, for canonical pathways gene sets derived from the
KEGG pathway database;
C2-CP-PID, for canonical pathways gene sets derived from the
PID pathway database;
C2-CP-REACTOME, for canonical pathways gene sets derived from the
Reactome pathway database;
C2-CP-WIKIPATHWAYS, for canonical pathways gene sets derived from the
WikiPathways database;
C3-MIR-MIRDB, for gene sets containing high-confidence gene-level
predictions of human miRNA targets as catalogued by miRDB v6.0 algorithm;
C3-MIR-MIR_Legacy, for older gene sets that contain genes sharing
putative target sites of human mature miRNA in their 3'-UTRs;
C3-TFT-GTRD, for genes that share GTRD predicted transcription factor
binding sites in the region -1000,+100 bp around the TSS for the
indicated transcription factor;
C3-TFT-TFT_Legacy, for older gene sets that share upstream
cis-regulatory motifs which can function as potential transcription factor
binding sites;
C4-CGN, for gene sets defined by expression neighborhoods centered
on 380 cancer-associated genes;
C4-CM, for cancer modules as defined by Segal et al. 2004;
C5-GO-BP, for GO - biological process ontology;
C5-GO-CC, for GO - cellular component ontology;
C5-GO-MF, for GO - molecular function ontology;
C5-HPO, for Human Phenotype ontology (HPO);
C6, for gene sets that represent signatures of cellular pathways which
are often dis-regulated in cancer;
C7-IMMUNESIGDB, for manually curated gene sets representing chemical
and genetic perturbations of the immune system;
C7-VAX, for gene sets deriving from the Human Immunology Project
Consortium (HIPC) describing human transcriptomic immune responses to
vaccinations;
C8, for gene sets that contain curated cluster markers for cell types;
WikiPathways;
Reactome;
Enrichr:
All avaliable gene sets can be listed through
geneset::enrichr_metadata
Disease Ontology (DO);
Network of Cancer Genes (NCG):
v6, for the sixth version;
v7, for the seventh version;
DisGeNET;
COVID-19.
For each database, different organisms are supported. To check the supported
organisms for a given database, MIRit provides the supportedOrganisms()
function.
The ranking statistic used to order genes before conducting GSEA is able
to influence the biological interpretation of functional enrichment
results. Several metrics have been used in scientific literature. MIRit
implements the possibility of using signed.pval, logFC, and log.pval.
In particular, the simplest option is to rank genes according to their
logFC value. However, this procedure is biased by higher variance for
lowly abundant genes.
Therefore, we recommend to use the signed.pval metric, which consists in
the p-value of a gene multiplied for the sign of its logFC, i.e.
sign(logFC) * p-value. Alternatively, log,pval metric, which consist in
the product of logFC and p-value, i.e. logFC * p-value can also be used.
For method GSEA and CAMERA, this function produces an object of class
FunctionalEnrichment containing enrichment
results. Instead, when ORA is used, this function returns a list object
with two elements, namely 'upregulated' and 'downregulated',
each containing a FunctionalEnrichment
object storing enrichment results of upregulated and downregulated genes,
respectively.
To access results of FunctionalEnrichment
objects, the user can use the enrichmentResults() function. Additionally,
MIRit provides several functions to graphically represent enrichment
analyses, including enrichmentBarplot(), enrichmentDotplot(),
gseaPlot(), and gseaRidgeplot().
To download gene sets from the above mentioned databases, MIRit uses the
geneset R package. Moreover, to perform ORA and GSEA, MIRit implements the
fgsea algorithm, whereas for CAMERA, the limma package is used.
Jacopo Ronchi, [email protected]
Liu, Y., Li, G. Empowering biologists to decode omics data: the Genekitr R package and web server. BMC Bioinformatics 24, 214 (2023). https://doi.org/10.1186/s12859-023-05342-9.
Korotkevich G, Sukhov V, Sergushichev A (2019). “Fast gene set enrichment analysis.” bioRxiv. doi:10.1101/060012, http://biorxiv.org/content/early/2016/06/20/060012.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.
Wu D, Smyth GK. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 2012 Sep 1;40(17):e133. doi: 10.1093/nar/gks461. Epub 2012 May 25. PMID: 22638577; PMCID: PMC3458527.
# load example MirnaExperiment object obj <- loadExamples() # perform ORA with GO de_enr <- enrichGenes(obj, method = "ORA", database = "GO")# load example MirnaExperiment object obj <- loadExamples() # perform ORA with GO de_enr <- enrichGenes(obj, method = "ORA", database = "GO")
This function produces a barplot to show the results of functional
enrichment analyses carried out through over-representation analysis (ORA),
gene set enrichment analysis (GSEA), and competitive gene set test accounting
for inter-gene correlation (CAMERA). In particular, this function can take
as input enrichment results generated by the enrichGenes() function.
enrichmentBarplot( enrichment, showTerms = 10, showTermsParam = "ratio", splitDir = TRUE, title = NULL )enrichmentBarplot( enrichment, showTerms = 10, showTermsParam = "ratio", splitDir = TRUE, title = NULL )
enrichment |
An object of class
|
showTerms |
It is the number of terms to be shown, based on the
order determined by the parameter |
showTermsParam |
The order in which the top terms are selected as
specified by the |
splitDir |
Logical, if |
title |
The title of the plot. Default is |
When producing a barplot with this function, significant pathways are ordered on the x-axis on the basis of the ratio between the number of overlapping genes in that set, and the total number of genes in the set. Moreover, the color scale of dots is relative to the adjusted p-values of each category.
A ggplot graph with a barplot of enrichment results.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # plot results enrichmentBarplot(obj)# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # plot results enrichmentBarplot(obj)
This function accesses the database slot of a
FunctionalEnrichment object and returns
a the name of the database used by the enrichGenes() function to perform
the enrichment analysis.
enrichmentDatabase(object)enrichmentDatabase(object)
object |
An object of class
|
A character containing the name of the database, such as KEGG.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # see the database enrichmentDatabase(obj)# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # see the database enrichmentDatabase(obj)
This function produces a dotplot to show the results of functional
enrichment analyses carried out through over-representation analysis (ORA),
gene set enrichment analysis (GSEA), and competitive gene set test accounting
for inter-gene correlation (CAMERA). In particular, this function can take
as input enrichment results generated by the enrichGenes() function.
enrichmentDotplot( enrichment, showTerms = 10, showTermsParam = "ratio", splitDir = TRUE, title = NULL )enrichmentDotplot( enrichment, showTerms = 10, showTermsParam = "ratio", splitDir = TRUE, title = NULL )
enrichment |
An object of class
|
showTerms |
It is the number of terms to be shown, based on the
order determined by the parameter |
showTermsParam |
The order in which the top terms are selected as
specified by the |
splitDir |
Logical, if |
title |
The title of the plot. Default is |
When producing a dotplot with this function, significant pathways are ordered on the x-axis on the basis of the ratio between the number of overlapping genes in that set, and the total number of genes in the set. Moreover, the size of each dot is proportional to the number of overlapping features. Finally, the color scale of dots is relative to the adjusted p-values of each category.
A ggplot graph with a dotplot of enrichment results.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # plot results enrichmentDotplot(obj)# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # plot results enrichmentDotplot(obj)
This function accesses the method slot of a
FunctionalEnrichment object and returns
a the name of the enrichment strategy used by the enrichGenes() function
to perform the enrichment analysis.
enrichmentMethod(object)enrichmentMethod(object)
object |
An object of class
|
A character containing the enrichment method, such as GSEA.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # see the method enrichmentMethod(obj)# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # see the method enrichmentMethod(obj)
This function accesses the statistic slot of a
FunctionalEnrichment object and returns
a numeric vector with the metric used to rank genes in GSEA.
enrichmentMetric(object)enrichmentMetric(object)
object |
An object of class
|
A numeric vector containing the ranking metric.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # extract the ranking metric rmet <- enrichmentMetric(obj)# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # extract the ranking metric rmet <- enrichmentMetric(obj)
This function accesses the data slot of a
FunctionalEnrichment object and returns
a data.frame with enrichment results.
enrichmentResults(object)enrichmentResults(object)
object |
An object of class
|
A data.frame object containing the results of functional enrichment
analyses, as returned by the enrichGenes() function.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # extract results de_df <- enrichmentResults(obj)# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # extract results de_df <- enrichmentResults(obj)
This function allows to perform an over-representation analysis (ORA) of integrated miRNA targets in order to explore the biological effects of targets that are statistically associated/correlated with DE-miRNAs. The enrichment analysis can be performed using different databases, namely Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (DO), Network of Cancer Genes (NCG), DisGeNET, and COVID19.
enrichTargets( mirnaObj, database = "GO", category = NULL, organism = "Homo sapiens", pCutoff = 0.05, pAdjustment = "fdr", minSize = 10L, maxSize = 500L )enrichTargets( mirnaObj, database = "GO", category = NULL, organism = "Homo sapiens", pCutoff = 0.05, pAdjustment = "fdr", minSize = 10L, maxSize = 500L )
mirnaObj |
A |
database |
The name of the database used for the enrichment analysis.
It must be one of: |
category |
The desired subcategory of gene sets present in |
organism |
The name of the organism under consideration. The different
databases have different supported organisms. To see the list of supported
organisms for a given database, use the |
pCutoff |
The adjusted p-value cutoff to use for statistical
significance. The default value is |
pAdjustment |
The p-value correction method for multiple testing. It
must be one of: |
minSize |
The minimum size for a gene set. All gene sets containing less than this number of genes will not be considered. Default is 10 |
maxSize |
The maximum size for a gene set. All gene sets containing more than this number of genes will not be considered. Default is 500 |
For each database, different organisms are supported. To check the supported
organisms for a given database, MIRit provides the supportedOrganisms()
function.
