Version: 1.2.4
Date: 2024-03-15
Title: Data Visualization for QTL Experiments
Description: Functions to plot QTL (quantitative trait loci) analysis results and related diagnostics. Part of 'qtl2', an upgrade of the 'qtl' package to better handle high-dimensional data and complex cross designs.
Depends: R (≥ 3.1.0)
Imports: Rcpp (≥ 0.12.7), assertthat, dplyr, ggplot2, purrr, stringr, tidyr, rlang, graphics, RColorBrewer, grid, qtl2, ggrepel
Suggests: devtools, testthat, roxygen2, knitr, rmarkdown
VignetteBuilder: knitr
License: GPL-3
URL: https://github.com/byandell/qtl2ggplot, https://kbroman.org/qtl2/
BugReports: https://github.com/byandell/qtl2ggplot/issues
Encoding: UTF-8
ByteCompile: true
LinkingTo: Rcpp
RoxygenNote: 7.3.1
NeedsCompilation: yes
Packaged: 2024-03-15 15:45:51 UTC; brianyandell
Author: Brian S Yandell [aut, cre], Karl W Broman [aut]
Maintainer: Brian S Yandell <brian.yandell@wisc.edu>
Repository: CRAN
Date/Publication: 2024-03-15 16:10:02 UTC

Set up col, pattern and group for plotting.

Description

Set up col, pattern and group for plotting.

Usage

color_patterns_get(scan1ggdata, col, palette = NULL)

Arguments

scan1ggdata

data frame to be used for plotting

col

Color for color column in scan1ggdata

palette

for colors (default NULL uses "Dark2" from RColorBrewer package)

Value

list of colors and shapes.

Set up col, pattern, shape and group for plotting.

Description

Set up col, pattern, shape and group for plotting.

Usage

color_patterns_pheno(
  scan1ggdata,
  lod,
  pattern,
  col,
  shape,
  patterns,
  facet = NULL
)

Arguments

scan1ggdata

data frame to be used for plotting

lod

matrix of LOD scores by position and pheno

pattern

allele pattern of form AB:CDEFGH

col

Color for color column in scan1ggdata

shape

Shape for shape column in scan1ggdata

patterns

Connect SDP patterns: one of c("none","all","hilit")

facet

use facet_wrap if not NULL

Value

data frame scan1ggdata with additional objects.

Set up colors for patterns or points

Description

Set up colors for patterns or points

Usage

color_patterns_set(
  scan1output,
  snpinfo,
  patterns,
  col,
  pattern,
  show_all_snps,
  col_hilit,
  drop_hilit,
  maxlod
)

Arguments

scan1output

output of linear mixed model for phename (see scan1)

snpinfo

Data frame with snp information

patterns

Connect SDP patterns: one of c("none","all","hilit").

col

Color of other points, or colors for patterns

pattern

allele pattern as of form AB:CDEFGH

show_all_snps

show all SNPs if TRUE

col_hilit

Color of highlighted points

drop_hilit

SNPs with LOD score within this amount of the maximum SNP association will be highlighted.

maxlod

Maximum LOD for drop of drop_hilit

Value

list of col and pattern.

Plot QTL effects along chromosome

Description

Plot estimated QTL effects along a chromosomes.

Usage

ggplot_coef(
  object,
  map,
  columns = NULL,
  col = NULL,
  scan1_output = NULL,
  gap = 25,
  ylim = NULL,
  bgcolor = "gray90",
  altbgcolor = "gray85",
  ylab = "QTL effects",
  xlim = NULL,
  ...
)

ggplot_coefCC(object, map, colors = qtl2::CCcolors, ...)

## S3 method for class 'scan1coef'
autoplot(object, ...)

Arguments

object

Estimated QTL effects ("coefficients") as obtained from scan1coef.

map

A list of vectors of marker positions, as produced by insert_pseudomarkers.

columns

Vector of columns to plot

col

Vector of colors, same length as columns. If NULL, some default choices are made.

scan1_output

If provided, we make a two-panel plot with coefficients on top and LOD scores below. Should have just one LOD score column; if multiple, only the first is used.

gap

Gap between chromosomes.

ylim

y-axis limits. If NULL, we use the range of the plotted coefficients.

bgcolor

Background color for the plot.

altbgcolor

Background color for alternate chromosomes.

ylab

y-axis label

xlim

x-axis limits. If NULL, we use the range of the plotted coefficients.

