R: Read data for a QTL experiment

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=TRUE, 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.
`dir` | Directory in which the data files will be found. In Windows, use forward slashes (`"/"`) or double backslashes (`"\\"`) to specify directory trees.
`file` | The main input file for formats `csv`, `csvr` and `mm`.
`genfile` | File with genotype data (formats `csvs`, `csvsr`, `karl`, `gary` and `mapqtl` only).
`mapfile` | File with marker position information (all except the `csv` formats).
`phefile` | File with phenotype data (formats `csvs`, `csvsr`, `karl`, `gary` and `mapqtl` only).
`chridfile` | File with chromosome ID for each marker (`gary` format only).
`mnamesfile` | File with marker names (`gary` format only).
`pnamesfile` | File with phenotype names (`gary` format only).
`na.strings` | A vector of strings which are to be interpreted as missing values (`csv` and `gary` formats only). For the `csv` formats, these are interpreted globally for the entire file, so missing value codes in phenotypes must not be valid genotypes, and vice versa. For the `gary` format, these are used only for the phenotype data.
`genotypes` | A vector of character strings specifying the genotype codes (`csv` formats only). Generally this is a vector of length 5, with the elements corresponding to AA, AB, BB, not BB (i.e., AA or AB), and not AA (i.e., AB or BB). Note: Pay careful attention to the third and fourth of these; the order of these can be confusing. If you are trying to read 4-way cross data, your file must have genotypes coded as described below, and you need to set `genotypes=NULL` so that no re-coding gets done.
`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 `qtlcart`, `mapqtl`, and `karl`: if TRUE and marker positions are not included in the input files, the genetic map is estimated using the function `est.map`.
`convertXdata` | If TRUE, any X chromosome genotype data is converted to the internal standard, using columns `sex` and `pgm` in the phenotype data if they available or by inference if they are not. If FALSE, the X chromsome data is read as is.
`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 `"bcsft"`.
`F.gen` | Used only for cross type `"bcsft"`.
`crosstype` | Optional character string to force a particular cross type.
`...` | Additional arguments, 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.

### 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 http://www.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. `names(geno)` contains the names of the chromsomes. Each chromosome is itself a list, and is given class `A` or `X` according to whether it is autosomal or the X chromosome. There are two components for each chromosome: `data`, a matrix whose rows are individuals and whose columns are markers, and `map`, either a vector of marker positions (in cM) or a matrix of dim (`2 x n.mar`) where the rows correspond to marker positions in female and male genetic distance, respectively. 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 (`n.ind x n.phe`) containing the phenotypes. If a phenotype with the name `id` or `ID` is included, these identifiers will be used in `top.errorlod`, `plotErrorlod`, and `plotGeno` as identifiers for the individual.

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, kbroman@biostat.wisc.edu; Brian S. Yandell; Aaron Wolen

### References

Broman, K. W. and Sen, Ś. (2009) A guide to QTL mapping with R/qtl. Springer. http://www.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 http://www.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", "http://www.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)

[Package qtl version 1.41-6 Index]