Moreover, since different database support multiple subcategories, the
category parameter can be set to specify the desired resource. For
specific information regarding the available categories for the different
databases, check the details section of the enrichGenes() documentation.
This function produces a list object with two elements, namely
'upregulated' and 'downregulated', each containing a
FunctionalEnrichment
object storing enrichment results of upregulated and downregulated target
genes, respectively.
To access results of FunctionalEnrichment
objects, the user can use the enrichmentResults() function. Additionally,
MIRit provides several functions to graphically represent enrichment
analyses, including enrichmentBarplot(), and enrichmentDotplot().
To download gene sets from the above mentioned databases, MIRit uses the
geneset R package. Moreover, to perform ORA, MIRit implements the
fgsea package in Bioconductor.
Jacopo Ronchi, [email protected]
Liu, Y., Li, G. Empowering biologists to decode omics data: the Genekitr R package and web server. BMC Bioinformatics 24, 214 (2023). https://doi.org/10.1186/s12859-023-05342-9.
Korotkevich G, Sukhov V, Sergushichev A (2019). “Fast gene set enrichment analysis.” bioRxiv. doi:10.1101/060012, http://biorxiv.org/content/early/2016/06/20/060012.
# load example MirnaExperiment object obj <- loadExamples() # perform enrichment analysis of integrated targets with DO targets_enrichment <- enrichTargets(obj, database = "DO")# load example MirnaExperiment object obj <- loadExamples() # perform enrichment analysis of integrated targets with DO targets_enrichment <- enrichTargets(obj, database = "DO")
This function allows to identify disease-associated genomic variants
affecting differentially expressed miRNA genes or their host genes.
To do so, this function uses gwasrapidd to retrieve SNPs-disease
associations, and then retains only SNPs that affect DE-miRNA genes or their
relative host genes (for intronic miRNAs).
findMirnaSNPs(mirnaObj, diseaseEFO)findMirnaSNPs(mirnaObj, diseaseEFO)
mirnaObj |
A |
diseaseEFO |
The EFO identifier of a disease of interest. This can be
identified with the |
SNPs occurring within miRNAs may have important effects on the biological function of these transcripts. Indeed, a SNP present within a miRNA gene might alter its expression or the spectrum of miRNA targets.
To retrieve disease-SNPs, this function uses gwasrapidd package, which
directly queries the NHGRI-EBI Catalog of published genome-wide association
studies. After running this function, the user can use the mirVariantPlot()
function to produce a trackplot for visualizing the genomic location of
SNPs within miRNA genes.
A data.frame containing details about disease-SNPs and the associated
differentially expressed miRNAs:
variant contains SNP identifiers;
gene defines the gene affected by a disease-SNP (it may be the miRNA
gene itself or the host gene of an intronic miRNA);
miRNA.gene specifies the DE-miRNA gene present;
miRNA.precursor specifies the name of the miRNA precursor affected
by disease-SNPs;
chr indicates the chromosome of SNPs;
position shows the SNP position;
allele displays possible alleles for this SNPs;
distance specifies the distance between SNPs and miRNAs;
is_upstream indicates whether SNP is upstream of miRNA gene;
is_downstream indicates whether SNP is downstream of miRNA gene;
mirnaStrand shows the strand;
mirnaStartPosition displays the start position of DE-miRNA gene;
mirnaEndPosition displays the end position of DE-miRNA gene.
To retrieve disease-associated SNPs, this function makes use of the
gwasrapidd package.
Jacopo Ronchi, [email protected]
Ramiro Magno, Ana-Teresa Maia, gwasrapidd: an R package to query, download and wrangle GWAS catalog data, Bioinformatics, Volume 36, Issue 2, January 2020, Pages 649–650, https://doi.org/10.1093/bioinformatics/btz605
# load example MirnaExperiment object obj <- loadExamples() # search disease searchDisease("response to antidepressant") disId <- "response to antidepressant" # retrieve associated SNPs association <- findMirnaSNPs(obj, disId)# load example MirnaExperiment object obj <- loadExamples() # search disease searchDisease("response to antidepressant") disId <- "response to antidepressant" # retrieve associated SNPs association <- findMirnaSNPs(obj, disId)
FunctionalEnrichment classThis class introduces the possibility to store the results of functional
enrichment analyses such as over-representation analysis (ORA), gene set
enrichment analysis (GSEA), and competitive gene set test accounting for
inter-gene correlation (CAMERA). The different slots contained in this class
are used to store enrichment results generated through the enrichGenes()
function.
## S4 method for signature 'FunctionalEnrichment' enrichmentResults(object) ## S4 method for signature 'FunctionalEnrichment' enrichmentDatabase(object) ## S4 method for signature 'FunctionalEnrichment' enrichmentMethod(object) ## S4 method for signature 'FunctionalEnrichment' geneSet(object) ## S4 method for signature 'FunctionalEnrichment' enrichmentMetric(object) ## S4 method for signature 'FunctionalEnrichment' enrichedFeatures(object) ## S4 method for signature 'FunctionalEnrichment' show(object)## S4 method for signature 'FunctionalEnrichment' enrichmentResults(object) ## S4 method for signature 'FunctionalEnrichment' enrichmentDatabase(object) ## S4 method for signature 'FunctionalEnrichment' enrichmentMethod(object) ## S4 method for signature 'FunctionalEnrichment' geneSet(object) ## S4 method for signature 'FunctionalEnrichment' enrichmentMetric(object) ## S4 method for signature 'FunctionalEnrichment' enrichedFeatures(object) ## S4 method for signature 'FunctionalEnrichment' show(object)
object |
An object of class
|
the enrichmentResults() function returns a data.frame object
with the results of the functional enrichment analysis.
the enrichmentDatabase() function returns a character that
specifies the database that provided the gene-set collection.
the enrichmentMethod() function returns a character with the
enrichment method used for the analysis.
the geneSet() function returns a list with the
gene-set collections retrieved.
the enrichmentMetric() function returns a numeric object with
ranked gene statistic used for GSEA.
the enrichmentMetric() function returns a character object with
the names of the ranked features used for GSEA.
enrichmentResults(FunctionalEnrichment): Access the data slot to take a
closer look at all the enriched terms of an enrichment analysis
enrichmentDatabase(FunctionalEnrichment): See the database used for the
functional enrichment
enrichmentMethod(FunctionalEnrichment): Visualize the approach used for the
functional enrichment analysis
geneSet(FunctionalEnrichment): Access the geneSet slot to see
the collection of gene sets used for GSEA
enrichmentMetric(FunctionalEnrichment): View the ranking metric used for GSEA
enrichedFeatures(FunctionalEnrichment): View the names of the pre-ranked
features used for GSEA
show(FunctionalEnrichment): Show method for objects of
class FunctionalEnrichment
dataA data.frame object holding the output of enrichment analysis
methodThe method used to perform functional enrichment analysis
(e.g. Gene Set Enrichment Analysis (GSEA))
organismThe name of the organism under consideration (e.g.
Homo sapiens)
databaseThe name of the database used for the enrichment analysis
(e.g. KEGG)
pCutoffA numeric value defining the threshold used for
statistical significance in the enrichment analysis (e.g. 0.05)
pAdjustmentA character indicating the method used to correct
p-values for multiple testing (e.g. fdr)
featuresA character vector containing the list of features used for
the enrichment
statisticA numeric vector containing the statistic used to run
GSEA. This parameter is empty for ORA and CAMERA
universeThe background universe of features. Typically, this is equal to the complete list of features assayed. This slot is NULL for GSEA
geneSetThe gene set used for the functional enrichment analysis. It
is a list object where each element contains the list of genes belonging
to a specific pathway.
Jacopo Ronchi, [email protected]
This dataset contains a gene expression matrix resulting from an RNA-Seq analysis of thyroid cancer. Specifically, these data originate from Riesco-Eizaguirre et al (2015), where they sequenced 8 papillary thyroid carcinomas (PTC) together with paired samples of normal thyroid tissue. The same thing was done for microRNAs in order to investigate the effects on target genes. Data included in this package have been obtained through the Gene Expression Omnibus (GEO accession: GSE63511).
data(geneCounts)data(geneCounts)
geneCountsA matrix object containing samples as columns and genes as rows.
A matrix object with 23710 rows and 16 columns.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63511
Garcilaso Riesco-Eizaguirre et al., “The MiR-146b-3p/PAX8/NIS Regulatory Circuit Modulates the Differentiation Phenotype and Function of Thyroid Cells during Carcinogenesis,” Cancer Research 75, no. 19 (September 30, 2015): 4119–30, https://doi.org/10.1158/0008-5472.CAN-14-3547.
This function accesses the geneSet slot of a
FunctionalEnrichment object and returns
a list with the collection of genes used for the enrichment.
geneSet(object)geneSet(object)
object |
An object of class
|
A list containing the gene-sets.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # extract the gene-sets gs <- geneSet(obj)# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # extract the gene-sets gs <- geneSet(obj)
This function returns the biomedical evidence that supports the association between a particular SNP and a phenotypic trait.
getEvidence(variant, diseaseEFO)getEvidence(variant, diseaseEFO)
variant |
The SNP ID of a particular variant of interest (e.g. 'rs394581') |
diseaseEFO |
The EFO identifier of a disease of interest. This can be
identified with the function |
A tbl_df dataframe containing information about literature evidences for a
disease-SNP association.
To retrieve evidences of disease-SNP association, this function makes use of
the gwasrapidd package.
