| read.cross {qtl} | R Documentation |
Read data for a QTL experiment
Description
Data for a QTL experiment is read from a set of files and converted
into an object of class cross. The comma-delimited format
(csv) is recommended. All formats require chromosome
assignments for the genetic markers, and assume that markers are in
their correct order.
Usage
read.cross(format=c("csv", "csvr", "csvs", "csvsr", "mm", "qtx",
"qtlcart", "gary", "karl", "mapqtl", "tidy"),
dir="", file, genfile, mapfile, phefile, chridfile,
mnamesfile, pnamesfile, na.strings=c("-","NA"),
genotypes=c("A","H","B","D","C"), alleles=c("A","B"),
estimate.map=FALSE, convertXdata=TRUE, error.prob=0.0001,
map.function=c("haldane", "kosambi", "c-f", "morgan"),
BC.gen=0, F.gen=0, crosstype, ...)
Arguments
format |
Specifies the format of the data file or files. Details on the various file formats are provided below. |
dir |
Directory in which the data files will be found. In
Windows, use forward slashes ( |
file |
The main input file for formats |
genfile |
File with genotype data (formats |
mapfile |
File with marker position information (all
except the |
phefile |
File with phenotype data (formats |
chridfile |
File with chromosome ID for each marker ( |
mnamesfile |
File with marker names ( |
pnamesfile |
File with phenotype names ( |
na.strings |
A vector of strings which are to be interpreted as
missing values ( |
genotypes |
A vector of character strings specifying the genotype
codes ( If you are trying to read 4-way cross data, your file must have
genotypes coded as described below, and you need to set
|
alleles |
A vector of two one-letter character strings (or four, for the four-way cross), to be used as labels for the two alleles. |
estimate.map |
For all formats but |
convertXdata |
If TRUE, any X chromosome genotype data is
converted to the internal standard, using columns |
error.prob |
In the case that the marker map must be estimated: Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). |
map.function |
In the case that the marker map must be estimated: Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions. (Ignored if m > 0.) |
BC.gen |
Used only for cross type |
F.gen |
Used only for cross type |
crosstype |
Optional character string to force a particular cross type. |
... |
Additional arguments, passed to the function
|
Details
The available formats are comma-delimited (csv), rotated
comma-delimited (csvr), comma-delimited with separate files for
genotype and phenotype data (csvs), rotated comma-delimited
with separate files for genotype and phenotype data (csvsr),
Mapmaker (mm), Map Manager QTX (qtx), Gary Churchill's
format (gary), Karl Broman's format (karl) and
MapQTL/JoinMap (mapqtl). The required files and their
specification for each format appears below. The comma-delimited
formats are recommended. Note that most of these formats work only
for backcross and intercross data.
The sampledata directory in the package distribution contains
sample data files in multiple formats. Also see
https://rqtl.org/sampledata/.
The ... argument enables additional arguments to be passed to
the function read.table in the case of csv
and csvr formats. In particular, one may use the argument
sep to specify the field separator (the default is a comma),
dec to specify the character used for the decimal point (the
default is a period), and comment.char to specify a character
to indicate comment lines.
Value
An object of class cross, which is a list with two components:
geno |
This is a list with elements corresponding to
chromosomes. There are two components for each chromosome: The genotype data gets converted into numeric codes, as follows. The genotype data for a backcross is coded as NA = missing, 1 = AA, 2 = AB. For an F2 intercross, the coding is NA = missing, 1 = AA, 2 = AB, 3 = BB, 4 = not BB (i.e. AA or AB; D in Mapmaker/qtl), 5 = not AA (i.e. AB or BB; C in Mapmaker/qtl). For a 4-way cross, the mother and father are assumed to have genotypes AB and CD, respectively. The genotype data for the progeny is assumed to be phase-known, with the following coding scheme: NA = missing, 1 = AC, 2 = BC, 3 = AD, 4 = BD, 5 = A = AC or AD, 6 = B = BC or BD, 7 = C = AC or BC, 8 = D = AD or BD, 9 = AC or BD, 10 = AD or BC, 11 = not AC, 12 = not BC, 13 = not AD, 14 = not BD. |
pheno |
data.frame of size ( |
While the data format is complicated, there are a number of functions,
such as subset.cross, to
assist in pulling out portions of the data.
X chromosome
The genotypes for the X chromosome require special care!
The X chromosome should be given chromosome identifier X or
x. If it is labeled by a number or by Xchr, it will be
interpreted as an autosome.