...

Additional graphics parameters.

colors

Colors to use for plotting.

Details

ggplot_coefCC() is the same as ggplot_coef(), but forcing columns=1:8 and using the Collaborative Cross colors, CCcolors.

Value

object of class ggplot.

See Also

ggplot_scan1, ggplot_snpasso

Examples

# read data
iron <- qtl2::read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

# insert pseudomarkers into map
map <- qtl2::insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- qtl2::calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno[,1]
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)

# calculate coefficients for chromosome 7
coef <- qtl2::scan1coef(probs[,7], pheno, addcovar=covar)

# plot QTL effects
ggplot2::autoplot(coef, map[7], columns=1:3)

Plot gene locations for a genomic interval

Description

Plot gene locations for a genomic interval, as rectangles with gene symbol (and arrow indicating strand/direction) below.

Usage

ggplot_genes(
  object,
  xlim = NULL,
  minrow = 4,
  padding = 0.2,
  colors = c("black", "red3", "green4", "blue3", "orange"),
  ...
)

## S3 method for class 'genes'
autoplot(object, ...)

Arguments

object

Object of class object

xlim

x-axis limits (in Mbp)

minrow

Minimum number of rows of object

padding

Proportion to pad with white space around the object

colors

Vectors of colors, used sequentially and then re-used.

...

Optional arguments passed to plot.

Value

None.

Examples

filename <- file.path("https://raw.githubusercontent.com/rqtl",
                      "qtl2data/master/DOex", 
                      "c2_genes.rds")
tmpfile <- tempfile()
download.file(filename, tmpfile, quiet=TRUE)
gene_tbl <- readRDS(tmpfile)
unlink(tmpfile)

ggplot_genes(gene_tbl)

GGPlot internal routine for ggplot_genes

Description

Plot genes at positions

Usage

ggplot_genes_internal(
  start,
  end,
  strand,
  rect_top,
  rect_bottom,
  colors,
  space,
  y,
  dir_symbol,
  name,
  xlim,
  xlab = "Position (Mbp)",
  ylab = "",
  bgcolor = "gray92",
  xat = NULL,
  legend.position = "none",
  vlines = NULL,
  ...
)

Arguments

start, end, strand, rect_top, rect_bottom, colors, space, y, dir_symbol, name, xlim

usual parameters

legend.position, vlines, xlab, ylab, bgcolor, xat

hidden parameters

...

Additional graphics parameters.

Value

object of class ggplot.

Plot of object of class listof_scan1coeff

Description

Plot object of class listof_scan1coeff, which is a list of objects of class scan1coef.

Usage

ggplot_listof_scan1coef(
  object,
  map,
  columns = NULL,
  col = NULL,
  scan1_output = NULL,
  facet = "pattern",
  ...
)

## S3 method for class 'listof_scan1coef'
autoplot(object, ...)

Arguments

object

object of class listof_scan1coeff

map

A list of vectors of marker positions, as produced by insert_pseudomarkers.

columns

Vector of columns to plot

col

Vector of colors, same length as columns. If NULL, some default choices are made.

scan1_output

If provided, we make a two-panel plot with coefficients on top and LOD scores below. Should have just one LOD score column; if multiple, only the first is used.

facet

Plot facets if multiple phenotypes and group provided (default = "pattern").

...

arguments for ggplot_coef

pattern

Use phenotype names as pattern.

Value

object of class ggplot

Author(s)

Brian S Yandell, brian.yandell@wisc.edu

Plot one individual's genome-wide genotypes

Description

Plot one individual's genome-wide genotypes

Usage

ggplot_onegeno(
  geno,
  map,
  ind = 1,
  chr = NULL,
  col = NULL,
  shift = FALSE,
  chrwidth = 0.5,
  ...
)

Arguments

geno

Imputed phase-known genotypes, as a list of matrices (as produced by maxmarg) or a list of three-dimensional arrays (as produced by guess_phase).

map

Marker map (a list of vectors of marker positions).

ind

Individual to plot, either a numeric index or an ID (can be a vector).

chr

Selected chromosomes to plot; a vector of character strings.

col

Vector of colors for the different genotypes.

shift

If TRUE, shift the chromosomes so they all start at 0.

chrwidth

Total width of rectangles for each chromosome, as a fraction of the distance between them.

