Version: 0.30
Date: 2024-04-04
Title: Convert Data among QTL Mapping Packages
Description: Functions to convert data structures among the 'qtl2', 'qtl', and 'DOQTL' packages for mapping quantitative trait loci (QTL).
Author: Karl W Broman ORCID iD [aut, cre]
Maintainer: Karl W Broman <broman@wisc.edu>
Depends: R (≥ 3.1.0)
Imports: Rcpp (≥ 0.12.12), qtl (≥ 1.40-8), qtl2 (≥ 0.18), utils, stats
Suggests: testthat, devtools, roxygen2
License: GPL-3
URL: https://kbroman.org/qtl2/, https://github.com/rqtl/qtl2convert
BugReports: https://github.com/rqtl/qtl2convert/issues
LinkingTo: Rcpp
Encoding: UTF-8
ByteCompile: true
RoxygenNote: 7.3.1
NeedsCompilation: yes
Packaged: 2024-04-04 18:31:38 UTC; kbroman
Repository: CRAN
Date/Publication: 2024-04-04 19:40:02 UTC

qtl2convert: Convert Data among QTL Mapping Packages

Description

Functions to convert data structures among the 'qtl2', 'qtl', and 'DOQTL' packages for mapping quantitative trait loci (QTL).

Vignettes

Author(s)

Maintainer: Karl W Broman broman@wisc.edu (ORCID)

See Also

Useful links:

Combine matrices by columns, replacing matching ones and adding unique ones

Description

This is like base::cbind() but if a column in the second matrix has the same name as a column in the first matrix, the column in the first matrix is deleted and that in the second matrix is used in its place.

Usage

cbind_smother(mat1, mat2)

Arguments

mat1

A matrix

mat2

Another matrix, with the same number of rows as mat.

Value

The two matrices combined by columns, but columns in the first matrix that also appear in the second matrix are deleted and replaced by those in the second matrix. Uses the row names to align the rows in the two matrices, and to expand them as needed.

Examples

df1 <- data.frame(x=c(1,2,3,NA,4), y=c(5,8,9,10,11), row.names=c("A", "B", "C", "D", "E"))
df2 <- data.frame(z=c(7,8,0,9,10), y=c(6,NA,NA,9,10), row.names=c("A", "B", "F", "C", "D"))
df1n2 <- cbind_smother(df1, df2)

Count the unique genotypes for each row of a genotype matrix

Description

For genotype data (markers x individuals) on a set of individuals, count the unique genotypes for each marker

Usage

count_unique_geno(genotypes, na.strings = c("N", "H", "NA", ""), cores = 1)

Arguments

genotypes

Matrix of genotypes (markers x individuals)

na.strings

Genotypes to be considered as missing values.

cores

Number of CPU cores to use, for parallel calculations. (If 0, use parallel::detectCores().) Alternatively, this can be links to a set of cluster sockets, as produced by parallel::makeCluster().

Value

Vector of counts of unique genotypes.

See Also

find_unique_geno()

Examples

g <- rbind(c("NA", "A",  "A",  "A", "T"),
           c("NA", "NA", "NA", "A", "A"),
           c("A",  "A",  "T",  "G", "G"),
           c("C", "C",  "G",  "G", "NA"))
counts <- count_unique_geno(g)

Convert cross2 object from do to genail8

Description

Convert a cross2 object from cross type "do" to cross type "genail8".

Usage

cross2_do_to_genail8(cross)

cross2_do_to_genail(cross)

Arguments

cross

Object of class "cross2", as produced by qtl2::read_cross2().

Value

The input object cross with cross type changed to class "genail8" and the cross information revised to match.

Examples

## Not run: 
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/master/DOex/DOex.zip")
DOex <- read_cross2(file)

DOex_genail <- cross2_do_to_genail8(DOex)

## End(Not run)

Convert cross2 object from ril to genril

Description

Convert a cross2 object from cross type "risibn" to cross type "genriln".

