crlmm (version 1.30.0)

genotype.Illumina: Preprocessing and genotyping of Illumina Infinium II arrays.

Description

Preprocessing and genotyping of Illumina Infinium II arrays.

Usage

genotype.Illumina(sampleSheet=NULL, arrayNames=NULL, ids=NULL, path=".", arrayInfoColNames=list(barcode="SentrixBarcode_A", position="SentrixPosition_A"), highDensity=FALSE, sep="_", fileExt=list(green="Grn.idat", red="Red.idat"), XY=NULL, call.method="crlmm", trueCalls=NULL, cdfName, copynumber=TRUE, batch=NULL, saveDate=FALSE, stripNorm=TRUE,  useTarget=TRUE, quantile.method="between", mixtureSampleSize=10^5, fitMixture=TRUE,                                eps =0.1, verbose = TRUE, seed = 1, sns, probs = rep(1/3, 3), DF = 6, SNRMin = 5,  recallMin = 10, recallRegMin = 1000, gender = NULL, returnParams = TRUE, badSNP = 0.7)

Arguments

sampleSheet

data.frame containing Illumina sample sheet information (for required columns, refer to BeadStudio Genotyping guide - Appendix A).

arrayNames

character vector containing names of arrays to be read in. If NULL, all arrays that can be found in the specified working directory will be read in.

ids

vector containing ids of probes to be read in. If NULL all probes found on the first array are read in.

path

character string specifying the location of files to be read by the function

arrayInfoColNames

(used when sampleSheet is specified) list containing elements 'barcode' which indicates column names in the sampleSheet which contains the arrayNumber/barcode number and 'position' which indicates the strip number. In older style sample sheets, this information is combined (usually in a column named 'SentrixPosition') and this should be specified as list(barcode=NULL, position="SentrixPosition")

highDensity

logical (used when sampleSheet is specified). If TRUE, array extensions '\_A', '\_B' in sampleSheet are replaced with 'R01C01', 'R01C02' etc.

sep

character string specifying separator used in .idat file names.

fileExt

list containing elements 'Green' and 'Red' which specify the .idat file extension for the Cy3 and Cy5 channels.

XY

NChannelSet containing X and Y intensities.

call.method

character string specifying the genotype calling algorithm to use ('crlmm' or 'krlmm').

trueCalls

matrix specifying known Genotype calls(can contain some NAs) for a subset of samples and features (1 - AA, 2 - AB, 3 - BB).

cdfName

annotation package (see also validCdfNames)

copynumber

'logical.' Whether to store copy number intensities with SNP output.

batch

character vector indicating the batch variable. Must be the same length as the number of samples. See details.

saveDate

'logical'. Should the dates from each .idat be saved with sample information?

stripNorm

'logical'. Should the data be strip-level normalized?

useTarget

'logical' (only used when stripNorm=TRUE). Should the reference HapMap intensities be used in strip-level normalization?

quantile.method

character string specifying the quantile normalization method to use ('within' or 'between' channels).

mixtureSampleSize

Sample size to be use when fitting the mixture model.

fitMixture

'logical.' Whether to fit per-array mixture model.

eps

Stop criteria.

verbose

'logical.' Whether to print descriptive messages during processing.

seed

Seed to be used when sampling. Useful for reproducibility

sns

The sample identifiers. If missing, the default sample names are basename(filenames)

probs

'numeric' vector with priors for AA, AB and BB.

DF

'integer' with number of degrees of freedom to use with t-distribution.

SNRMin

'numeric' scalar defining the minimum SNR used to filter out samples.

recallMin

Minimum number of samples for recalibration.

recallRegMin

Minimum number of SNP's for regression.

gender

integer vector ( male = 1, female = 2 ) or missing, with same length as filenames. If missing, the gender is predicted.

returnParams

'logical'. Return recalibrated parameters from crlmm.

badSNP

'numeric'. Threshold to flag as bad SNP (affects batchQC)

Value

SnpSuperSet instance.

