Perform an enrichment analysis of integrated microRNA targets

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.

Usage

enrichTargets(
  mirnaObj,
  database = "GO",
  category = NULL,
  organism = "Homo sapiens",
  pCutoff = 0.05,
  pAdjustment = "fdr",
  minSize = 10L,
  maxSize = 500L
)

Arguments

mirnaObj: A MirnaExperiment object containing miRNA and gene data
database: The name of the database used for the enrichment analysis. It must be one of: GO, KEGG, MsigDB, WikiPathways, Reactome, Enrichr, DO, NCG, DisGeNET, COVID19. Default is GO
category: The desired subcategory of gene sets present in database. Please, see the details section to check the available categories for each database. Default is NULL to use default categories
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 supportedOrganisms() function. Default is Homo sapiens
pCutoff: The adjusted p-value cutoff to use for statistical significance. The default value is 0.05
pAdjustment: The p-value correction method for multiple testing. It must be one of: fdr (default), BH, none, holm, hochberg, hommel, bonferroni, BY
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

Value

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().

Details

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.

Note

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.

References

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.

Author

Jacopo Ronchi, jacopo.ronchi@unimib.it

Examples

# load example MirnaExperiment object
obj <- loadExamples()

# perform enrichment analysis of integrated targets with DO
targets_enrichment <- enrichTargets(obj, database = "DO")
#> Reading DO gene-sets from cache...
#> Performing the enrichment of upregulated genes...
#> Performing the enrichment of downregulated genes...
#> The enrichment of genes reported 113 significantly enriched terms for downregulated genes and 0 for upregulated genes.