Usage

cross2_ril_to_genril(cross)

Arguments

cross

Object of class "cross2", as produced by qtl2::read_cross2().

Value

The input object cross with cross type changed to class "genriln" for some value of n, and the cross information revised to match.

Examples

## Not run: 
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/master/CC/cc.zip")
cc <- read_cross2(file)

cc_genril <- cross2_ril_to_genril(cc)

## End(Not run)

## Not run: 
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/master/ArabMAGIC/arabmagic_tair9.zip")
arab <- read_cross2(file)

arab_genril <- cross2_ril_to_genril(arab)

## End(Not run)

Encode a matrix of genotypes using a set of allele codes

Description

Encode a matrix of genotypes using a set of allele codes.

Usage

encode_geno(
  geno,
  allele_codes,
  output_codes = c("-", "A", "H", "B"),
  cores = 1
)

Arguments

geno

Character matrix of genotypes (rows as markers, columns as individuals)

allele_codes

Two-column matrix of alleles (rows as markers)

output_codes

Vector of length four, with missing, AA, AB, BB codes

cores

Number of CPU cores to use, for parallel calculations. (If 0, use parallel::detectCores().) Alternatively, this can be links to a set of cluster sockets, as produced by parallel::makeCluster().

Value

Matrix of same dimensions as geno, but with values in output_codes.

See Also

find_consensus_geno(), find_unique_geno()

Examples

geno <- rbind(c("C", "G", "C",  "GG", "CG"),
              c("A", "A", "AT", "TA", "TT"),
              c("T", "G", NA,   "GT", "TT"))
codes <- rbind(c("C", "G"), c("A", "T"), c("T", "G"))
geno_encoded <- encode_geno(geno, codes)

Find the consensus genotype for each row of a genotype matrix

Description

For genotype data (markers x individuals) on a set of individuals from a single inbred line, find the consensus genotype at each marker.

Usage

find_consensus_geno(genotypes, na.strings = c("N", "H", "NA", ""), cores = 1)

Arguments

genotypes

Matrix of genotypes (markers x individuals)

na.strings

Genotypes to be considered as missing values.

cores

Number of CPU cores to use, for parallel calculations. (If 0, use parallel::detectCores().) Alternatively, this can be links to a set of cluster sockets, as produced by parallel::makeCluster().

Value

Vector of consensus genotypes, one value per row of genotypes

See Also

find_unique_geno(), encode_geno()

Examples

g <- rbind(c("NA", "N", "A", "A", "T", "G", NA, "H"),
           c("C",  "C", "G", "G", "A", NA,  NA, NA),
           rep(NA, 8),
           c("C", "C", "G", "G", "G", "C", "G", "G"))
consensus <- find_consensus_geno(g)

Find the unique genotypes for each row of a genotype matrix

Description

For genotype data (markers x individuals) on a set of individuals, find the unique genotypes for each marker, provided that there are exactly two. (If more than two or fewer than two, return NAs.)

Usage

find_unique_geno(genotypes, na.strings = c("N", "H", "NA", ""), cores = 1)

Arguments

genotypes

Matrix of genotypes (markers x individuals)

na.strings

Genotypes to be considered as missing values.

cores

Number of CPU cores to use, for parallel calculations. (If 0, use parallel::detectCores().) Alternatively, this can be links to a set of cluster sockets, as produced by parallel::makeCluster().

Value

Matrix with two columns. Each row corresponds to a marker, and has the two unique genotypes, or NAs (if >2 or <2 unique genotypes).

