strvalidator (version 1.3.0)

calculateDropout: Calculate drop-out events

Description

calculateDropout calculate drop-out events (allele and locus) and records the surviving peak height.

Usage

calculateDropout(data, ref, threshold = NULL, method = c("1", "2", "X",
  "L"), ignoreCase = TRUE, debug = FALSE)

Arguments

data

data frame in GeneMapper format containing at least a column 'Allele'.

ref

data frame in GeneMapper format.

threshold

numeric, threshold in RFU defining a dropout event. Default is 'NULL' and dropout is scored purely on the absence of a peak.

method

character vector, specifying which scoring method(s) to use. Method 'X' for random allele, '1' or '2' for the low/high molecular weight allele, and 'L' for the locus method (the option is case insensitive).

ignoreCase

logical, default TRUE for case insensitive.

debug

logical indicating printing debug information.

Value

• data.frame with columns 'Sample.Name', 'Marker', 'Allele', 'Height', 'Dropout', 'Rfu', 'Heterozygous', and 'Model'. Dropout: 0 indicate no dropout, 1 indicate allele dropout, and 2 indicate locus dropout. Rfu: height of surviving allele. Heterozygous: 1 for heterozygous and 0 for homozygous. And any of the following containing the response (or explanatory) variable used for modelling by logistic regression in function modelDropout: 'MethodX', 'Method1', 'Method2', 'MethodL' and 'MethodL.Ph'.

Details

Calculates drop-out events. In case of allele dropout the peak height of the surviving allele is given. Homozygous alleles in the reference set can be either single or double notation (X or X X). Markers present in the reference set but not in the data set will be added to the result. NB! "Sample Names" in 'ref' must be unique 'core' name of replicate sample names in 'data'. Use checkSubset to make sure subsetting works as intended. NB! There are several methods of scoring drop-out events for regression. Currently the 'MethodX', 'Method1', and 'Method2' are endorsed by the DNA commission (see Appendix B in ref 1). However, an alternative method is to consider the whole locus and score drop-out if any allele is missing. Explanation of the methods: Dropout - all alleles are scored according to LDT. This is pure observations and is not used for modelling. MethodX - a random reference allele is selected and drop-out is scored in relation to the the partner allele. Method1 - the low molecular weight allele is selected and drop-out is scored in relation to the partner allele. Method2 - the high molecular weight allele is selected and drop-out is scored in relation to the partner allele. MethodL - drop-out is scored per locus i.e. drop-out if any allele has dropped out. Method X/1/2 records the peak height of the partner allele to be used as the explanatory variable in the logistic regression. The locus method L also do this when there has been a drop-out, if not the the mean peak height for the locus is used. Peak heights for the locus method are stored in a separate column.

References

Peter Gill et.al., DNA commission of the International Society of Forensic Genetics: Recommendations on the evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods, Forensic Science International: Genetics, Volume 6, Issue 6, December 2012, Pages 679-688, ISSN 1872-4973, 10.1016/j.fsigen.2012.06.002. http://www.sciencedirect.com/science/article/pii/S1872497312001354 Peter Gill, Roberto Puch-Solis, James Curran, The low-template-DNA (stochastic) threshold-Its determination relative to risk analysis for national DNA databases, Forensic Science International: Genetics, Volume 3, Issue 2, March 2009, Pages 104-111, ISSN 1872-4973, 10.1016/j.fsigen.2008.11.009. http://www.sciencedirect.com/science/article/pii/S1872497308001798

Examples

data(set4)
data(ref4)
drop <- calculateDropout(data=set4, ref=ref4, ignoreCase=TRUE)