createDataFile {GWASTools} | R Documentation

## Write genotypic calls and/or associated metrics to a GDS or netCDF file.

### Description

Genotypic calls and/or associated quantitative variables (e.g. quality score, intensities) are read from text files and written to a GDS or netCDF file.

### Usage

    createDataFile(path=".", filename, file.type=c("gds", "ncdf"),
                   variables="genotype", snp.annotation, scan.annotation,
                   sep.type, skip.num, col.total, col.nums, scan.name.in.file,
    	       allele.coding=c("AB", "nucleotide"),
    	       precision="single", compress="LZMA_RA:1M", compress.geno="", compress.annot="LZMA_RA",
    	       array.name=NULL, genome.build=NULL,
                   diagnostics.filename="createDataFile.diagnostics.RData",
                   verbose=TRUE)

    createAffyIntensityFile(path=".",  filename, file.type=c("gds", "ncdf"),
                            snp.annotation, scan.annotation,
    	                precision="single", compress="LZMA_RA:1M", compress.annot="LZMA_RA",
    	                array.name=NULL, genome.build=NULL,
                     	diagnostics.filename="createAffyIntensityFile.diagnostics.RData",
                     	verbose=TRUE)

    checkGenotypeFile(path=".", filename, file.type=c("gds", "ncdf"),
                      snp.annotation, scan.annotation,
                      sep.type, skip.num, col.total, col.nums, scan.name.in.file,
    		  check.scan.index, n.scans.loaded,
    	          allele.coding=c("AB", "nucleotide"),
                      diagnostics.filename="checkGenotypeFile.diagnostics.RData",
                      verbose=TRUE)

    checkIntensityFile(path=".", filename, file.type=c("gds", "ncdf"),
                       snp.annotation, scan.annotation,
                       sep.type, skip.num, col.total, col.nums, scan.name.in.file,
    		   check.scan.index, n.scans.loaded, affy.inten=FALSE,
                       diagnostics.filename="checkIntensityFile.diagnostics.RData",
                       verbose=TRUE)

### Arguments

`path` | Path to the raw text files.
`filename` | The name of the genotype GDS or netCDF file to create
`file.type` | The type of file to create ("gds" or "ncdf")
`variables` | A character vector containing the names of the variables to create (must be one or more of `c("genotype", "quality", "X", "Y", "rawX", "rawY", "R", "Theta", "BAlleleFreq", "LogRRatio")`)
`snp.annotation` | Snp annotation dataframe with columns "snpID", "chromosome", "position" and "snpName". snpID should be a unique integer vector, sorted with respect to chromosome and position. snpName should match the snp identifiers inside the raw genoypic data files If `file.type="gds"`, optional columns "alleleA", and "alleleB" will be written if present.
`scan.annotation` | Scan annotation data.frame with columns "scanID" (unique id of genotyping instance), "scanName", (sample name inside the raw data file) and "file" (corresponding raw data file name).
`sep.type` | Field separator in the raw text files.
`skip.num` | Number of rows to skip, which should be all rows preceding the genotypic or quantitative data (including the header).
`col.total` | Total number of columns in the raw text files.
`col.nums` | An integer vector indicating which columns of the raw text file contain variables for input. `names(col.nums)` must be a subset of c("snp", "sample", "geno", "a1", "a2", "quality", "X", "Y", "rawX", "rawY", "R", "Theta", "BAlleleFreq", "LogRRatio"). The element "snp" is the column of SNP ids, "sample" is sample ids, "geno" is diploid genotype (in AB format), "a1" and "a2" are alleles 1 and 2 (in AB format), "quality" is quality score, "X" and "Y" are normalized intensities, "rawX" and "rawY" are raw intensities, "R" is the sum of normalized intensities, "Theta" is angular polar coordinate, "BAlleleFreq" is the B allele frequency, and "LogRRatio" is the Log R Ratio.
`scan.name.in.file` | An indicator for the presence of sample name within the file. A value of 1 indicates a column with repeated values of the sample name (Illumina format), -1 indicates sample name embedded in a column heading (Affymetrix format) and 0 indicates no sample name inside the raw data file.
`allele.coding` | Whether the genotypes in the file are coded as "AB" (recognized characters are A,B) or "nucleotide" (recognized characters are A,C,G,T). If `allele.coding="nucelotide"`, the columns "alleleA" and "alleleB" must be present in `snp.annotation` to map the genotypes to integer format (number of A alleles).
`check.scan.index` | An integer vector containing the indices of the sample dimension of the GDS or netCDF file to check.
`n.scans.loaded` | Number of scans loaded in the GDS or netCDF file.
`affy.inten` | Logical value indicating whether intensity files are in Affymetrix format (two lines per SNP).
`precision` | A character value indicating whether floating point numbers should be stored as "double" or "single" precision.
`compress` | The compression level for floating-point variables in a GDS file (see `add.gdsn` for options.
`compress.geno` | The compression level for genotypes in a GDS file (see `add.gdsn` for options.
`compress.annot` | The compression level for annotation variables in a GDS file (see `add.gdsn` for options.
`array.name` | Name of the array, to be stored as an attribute in the netCDF file.
`genome.build` | Genome build used in determining chromosome and position, to be stored as an attribute in the netCDF file.
`diagnostics.filename` | Name of the output file to save diagnostics.
`verbose` | Logical value specifying whether to show progress information.