Jacopo Ronchi, [email protected]
Ramiro Magno, Ana-Teresa Maia, gwasrapidd: an R package to query, download and wrangle GWAS catalog data, Bioinformatics, Volume 36, Issue 2, January 2020, Pages 649–650, https://doi.org/10.1093/bioinformatics/btz605.
# searchDisease("Alzheimer disease") evidence <- getEvidence("rs2075650", diseaseEFO = "Alzheimer disease")# searchDisease("Alzheimer disease") evidence <- getEvidence("rs2075650", diseaseEFO = "Alzheimer disease")
This function allows to obtain human miRNA-target interactions using two databases, namely miRTarBase v9, which contains experimentally validated interactions, and 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, as demonstrated by Tokar et al. 2018, mirDIP reports more accurate predictions compared to those of individual tools. However, for species other than Homo sapiens only validated interactions are returned, since mirDIP is only available for human miRNAs.
getTargets( mirnaObj, organism = "Homo sapiens", score = "High", includeValidated = TRUE, evidence = "all" )getTargets( mirnaObj, organism = "Homo sapiens", score = "High", includeValidated = TRUE, evidence = "all" )
mirnaObj |
A |
organism |
The specie for which you are retrieving miRNA target genes.
Available species are: |
score |
The minimum mirDIP confidence score. It must be one of
|
includeValidated |
Logical, whether to include validated interactions from miRTarBase or not. Default is TRUE in order to retrieve both predicted and validated targets. Note that for species other than Homo sapines only validated interactions are considered. |
evidence |
The support evidence required for miRTarBase validated
interactions. The possible options are |
To define miRNA target genes, we can consider both experimentally validated and computationally predicted interactions. Interactions of the former type are generally preferred, since they are corroborated by biomolecular experiments. However, they are often not sufficient, thus making it necessary to consider the predicted interactions as well. The downside of miRNA target prediction algorithms is the scarce extend of overlap existing between the 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 including mirDIP started using other statistics to rank miRNA-target predictions obtained by multiple algorithms. For additional information on mirDIP database and its ranking metric check Tokar et al. 2018 and Hauschild et al. 2023.
This function defines miRNA targets by considering both validated interactions present in miRTarBase (version 9), and predicted interactions identified by mirDIP. Please note that for species other than Homo sapiens, only miRTarBase interactions are available. Further, it is possible to include all validated miRNA-target interactions, or limit the retrieval to interactions with strong supporting evidence.
A MirnaExperiment object containing miRNA targets
stored in the targets slot. Results can be accessed with the
mirnaTargets() function.
To access mirDIP database at https://ophid.utoronto.ca/mirDIP/, this function directly use mirDIP API through R.
Jacopo Ronchi, [email protected]
Tomas Tokar and others, mirDIP 4.1—integrative database of human microRNA target predictions, Nucleic Acids Research, Volume 46, Issue D1, 4 January 2018, Pages D360–D370, https://doi.org/10.1093/nar/gkx1144.
Anne-Christin Hauschild and others, MirDIP 5.2: tissue context annotation and novel microRNA curation, Nucleic Acids Research, Volume 51, Issue D1, 6 January 2023, Pages D217–D225, https://doi.org/10.1093/nar/gkac1070.
Hsi-Yuan Huang and others, miRTarBase update 2022: an informative resource for experimentally validated miRNA–target interactions, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D222–D230, https://doi.org/10.1093/nar/gkab1079.
# load example MirnaExperiment object obj <- loadExamples() # retrieve targets obj <- getTargets(mirnaObj = obj) # access targets tg <- mirnaTargets(obj)# load example MirnaExperiment object obj <- loadExamples() # retrieve targets obj <- getTargets(mirnaObj = obj) # access targets tg <- mirnaTargets(obj)
This function creates a classic enrichment plot to show the results of
gene set enrichment analyses (GSEA). In particular, this function takes as
input GSEA results originating from the enrichGenes() function, and
returns a ggplot2 object with GSEA plot. In this kind of plots, the
running enrichment score (ES) for a given pathway is shown on the y-axis,
whereas gene positions in the ranked list are reported on the x-axis.
gseaPlot( enrichment, pathway, showTitle = TRUE, rankingMetric = FALSE, lineColor = "green", lineSize = 1, vlineColor = "red", vlineSize = 0.6 )gseaPlot( enrichment, pathway, showTitle = TRUE, rankingMetric = FALSE, lineColor = "green", lineSize = 1, vlineColor = "red", vlineSize = 0.6 )
enrichment |
An object of class
|
pathway |
It must be the name of a significantly enriched term/pathway for which we want to produce a GSEA plot (e.g. 'Thyroid hormone synthesis') |
showTitle |
Logical, whether to add the name of the pathway/term as plot title. Default is TRUE |
rankingMetric |
Logical, whether to show the variations of the ranking metric below the plot. Default is FALSE |
lineColor |
It must be an R color name that specifies the color of
the running score line. Default is |
lineSize |
The line width of the running score line. Default is |
vlineColor |
It must be an R color name that specifies the color of
the vertical line indicating the enrichment score (ES). Default is |
vlineSize |
The line width of the vertical line indicating the
enrichment score (ES). Default is |
An object of class ggplot containing the GSEA plot.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # plot results gseaPlot(obj, pathway = "Thyroid hormone synthesis")# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # plot results gseaPlot(obj, pathway = "Thyroid hormone synthesis")
This function creates a ridgeplot that is useful for showing the results
of GSEA analyses. The output of this function is a plot where enriched
terms/pathways found with enrichGenes() function are visualized on the
basis of the ranking metric used for the analysis. The resulting
areas represent the density of signed p-values, log2 fold changes, or
log.p-values belonging to genes annotated to that category.
gseaRidgeplot( enrichment, showTerms = 10, showTermsParam = "padj", title = NULL )gseaRidgeplot( enrichment, showTerms = 10, showTermsParam = "padj", title = NULL )
enrichment |
An object of class
|
showTerms |
It is the number of terms to be shown, based on the
order determined by the parameter |
showTermsParam |
The order in which the top terms are selected as
specified by the |
title |
The title of the plot. Default is |
An object of class ggplot containing the ridgeplot of GSEA results.
Jacopo Ronchi, [email protected]
# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # plot results gseaRidgeplot(obj)# load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment") # plot results gseaRidgeplot(obj)
This function accesses the data slot of a
FunctionalEnrichment object and returns
a data.frame with the results of an integrative topological analysis
carried out through the topologicalAnalysis() function.
integratedPathways(object)integratedPathways(object)
object |
An object of class
|
A data.frame object containing the results of the topological analysis,
as returned by the topologicalAnalysis() function.
Jacopo Ronchi, [email protected]
# load the example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # extract results taipaRes <- integratedPathways(obj)# load the example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # extract results taipaRes <- integratedPathways(obj)
After performing the integration analysis between miRNA and gene expression
values with the mirnaIntegration() function, the results are stored
in the integration slot of a
MirnaExperiment object and can be explored with
this function.
integration(object, param = FALSE)integration(object, param = FALSE)
object |
A |
param |
Logical, whether to return the complete |
If param is FALSE, then this functions returns a data.frame object
containing the results of the integration analysis. Otherwise, a list
object including the parameters used for the analysis will be returned.
Jacopo Ronchi, [email protected]
# load example MirnaExperiment object obj <- loadExamples() # visualize the results of correlation analysis res <- integration(obj) res# load example MirnaExperiment object obj <- loadExamples() # visualize the results of correlation analysis res <- integration(obj) res
This function accesses the database slot of a
FunctionalEnrichment object and returns
the name of the database used by the topologicalAnalysis() function to
perform the integrative topological analysis.
integrationDatabase(object)integrationDatabase(object)
object |
An object of class
|
A character object with the name of the database used by the
topologicalAnalysis() function, such as KEGG.
Jacopo Ronchi, [email protected]
# load the example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # see the database integrationDatabase(obj)# load the example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # see the database integrationDatabase(obj)
This function produces a dotplot that depicts the results of a
topologically-aware integrative pathway analysis (TAIPA) carried out through
the topologicalAnalysis() function.
integrationDotplot( object, showTerms = 10, showTermsParam = "normalized.score", title = NULL )integrationDotplot( object, showTerms = 10, showTermsParam = "normalized.score", title = NULL )
object |
An object of class
|
showTerms |
It is the number of pathways to be shown, based on the
order determined by the parameter |
showTermsParam |
The order in which the top pathways are selected as
specified by the |
title |
The title of the plot. Default is |
When producing the dotplot with this function, significant pathways
are ordered on the x-axis on the basis of their normalized pathway score
computed by topologicalAnalysis(). The higher is this score, and the more
affected a pathway is between biological conditions. Moreover, the size
of each dot is equal to the ratio between the number of nodes for which a
measurement is available, and the total number of nodes (pathway coverage).
Finally, the color scale of dots is relative to the adjusted p-values of
each pathway.
A ggplot graph with a dotplot of integrated pathways.
Jacopo Ronchi, [email protected]
# load example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # create a dotplot of integrated pathways integrationDotplot(obj)# load example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # create a dotplot of integrated pathways integrationDotplot(obj)
IntegrativePathwayAnalysis classThis class stores the output of integrative multi-omic pathway analyses.
In particular, the slots of this class are suitable to represent the results
of topologically-aware integrative pathway analysis (TAIPA) returned from
the topologicalAnalysis() function.