See Also

count_unique_geno(), encode_geno()

Examples

g <- rbind(c("NA", "A",  "A",  "A", "T"),
           c("NA", "NA", "NA", "A", "A"),
           c("A",  "A",  "T",  "G", "G"),
           c("C", "C",  "G",  "G", "NA"))
ug <- find_unique_geno(g)

Marker map data frame to list

Description

Convert a marker map organized as data frame to a list

Usage

map_df_to_list(
  map,
  chr_column = "chr",
  pos_column = "cM",
  marker_column = "marker",
  Xchr = c("x", "X")
)

Arguments

map

Data frame with marker map

chr_column

Name of the column in map that contains the chromosome IDs.

pos_column

Name of the column in map that contains the marker positions.

marker_column

Name of the column in map that contains the marker names. If NULL, use the row names.

Xchr

Vector of character strings indicating the name or names of the X chromosome. If NULL, assume there's no X chromosome.

Value

A list of vectors of marker positions, one component per chromosome

See Also

map_list_to_df()

Examples

map <- data.frame(chr=c(1,1,1,  2,2,2,   "X","X"),
                  pos=c(0,5,10, 0,8,16,  5,20),
                  marker=c("D1M1","D1M2","D1M3",    "D2M1","D2M2","D2M3",   "DXM1","DXM2"))
map_list <- map_df_to_list(map, pos_column="pos")

Marker map list to data frame

Description

Convert a marker map organized as a list to a data frame

Usage

map_list_to_df(
  map_list,
  chr_column = "chr",
  pos_column = "pos",
  marker_column = "marker"
)

Arguments

map_list

List of vectors containing marker positions

chr_column

Name of the chromosome column in the output

pos_column

Name of the position column in the output

marker_column

Name of the marker column in the output. If NULL, just put them as row names.

Value

A data frame with the marker positions.

See Also

map_df_to_list()

Examples

library(qtl2)
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
iron_map <- map_list_to_df(iron$gmap)

Convert DOQTL genotype probabilities to R/qtl2 format

Description

Convert DOQTL genotype probabilities to R/qtl2 format

Usage

probs_doqtl_to_qtl2(
  probs,
  map,
  is_female = NULL,
  chr_column = "chr",
  pos_column = "cM",
  marker_column = "marker"
)

Arguments

probs

3d array of genotype probabilities as calculated from DOQTL (individuals x genotypes x positions)

map

Data frame with marker map

is_female

Optional logical vector indicating which individuals are female. Names should contain the individual identifiers, matching the row names in probs.

chr_column

Name of the column in map that contains the chromosome IDs.

pos_column

Name of the column in map that contains the marker positions.

marker_column

Name of the column in map that contains the marker names. If NULL, use the row names.

Details

We assume that the X chromosome is labeled "X" (must be upper-case) and that any other chromosomes are autosomes. We assume that the genotypes are labeled using the 8 letters A-H.

If the probabilities are for the full 36 states and the X chromosome is included but is_female is not provided, we'll guess which individuals are females based on their genotype probabilities. (If the average, across loci, of the sum of the heterozygote probabilities is small, we'll assume it's a female.)

Value

An object of the form produced by qtl2::calc_genoprob().

Convert R/qtl2 genotype probabilities to a 3d array

Description

Convert R/qtl2 genotype probabilities to a 3d array

Usage

probs_qtl2_to_array(probs)

Arguments

probs

A "calc_genoprob" object (a list of 3d arrays of genotype probabilities), as calculated by qtl2::calc_genoprob().

Details

We convert just the autosomal genotype probabilities, because they should all have the same number of genotypes (columns). The main application of this is for identifying possible sample mix-ups among batches of genotype probabilities (e.g., using the R/lineup2 package), and for this the autosomal genotype probabilities should be sufficient.

Value

A single three-dimensional array, with just the autosomal genotype probabilities.

Convert R/qtl2 genotype probabilities to DOQTL format

Description

Convert R/qtl2 genotype probabilities to DOQTL format

Usage

probs_qtl2_to_doqtl(probs)

Arguments

probs

A "calc_genoprob" object (a list of 3d arrays of genotype probabilities), as calculated by qtl2::calc_genoprob().

Details

If the arrays in probs all have 8 columns, they're assumed to be allele dosages and we paste them all together into one big array.