...

Additional graphics parameters

Value

object of class ggplot.

Examples

# load qtl2 package for data and genoprob calculation
library(qtl2)

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# inferred genotypes
geno <- maxmarg(probs)

# plot the inferred genotypes for the first individual
ggplot_onegeno(geno, map, shift = TRUE)

# plot the inferred genotypes for the first four individuals
ggplot_onegeno(geno, map, ind=1:4)

Plot QTL peak locations

Description

Plot QTL peak locations (possibly with intervals) for multiple traits.

Usage

ggplot_peaks(
  peaks,
  map,
  chr = NULL,
  tick_height = 0.3,
  gap = 25,
  bgcolor = "gray90",
  altbgcolor = "gray85",
  ...
)

Arguments

peaks

Data frame such as that produced by find_peaks) containing columns chr, pos, lodindex, and lodcolumn. May also contain columns ci_lo and ci_hi, in which case intervals will be plotted.

map

Marker map, used to get chromosome lengths (and start and end positions).

chr

Selected chromosomes to plot; a vector of character strings.

tick_height

Height of tick marks at the peaks (a number between 0 and 1).

gap

Gap between chromosomes.

bgcolor

Background color for the plot.

altbgcolor

Background color for alternate chromosomes.

...

Additional graphics parameters

Value

None.

See Also

find_peaks

Examples

# load qtl2 package for data and genoprob calculation
library(qtl2)

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)
Xcovar <- get_x_covar(iron)

# perform genome scan
out <- scan1(probs, pheno, addcovar=covar, Xcovar=Xcovar)

# find peaks above lod=3.5 (and calculate 1.5-LOD support intervals)
peaks <- find_peaks(out, map, threshold=3.5, drop=1.5)

# color peaks above 6 red; only show chromosomes with peaks
plot_peaks(peaks, map)
peaks$col <- (peaks$lod > 6)

ggplot_peaks(peaks, map[names(map) %in% peaks$chr], col = c("blue","red"),
           legend.title = "LOD > 6")

Plot phenotype vs genotype

Description

Plot phenotype vs genotype for a single putative QTL and a single phenotype.

Usage

ggplot_pxg(
  geno,
  pheno,
  sort = TRUE,
  SEmult = NULL,
  pooledSD = TRUE,
  jitter = 0.2,
  bgcolor = "gray90",
  seg_width = 0.4,
  seg_lwd = 2,
  seg_col = "black",
  hlines = NULL,
  hlines_col = "white",
  hlines_lty = 1,
  hlines_lwd = 1,
  vlines_col = "gray80",
  vlines_lty = 1,
  vlines_lwd = 3,
  force_labels = TRUE,
  alternate_labels = FALSE,
  omit_points = FALSE,
  ...
)

mean_pxg(geno, pheno, dataframe = NULL)

Arguments

geno

Vector of genotypes, as produced by maxmarg with specific chr and pos.

pheno

Vector of phenotypes.

sort

If TRUE, sort genotypes from largest to smallest.

SEmult

If specified, interval estimates of the within-group averages will be displayed, as mean +/- SE * SEmult.

pooledSD

If TRUE and SEmult is specified, calculated a pooled within-group SD. Otherwise, get separate estimates of the within-group SD for each group.

jitter

Amount to jitter the points horizontally, if a vector of length > 0, it is taken to be the actual jitter amounts (with values between -0.5 and 0.5).

bgcolor

Background color for the plot.

seg_width

Width of segments at the estimated within-group averages

seg_lwd

Line width used to plot estimated within-group averages

seg_col

Line color used to plot estimated within-group averages

hlines

Locations of horizontal grid lines.

hlines_col

Color of horizontal grid lines

hlines_lty

Line type of horizontal grid lines

hlines_lwd

Line width of horizontal grid lines

vlines_col

Color of vertical grid lines

vlines_lty

Line type of vertical grid lines

vlines_lwd

Line width of vertical grid lines

force_labels

If TRUE, force all genotype labels to be shown.

alternate_labels

If TRUE, place genotype labels in two rows

omit_points

If TRUE, omit the points, just plotting the averages (and, potentially, the +/- SE intervals).

