R/peak_overlap_plot.R
peak_overlap_plot.RdPlot overlap between some SNP group and various epigenomic data
peak_overlap_plot(
merged_DT,
snp_filter = "Consensus_SNP==TRUE",
force_new = FALSE,
include.NOTT2019_peaks = TRUE,
include.NOTT2019_enhancers_promoters = TRUE,
include.NOTT2019_PLACseq = TRUE,
include.CORCES2020_scATACpeaks = TRUE,
include.CORCES2020_Cicero_coaccess = TRUE,
include.CORCES2020_bulkATACpeaks = TRUE,
include.CORCES2020_HiChIP_FitHiChIP_coaccess = TRUE,
include.CORCES2020_gene_annotations = TRUE,
plot_celltype_specificity = TRUE,
plot_celltype_specificity_genes = FALSE,
facets_formula = ". ~ Cell_type",
show_plot = TRUE,
label_yaxis = TRUE,
x_strip_angle = 90,
x_tick_angle = 40,
drop_empty_cols = FALSE,
fill_title = paste(snp_filter, "\nin epigenomic peaks"),
save_path = FALSE,
height = 11,
width = 12,
subplot_widths = c(1, 0.5),
verbose = TRUE
)Nott et al., 2019 (The Lancet Neurology) (doi:10.1126/science.aay0793 ) Corces et al., 2020 (Nature Genetics) (doi:10.1038/s41588-020-00721-x )
Don't use previously downloaded files.
Plot SNP subset overlap with peaks from cell-type-specific bulk ATAC, H3K27ac, and H3K4me3 assays.
Plot SNP subset overlap with cell enhancers and promoters.
Plot SNP subset overlap with cell-type-specific scATAC-seq peaks.
Plot SNP subset overlap with Cicero coaccessibility peaks (derived from scATACseq).
Other summarise:
CS_bin_plot(),
CS_counts_plot(),
plot_dataset_overlap(),
super_summary_plot()