plot_locus: Generate a locus plot

In RajLabMSSM/echoplot: echoverse module: Locus plot creation for fine-mapping and colocalization studies

Generate a locus plot

Description

Generate a locus-specific plot with multiple selectable tracks. Users can also generate multiple zoomed in views of the plot at multiple resolutions.

Usage

plot_locus(
  dat,
  locus_dir,
  LD_matrix = NULL,
  LD_reference = NULL,
  facet_formula = "Method~.",
  dataset_type = "GWAS",
  color_r2 = TRUE,
  finemap_methods = c("ABF", "FINEMAP", "SUSIE", "POLYFUN_SUSIE"),
  track_order = NULL,
  track_heights = NULL,
  plot_full_window = TRUE,
  dot_summary = FALSE,
  qtl_suffixes = NULL,
  mean.PP = TRUE,
  credset_thresh = 0.95,
  consensus_thresh = 2,
  sig_cutoff = 5e-08,
  gene_track = TRUE,
  tx_biotypes = NULL,
  point_size = 1,
  point_alpha = 0.6,
  density_adjust = 0.2,
  snp_group_lines = c("Lead", "UCS", "Consensus"),
  labels_subset = c("Lead", "CS", "Consensus"),
  xtext = FALSE,
  show_legend_genes = TRUE,
  xgr_libnames = NULL,
  xgr_n_top = 5,
  roadmap = FALSE,
  roadmap_query = NULL,
  roadmap_n_top = 7,
  zoom_exceptions_str = "*full window$|zoom_polygon",
  nott_epigenome = FALSE,
  nott_regulatory_rects = TRUE,
  nott_show_placseq = TRUE,
  nott_binwidth = 200,
  nott_bigwig_dir = NULL,
  save_plot = FALSE,
  show_plot = TRUE,
  genomic_units = "Mb",
  strip.text.y.angle = 0,
  max_transcripts = 1,
  zoom = c("1x"),
  dpi = 300,
  height = 12,
  width = 10,
  plot_format = "png",
  save_RDS = FALSE,
  return_list = FALSE,
  conda_env = "echoR_mini",
  nThread = 1,
  verbose = TRUE
)