...

Additional graphics parameters, passed to plot.

dataframe

Supplied data frame, or constructed from geno and pheno if NULL.

Value

object of class ggplot.

See Also

plot_coef

Examples

# load qtl2 package for data and genoprob calculation
library(qtl2)

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# inferred genotype at a 28.6 cM on chr 16
geno <- maxmarg(probs, map, chr=16, pos=28.6, return_char=TRUE)

# plot phenotype vs genotype
ggplot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)))

# include +/- 2 SE intervals
ggplot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)),
         SEmult=2)

# plot just the means
ggplot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)),
         omit_points=TRUE)

# plot just the means +/- 2 SEs
ggplot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)),
         omit_points=TRUE, SEmult=2)

Plot a genome scan

Description

Plot LOD curves for a genome scan

Plot LOD curves for a genome scan

Usage

ggplot_scan1(
  object,
  map,
  lodcolumn = 1,
  chr = NULL,
  gap = 25,
  bgcolor = "gray90",
  altbgcolor = "gray85",
  ...
)

## S3 method for class 'scan1'
autoplot(object, ...)

ggplot_scan1_internal(
  map,
  lod,
  gap = 25,
  col = NULL,
  shape = NULL,
  pattern = NULL,
  facet = NULL,
  patterns = c("none", "all", "hilit"),
  chrName = "Chr",
  ...
)

Arguments

object

Output of scan1.

map

Map of pseudomarker locations.

lodcolumn

LOD score column to plot (a numeric index, or a character string for a column name). One or more value(s) allowed.

chr

Selected chromosomes to plot; a vector of character strings.

gap

Gap between chromosomes.

bgcolor

Background color for the plot.

altbgcolor

Background color for alternate chromosomes.

...

Additional graphics parameters.

lod

Matrix of lod (or other) values.

col

Colors for points or lines, with labels.

shape

Shapes for points.

pattern

Use to group values for plotting (default = NULL); typically provided by plot_snpasso internal routine.

facet

Plot facets if multiple phenotypes and group provided (default = NULL).

patterns

Connect SDP patterns: one of c("none","all","hilit").

chrName

Add prefix chromosome name (default "Chr").

Value

None.

See Also

ggplot_coef, ggplot_snpasso

Examples

# load qtl2 package for data and genoprob calculation
library(qtl2)

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)
Xcovar <- get_x_covar(iron)

# perform genome scan
out <- scan1(probs, pheno, addcovar=covar, Xcovar=Xcovar)

# plot the results for selected chromosomes
chr <- c(2,7,8,9,15,16)
ggplot_scan1(out, map, lodcolumn=1:2, chr=chr, col=c("darkslateblue","violetred"),
     legend.position=c(0.1,0.9))

# plot just one chromosome
ggplot_scan1(out, map, chr=8, lodcolumn=1:2, col=c("darkblue","violetred"))

# can also use autoplot from ggplot2
# lodcolumn can also be a column name
library(ggplot2)
autoplot(out, map, chr=8, lodcolumn=c("liver","spleen"), col=c("darkblue","violetred"))

Plot SNP associations

Description

Plot SNP associations, with possible expansion from distinct snps to all snps.

Usage

ggplot_snpasso(
  scan1output,
  snpinfo,
  genes = NULL,
  lodcolumn = 1,
  show_all_snps = TRUE,
  drop_hilit = NA,
  col_hilit = "violetred",
  col = "darkslateblue",
  ylim = NULL,
  gap = 25,
  minlod = 0,
  bgcolor = "gray90",
  altbgcolor = "gray85",
  ...
)

Arguments

scan1output

Output of scan1. Should contain an attribute, "snpinfo", as when scan1 are run with SNP probabilities produced by genoprob_to_snpprob.

snpinfo

Data frame with SNP information with the following columns (the last three are generally derived from with index_snps):

  • chr - Character string or factor with chromosome

  • pos - Position (in same units as in the "map" attribute in genoprobs.

