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Perform functional enrichment analysis of genes

Source: R/enrichment.R
enrichGenes.Rd

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.

Usage

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
)

Arguments

mirnaObj

A MirnaExperiment object containing miRNA and gene data

method

The functional enrichment analysis to perform. It must be one of ORA, GSEA (default), and CAMERA. For additional information, see the details section

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

rankMetric

The ranking statistic used to order genes before performing GSEA. It must be one of signed.pval (default), logFC, and log.pval. For additional information, refer to the details section

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

Value

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().

Details

Enrichment method

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.

Databases and categories

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:

  • Disease Ontology (DO);

  • Network of Cancer Genes (NCG):

    • v6, for the sixth version;

    • v7, for the seventh version;

  • DisGeNET;

  • COVID-19.

Supported organisms

For each database, different organisms are supported. To check the supported organisms for a given database, MIRit provides the supportedOrganisms() function.

GSEA ranking statistic

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.

Note

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.

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.

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.

Author

Jacopo Ronchi, jacopo.ronchi@unimib.it

Examples

# \donttest{
# load example MirnaExperiment object
obj <- loadExamples()

# perform ORA with GO
de_enr <- enrichGenes(obj, method = "ORA", database = "GO")
#> Since not specified, 'category' for GO database is set to bp (default).
#> Reading GO gene-sets from cache...
#> Performing the enrichment of upregulated genes...
#> Performing the enrichment of downregulated genes...
#> The enrichment of genes reported 9 significantly enriched terms for downregulated genes and 0 for upregulated genes.
# }