## S4 method for signature 'IntegrativePathwayAnalysis' integratedPathways(object) ## S4 method for signature 'IntegrativePathwayAnalysis' integrationDatabase(object) ## S4 method for signature 'IntegrativePathwayAnalysis' augmentedPathways(object) ## S4 method for signature 'IntegrativePathwayAnalysis' show(object)## S4 method for signature 'IntegrativePathwayAnalysis' integratedPathways(object) ## S4 method for signature 'IntegrativePathwayAnalysis' integrationDatabase(object) ## S4 method for signature 'IntegrativePathwayAnalysis' augmentedPathways(object) ## S4 method for signature 'IntegrativePathwayAnalysis' show(object)
object |
An object of class
|
The data slot of this class consists in a data.frame object with six
columns, namely:
pathway, which indicates the name of the biological network;
coverage, which specifies the fraction of nodes with expression
measurement available;
score, which expresses the score of each individual pathway;
normalized.score, which indicates the pathway scores after standardizing
the values for the null distribution computed through permutations;
P.Val, the resulting p-value of each pathway;
adj.P.Val, the p-value adjusted for multiple testing.
The organism and database slots specify the organism in study and the
database used for retrieving biological interactions, respectively. In
particular, the topologicalAnalysis() function supports KEGG,
WikiPathways, and Reactome databases. Regarding organisms, the
supportedOrganisms() function can be used to retrieve the available
species for each database.
pCutoff and pAdjustment slots refer to the cutoff used for the analysis.
pCutoff is the threshold used for defining statistically significant
pathways, whereas pAdjustment refers to the multiple testing correction
method used.
Furthermore, since the statistical significance of each pathway is defined
on the basis of a permutation test, the number of permutations is also
specified in the nPerm slot.
The pathways slot contains a list with weighted graph objects, each
representing a biological pathway. These networks have been enlarged by
adding the observed miRNA-mRNA interactions. Each network has been
processed so that the weight of each edge is +1 for activation interactions,
and -1 for repression interactions, such as those occurring between miRNAs
and mRNAs.
The expression variation of all miRNAs and genes measured in the study is
stored in the expression slot. In particular, this slot consists of a
data.frame object with different information, including log2 fold changes,
node weights and p-values.
The minPc slot indicates the minimum percentage of miRNAs/mRNAs above
which pathways have been considered for the integrative analysis. This is
needed because often, when differential expression analysis is performed,
lowly expressed features are removed. Therefore, some pathways might result
significantly affected even if only 1% of nodes is perturbed.
the integratedPathways() function returns a data.frame with the
results of the integrative pathway analysis.
the integrationDatabase() function returns a character object
with the database used for the analysis.
the augmentedPathways() function returns a list object
containing the miRNA-augmented pathways considered for TAIPA.
integratedPathways(IntegrativePathwayAnalysis): Access the results of
integrative miRNA-mRNA pathway analysis
integrationDatabase(IntegrativePathwayAnalysis): View the database used for
the integrative pathway analysis
augmentedPathways(IntegrativePathwayAnalysis): Extract the list of biological
networks augmented with miRNA-mRNA interactions
show(IntegrativePathwayAnalysis): Show method for objects of
class IntegrativePathwayAnalysis
dataA data.frame object that contains the results of the
integrative pathway analysis. See the details section for further details
methodThe method used for the analysis
organismThe name of the organism under consideration (e.g.
Homo sapiens)
databaseThe name of the database used for retrieving biological
pathways (e.g. KEGG)
pCutoffA numeric value defining the threshold used for
statistical significance (e.g. 0.05)
pAdjustmentA character indicating the method used to correct
p-values for multiple testing (e.g. fdr)
pathwaysA list of graph objects containing the biological
networks retrieved from database, and augmented with
miRNA-mRNA interactions
expressionA data.frame object containing differential expression
results for both miRNAs and genes
minPcThe minimum percentage of measured features that a pathway must have for being considered in the analysis
nPermThe number of permutation used for assessing the statistical significance of each pathway
Jacopo Ronchi, [email protected]
This function can be used to retrieve a list of valid biological pathways present in KEGG, Reactome and WikiPathways.
listPathways(organism, database)listPathways(organism, database)
organism |
The name of the organism under consideration. The different
databases have different supported organisms. To see the list of supported
organisms for a given database, use the |
database |
The name of the database to use. It must be one of: |
A character vector containing the pathway names present in the
specified database.
This function uses the graphite package to retrieve biological pathways
from KEGG, Reactome and WikiPathways.
Jacopo Ronchi, [email protected]
Sales, G., Calura, E., Cavalieri, D. et al. graphite - a Bioconductor package to convert pathway topology to gene network. BMC Bioinformatics 13, 20 (2012), https://doi.org/10.1186/1471-2105-13-20.
# list the mouse pathways present in WikiPathways listPathways("Mus musculus", "WikiPathways")# list the mouse pathways present in WikiPathways listPathways("Mus musculus", "WikiPathways")
This helper function allows to create a
MirnaExperiment object containing miRNA and gene
expression data deriving from Riesco-Eizaguirre et al (2015), an
IntegrativePathwayAnalysis object
containing TAIPA results for the same dataset, or a
FunctionalEnrichment with example GSEA
enrichment results.
loadExamples(class = "MirnaExperiment")loadExamples(class = "MirnaExperiment")
class |
It must be |
An example MirnaExperiment object, an IntegrativePathwayAnalysis
object, or a FunctionalEnrichment object.
Jacopo Ronchi, [email protected]
# load example MirnaExperiment object obj <- loadExamples() # load example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment")# load example MirnaExperiment object obj <- loadExamples() # load example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # load example FunctionalEnrichment object obj <- loadExamples("FunctionalEnrichment")
This dataset contains a gene expression matrix resulting from a miRNA-Seq analysis of thyroid cancer. Specifically, these data originate from Riesco-Eizaguirre et al (2015), where they sequenced 8 papillary thyroid carcinomas (PTC) together with paired samples of normal thyroid tissue. The same thing was done for mRNAs in order to investigate the effects on target genes. Data included in this package have been obtained through the Gene Expression Omnibus (GEO accession: GSE63511).
data(mirnaCounts)data(mirnaCounts)
mirnaCountsA matrix object containing samples as columns and microRNAs as rows.
A matrix object with 2576 rows and 16 columns.
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63511
Garcilaso Riesco-Eizaguirre et al., “The MiR-146b-3p/PAX8/NIS Regulatory Circuit Modulates the Differentiation Phenotype and Function of Thyroid Cells during Carcinogenesis,” Cancer Research 75, no. 19 (September 30, 2015): 4119–30, https://doi.org/10.1158/0008-5472.CAN-14-3547.
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.
MirnaExperiment(mirnaExpr, geneExpr, samplesMetadata, pairedSamples = TRUE)MirnaExperiment(mirnaExpr, geneExpr, samplesMetadata, pairedSamples = TRUE)
mirnaExpr |
A |
geneExpr |
A |
samplesMetadata |
A |
pairedSamples |
Logical, whether miRNA and gene expression levels
derive from the same subjects or not. Check the details section for
additional instructions. Default is |
This function requires data to be prepared as described below.
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 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.
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.
A valid MirnaExperiment object containing
information about miRNA and gene expression.
Jacopo Ronchi, [email protected]
# 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 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 )
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).
## S4 method for signature 'MirnaExperiment' mirnaDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE) ## S4 method for signature 'MirnaExperiment' geneDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE) ## S4 method for signature 'MirnaExperiment' significantMirnas(object) ## S4 method for signature 'MirnaExperiment' significantGenes(object) ## S4 method for signature 'MirnaExperiment' pairedSamples(object) ## S4 method for signature 'MirnaExperiment' mirnaTargets(object) ## S4 method for signature 'MirnaExperiment' integration(object, param = FALSE) ## S4 method for signature 'MirnaExperiment' show(object)## S4 method for signature 'MirnaExperiment' mirnaDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE) ## S4 method for signature 'MirnaExperiment' geneDE(object, onlySignificant = TRUE, param = FALSE, returnObject = FALSE) ## S4 method for signature 'MirnaExperiment' significantMirnas(object) ## S4 method for signature 'MirnaExperiment' significantGenes(object) ## S4 method for signature 'MirnaExperiment' pairedSamples(object) ## S4 method for signature 'MirnaExperiment' mirnaTargets(object) ## S4 method for signature 'MirnaExperiment' integration(object, param = FALSE) ## S4 method for signature 'MirnaExperiment' show(object)
object |
An object of class |
onlySignificant |
Logical, if |
param |
Logical, whether to return the complete |
returnObject |
Logical, if |
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.
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
ExperimentListAn ExperimentList class object for each assay dataset
colDataA DataFrame of all clinical/specimen data available across
experiments
sampleMapA DataFrame of translatable identifiers of samples and
participants
metadataAdditional data describing the object
dropsA metadata list of dropped information
mirnaDEA list object containing the results of miRNA differential
expression
geneDEA list object containing the results of gene differential
expression
pairedSamplesA logical parameter that specifies whether miRNA and
gene expression measurements derive from the same individuals (TRUE) or
from different subjects (FALSE)
targetsA data.frame object containing miRNA-target pairs. This
slot is commonly populated by the getTargets() function
integrationA list object containing the results
of the integration analysis between miRNA and gene expression values. This
slot is commonly populated by the mirnaIntegration() function
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.
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.
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 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.
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 is a data.frame with miRNA-target interactions, as retrieved by
getTargets() function.
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.
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.
Jacopo Ronchi, [email protected]
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
See MultiAssayExperiment-class
for additional information.