  • sdp - Strain distribution pattern: an integer, between 1 and 2^n - 2 where n is the number of strains, whose binary encoding indicates the founder genotypes

  • snp - Character string with SNP identifier (if missing, the rownames are used).

  • index - Indices that indicate equivalent groups of SNPs.

  • intervals - Indexes that indicate which marker intervals the SNPs reside.

  • on_map - Indicate whether SNP coincides with a marker in the genoprobs

genes

Optional data frame containing gene information for the region, with columns 'start' and 'stop' in Mbp, 'strand' (as '"-"', '"+"', or 'NA'), and 'Name'. If included, a two-panel plot is produced, with SNP associations above and gene locations below.

lodcolumn

LOD score column to plot (a numeric index, or a character string for a column name). One or more value(s) allowed.

show_all_snps

If TRUE, expand to show all SNPs.

drop_hilit

SNPs with LOD score within this amount of the maximum SNP association will be highlighted.

col_hilit

Color of highlighted points

col

Color of other points

ylim

y-axis limits

gap

Gap between chromosomes.

minlod

Minimum LOD to display. (Mostly for GWAS, in which case using 'minlod=1' will greatly increase the plotting speed, since the vast majority of points would be omittted.

bgcolor

Background color for the plot.

altbgcolor

Background color for alternate chromosomes.

...

Additional graphics parameters.

Value

object of class ggplot.

Hidden graphics parameters

A number of graphics parameters can be passed via '...'. For example, 'bgcolor' to control the background color and 'altbgcolor' to control the background color on alternate chromosomes. 'cex' for character expansion for the points (default 0.5), 'pch' for the plotting character for the points (default 16), and 'ylim' for y-axis limits.

See Also

ggplot_scan1, ggplot_coef

Examples

dirpath <- "https://raw.githubusercontent.com/rqtl/qtl2data/master/DOex"

# Read DOex example cross from 'qtl2data'
DOex <- subset(qtl2::read_cross2(file.path(dirpath, "DOex.zip")), chr = "2")

# Download genotype probabilities
tmpfile <- tempfile()
download.file(file.path(dirpath, "DOex_genoprobs_2.rds"), tmpfile, quiet=TRUE)
pr <- readRDS(tmpfile)
unlink(tmpfile)

# Download SNP info for DOex from web and read as RDS.
tmpfile <- tempfile()
download.file(file.path(dirpath, "c2_snpinfo.rds"), tmpfile, quiet=TRUE)
snpinfo <- readRDS(tmpfile)
unlink(tmpfile)
snpinfo <- dplyr::rename(snpinfo, pos = pos_Mbp)

# Convert to SNP probabilities
snpinfo <- qtl2::index_snps(DOex$pmap, snpinfo)
snppr <- qtl2::genoprob_to_snpprob(pr, snpinfo)

# Scan SNPs.
scan_snppr <- qtl2::scan1(snppr, DOex$pheno)

# plot results
ggplot_snpasso(scan_snppr, snpinfo, show_all_snps=FALSE, patterns="all", drop_hilit=1.5)


# can also just type autoplot() if ggplot2 attached
library(ggplot2)

# plot just subset of distinct SNPs
autoplot(scan_snppr, snpinfo, show_all_snps=FALSE, drop_hilit=1.5)

# highlight SDP patterns in SNPs; connect with lines.
autoplot(scan_snppr, snpinfo, patterns="all",drop_hilit=4)

# query function for finding genes in region
gene_dbfile <- system.file("extdata", "mouse_genes_small.sqlite", package="qtl2")
query_genes <- qtl2::create_gene_query_func(gene_dbfile)
genes <- query_genes(2, 97, 98)

# plot SNP association results with gene locations
autoplot(scan_snppr, snpinfo, patterns="hilit", drop_hilit=1.5, genes=genes)

List of scan1coef objects

Description

Create a list of scan1coef objects using scan1coef.

Summary of object of class listof_scan1coef, which is a list of objects of class scan1coef.

Summary of object of class listof_scan1coef, which is a list of objects of class scan1coef.

Subset of object of class listof_scan1coef, which is a list of objects of class scan1coef.