### Details

These functions read genotypic and associated data from raw text files. The files to be read and processed are specified in the sample annotation. `createDataFile` expects one file per sample, with each file having one row of data per SNP probe. The `col.nums` argument allows the user to select and identify specific fields for writing to the GDS or netCDF file. Illumina text files and Affymetrix ".CHP" files can be used here (but not Affymetrix "ALLELE_SUMMARY" files).

A SNP annotation data.frame is a pre-requisite for this function. It has the same number of rows (one per SNP) as the raw text file and a column of SNP names matching those within the raw text file. It also has a column of integer SNP ids to be used as a unique key for each SNP in the GDS or netCDF file.

A sample annotation data.frame is also a pre-requisite. It has one row per sample with columns corresponding to sample name (as it occurs within the raw text file), name of the raw text file for that sample and a unique sample id (to be written as the "sampleID" variable in the GDS or netCDF file). If `file.type="ncdf"`, the unique id must be an integer.

The genotype calls in the raw text file may be either one column of diploid calls or two columns of allele calls. The function takes calls in "AB" or "nucleotide" format and converts them to a numeric code indicating the number of "A" alleles in the genotype (i.e. AA=2, AB=1, BB=0 and missing=-1). If the genotype calls are nucleotides (A,C,G,T), the columns "alleleA" and "alleleB" in `snp.annotation` are used to map to AB format.

While each raw text file is being read, the functions check for errors and irregularities and records the results in a list of vectors. If any problem is detected, that raw text file is skipped.

`createAffyIntensityFile` create an intensity data file from Affymetrix "ALLELE_SUMMARY" files. The "ALLELE_SUMMARY" files have two rows per SNP, one for X (A allele) and one for Y (B allele). These are reformatted to one row per SNP and and ordered according to the SNP integer id. The correspondence between SNP names in the "ALLELE_SUMMARY" file and the SNP integer ids is made using the SNP annotation data.frame.

`checkGenotypeFile` and `checkIntensityFile` check the contents of GDS or netCDF files against raw text files.

### Value

The GDS or netCDF file specified in argument `filename` is populated with genotype calls and/or associated quantitative variables. A list of diagnostics with the following components is returned. Each vector has one element per raw text file processed.

`read.file` | A vector indicating whether (1) or not (0) each file was read successfully.
`row.num` | A vector of the number of rows read from each file. These should all be the same and equal to the number of rows in the SNP annotation data.frame.
`samples` | A list of vectors containing the unique sample names in the sample column of each raw text file. Each vector should have just one element.
`sample.match` | A vector indicating whether (1) or not (0) the sample name inside the raw text file matches that in the sample annotation data.frame
`missg` | A list of vectors containing the unique character string(s) for missing genotypes (i.e. not AA,AB or BB) for each raw text file.
`snp.chk` | A vector indicating whether (1) or not (0) the raw text file has the expected set of SNP names (i.e. matching those in the SNP annotation data.frame).
`chk` | A vector indicating whether (1) or not (0) all previous checks were successful and the data were written to the netCDF file.

`checkGenotypeFile` returns the following additional list items.

`snp.order` | A vector indicating whether (1) or not (0) the snp ids are in the same order in each file.
`geno.chk` | A vector indicating whether (1) or not (0) the genotypes in the netCDF match the text file.

`checkIntensityFile` returns the following additional list items.

`qs.chk` | A vector indicating whether (1) or not (0) the quality scores in the netCDF match the text file.
`read.file.inten` | A vector indicating whether (1) or not (0) each intensity file was read successfully (if intensity files are separate).
`sample.match.inten` | A vector indicating whether (1) or not (0) the sample name inside the raw text file matches that in the sample annotation data.frame (if intensity files are separate).
`rows.equal` | A vector indicating whether (1) or not (0) the number of rows read from each file are the same and equal to the number of rows in the SNP annotation data.frame (if intensity files are separate).
`snp.chk.inten` | A vector indicating whether (1) or not (0) the raw text file has the expected set of SNP names (i.e. matching those in the SNP annotation data.frame) (if intensity files are separate).
`inten.chk` | A vector for each intensity variable indicating whether (1) or not (0) the intensities in the netCDF match the text file.