Details

For large datasets it is important to utilize the large data support by installing and loading the ff package before calling the genotype function. In previous versions of the crlmm package, we used different functions for genotyping depending on whether the ff package is loaded, namely genotype and genotype2. The genotype function now handles both instances.

genotype.Illumina is a wrapper of the crlmm function for genotyping. Differences include (1) that the copy number probes (if present) are also quantile-normalized and (2) the class of object returned by this function, CNSet, is needed for subsequent copy number estimation. Note that the batch variable (a character string) that must be passed to this function has no effect on the normalization or genotyping steps. Rather, batch is required in order to initialize a CNSet container with the appropriate dimensions.

The new 'krlmm' option is available for certain chip types. Optional argument trueCalls matrix contains known Genotype calls (1 - AA, 2 - AB, 3 - BB) for a subset of samples and features. This will used to compute KRLMM coefficients by calling vglm function from VGAM package.

The 'krlmm' method makes use of functions provided in parallel package to speed up the process. It by default initialises up to 8 clusters. This is configurable by setting up an option named "krlmm.cores", e.g. options("krlmm.cores" = 16).

References

Ritchie ME, Carvalho BS, Hetrick KN, Tavar\'e S, Irizarry RA. R/Bioconductor software for Illumina's Infinium whole-genome genotyping BeadChips. Bioinformatics. 2009 Oct 1;25(19):2621-3.

Carvalho B, Bengtsson H, Speed TP, Irizarry RA. Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data. Biostatistics. 2007 Apr;8(2):485-99. Epub 2006 Dec 22. PMID: 17189563.

Carvalho BS, Louis TA, Irizarry RA. Quantifying uncertainty in genotype calls. Bioinformatics. 2010 Jan 15;26(2):242-9.

See Also

crlmmIlluminaV2, ocSamples, ldOpts

Examples

## Not run: 
# 	# example for 'crlmm' option
# 	library(ff)
# 	library(crlmm)
# 	## to enable paralellization, set to TRUE
# 	if(FALSE){
# 		library(snow)
# 		library(doSNOW)
# 		## with 10 workers
# 		cl <- makeCluster(10, type="SOCK")
# 		registerDoSNOW(cl)
# 	}
# 	## path to idat files
# 	datadir <- "/thumper/ctsa/snpmicroarray/illumina/IDATS/370k"
# 	## read in your samplesheet
# 	samplesheet = read.csv(file.path(datadir, "HumanHap370Duo_Sample_Map.csv"), header=TRUE, as.is=TRUE)
# 	samplesheet <- samplesheet[-c(28:46,61:75,78:79), ]
# 	arrayNames <- file.path(datadir, unique(samplesheet[, "SentrixPosition"]))
# 	arrayInfo <- list(barcode=NULL, position="SentrixPosition")
# 	cnSet <- genotype.Illumina(sampleSheet=samplesheet,
# 				   arrayNames=arrayNames,
# 				   arrayInfoColNames=arrayInfo,
# 				   cdfName="human370v1c",
# 				   batch=rep("1", nrow(samplesheet)))
# 
# ## End(Not run)
## Not run: 
# 	# example for 'krlmm' option
# 	library(crlmm)
# 	library(ff)
# 	# line below is an optional step for krlmm to initialise 16 workers 
# 	# options("krlmm.cores" = 16)
# 	# read in raw X and Y intensities output by GenomeStudio's GenCall genotyping module
# 	XY = readGenCallOutput(c("HumanOmni2-5_4v1_FinalReport_83TUSCAN.csv","HumanOmni2-5_4v1_FinalReport_88CHB-JPT.csv"),
# 				cdfName="humanomni25quadv1b",
# 				verbose=TRUE)
# 	krlmmResult = genotype.Illumina(XY=XY, 
# 		      			cdfName=ThiscdfName, 
# 					call.method="krlmm", 
# 					verbose=TRUE)
# 
# 	# example for 'krlmm' option with known genotype call for some SNPs and samples
# 	library(VGAM)
# 	hapmapCalls = load("hapmapCalls.rda")
# 	# hapmapCalls should have rownames and colnames corresponding to XY featureNames and sampleNames
# 	krlmmResult = genotype.Illumina(XY=XY,
# 					cdfName=ThiscdfName, 
# 					call.method="krlmm", 
# 					trueCalls=hapmapCalls, 
# 					verbose=TRUE)		
# ## End(Not run)