ctcisQTL: Cell-Type-Specific QTL analysis
In omicwas: Cell-Type-Specific Association Testing in Bulk Omics Experiments

Description Usage Arguments Details Value See Also Examples

Cell-Type-Specific QTL analysis

ctcisQTL(
  X,
  Xpos,
  W,
  Y,
  Ypos,
  C = NULL,
  max.pos.diff = 1e+06,
  outdir = tempdir(),
  outfile = "ctcisQTL.out.txt"
)

`X`	Matrix (or vector) of SNP genotypes; SNPs x samples.
`Xpos`	Vector of the physical position of X
`W`	Matrix of cell type composition; samples x cell types.
`Y`	Matrix (or vector) of bulk omics measurements; markers x samples.
`Ypos`	Vector of the physical position of Y
`C`	Matrix (or vector) of covariates; samples x covariates. X, Xpos, W, Y, Ypos, C should be numeric.
`max.pos.diff`	Maximum positional difference to compute cis-QTL. Association analysis is performed between a row of X and a row of Y, only when they are within this limit. Since the limiting is only by position, the function needs to be run separately for each chromosome.
`outdir`	Output directory.
`outfile`	Output file.

A function for analyses of QTL, such as eQTL, mQTL, pQTL. The statistical test is almost identical to ctassoc(test = "nls.identity", regularize = "TRUE"). Association analysis is performed between each row of Y and each row of X. Usually, the former will be a methylation/expression marker, and the latter will be a SNP. To cope with the large number of combinations, the testing is limited to pairs whose position is within the difference specified by max.pos.diff; i.e., limited to cis-QTL. In detail, this function performs linear ridge regression, whereas ctassoc(test = "nls.identity", regularize = "TRUE") actually is nonlinear regression but with f = identity as normalizing transformation. In order to speed up computation, first, the parameters α_{h j} and γ_{j l} are fit by ordinary linear regression assuming β_{h j k} = 0. Next, β_{h j k} are fit and tested by linear ridge regression (see documentation for ctassoc).

The estimate, statistic, p.value are written to the specified file.

ctassoc

data(GSE79262small)
X    = GSE79262small$X
Xpos = GSE79262small$Xpos
W    = GSE79262small$W
Y    = GSE79262small$Y
Ypos = GSE79262small$Ypos
C    = GSE79262small$C
X    = X[seq(1, 3601, 100), ] # for brevity
Xpos = Xpos[seq(1, 3601, 100)]
ctcisQTL(X, Xpos, W, Y, Ypos, C = C)