### Author(s)

Stephanie Gogarten, Cathy Laurie

### See Also

gdsfmt, ncdf4-package

### Examples

    library(GWASdata)

    #############
    # Illumina - genotype file
    #############
    gdsfile <- tempfile()
    path <- system.file("extdata", "illumina_raw_data", package="GWASdata")
    data(illumina_snp_annot, illumina_scan_annot)
    snpAnnot <- illumina_snp_annot[,c("snpID", "rsID", "chromosome",
    	                          "position", "alleleA", "alleleB")]
    names(snpAnnot)[2] <-  "snpName"
    # subset of samples for testing
    scanAnnot <- illumina_scan_annot[1:3, c("scanID", "genoRunID", "file")]
    names(scanAnnot)[2] <- "scanName"
    col.nums <- as.integer(c(1,2,12,13))
    names(col.nums) <- c("snp", "sample", "a1", "a2")
    diagfile <- tempfile()
    res <- createDataFile(path, gdsfile, file.type="gds", variables="genotype",
                          snpAnnot, scanAnnot, sep.type=",",
                          skip.num=11, col.total=21, col.nums=col.nums,
                          scan.name.in.file=1, diagnostics.filename=diagfile)

    file.remove(diagfile)
    file.remove(gdsfile)

    #############
    # Affymetrix - genotype file
    #############
    gdsfile <- tempfile()
    path <- system.file("extdata", "affy_raw_data", package="GWASdata")
    data(affy_snp_annot, affy_scan_annot)
    snpAnnot <- affy_snp_annot[,c("snpID", "probeID", "chromosome", "position")]
    names(snpAnnot)[2] <- "snpName"
    # subset of samples for testing
    scanAnnot <- affy_scan_annot[1:3, c("scanID", "genoRunID", "chpFile")]
    names(scanAnnot)[2:3] <- c("scanName", "file")
    col.nums <- as.integer(c(2,3)); names(col.nums) <- c("snp", "geno")
    diagfile <- tempfile()
    res <- createDataFile(path, gdsfile, file.type="gds", variables="genotype",
                          snpAnnot, scanAnnot, sep.type="\t",
                          skip.num=1, col.total=6, col.nums=col.nums,
                          scan.name.in.file=-1, diagnostics.filename=diagfile)
    file.remove(diagfile)

    # check
    diagfile <- tempfile()
    res <- checkGenotypeFile(path, gdsfile, file.type="gds", snpAnnot, scanAnnot,
                            sep.type="\t", skip.num=1, col.total=6, col.nums=col.nums,
    			scan.name.in.file=-1,
    			check.scan.index=1:3, n.scans.loaded=3,
    			diagnostics.filename=diagfile)
    file.remove(diagfile)
    file.remove(gdsfile)

    #############
    # Affymetrix - intensity file
    #############
    gdsfile <- tempfile()
    path <- system.file("extdata", "affy_raw_data", package="GWASdata")
    data(affy_snp_annot, affy_scan_annot)
    snpAnnot <- affy_snp_annot[,c("snpID", "probeID", "chromosome", "position")]
    names(snpAnnot)[2] <- "snpName"
    # subset of samples for testing
    scanAnnot <- affy_scan_annot[1:3, c("scanID", "genoRunID", "alleleFile")]
    names(scanAnnot)[2:3] <- c("scanName", "file")
    diagfile <- tempfile()
    res <- createAffyIntensityFile(path, gdsfile, file.type="gds", snpAnnot, scanAnnot,
    		     	       diagnostics.filename=diagfile)
    file.remove(diagfile)

    # check
    diagfile <- tempfile()
    res <- checkIntensityFile(path, gdsfile, file.type="gds", snpAnnot, scanAnnot,
                              sep.type="\t", skip.num=1, col.total=2,
    			  col.nums=setNames(as.integer(c(1,2,2)), c("snp", "X", "Y")),
    		  	  scan.name.in.file=-1, affy.inten=TRUE,
                              check.scan.index=1:3, n.scans.loaded=3,
    		   	  diagnostics.filename=diagfile)
    file.remove(diagfile)
    file.remove(gdsfile)

[Package GWASTools version 1.28.0 Index]