Enrichment analyses

Perform GO or KEGG enrichment analysis based on the clusterProfiler package.

Usage

enrichEWAS(
  input,
  filename = "default",
  method = "GO",
  filterP = "PVAL",
  cutoff = 0.05,
  ont = "BP",
  pool = FALSE,
  plot = TRUE,
  plotType = "dot",
  plotcolor = "p.adjust",
  x = "GeneRatio",
  showCategory = 10,
  width = 11,
  height = 7,
  pvalueCutoff = 0.05,
  pAdjustMethod = "BH",
  qvalueCutoff = 0.2
)

Arguments

• input:

  An R6 class integrated with all the information obtained from the startEWAS or plotEWAS or bootEWAS function.

• filename:

  User-customized .xlsx file name for storing EWAS results. If "default" is chosen, it will be named as "enrichresult".

• method:

  Methods of enrichment analysis, including "GO" and "KEGG".

• filterP:

  The name of the p value columns such as "PVAL", "FDR", and "Bonfferoni." Users use this P-value to screen for significance sites and further conduct enrichment analysis.

• cutoff:

  The cutoff value of the P-value used to filter for further enrichment analysis. The default is 0.05.

• ont:

  When choosing GO enrichment analysis, select the GO sub-ontology for which the enrichment analysis will be performed. One of "BP", "MF", and "CC" sub-ontologies, or "ALL" for all three. Default to "BP".

• pool:

  If ont='ALL', whether pool three GO sub-ontologies.

• plot:

  Whether the results of enrichment analysis need to be visualized, the default is TRUE

• plotType:

  Whether to draw a bar plot ("bar") or a dot plot ("dot"), the default is "dot".

• plotcolor:

  It is the vertical axis of the picture of the enrichment analysis results. Users can choose "pvalue" or "p.adjust" or "qvalue". The default is "p.adjust".

• x:

  Character string specifying the variable to be used on the x-axis of the plot. Common options are "GeneRatio" or "Count".

  • "GeneRatio": ratio of input genes annotated to a given term.

  • "Count": the number of input genes annotated to the term.

• showCategory:

  The number of categories which will be displayed in the plots. Default to 10.

• width:

  Width of the PDF output in inches. Default is 11.

• height:

  Height of the PDF output in inches. Default is 7.

• pvalueCutoff:

  The p-value threshold used to filter enrichment results. Only results that pass the p-value test (i.e., those smaller than this value) will be reported. This value refers to the p-value before adjustment. The p-value represents the probability of observing the current level of enrichment under the assumption of no enrichment. The smaller the p-value, the more significant the enrichment result.

• pAdjustMethod:

  The p-value adjustment method used for multiple hypothesis testing, aimed at reducing false positives caused by multiple comparisons. One of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

• qvalueCutoff:

  qvalue cutoff on enrichment tests to report as significant. The q-value is the result of controlling the false discovery rate (FDR) and represents the proportion of false positives that may occur when conducting multiple tests.Tests must pass i) pvalueCutoff on unadjusted pvalues, ii) pvalueCutoff on adjusted pvalues and iii) qvalueCutoff on qvalues to be reported. The default is 0.2.

Value

input, An R6 class object integrating all information.

Examples

if (FALSE) { # \dontrun{
res <- initEWAS(outpath = "default")
res <- loadEWAS(input = res, ExpoData = "default", MethyData = "default")
res <- transEWAS(input = res, Vars = "cov1", TypeTo = "factor")
res <- startEWAS(input = res, chipType = "EPICV2", model = "lm", expo = "default", adjustP = TRUE)
res <- plotEWAS(input = res, pval = "PVAL")
res <- bootEWAS(input = res, filterP = "PVAL", cutoff = 0.05, times = 100)
res <- enrichEWAS(input = res, method = "GO", filterP = "PVAL", cutoff = 0.05, pAdjustMethod = "BH")
} # }