Otherwise, it should be that the autosomes all have 36 columns the X chromosome has 44. In this case, the male hemizygotes on the X are placed where the female homozygotes are, and then we reorder the genotypes into alphabetical order.

Value

A single three-dimensional array, for use with DOQTL.

Convert R/qtl genotype probabilities to R/qtl2 format

Description

Convert R/qtl genotype probabilities to R/qtl2 format

Usage

probs_qtl_to_qtl2(cross)

Arguments

cross

An R/qtl "cross" object (see qtl::read.cross() for details.) Must contain genotype probabilities as calculated by qtl::calc.genoprob().

Value

A list with two components:

Examples

library(qtl)
data(hyper)
hyper <- calc.genoprob(hyper, step=1, error.prob=0.002)
result <- probs_qtl_to_qtl2(hyper)
pr <- result$probs
map <- result$map

Convert scan1 results to the scanone format

Description

Convert the results of qtl2::scan1() to the form used by the R/qtl function qtl::scanone().

Usage

scan_qtl2_to_qtl(scan1_output, map)

Arguments

scan1_output

Matrix of LOD scores, as calculated by qtl2::scan1().

map

Map of markers/pseudomarkers (as a list of vectors).

Value

A data frame with class "scanone", containing chromosome and position columns followed by the LOD scores in scan1_output.

See Also

scan_qtl2_to_qtl()

Examples

library(qtl2)
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
map <- insert_pseudomarkers(iron$gmap, step=1)
probs <- calc_genoprob(iron, map, error_prob=0.002)
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)
Xcovar <- get_x_covar(iron)
out <- scan1(probs, pheno, addcovar=covar, Xcovar=Xcovar)

out_rev <- scan_qtl2_to_qtl(out, map)

Convert R/qtl scanone results to R/qtl2 scan1 format

Description

Convert the results of R/qtl1 qtl::scanone() to the form used by the R/qtl2 qtl2::scan1().

Usage

scan_qtl_to_qtl2(scanone_output)

Arguments

scanone_output

Data frame as output by the R/qtl1 function qtl::scanone().

Value

List with two objects: the LOD scores in qtl2::scan1() format, and the map (as a list of marker/pseudomarker positions).

See Also

scan_qtl_to_qtl2()

Examples

library(qtl)
data(hyper)
hyper <- calc.genoprob(hyper, step=1, error.prob=0.002)
out <- scanone(hyper)
out2 <- scan_qtl_to_qtl2(out)

Write a data frame to a CSV file

Description

Write a data frame to a CSV file in a special form, with info about the number of rows and columns.

Usage

write2csv(
  df,
  filename,
  comment = "",
  sep = ",",
  comment.char = "#",
  row.names = NULL,
  overwrite = FALSE
)

Arguments

df

A data frame (or matrix)

filename

File name to write

comment

Comment to place on the first line

sep

Field separator

comment.char

Character to use to initiate the comment lines

row.names

If NA or NULL (the default), row names are not included in the output file. Otherwise, the row names are included as the first column of the output, and this is taken to be the name for that column.

overwrite

If TRUE, overwrite file if it exists. If FALSE (the default) and the file exists, stop with an error.

Details

If the file already exists, the function will refuse to write over it.

The file will include comments at the top, using ⁠#⁠ as a comment character, including the number of rows (not including the header) and the number of columns.

By default, row names are not included. But with the option row.names provided as a character string, they are added as an initial column, with the value of this argument defining the name for that column. If a column with that name already exists, the function halts with an error.

Value

None.

Examples

nr <- 10
nc <- 5
x <- data.frame(id=paste0("ind", 1:nr),
                matrix(rnorm(nr*nc), ncol=nc))
colnames(x)[1:nc + 1] <- paste0("col", 1:nc)

testfile <- file.path(tempdir(), "tmpfile.csv")
write2csv(x, testfile, "A file created by write2csv")

# Remove the file, to clean up temporary directory
unlink(testfile)