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Make a plot that lines up manhattan and gene locations

Source: R/plot_panvar.R
plot_panvar.Rd

Make a plot that lines up manhattan and gene locations

Usage

plot_panvar(
  panvar.table.list = NULL,
  gwas.res = NULL,
  ld.list = NULL,
  annotation.table = NULL,
  pvals.in.log = T,
  plot.r2.thresh = 0.2,
  unplotted.alpha = 0.4,
  window,
  sig.line,
  point.shape.variable = NULL,
  point.shape.scale = NULL,
  point.fill.variable.c = NULL,
  point.fill.scale.c = NULL,
  point.fill.variable.d = NULL,
  point.fill.scale.d = NULL,
  plot.title = "",
  include.gene.id = F,
  highlight.gene.ids = NULL,
  gene.highlight.color = "red",
  annotation.point.variable = "LD",
  annotation.point.scale = NULL,
  plot.effect = F,
  plot.text.size = 11,
  plot.legend.size = 1.2,
  snp.highlight.df = NULL,
  snp.highlight.point.size = 4,
  snp.highlight.shape.var = NULL,
  snp.highlight.shape.scale = NULL,
  snp.highlight.color.var = NULL,
  snp.highlight.color.scale = NULL
)

Arguments

panvar.table.list

list, output from make_panvar_tables. Provide either this list or both gwas.res and ld.list.

gwas.res

data.frame of all gwas results, should contain columns (CHR, POS, PVAL), corresponding to (chromosome, physical position, and pvalue).

ld.list

list, output of get_ld_in_window

annotation.table

table with annotations with columns (geneID, CHR, start, end, annotation). start and end correspond to base-pair coordinates of start and end of gene. CHR is chromosome of gene.

pvals.in.log

boolean, if TRUE PVAL column has already been converted to -log10(pvalue)

plot.r2.thresh

minimum LD with qtl snps to plot snps colored by LD

unplotted.alpha

numeric, number from 0 to 1 to indicate alpha values of snps below the plot.r2.thresh. To not plot these snps set value to 0.

window

numeric, total window size in KB, all variants within .5 * window are calculated.

sig.line

numeric, -log10(p) value to draw line on plot

point.shape.variable

character, column in gwas.res that contains qualitative annotations to be mapped to point shapes. For example impact grades from snpeff. See format_snpeff_annotations. Only accepts up to 5 classes. "IMPACT" and "IMPACT_PLUS" are special cases that will have a pre-assigned scale used if supplied here.

point.shape.scale

ggplot scale, an object with a stored call to ggplot2::scale_shape_manual. More often an output of the function make_consistent_scale.

point.fill.variable.c

character, column in gwas.res that contains quantitative annotations to be plotted as a continuous variable mapped to point fill. For example, variant effect scores. Only provide either continuous or discrete quantitative annotations.

point.fill.scale.c

character or scale object, either a character indicating the option parameter passed to ggplot2::scale_fill_viridis_b that alters the color scale used. Or a previous call to a ggplot2 continuous fill scale for example ggplot2::scale_fill_stepsn.

point.fill.variable.d

character, column in gwas.res that contains annotations to be plotted as a discrete variable mapped to point fill. For example, Year or Trial if combining multiple gwas results. Only provide either continuous or discrete quantitative annotations.

point.fill.scale.d

character or scale object, either a character indicating the option parameter passed to ggplot2::scale_fill_viridis_d that alters the color scale used. Or a previous call to a ggplot2 discrete fill scale for example ggplot2::scale_fill_discrete.

plot.title

character, title of plot.

include.gene.id

boolean, if TRUE, geneID column will be included in annotation plot.

highlight.gene.ids

character, vector of geneID's that will be highlighted in the plot.

gene.highlight.color

character, a color to highlight specific geneIDs

annotation.point.variable

character, variable in annotation.table that indicates how to color points plotted next to gene descriptions. If not supplied, no points are plotted. The input "LD" is reserved and will use LD.

annotation.point.scale

ggplot2 scale object, a color scale to customize how point.color is displayed.

plot.effect

boolean, if TRUE include volcano style effect vs pvalue plot as inset.

plot.text.size

numeric, size of text in plot.

plot.legend.size

numeric, size of legend. Passed to ggplot2::theme, legend.key.size.

snp.highlight.df

data.frame, table of specific snps that will be highlighted. By default the point size will be larger and they will be plotted red. Data.frame should contain columns (CHR, POS, PVAL). PVAL column format should reflect pvals.in.log.

snp.highlight.point.size

numeric, size of points plotted from snp.highlight.df.

snp.highlight.shape.var

character, column in snp.highlight.df to be mapped to shape of points. Only one of point.shape.variable or snp.highlight.shape.var should be provided.

snp.highlight.shape.scale

ggplot scale, an object with a stored call to ggplot2::scale_shape_manual. More often an output of the function make_consistent_scale. Works best with shapes 15-20 whose color aesthetics map to the entire shape instead of just the outline.

snp.highlight.color.var

character, column in snp.highlight.df to be mapped to color of highlighted points.

snp.highlight.color.scale

ggplot scale, an object with a stored call to ggplot2::scale_shape_manual. More often an output of the function make_consistent_scale.

Value

ggplot2 object of plot with manhattan plot alongside genes for a given genomic window.

Examples

# organize options
tag.snp <- "Chr_05-6857045"
gwas.df <- read.csv(system.file(
    "extdata",
    "PanvarExample_GLM_GWASresults.csv",
    package = "panvaR"))
annotation.table <- read.csv(system.file(
    "extdata",
    "Setaria_shattering_annotation.csv",
    package = "panvaR"))
plink.path <- bigsnpr::download_plink2()
temp.dir <- file.path(tempdir(), "panvar_ex")
dir.create(temp.dir, showWarnings = FALSE)
geno.bed.filename <- "Setaria_shattering_example_pruned.bed"
geno.bed.directory <- system.file("extdata", package="panvaR")

# make input tables
tables <- make_panvar_tables(
  gwas.res = gwas.df,
  tag.snp = tag.snp,
  annotation.table = annotation.table,
  plink.path = plink.path,
  pvals.in.log = F,
  geno.bed.filename = geno.bed.filename,
  geno.bed.directory = geno.bed.directory,
  window = 25,
  temp.dir = temp.dir,
  compute.scores = FALSE,
  snp.to.gene.buffer = 0)
#> Calculating LD
#> Generating snp to gene correspondence
  
# make plot
plot_panvar(
  panvar.table.list = tables,
  pvals.in.log = FALSE,
  window = 25,
  sig.line = 6)
#> Making manhattan
#> Making annotation plot

  
# clean up 
unlink(temp.dir, recursive = TRUE)