This function allows to identify microRNAs that are significantly associated/correlated with their targets. The principle is that, since the biological role of miRNAs is mainly to negatively regulate gene expression post-transcriptionally, the expression of a microRNA should be negatively correlated with the expression of its targets. To test this assumption for matched-sample data, this function performs a correlation analysis. On the other hand, for unpaired data, it offers different one-sided association tests to estimate if targets of down-regulated miRNAs are enriched in up-regulated genes and vice versa. Additionally, for unpaired data, miRNA effects on target gene expression can also be quantified through a fast approximation to rotation gene-set testing ('fry' method). For correlation analyses, the default behavior is to use Spearman's correlation analysis, whereas for association tests the default option makes use of a one-sided Boschloo's exact test. See the details section for further information.
mirnaIntegration( mirnaObj, test = "auto", pCutoff = 0.05, pAdjustment = "fdr", corMethod = "spearman", corCutoff = 0.5, associationMethod = "boschloo", nuisanceParam = 100, BPPARAM = bpparam() )mirnaIntegration( mirnaObj, test = "auto", pCutoff = 0.05, pAdjustment = "fdr", corMethod = "spearman", corCutoff = 0.5, associationMethod = "boschloo", nuisanceParam = 100, BPPARAM = bpparam() )
mirnaObj |
A |
test |
The statistical test to evaluate the association between miRNAs
and genes. It must be one of |
pCutoff |
The adjusted p-value cutoff to use for statistical
significance. The default value is |
pAdjustment |
The p-value correction method for multiple testing. It
must be one of: |
corMethod |
The correlation method to be used for correlation analysis.
It must be one of: |
corCutoff |
The minimum (negative) value of correlation coefficient to
consider meaningful a miRNA-target relationship. Default is |
associationMethod |
The statistical test used for evaluating the
association between miRNAs and their targets for unpaired data. It must be
one of |
nuisanceParam |
The number of nuisance parameter values considered for
p-value calculation in |
BPPARAM |
The desired parallel computing behavior. This parameter
defaults to |
As already pointed out, if miRNA and gene expression data derive from the same samples, a correlation analysis is used. For evaluating these relationships, the default method used is Spearman's correlation coefficient, as:
it does not need normally distributed data;
it does not assume linearity;
it is much more resistant to outliers.
However, the user can also decide to use other correlation methods,
such as Pearson's and Kendall's correlation. Nevertheless, for NGS data
it may happen that a certain number of ties is present in the expression
values. This can be handled by spearman method as it computes a
tie-corrected version of Spearman's coefficients. However, another
correlation method that is suitable to perform rank correlation on tied data
is the Kendall's tau-b method, usable with kendall.
Regarding correlation direction, since miRNAs mainly act as negative regulators, only negatively correlated miRNA-target pairs are evaluated, and statistical significance is calculated through a one-tailed t-test.
Please notice that if strong batch effects are noticed in expression data,
it is recommended to remove them through the batchCorrection() function
implemented in MIRit.
Moreover, if gene expression data and miRNA expression data derive from different samples (unpaired data), a correlation analysis can't be performed. However, one-sided association tests can be applied in these cases to evaluate if targets of down-regulated miRNAs are statistically enriched in up-regulated genes, and, conversely, if targets of up-regulated miRNAs are statistically enriched in down-regulated genes. In this case, Fisher's exact test can be used to assess the statistical significance of this inverse association. Moreover, Lancaster's mid-p adjustment can be applied since it has been shown that it increases statistical power while retaining Type I error rates. However, Fisher's exact test is 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 test is uniformly more powerful compared to Fisher's exact test.
Finally, for unpaired data, the effect of DE-miRNAs on the expression of
target genes can be estimated through rotation gene-set tests. In particular,
a fast approximation to rotation gene-set testing called fry, implemented
in the limma package, can be used to statistically quantify the influence
of miRNAs on the expression changes of their target genes.
To speed up the identification of anti-correlated or anti-associated
miRNA-target pairs, this function implements parallel computation via
BiocParallel::bpparam(). In this regard, the parallelization behavior can
be specified via the BPPARAM parameter.
A MirnaExperiment object containing integration
results. To access these results, the user can make use of the
integration() function. For additional details on how to
interpret the results of miRNA-gene integrative analysis, please see
MirnaExperiment.
Jacopo Ronchi, [email protected]
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.
Di Wu and others, ROAST: rotation gene set tests for complex microarray experiments, Bioinformatics, Volume 26, Issue 17, September 2010, Pages 2176–2182, https://doi.org/10.1093/bioinformatics/btq401.
Routledge, R. D. (1994). Practicing Safe Statistics with the Mid-p. The Canadian Journal of Statistics / La Revue Canadienne de Statistique, 22(1), 103–110, https://doi.org/10.2307/3315826.
Boschloo R.D. (1970). "Raised Conditional Level of Significance for the 2x2-table when Testing the Equality of Two Probabilities". Statistica Neerlandica. 24: 1–35. doi:10.1111/j.1467-9574.1970.tb00104.x.
# load example MirnaExperiment object obj <- loadExamples() # perform integration analysis with default settings obj <- mirnaIntegration(obj)# load example MirnaExperiment object obj <- loadExamples() # perform integration analysis with default settings obj <- mirnaIntegration(obj)
This function accesses the targets slot of a
MirnaExperiment
object. After retrieving miRNA targets with the getTargets() function,
the interactions between miRNAs and target genes are stored in the
targets slot and can be explored with this function.
mirnaTargets(object)mirnaTargets(object)
object |
A |
A data.frame object containing the interactions between miRNAs and target
genes, as retrieved with the getTargets() function.
Jacopo Ronchi, [email protected]
# load example MirnaExperiment object obj <- loadExamples() # visualize targets targets_df <- mirnaTargets(obj)# load example MirnaExperiment object obj <- loadExamples() # visualize targets targets_df <- mirnaTargets(obj)
This function plots a trackplot that shows the genomic position of disease-associated SNPs that affect miRNA genes. This is useful to visualize the genomic position and context of disease-associated variants that may affect miRNA expression.
mirVariantPlot( variantId, snpAssociation, showContext = FALSE, showSequence = TRUE, snpFill = "lightblue", mirFill = "orange", from = NULL, to = NULL, title = NULL, ... )mirVariantPlot( variantId, snpAssociation, showContext = FALSE, showSequence = TRUE, snpFill = "lightblue", mirFill = "orange", from = NULL, to = NULL, title = NULL, ... )
variantId |
A valid name of a SNP variant! (e.g. |
snpAssociation |
A |
showContext |
Logical, if |
showSequence |
Logical, whether to display a color-coded sequence at
the bottom of the trackplot. Default is |
snpFill |
It must be an R color name that specifies the fill color of
the SNP locus. Default is |
mirFill |
It must be an R color name that specifies the fill color of
the miRNA locus. Default is |
from |
The start position of the plotted genomic range. Default is NULL to automatically determine an appropriate position |
to |
The end position of the plotted genomic range. Default is NULL to automatically determine an appropriate position |
title |
The title of the plot. Default is |
... |
Other parameters that can be passed to |
A trackplot with information about chromosome, SNP and miRNA gene location.
This function retrieves genomic coordinates from the output of
findMirnaSNPs() function and then uses Gviz package to build
the trackplot.
Jacopo Ronchi, [email protected]
Hahne, F., Ivanek, R. (2016). Visualizing Genomic Data Using Gviz and Bioconductor. In: Mathé, E., Davis, S. (eds) Statistical Genomics. Methods in Molecular Biology, vol 1418. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3578-9_16
# load example MirnaExperiment object obj <- loadExamples() # retrieve associated SNPs association <- findMirnaSNPs(obj, "response to antidepressant") # visualize association as a trackplot mirVariantPlot(variantId = "rs2402960", snpAssociation = association)# load example MirnaExperiment object obj <- loadExamples() # retrieve associated SNPs association <- findMirnaSNPs(obj, "response to antidepressant") # visualize association as a trackplot mirVariantPlot(variantId = "rs2402960", snpAssociation = association)
This function allows to access the pairedSamples slot of a
MirnaExperiment object. The MirnaExperiment
class is able to contain miRNA and gene expression measurements deriving
from the same individuals (paired samples), or from different subjects
(unpaired samples).
pairedSamples(object)pairedSamples(object)
object |
A |
A logical value that is either TRUE for paired samples, or FALSE for
unpaired samples.
Jacopo Ronchi, [email protected]
# load example MirnaExperiment object obj <- loadExamples() # check if an existing MirnaExperiment object derive from paired samples pairedSamples(obj)# load example MirnaExperiment object obj <- loadExamples() # check if an existing MirnaExperiment object derive from paired samples pairedSamples(obj)
This function creates a scatter plot that shows the correlation between
miRNA and gene expression levels. This is useful after correlation
analysis performed through the mirnaIntegration() function, to
graphically visualize the quantitative effect of miRNA dysregulations on
target gene expression. Furthermore, this function performs linear/monotonic
regression to better represent the relationships between miRNA-target pairs.