The phenotype data should contain a column named "sex" which
indicates the sex of each individual, either coded as 0=female and
1=male, or as a factor with levels female/male or
f/m. Case will be
ignored both in the name and in the factor levels. If no such
phenotype column is included, it will be assumed that all individuals
are of the same sex.
In the case of an intercross, the phenotype data may also contain a
column named "pgm" (for "paternal grandmother") indicating the
direction of the cross. It should be coded as 0/1 with 0 indicating
the cross (AxB)x(AxB) or (BxA)x(AxB) and 1 indicating the cross
(AxB)x(BxA) or (BxA)x(BxA). If no such phenotype column is included,
it will be assumed that all individuals come from the same direction
of cross.
The internal storage of X chromosome data is quite different from that
of autosomal data. Males are coded 1=AA and 2=BB; females with pgm==0
are coded 1=AA and 2=AB; and females with pgm==1 are coded 1=BB and
2=AB. If the argument convertXdata is TRUE, conversion to this
format is made automatically; if FALSE, no conversion is done,
summary.cross will likely return a warning, and
most analyses will not work properly.
Use of convertXdata=FALSE (in which case the X chromosome
genotypes will not be converted to our internal standard) can be
useful for diagnosing problems in the data, but will require some
serious mucking about in the internal data structure.
CSV format
The input file is a comma-delimited text file. A different field
separator may be specified via the argument sep, which will be passed
to the function read.table). For example, in
Europe, it is common to use a comma in place of the decimal point in
numbers and so a semi-colon in place of a comma as the field
separator; such data may be read by using sep=";" and
dec=",".
The first line should contain the phenotype names followed by the marker names. At least one phenotype must be included; for example, include a numerical index for each individual.
The second line should contain blanks in the phenotype columns,
followed by chromosome identifiers for each marker in all other
columns. If a chromosome has the identifier X or x, it
is assumed to be the X chromosome; otherwise, it is assumed to be an
autosome.
An optional third line should contain blanks in the phenotype columns, followed by marker positions, in cM.
Marker order is taken from the cM positions, if provided; otherwise, it is taken from the column order.
Subsequent lines should give the data, with one line for each individual, and with phenotypes followed by genotypes. If possible, phenotypes are made numeric; otherwise they are converted to factors.
The genotype codes must be the same across all markers. For example, you can't have one marker coded AA/AB/BB and another coded A/H/B. This includes genotypes for the X chromosome, for which hemizygous individuals should be coded as if they were homoyzogous.
The cross is determined to be a backcross if only the first two elements
of the genotypes string are found; otherwise, it is assumed to
be an intercross.
CSVr format
This is just like the csv format, but rotated (or really
transposed), so that rows are columns and columns are rows.
CSVs format
This is like the csv format, but with separate files for the
genotype and phenotype data.
The first column in the genotype data must specify individuals'
identifiers, and there must be a column in the phenotype data with
precisely the same information (and with the same name). These IDs
will be included in the data as a phenotype. If the name id or
ID is used, these identifiers will be used in
top.errorlod, plotErrorlod, and
plotGeno as identifiers for the individual.
The first row in each file contains the column names. For the
phenotype file, these are the names of the phenotypes. For the
genotype file, the first cell will be the name of the identifier
column (id or ID) and the subsequent fields will be the
marker names.
In the genotype data file, the second row gives the chromosome IDs. The cell in the second row, first column, must be blank. A third row giving cM positions of markers may be included, in which case the cell in the third row, first column, must be blank.
There need be no blank rows in the phenotype data file.
CSVsr format
This is just like the csvs format, but with each file rotated
(or really transposed), so that rows are columns and columns are rows.
Mapmaker format
This format requires two files. The so-called rawfile, specified by
the argument file, contains the genotype and phenotype
data. Rows beginning with the symbol # are ignored. The first
line should be either data type f2 intercross or
data type f2 backcross. The second line should begin with
three numbers indicating the numbers of individuals, markers and
phenotypes in the file. This line may include the word symbols
followed by symbol assignments (see the documentation for mapmaker,
and cross your fingers). The rest of the lines give genotype data
followed by phenotype data, with marker and phenotype names always
beginning with the * symbol.
A second file contains the genetic map information, specified with
the argument mapfile. The map file may be in
one of two formats. The function will determine which format of map
file is presented.
The simplest format for the map file is not standard for the Mapmaker software, but is easy to create. The file contains two or three columns separated by white space and with no header row. The first column gives the chromosome assignments. The second column gives the marker names, with markers listed in the order along the chromosomes. An optional third column lists the map positions of the markers.
Another possible format for the map file is the .maps
format, which is produced by Mapmaker. The code for reading this
format was written by Brian Yandell.