Arguments

dat	Data to query transcripts with.
locus_dir	Storage directory to use.
LD_matrix	LD matrix.
LD_reference	LD reference to use: "1KGphase1" : 1000 Genomes Project Phase 1 (genome build: hg19). "1KGphase3" : 1000 Genomes Project Phase 3 (genome build: hg19). "UKB" : Pre-computed LD from a British European-decent subset of UK Biobank. Genome build : hg19 "<vcf_path>" : User-supplied path to a custom VCF file to compute LD matrix from. Accepted formats: .vcf / .vcf.gz / .vcf.bgz Genome build : defined by user with target_genome. "<matrix_path>" : User-supplied path to a pre-computed LD matrix Accepted formats: .rds / .rda / .csv / .tsv / .txt Genome build : defined by user with target_genome.
facet_formula	Formula to facet plots by. See facet_grid for details.
dataset_type	Dataset type (e.g. "GWAS" or "eQTL").
color_r2	Whether to color data points (SNPs) by how strongly they correlate with the lead SNP (i.g. LD measured in terms of r2).
finemap_methods	Fine-mapping methods to plot tracks for, where the y-axis show the Posterior Probabilities (PP) of each SNP being causal.
track_order	The order in which tracks should appear (from top to bottom).
track_heights	The height of each track (from top to bottom).
plot_full_window	Include a track with a Manhattan plot of the full GWAS/eQTL locus (not just the zoomed-in portion).
dot_summary	Include a dot-summary plot that highlights the Lead, Credible Set, and Consensus SNPs.
qtl_suffixes	If columns with QTL data is included in dat, you can indicate which columns those are with one or more string suffixes (e.g. qtl_suffixes=c(".eQTL1",".eQTL2") to use the columns "P.QTL1", "Effect.QTL1", "P.QTL2", "Effect.QTL2").
mean.PP	Include a track showing mean Posterior Probabilities (PP) averaged across all fine-mapping methods.
credset_thresh	The minimum fine-mapped posterior probability for a SNP to be considered part of a Credible Set. For example, credset_thresh=.95 means that all Credible Set SNPs will be 95% Credible Set SNPs.
consensus_thresh	The minimum number of fine-mapping tools in which a SNP is in the Credible Set in order to be included in the "Consensus_SNP" column.
sig_cutoff	Filters out SNPs to plot based on an (uncorrected) p-value significance cutoff.
gene_track	Include a track showing gene bodies.
tx_biotypes	Transcript biotypes to include in the gene model track. By default (NULL), all transcript biotypes will be included. See get_tx_biotypes for a full list of all available biotypes
point_size	Size of each data point.
point_alpha	Opacity of each data point.
density_adjust	Passed to adjust argument in geom_density.
snp_group_lines	Include vertical lines to help highlight SNPs belonging to one or more of the following groups: Lead, Credible Set, Consensus.
labels_subset	Include colored shapes and RSID labels to help highlight SNPs belonging to one or more of the following groups: Lead, Credible Set, Consensus.
xtext	Include x-axis title and text for each track (not just the lower-most one).
show_legend_genes	Show the legend for the gene_track.
xgr_libnames	Passed to XGR_plot. Which XGR annotations to check overlap with. For full list of libraries see here. Passed to the RData.customised argument in xRDataLoader. Examples: "ENCODE_TFBS_ClusteredV3_CellTypes" "ENCODE_DNaseI_ClusteredV3_CellTypes" "Broad_Histone"
xgr_n_top	Passed to XGR_plot. Number of top annotations to be plotted (passed to XGR_filter_sources and then XGR_filter_assays).
roadmap	Find and plot annotations from Roadmap.
roadmap_query	Only plot annotations from Roadmap whose metadata contains a string or any items from a list of strings (e.g. "brain" or c("brain","liver","monocytes")).
roadmap_n_top	Passed to ROADMAP_plot. Number of top annotations to be plotted (passed to ROADMAP_query).
zoom_exceptions_str	Names of tracks to exclude when zooming.
nott_epigenome	Include tracks showing brain cell-type-specific epigenomic data from Nott et al. (2019).
nott_regulatory_rects	Include track generated by NOTT2019_epigenomic_histograms.
nott_show_placseq	Include track generated by NOTT2019_plac_seq_plot.
nott_binwidth	When including Nott et al. (2019) epigenomic data in the track plots, adjust the bin width of the histograms.
nott_bigwig_dir	Instead of pulling Nott et al. (2019) epigenomic data from the UCSC Genome Browser, use a set of local bigwig files.
save_plot	Save plot as RDS file.
show_plot	Print plot to screen.
genomic_units	Which genomic units to return window limits in.
strip.text.y.angle	Angle of the y-axis facet labels.
max_transcripts	Maximum number of transcripts per gene.
zoom	Zoom into the center of the locus when plotting (without editing the fine-mapping results file). You can provide either: The size of your plot window in terms of basepairs (e.g. zoom=50000 for a 50kb window). How much you want to zoom in (e.g. zoom="1x" for the full locus, zoom="2x" for 2x zoom into the center of the locus, etc.). You can pass a list of window sizes (e.g. c(50000,100000,500000)) to automatically generate multiple views of each locus. This can even be a mix of different style inputs: e.g. c("1x","4.5x",25000).
dpi	dpi to use for raster graphics
height	height (defaults to the height of current plotting window)
width	width (defaults to the width of current plotting window)
plot_format	Format to save plot as when saving with ggsave.
save_RDS	Save the tracks as an RDS file (Warning: These plots take up a lot disk space).
return_list	Return a named list with each track as a separate plot (default: FALSE). If TRUE, will return a merged plot using wrap_plots.
conda_env	Conda environments to search in. If NULL (default), will search all conda environments.
nThread	Number of threads to parallelize over.
verbose	Print messages.

Examples

dat1 <- echodata::BST1
LD_matrix <- echodata::BST1_LD_matrix
locus_dir <- file.path(tempdir(),echodata::locus_dir)
plt <- echoplot::plot_locus(dat = dat1,
                            locus_dir = locus_dir,
                            LD_matrix = LD_matrix,
                            show_plot = TRUE)

RajLabMSSM/echoplot documentation built on Oct. 24, 2023, 2:41 a.m.