plotCorrelation( mirnaObj, mirna, gene, condition = NULL, showCoeff = TRUE, regression = TRUE, useRanks = FALSE, lineCol = "red", lineType = "dashed", lineWidth = 0.8, pointSize = 3, colorScale = NULL, fontSize = 12, fontFamily = "", legend = "top", borderWidth = 1, allBorders = TRUE, grid = TRUE )plotCorrelation( mirnaObj, mirna, gene, condition = NULL, showCoeff = TRUE, regression = TRUE, useRanks = FALSE, lineCol = "red", lineType = "dashed", lineWidth = 0.8, pointSize = 3, colorScale = NULL, fontSize = 12, fontFamily = "", legend = "top", borderWidth = 1, allBorders = TRUE, grid = TRUE )
mirnaObj |
A |
mirna |
The name of the miRNA for which we want to observe the correlation |
gene |
The name of the gene for which we want to observe the correlation |
condition |
It must be NULL (default) to plot expression based on the group variable used for differential expression analysis. Alternatively, it must be a character/factor object that specifies group memberships (eg. c("healthy, "healthy", "disease", "disease")) |
showCoeff |
Logical, whether to show the correlation coeffficient or not. Note that the "R" is used for Pearson's correlation", "rho" for Spearman's correlation, and "tau" for Kendall's correlation. Default is TRUE |
regression |
Logical, whether to display a linear/monotonic regression line that fits miRNA-gene correlation data. Default is TRUE |
useRanks |
Logical, whether to represent non-parametric correlation analyses (Spearman's and Kendall's correlations) through rank-transformed data. Note that in this case, linear regression is performed on ranked data instead of monotonic regression. Default is FALSE |
lineCol |
It must be an R color name that specifies the color of
the regression line. Default is |
lineType |
It specifies the line type used for the regression line. It must be either 'blank', 'solid', 'dashed' (default), 'dotted', 'dotdash', 'longdash' or 'twodash' |
lineWidth |
The width of the fitted regression line (default is 0.8) |
pointSize |
The size of points in the correlation plot (default is 3) |
colorScale |
It must be a named character vector where values
correspond to R colors, while names coincide with the groups specified in
the |
fontSize |
The base size for text elements within the plot. Default is 12 |
fontFamily |
The base family for text elements within the plot |
legend |
The position of the legend. Allowed values are |
borderWidth |
The width of plot borders (default is 1) |
allBorders |
Logical, whetether to show all panel borders, or just the bottom and left borders. Default is TRUE |
grid |
Logical, whether to show grid lines or not. Default is TRUE |
When non-parametric correlation has been performed with the
mirnaIntegration() function, a regression line can be fitted through
monotonic regression on expression levels, or through linear regression
performed on rank-transformed data. Since, ranks do not correspond to real
expression values, the default option is to perform monotonic regression
to fit a monotonic curve. To do so, this function makes use of the MonoPoly
R package, which implements the algorithm proposed by Murray et al. in 2016.
An object of class ggplot containing the correlation scatter plot.
Jacopo Ronchi, [email protected]
K. Murray, S. Müller & B. A. Turlach (2016) Fast and flexible methods for monotone polynomial fitting, Journal of Statistical Computation and Simulation, 86:15, 2946-2966, DOI: 10.1080/00949655.2016.1139582.
# load example MirnaExperiment object obj <- loadExamples() # plot correlation between miR-146b and PAX8 with monotonic regression curve plotCorrelation(obj, "hsa-miR-146b-5p", "PAX8", condition = "disease")# load example MirnaExperiment object obj <- loadExamples() # plot correlation between miR-146b and PAX8 with monotonic regression curve plotCorrelation(obj, "hsa-miR-146b-5p", "PAX8", condition = "disease")
This function is able to produce boxplots, barplots and violinplots that are
useful to visualize miRNA and gene differential expression. The user just
has to provide a vector of interesting miRNA/genes that he wants to plot
(e.g. "hsa-miR-34a-5p", "hsa-miR-146b-5p", "PAX8"). The chart type can be
specified through the graph parameter.
plotDE( mirnaObj, features, condition = NULL, graph = "boxplot", linear = TRUE, showSignificance = TRUE, starSig = TRUE, pCol = "adj.P.Val", sigLabelSize = 7, digits = 3, nameAsTitle = FALSE, colorScale = NULL, fontSize = 12, fontFamily = "", legend = "top", borderWidth = 1, allBorders = FALSE, grid = FALSE )plotDE( mirnaObj, features, condition = NULL, graph = "boxplot", linear = TRUE, showSignificance = TRUE, starSig = TRUE, pCol = "adj.P.Val", sigLabelSize = 7, digits = 3, nameAsTitle = FALSE, colorScale = NULL, fontSize = 12, fontFamily = "", legend = "top", borderWidth = 1, allBorders = FALSE, grid = FALSE )
mirnaObj |
A |
features |
A character vector containing the genes/miRNAs to plot |
condition |
It must be NULL (default) to plot expression based on the group variable used for differential expression analysis. Alternatively, it must be a character/factor object that specifies group memberships (eg. c("healthy, "healthy", "disease", "disease")) |
graph |
The type of plot to produce. It must be one of |
linear |
Logical, whether to plot expression levels in linear scale or in log2 space. Default is TRUE in order to use the linear space |
showSignificance |
Logical, whether to display statistical significance or not. Default is TRUE |
starSig |
Logical, whether to represent statistical significance through
stars. Default is TRUE, and the significance scale is: * for |
pCol |
The statistics used to evaluate comparison significance. It must
be one of |
sigLabelSize |
The size for the labels used to show statistical
significance. Default is 7, which is well suited for representing p-values
as significance stars. However, if |
digits |
The number of digits to show when p-values are reported as
numbers (when |
nameAsTitle |
Logical, if set to TRUE, the miRNA/gene name will be
added as plot title, and the x-axis and legend will be removed. Note that
this option is only considered if |
colorScale |
It must be a named character vector where values
correspond to R colors, while names coincide with the groups specified in
the |
fontSize |
The base size for text elements within the plot. Default is 12 |
fontFamily |
The base family for text elements within the plot |
legend |
The position of the legend. Allowed values are |
borderWidth |
The width of plot borders (default is 1) |
allBorders |
Logical, whetether to show all panel borders, or just the bottom and left borders. Default is FALSE |
grid |
Logical, whether to show grid lines or not. Default is FALSE |
An object of class ggplot containing the plot.
Jacopo Ronchi, [email protected]
# load example MirnaExperiment object obj <- loadExamples() # produce a boxplot for PAX8 and miR-34a-5p plotDE(obj, features = c("hsa-miR-34a-5p", "PAX8"))# load example MirnaExperiment object obj <- loadExamples() # produce a boxplot for PAX8 and miR-34a-5p plotDE(obj, features = c("hsa-miR-34a-5p", "PAX8"))
This function performs multidimensional scaling in order to produce a simple
scatterplot that shows miRNA/gene expression variations among samples. In
particular, starting from a MirnaExperiment
object, this functions allows to visualize both miRNA and gene expression in
the multidimensional space. Moreover, it is possible to color samples on the
basis of specific variables, and this is extremely useful to assess
miRNA/gene expression variations between distinct biological groups.
plotDimensions( mirnaObj, assay, condition = NULL, dimensions = c(1, 2), labels = FALSE, boxedLabel = TRUE, pointSize = 3, pointAlpha = 1, colorScale = NULL, title = NULL, fontSize = 12, fontFamily = "", legend = "top", borderWidth = 1, allBorders = TRUE, grid = FALSE, ... )plotDimensions( mirnaObj, assay, condition = NULL, dimensions = c(1, 2), labels = FALSE, boxedLabel = TRUE, pointSize = 3, pointAlpha = 1, colorScale = NULL, title = NULL, fontSize = 12, fontFamily = "", legend = "top", borderWidth = 1, allBorders = TRUE, grid = FALSE, ... )
mirnaObj |
A |
assay |
The results to display. It must be either 'microRNA', to plot miRNA expression, or 'genes', to produce MDS plot for genes |
condition |
It must be the column name of a variable specified in the
metadata (colData) of a |
dimensions |
It is a numeric vector of length 2 that indicates the two
dimensions to represent on the plot. Default is |
labels |
Logical, whether to display labels or not. Default is FALSE |
boxedLabel |
Logical, whether to show labels inside a rectangular shape (default) or just as text elements |
pointSize |
The size of points in the MDS plot (default is 3) |
pointAlpha |
The transparency of points in the MDS plot (default is 1) |
colorScale |
It must be a named character vector where values
correspond to R colors, while names coincide with the groups specified in
the |
title |
The title of the plot. Default is |
fontSize |
The base size for text elements within the plot. Default is 12 |
fontFamily |
The base family for text elements within the plot |
legend |
The position of the legend. Allowed values are |
borderWidth |
The width of plot borders (default is 1) |
allBorders |
Logical, whetether to show all panel borders, or just the bottom and left borders. Default is TRUE |
grid |
Logical, whether to show grid lines or not. Default is FALSE |
... |
Other parameters that can be passed to |
An object of class ggplot containing the plot.
To perform multidimensional scaling, this function internally uses
limma::plotMDS() function provided by limma package.
Jacopo Ronchi, [email protected]
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.
# load example MirnaExperiment object obj <- loadExamples() # produce MDS plot for genes with condition color plotDimensions(obj, "genes", condition = "disease")# load example MirnaExperiment object obj <- loadExamples() # produce MDS plot for genes with condition color plotDimensions(obj, "genes", condition = "disease")
This function allows the user to create publication-quality volcano plots to represent the results of miRNA/gene differential expression. In this kind of plots, the x-axis is relative to the log2 fold change between biological conditions, while the y-axis contains the negative base-10 logarithm of the p-value. Note, that even if volcano plots display unadjusted p-values on the y-axis, the cutoff level shown in this plot derive from the adjusted p-value cutoff used for differential expression analysis.
plotVolcano( mirnaObj, assay, labels = NULL, boxedLabel = TRUE, pointSize = 3, pointAlpha = 0.4, interceptWidth = 0.6, interceptColor = "black", interceptType = "dashed", colorScale = c("blue", "grey", "red"), title = NULL, fontSize = 12, fontFamily = "", legend = "none", borderWidth = 1, allBorders = TRUE, grid = FALSE )plotVolcano( mirnaObj, assay, labels = NULL, boxedLabel = TRUE, pointSize = 3, pointAlpha = 0.4, interceptWidth = 0.6, interceptColor = "black", interceptType = "dashed", colorScale = c("blue", "grey", "red"), title = NULL, fontSize = 12, fontFamily = "", legend = "none", borderWidth = 1, allBorders = TRUE, grid = FALSE )
mirnaObj |
A |
assay |
The results to display. It must be either 'microRNA', to plot miRNA differential expression, or 'genes', to show the results for genes |
labels |
The labels to show on the graph. Default is NULL not to include labels. This parameter can be a character vector containing the IDs of the features that you want to display. Alternatively, this parameter can also be the number of most significant features for which we want to plot labels |
boxedLabel |
Logical, whether to show labels inside a rectangular shape (default) or just as text elements |
pointSize |
The size of points in the volcano plot (default is 3) |
pointAlpha |
The transparency of points in the volcano plot (default is 0.4) |
interceptWidth |
The width of cutoff intercepts (default is 0.6) |
interceptColor |
It must be an R color name that specifies the color of
cutoff intercepts. Default is |
interceptType |
It specifies the line type used for cutoff intercepts. It must be either 'blank', 'solid', 'dashed' (default), 'dotted', 'dotdash', 'longdash' or 'twodash' |
colorScale |
It must be a character vector of length 3 containing valid
R color names for downregulated, non significant, and upregulated features,
respectively. Default value is |
title |
The title of the plot. Default is |
fontSize |
The base size for text elements within the plot. Default is 12 |
fontFamily |
The base family for text elements within the plot |
legend |
The position of the legend. Allowed values are |
borderWidth |
The width of plot borders (default is 1) |
allBorders |
Logical, whetether to show all panel borders, or just the bottom and left borders. Default is TRUE |
grid |
Logical, whether to show grid lines or not. Default is FALSE |
An object of class ggplot containing the plot.
