xxt {snpStats} | R Documentation

## X.X-transpose for a standardized SnpMatrix

### Description

The input SnpMatrix is first standardized by subtracting the mean (or stratum mean) from each call and dividing by the expected standard deviation under Hardy-Weinberg equilibrium. It is then post-multiplied by its transpose. This is a preliminary step in the computation of principal components.

### Usage

    xxt(snps, strata = NULL, correct.for.missing = FALSE, lower.only = FALSE,
      uncertain = FALSE)

### Arguments

`snps` | The input matrix, of type `"SnpMatrix"`
`strata` | A `factor` (or an object which can be coerced into a `factor`) with length equal to the number of rows of `snps` defining stratum membership
`correct.for.missing` | If `TRUE`, an attempt is made to correct for the effect of missing data by use of inverse probability weights. Otherwise, missing observations are scored zero in the standardized matrix
`lower.only` | If `TRUE`, only the lower triangle of the result is returned and the upper triangle is filled with zeros. Otherwise, the complete symmetric matrix is returned
`uncertain` | If `TRUE`, uncertain genotypes are replaced by posterior expectations. Otherwise these are treated as missing values

### Details

This computation forms the first step of the calculation of principal components for genome-wide SNP data. As pointed out by Price et al. (2006), when the data matrix has more rows than columns it is most efficient to calculate the eigenvectors of X.X-transpose, where X is a `SnpMatrix` whose columns have been standardized to zero mean and unit variance. For autosomes, the genotypes are given codes 0, 1 or 2 after subtraction of the mean, 2p, are divided by the standard deviation sqrt(2p(1-p)) (p is the estimated allele frequency). For SNPs on the X chromosome in male subjects, genotypes are coded 0 or 2. Then the mean is still 2p, but the standard deviation is 2sqrt(p(1-p)). If the `strata` is supplied, a stratum-specific estimate value for p is used for standardization.

Missing observations present some difficulty. Price et al. (2006) recommended replacing missing observations by their means, this being equivalent to replacement by zeros in the standardized matrix. However this results in a biased estimate of the complete data result. Optionally this bias can be corrected by inverse probability weighting. We assume that the probability that any one call is missing is small, and can be predicted by a multiplicative model with row (subject) and column (locus) effects. The estimated probability of a missing value in a given row and column is then given by m = RC/T, where R is the row total number of no-calls, C is the column total of no-calls, and T is the overall total number of no-calls. Non-missing contributions to X.X-transpose are then weighted by w=1/(1-m) for contributions to the diagonal elements, and products of the relevant pairs of weights for contributions to off–diagonal elements.

### Value

A square matrix containing either the complete X.X-transpose matrix, or just its lower triangle

### Warning

The correction for missing observations can result in an output matrix which is not positive semi-definite. This should not matter in the application for which it is intended

### Note

In genome-wide studies, the SNP data will usually be held as a series of objects (of class `"SnpMatrix"` or`"XSnpMatrix"`), one per chromosome. Note that the X.X-transpose matrices produced by applying the `xxt` function to each object in turn can be added to yield the genome-wide result.

If the matrix is converted to a correlation matrix by pre- and post-multiplying by the sqrt of the inverse of its diagonal, then this is an unbiased estimate of twice the kinship matrix.

### Author(s)

David Clayton dc208@cam.ac.uk

### References

Price et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38:904-9

### Examples

    # make a SnpMatrix with a small number of rows
    data(testdata)
    small <- Autosomes[1:100,]
    # Calculate the X.X-transpose matrix
    xx <- xxt(small, correct.for.missing=TRUE)
    # Calculate the principal components
    pc <- eigen(xx, symmetric=TRUE)$vectors

[Package snpStats version 1.30.0 Index]