Given the results from `cinaR` it produces dot plots for enrichment analyses.

dot_plot(results, fdr.cutoff = 0.1, filter.pathways = FALSE)

Arguments

results

cinaR result object

fdr.cutoff

Pathways with smaller fdr values than the cut-off will be shown as dots.

filter.pathways

logical, it will filter the pathways from dot plot with fdr values less than `fdr.cutoff`.

Value

ggplot object

Examples

# \donttest{ library(cinaR) data(atac_seq_consensus_bm) # calls 'bed' # a vector for comparing the examples contrasts <- sapply(strsplit(colnames(bed), split = "-", fixed = TRUE), function(x){x[1]})[4:25] results <- cinaR(bed, contrasts, reference.genome = "mm10")
#> >> Experiment type: ATAC-Seq
#> >> Matrix is filtered!
#> >> preparing features information... 2021-04-07 22:05:11 #> >> identifying nearest features... 2021-04-07 22:05:11 #> >> calculating distance from peak to TSS... 2021-04-07 22:05:12 #> >> assigning genomic annotation... 2021-04-07 22:05:12 #> >> assigning chromosome lengths 2021-04-07 22:05:13 #> >> done... 2021-04-07 22:05:13
#> >> Method: edgeR #> FDR:0.05& abs(logFC)<0
#> >> Estimating dispersion...
#> >> Fitting GLM...
#> >> DA peaks are found!
#> >> No `geneset` is specified so immune modules (Chaussabel, 2008) will be used!
#> >> enrichment.method` is not selected. Hyper-geometric p-value (HPEA) will be used!
#> >> Mice gene symbols are converted to human symbols!
#> >> Enrichment results are ready...
#> >> Done!
dot_plot(results)
#> Warning: Removed 54 rows containing missing values (geom_point).
# }