Marker order is taken from the map file, either by the order they are presented or by the cM positions, if specified.
Map Manager QTX format
This format requires a single file (that produced by the Map Manager QTX program).
QTL Cartographer format
This format requires two files: the .cro and .map files
for QTL Cartographer (produced by the QTL Cartographer
sub-program, Rmap and Rcross).
Note that the QTL Cartographer cross types are converted as follows: RF1 to riself, RF2 to risib, RF0 (doubled haploids) to bc, B1 or B2 to bc, RF2 or SF2 to f2.
Tidy format
This format requires three simple CSV files, separating the genotype, phenotype, and marker map information so that each file may be of a simple form.
Gary format
This format requires the six files. All files have default names, and so the file names need not be specified if the default names are used.
genfile (default = "geno.dat") contains the genotype
data. The file contains one line per individual, with genotypes for
the set of markers separated by white space. Missing values are
coded as 9, and genotypes are coded as 0/1/2 for AA/AB/BB.
mapfile (default = "markerpos.txt") contains two
columns with no header row: the marker names in the first column and
their cM position in the second column. If marker positions are not
available, use mapfile=NULL, and a dummy map will be inserted.
phefile (default = "pheno.dat") contains the phenotype
data, with one row for each mouse and one column for each phenotype.
There should be no header row, and missing values are coded as
"-".
chridfile (default = "chrid.dat") contains the
chromosome identifier for each marker.
mnamesfile (default = "mnames.txt") contains the marker
names.
pnamesfile (default = "pnames.txt") contains the names
of the phenotypes. If phenotype names file is not available, use
pnamesfile=NULL; arbitrary phenotype names will then be
assigned.
Karl format
This format requires three files; all files have default names, and so need not be specified if the default name is used.
genfile (default = "gen.txt") contains the genotype
data. The file contains one line per individual, with genotypes
separated by white space. Missing values are coded 0; genotypes are
coded as 1/2/3/4/5 for AA/AB/BB/not BB/not AA.
mapfile (default = "map.txt") contains the map
information, in the following complicated format:
n.chr
n.mar(1) rf(1,1) rf(1,2) ... rf(1,n.mar(1)-1)
mar.name(1,1)
mar.name(1,2)
...
mar.name(1,n.mar(1))
n.mar(2)
...
etc.
phefile (default = "phe.txt") contains a matrix of
phenotypes, with one individual per line. The first line in the
file should give the phenotype names.
MapQTL format
This format requires three files, described in the manual of the MapQTL program (same as JoinMap).
genfile corresponds to the loc file containing the genotype
data. Each marker and its genotypes should be on a single line.
mapfile corresponds to the map file containing the linkage
group assignment, marker names and their map positions.
phefile corresponds to the qua file containing the phenotypes.
For the moment, only 4-way crosses are supported (CP population type in MapQTL).
Author(s)
Karl W Broman, broman@wisc.edu; Brian S. Yandell; Aaron Wolen
References
Broman, K. W. and Sen, Ś. (2009) A guide to QTL mapping with R/qtl. Springer. https://rqtl.org/book/
See Also
subset.cross, summary.cross,
plot.cross, c.cross, clean.cross,
write.cross, sim.cross, read.table.
The sampledata directory in the package distribution contains
sample data files in multiple formats. Also see
https://rqtl.org/sampledata/.
Examples
## Not run:
# CSV format
dat1 <- read.cross("csv", dir="Mydata", file="mydata.csv")
# CSVS format
dat2 <- read.cross("csvs", dir="Mydata", genfile="mydata_gen.csv",
phefile="mydata_phe.csv")
# you can read files directly from the internet
datweb <- read.cross("csv", "https://rqtl.org/sampledata",
"listeria.csv")
# Mapmaker format
dat3 <- read.cross("mm", dir="Mydata", file="mydata.raw",
mapfile="mydata.map")
# Map Manager QTX format
dat4 <- read.cross("qtx", dir="Mydata", file="mydata.qtx")
# QTL Cartographer format
dat5 <- read.cross("qtlcart", dir="Mydata", file="qtlcart.cro",
mapfile="qtlcart.map")
# Gary format
dat6 <- read.cross("gary", dir="Mydata", genfile="geno.dat",
mapfile="markerpos.txt", phefile="pheno.dat",
chridfile="chrid.dat", mnamesfile="mnames.txt",
pnamesfile="pnames.txt")
# Karl format
dat7 <- read.cross("karl", dir="Mydata", genfile="gen.txt",
phefile="phe.txt", mapfile="map.txt")
## End(Not run)