Jacopo Ronchi, [email protected]
# load example MirnaExperiment object obj <- loadExamples() # produce a volcano plot for genes plotVolcano(obj, "genes")# load example MirnaExperiment object obj <- loadExamples() # produce a volcano plot for genes plotVolcano(obj, "genes")
This function takes influential miRNA-mRNA interactions, identified by the
mirnaIntegration() function, and adds them to biological pathways
retrieved from a pathway database such as KEGG, WikiPathways and
Reactome. The pathways returned from this function are needed to perform
a topologically-aware integrative pathway analysis (TAIPA) through the
topologicalAnalysis() function.
preparePathways( mirnaObj, database = "KEGG", organism = "Homo sapiens", minPc = 10, size = NULL, BPPARAM = bpparam() )preparePathways( mirnaObj, database = "KEGG", organism = "Homo sapiens", minPc = 10, size = NULL, BPPARAM = bpparam() )
mirnaObj |
A |
database |
The name of the database to use. It must be one of: |
organism |
The name of the organism under consideration. The different
databases have different supported organisms. To see the list of supported
organisms for a given database, use the |
minPc |
The minimum percentage of measured features that a pathway must have for being considered in the analysis. Default is 10. See the details section for additional information |
size |
An optional numeric vector of length 2, containing the minimum
and maximum number of nodes for each pathway. For example, if
|
BPPARAM |
The desired parallel computing behavior. This parameter
defaults to |
To create augmented pathways, this function uses the graphite R package
to download biological networks from the above mentioned databases. Then,
each pathway is converted to a graph object, and significant miRNA-mRNA
interactions are added to the network. Further, edge weights are added
according to interaction type.
At this point, biological pathways with few nodes measured are excluded
from this analysis. This is required because, during differential expression
analysis, lowly expressed features are removed. Therefore, some pathways
might result significantly affected even if only 1% of nodes is perturbed.
The default behavior is to exclude pathways with less than 10% of
representation (minPc = 10).
Finally, this function performs a breadth-first search (BFS) algorithm to topologically sort pathway nodes so that each individual node occurs after all its upstream nodes. Nodes within cycles are considered leaf nodes.
Information about pathway coverage, i.e. the percentage of nodes with
expression measurments, edge weights, topological sorting order, and the
parameters used to create the networks are all stored in the graphData
slot of each graphNEL object.
A list object containing the miRNA-augmented pathways as graphNEL
objects.
Jacopo Ronchi, [email protected]
Sales, G., Calura, E., Cavalieri, D. et al. graphite - a Bioconductor package to convert pathway topology to gene network. BMC Bioinformatics 13, 20 (2012), https://doi.org/10.1186/1471-2105-13-20.
# load example MirnaExperiment object obj <- loadExamples() # perform integration analysis with default settings obj <- mirnaIntegration(obj) # retrieve pathways from KEGG and augment them with miRNA-gene interactions paths <- preparePathways(obj) # perform the integrative pathway analysis with 1000 permutations ipa <- topologicalAnalysis(obj, paths, nPerm = 1000) # access the results of pathway analysis integratedPathways(ipa) # create a dotplot of integrated pathways integrationDotplot(ipa) # explore a specific biological network visualizeNetwork(ipa, "Thyroid hormone synthesis")# load example MirnaExperiment object obj <- loadExamples() # perform integration analysis with default settings obj <- mirnaIntegration(obj) # retrieve pathways from KEGG and augment them with miRNA-gene interactions paths <- preparePathways(obj) # perform the integrative pathway analysis with 1000 permutations ipa <- topologicalAnalysis(obj, paths, nPerm = 1000) # access the results of pathway analysis integratedPathways(ipa) # create a dotplot of integrated pathways integrationDotplot(ipa) # explore a specific biological network visualizeNetwork(ipa, "Thyroid hormone synthesis")
This function allows to retrieve the Experimental Factor Ontology (EFO)
identifier of a particular disease. This ID is then needed to use the
function findMirnaSNPs().
searchDisease(diseaseName)searchDisease(diseaseName)
diseaseName |
The name of a particular disease
(ex. |
A character object containing EFO identifiers.
To retrieve EFO IDs for specific diseases, this function makes use of the
gwasrapidd package.
Jacopo Ronchi, [email protected]
Ramiro Magno, Ana-Teresa Maia, gwasrapidd: an R package to query, download and wrangle GWAS catalog data, Bioinformatics, Volume 36, Issue 2, January 2020, Pages 649–650, https://doi.org/10.1093/bioinformatics/btz605.
# search the EFO identifier of Alzheimer disease searchDisease("Alzheimer disease")# search the EFO identifier of Alzheimer disease searchDisease("Alzheimer disease")
The significantMirnas() and significantGenes() functions access the
significant features contained in the mirnaDE or geneDE slots
of a MirnaExperiment object, and can be used
to obtain the IDs of statistically differentially expressed miRNAs and genes.
significantMirnas(object) significantGenes(object)significantMirnas(object) significantGenes(object)
object |
A |
A character vector of miRNA IDs (e.g. 'hsa-miR-16-5p',
hsa-miR-29a-3p'...), or acharacter vector of gene symbols (e.g. 'TP53',
'FOXP2', 'TIGAR', CASP1'...).
significantMirnas(): Get the IDs of differentially expressed
miRNAs
significantGenes(): Get the IDs of differentially expressed
genes
Jacopo Ronchi, [email protected]
# load example MirnaExperiment object obj <- loadExamples() # extract significant DE-miRNAs sigMirnas <- significantMirnas(obj) # extract significant DEGs sigGenes <- significantGenes(obj)# load example MirnaExperiment object obj <- loadExamples() # extract significant DE-miRNAs sigMirnas <- significantMirnas(obj) # extract significant DEGs sigGenes <- significantGenes(obj)
This function provides the list of supported organisms for different databases, namely Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), MsigDB, WikiPathways, Reactome, Enrichr, Disease Ontology (DO), Network of Cancer Genes (NCG), DisGeNET, and COVID19.
supportedOrganisms(database)supportedOrganisms(database)
database |
The database name. It must be one of: |
A character vector listing all the supported organisms for the database
specified by the user.
To perform the functional enrichment of genes, MIRit uses the geneset R
package to download gene sets from the above mentioned databases.
Jacopo Ronchi, [email protected]
Liu, Y., Li, G. Empowering biologists to decode omics data: the Genekitr R package and web server. BMC Bioinformatics 24, 214 (2023). https://doi.org/10.1186/s12859-023-05342-9.
# get the supported organisms for GO database supportedOrganisms("GO") # get the supported organisms for Reactome supportedOrganisms("Reactome")# get the supported organisms for GO database supportedOrganisms("GO") # get the supported organisms for Reactome supportedOrganisms("Reactome")
This function allows to perform an integrative pathway analysis that aims
to identify the biological networks that are most affected by miRNomic
and transcriptomic dysregulations. This function takes miRNA-augmented
pathways, created by the preparePathways() function, and then calculates
a score that estimates the degree of impairment for each pathway. Later,
statistical significance is calculated through a permutation test. The main
advantages of this method are that it doesn't require matched samples, and
that it allows to perform an integrative miRNA-mRNA pathway analysis that
takes into account the topology of biological networks. See the details
section for additional information.
topologicalAnalysis( mirnaObj, pathways, pCutoff = 0.05, pAdjustment = "max-T", nPerm = 10000, progress = FALSE, tasks = 0, BPPARAM = bpparam() )topologicalAnalysis( mirnaObj, pathways, pCutoff = 0.05, pAdjustment = "max-T", nPerm = 10000, progress = FALSE, tasks = 0, BPPARAM = bpparam() )
mirnaObj |
A |
pathways |
A |
pCutoff |
The adjusted p-value cutoff to use for statistical
significance. The default value is |
pAdjustment |
The p-value correction method for multiple testing. It
must be one of: |
nPerm |
The number of permutation used for assessing the statistical significance of each pathway. Default is 10000. See the details section for additional information |
progress |
Logical, whether to show a progress bar during p-value
calculation or not. Default is FALSE, not to include a progress bar. Please
note that setting |
tasks |
An integer between 0 and 100 that specifies how frequently the
progress bar must be updated. Default is 0 to simply split the computation
among the workers. High values of |
BPPARAM |
The desired parallel computing behavior. This parameter
defaults to |
This analysis aims to identify the biological pathways that result affected
by miRNA and mRNA dysregulations. In this analysis, biological pathways are
retrieved from a pathway database such as KEGG, and the interplay between
miRNAs and genes is then added to the networks. Each network is defined as
a graph , where represents nodes, and
represents the relationships between nodes.
