Download cell type-specific epigenomic peaks — NOTT2019_get_epigenomic_peaks • echoannot

API access to brain cell type-specific epigenomic peaks (bed format) from Nott et al. (2019).

NOTT2019_get_epigenomic_peaks(
  assays = c("ATAC", "H3K27ac", "H3K4me3"),
  cell_types = c("neurons", "microglia", "oligo", "astrocytes"),
  convert_to_granges = TRUE,
  save_dir = tools::R_user_dir(package = "echoannot", which = "cache"),
  force_new = FALSE,
  nThread = 1,
  verbose = TRUE
)

Source

Nott et al. (2019)

Arguments

assays: Which epigenomic assays to import data from.
cell_types: Which cell-types to import data from.
convert_to_granges: Whether to convert the peaks to a GRanges object.
save_dir: Where to save the processed data.
force_new: If the saved data already exists, re-downloaded anyway.
nThread: Number of threads to parallelise downloads across.
verbose: Print messages.

See also

Other NOTT2019: NOTT2019_bigwig_metadata, NOTT2019_epigenomic_histograms(), NOTT2019_get_interactions(), NOTT2019_get_interactome(), NOTT2019_get_promoter_celltypes(), NOTT2019_get_promoter_interactome_data(), NOTT2019_get_regulatory_regions(), NOTT2019_plac_seq_plot(), NOTT2019_superenhancers(), get_NOTT2019_interactome(), get_NOTT2019_superenhancer_interactome()

Examples

PEAKS <- echoannot::NOTT2019_get_epigenomic_peaks() 
#> Importing previously downloaded files: /github/home/.cache/R/echoannot/NOTT2019_epigenomic_peaks.rds
#> ++ NOTT2019:: 634,540 ranges retrieved.