Usage

listof_scan1coef(
  probs,
  phe,
  K = NULL,
  covar = NULL,
  blups = FALSE,
  center = FALSE,
  ...
)

summary_listof_scan1coef(
  object,
  scan1_object,
  map,
  coef_names = dimnames(object[[1]])[[2]],
  center = TRUE,
  ...
)

## S3 method for class 'listof_scan1coef'
summary(object, ...)

summary_scan1coef(object, scan1_object, map, ...)

## S3 method for class 'scan1coef'
summary(object, ...)

subset_listof_scan1coef(x, elements, ...)

## S3 method for class 'listof_scan1coef'
subset(x, ...)

## S3 method for class 'listof_scan1coef'
x[...]

Arguments

probs

genotype probabilities object for one chromosome from calc_genoprob

phe

data frame of phenotypes

K

list of length 1 with kinship matrix

covar

matrix of covariates

blups

Create BLUPs if TRUE

center

center coefficients if TRUE

...

ignored

object

object of class listof_scan1coef

scan1_object

object from scan1

map

A list of vectors of marker positions, as produced by insert_pseudomarkers.

coef_names

names of effect coefficients (default is all coefficient names)

x

object of class listof_scan1coef

elements

indexes or names of list elements in x

Value

object of class listof_scan1coef

Author(s)

Brian S Yandell, brian.yandell@wisc.edu

Examples

# read data
iron <- qtl2::read_cross2(system.file("extdata", "iron.zip", package="qtl2"))

# insert pseudomarkers into map
map <- qtl2::insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- qtl2::calc_genoprob(iron, map, error_prob=0.002)

# Ensure that covariates have names attribute
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)

# Calculate scan1coef on all phenotypes,
# returning a list of \code{\link{scan1coef}} objects
out <- listof_scan1coef(probs[,7], iron$pheno, addcovar = covar, center = TRUE)

# Plot coefficients for all phenotypes
ggplot2::autoplot(out, map[7], columns = 1:3)

# Summary of coefficients at scan peak
scan_pr <- qtl2::scan1(probs[,7], iron$pheno)
summary(out, scan_pr, map[7])

Convert sdp to pattern

Description

Convert strain distribution pattern (sdp) to letter pattern. Taken from package 'qtl2pattern' for internal use here.

Usage

sdp_to_pattern(sdp, haplos, symmetric = TRUE)

Arguments

sdp

vector of sdp values

haplos

letter codes for haplotypes (required)

symmetric

make patterns symmetric if TRUE

Value

vector of letter patterns

Author(s)

Brian S Yandell, brian.yandell@wisc.edu

Summary of scan1 object

Description

Summary of scan1 object

Usage

summary_scan1(
  object,
  map,
  snpinfo = NULL,
  lodcolumn = seq_len(ncol(object)),
  chr = names(map),
  sum_type = c("common", "best"),
  drop = 1.5,
  show_all_snps = TRUE,
  ...
)

## S3 method for class 'scan1'
summary(object, ...)

Arguments

object

object from scan1

map

A list of vectors of marker positions, as produced by insert_pseudomarkers.

snpinfo

Data frame with SNP information with the following columns (the last three are generally derived from with index_snps):

  • chr - Character string or factor with chromosome

  • pos - Position (in same units as in the "map" attribute in genoprobs.

  • sdp - Strain distribution pattern: an integer, between 1 and 2^n - 2 where n is the number of strains, whose binary encoding indicates the founder genotypes

  • snp - Character string with SNP identifier (if missing, the rownames are used).

  • index - Indices that indicate equivalent groups of SNPs.

  • intervals - Indexes that indicate which marker intervals the SNPs reside.

  • on_map - Indicate whether SNP coincides with a marker in the genoprobs

lodcolumn

one or more lod columns

chr

one or more chromosome IDs

sum_type

type of summary

drop

LOD drop from maximum

show_all_snps

show all SNPs if TRUE

...

other arguments not used

Value

tbl summary

Author(s)

Brian S Yandell, brian.yandell@wisc.edu

Examples

# read data
iron <- qtl2::read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
# insert pseudomarkers into map
map <- qtl2::insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- qtl2::calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)
Xcovar <- qtl2::get_x_covar(iron)

# perform genome scan
out <- qtl2::scan1(probs, pheno, addcovar=covar, Xcovar=Xcovar)

# summary
summary(out, map)