Then, nodes that are not significantly differentially expressed are assigned
a weight , whereas differentially expressed nodes are assigned
a weight , where is
the linear fold change of the node. Moreover, to consider the biological
interaction between two nodes, namely and , we define an
interaction parameter for activation
interactions and for repression
interactions. Subsequently, the concordance coefficient
is defined as:
Later in the process, a breadth-first search (BFS) algorithm is applied to
topologically sort pathway nodes so that each individual node occurs after
all its upstream nodes. Nodes within cycles are considered leaf nodes. At
this point, a node score is calculated for each pathway node
as:
where represents the number of upstream nodes,
denotes the concordance coefficient, and
is a propagation factor defined as:
Finally, the pathway score is calculated as:
where represents the proportion of miRNAs in the pathway, and
represents the total number of nodes in the network.
Then, to compute the statistical significance of each pathway score, a
permutation procedure is applied. Later, both observed pathway scores and
permuted scores are standardized by subtracting the mean score of the
permuted sets and then dividing by the standard deviation
of the permuted scores .
Finally, the p-value is defined based on the fraction of permutations that reported a higher normalized pathway score than the observed one. However, to prevent p-values equal to zero, we define p-values as:
In the end, p-values are corrected for multiple testing either through the max-T procedure (default option) which is particularly suited for permutation tests, or through the standard multiple testing approaches.
For computational efficiency, pathway score computation has been implemented
in C++ language. Moreover, to define the statistical significance of each
network, a permutation test is applied following the number of permutations
specified with nPerm. The default setting is to perform 10000 permutations.
The higher is the number of permutations, the more stable are the calculated
p-values, even though the time needed will increase. In this regard, since
computing pathway score for 10000 networks for each pathway is
computationally intensive, parallel computing has been employed to reduce
running time. The user can modify the parallel computing behavior by
specifying the BPPARAM parameter. See BiocParallel::bpparam() for
further details. Further, a progress bar can also be included to show the
completion percentage by setting progress = TRUE. Moreover, the user can
define how frequently the progress bar gets updated by tweaking the tasks
parameter. When using progress = TRUE, setting tasks to 100 tells the
function to update the progress bar 100 times, so that the user can see
increases of 1%. Instead, setting tasks to 50, means that the progress bar
gets updated every 2% of completion. However, keep in mind that tasks
values from 50 to 100 lead to 15-30% slower p-value calculation due to
increased data transfer to the workers. Instead, lower tasks values like
20 determine less frequent progress updates but are only slightly less
efficient than not including a progress bar.
An object of class
IntegrativePathwayAnalysis that stores
the results of the analysis. See the relative help page for further details.
Jacopo Ronchi, [email protected]
Peter H. Westfall and S. Stanley Young. Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment. John Wiley & Sons. ISBN 978-0-471-55761-6.
# load example MirnaExperiment object obj <- loadExamples() # perform integration analysis with default settings obj <- mirnaIntegration(obj) # retrieve pathways from KEGG and augment them with miRNA-gene interactions paths <- preparePathways(obj) # perform the integrative pathway analysis with 1000 permutations ipa <- topologicalAnalysis(obj, paths, nPerm = 1000) # access the results of pathway analysis integratedPathways(ipa) # create a dotplot of integrated pathways integrationDotplot(ipa) # explore a specific biological network visualizeNetwork(ipa, "Thyroid hormone synthesis")# load example MirnaExperiment object obj <- loadExamples() # perform integration analysis with default settings obj <- mirnaIntegration(obj) # retrieve pathways from KEGG and augment them with miRNA-gene interactions paths <- preparePathways(obj) # perform the integrative pathway analysis with 1000 permutations ipa <- topologicalAnalysis(obj, paths, nPerm = 1000) # access the results of pathway analysis integratedPathways(ipa) # create a dotplot of integrated pathways integrationDotplot(ipa) # explore a specific biological network visualizeNetwork(ipa, "Thyroid hormone synthesis")
This function can be used to plot augmented pathways created by the
topologicalAnalysis() function. In particular, given a valid object of
class IntegrativePathwayAnalysis,
this function allows to produce a network graph for a specified biological
pathway, alongside with expression fold changes. In this way, augmented
pathways made of both miRNAs and genes can be visually explored to better
investigate the consequences of miRNA/gene dysregulations.
visualizeNetwork( object, pathway, algorithm = "dot", fontsize = 14, lfcScale = c("royalblue", "white", "red"), nodeBorderCol = "black", nodeTextCol = "black", edgeCol = "darkgrey", edgeWidth = 1, subgraph = NULL, highlightNodes = NULL, highlightCol = "gold", highlightWidth = 2, legendColorbar = TRUE, legendInteraction = TRUE, title = NULL, titleCex = 2, titleFace = 1 )visualizeNetwork( object, pathway, algorithm = "dot", fontsize = 14, lfcScale = c("royalblue", "white", "red"), nodeBorderCol = "black", nodeTextCol = "black", edgeCol = "darkgrey", edgeWidth = 1, subgraph = NULL, highlightNodes = NULL, highlightCol = "gold", highlightWidth = 2, legendColorbar = TRUE, legendInteraction = TRUE, title = NULL, titleCex = 2, titleFace = 1 )
object |
An object of class
|
pathway |
The name of the biological pathway to show. The available
pathways for a given database can be seen through the |
algorithm |
The layout algorithm used to arrange nodes in the network.
It must be one of |
fontsize |
The font size of each node in the graph. Default is 14 |
lfcScale |
It must be a |
nodeBorderCol |
It must be an R color name that specifies the color
of node borders. Default is |
nodeTextCol |
It must be an R color name that specifies the color of
miRNA/gene names. Default is |
edgeCol |
It must be an R color name that specifies the color of
edges between nodes. Default is |
edgeWidth |
The width of edges. Default is 1 |
subgraph |
An optional |
highlightNodes |
A |
highlightCol |
It must be an R color name that specifies the color of
edges and borders for highlighted nodes. Default is |
highlightWidth |
The width of edges between highlighted nodes. Default is 2 |
legendColorbar |
Logical, whether to add a legend with a color bar for log2 fold changes. Default is TRUE |
legendInteraction |
Logical, whether to add a legend that links edge types to biological interactions. Default is TRUE |
title |
The title of the plot. Default is |
titleCex |
The cex of the plot main title. Default is 2 |
titleFace |
An integer which specifies which font to use for title. 1 corresponds to plain text, 2 to bold face, 3 to italic, 4 to bold italic, and 5 to symbol font. Default is 1 |
The network created by this function is highly flexible, allowing to tweak different parameters that can influence the resulting graph, including node highlighting, layout algorithms, colors, and legends.
For huge messy networks, the user can specify the nodes to include in the
plot through the subgraph parameter, in order to represent only the
features that he wants to display. Alternatively, this parameter can be set
to NULL (default), to plot all nodes of that biological pathway.
One interesting feature offered by this function consists in highlighting
specific nodes and edges within a network. This results particularly useful
when we want to put in evidence affected routes in a biological pathway.
To highlight nodes, you must provide the highlightNodes parameter with a
character vector that lists all the desired nodes. As a result, the
borders of highlighted nodes will be colored according to highlightCol
parameter (default is 'gold'), and will have a width specified by
highlightWidth (default is 2). Notably, this function automatically
highlights in the same way the edges connecting the selected nodes.
Furthermore, this function allows to use different methods to lay out nodes
in the network by setting the algorithm parameter. In this regard, several
algorithms in Rgraphviz package can be used, namely:
dot (default), which is an algorithm attributed to Sugiyama et al. and
described by Gansner et al., that creates a ranked layout that is
particularly suited to display hierarchies and complex pathways;
circo, which uses a recursive radial algorithm resulting in a circular
layout;
fdp, which adopts a force-directed approach similar to that of
Fruchterman and Reingold;
neato, which relies on a spring model where an iterative solver finds
low energy configurations;
osage, which is a layout engine that recursively draws cluster subgraphs;
twopi, which places a node in the center of the network, and then
arranges the remaining nodes in a series of concentric circles around the
center.
For additional information on these algorithms, refer to Rgraphviz::GraphvizLayouts.
To customize the look of the resulting plot, this function allows to change different graphical parameters, including:
the color scale for log2 fold changes, that can be set with lfcScale;
the font size of nodes, which can be changed through fontsize;
the border color for nodes, which can be edited with nodeBorderCol;
the text color of nodes, which can be changed through nodeTextCol;
the color used for edges, set by edgeCol;
the width of edges, customizable with edgeWidth.
Additionally, this function allows to include handy legends that are useful for interpreting the biological consequences of network alterations. In particular:
a color bar legend displaying the log2 fold changes corresponding to each
fill color can be included with legendColorbar = TRUE (default); and
a legend that links the appearance of edges and arrow heads to the type of
biological interaction can be shown through legendInteraction = TRUE
(default).
Lastly, title, titleCex and titleFace parameters can be tweaked to
include a network title with the desired look.
A base R plot with the augmented pathway.
This function uses the Rgraphviz package to render the network object.
Jacopo Ronchi, [email protected]
# load example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # explore a specific biological network visualizeNetwork(obj, "Thyroid hormone synthesis")# load example IntegrativePathwayAnalysis object obj <- loadExamples("IntegrativePathwayAnalysis") # explore a specific biological network visualizeNetwork(obj, "Thyroid hormone synthesis")