PrecTpFpMatrix: Determine false positive and true positive rates for known...
In qtlhot: Inference for QTL Hotspots

Description Usage Arguments Details Value Author(s) See Also Examples

Determine how well different tests do to predict candidates of regulation.

FitAllTests(cross, pheno1, pheno2, Q.chr, Q.pos, verbose = TRUE)
JoinTestOutputs(comap, tests, file)
PrecTpFpMatrix(alpha, val.targets, all.orfs, tests, cand.reg, cis.cand.reg)
p.adjust.np(tests, method = "BH")

`cross`	object of class `cross`
`pheno1`	first phenotype column number or character string name
`pheno2`	second phenotype column number or character string name; if more than one, then all phenotypes will be tested against `pheno1`
`Q.chr`	QTL chromosome (number or label)
`Q.pos`	QTL position in cM
`verbose`	verbose printout if `TRUE`
`comap`	list result of `GetComappingTraits`
`alpha`	significance levels at which summaries are computed
`val.targets`	validated targets of candidate regulators
`all.orfs`	all trait names
`tests`	list object as list of `FitAllTests` results, or of joined output created by `JoinTestsOutputs`
`file`	prefix for file names when running `FitAllTests` in parallel and saving test results in separate files
`cand.reg`	object from `GetCandReg`
`cis.cand.reg`	object from `GetCisCandReg`
`method`	method for p-value adjustment; see `p.adjust`

FitAllTests invokes 7 tests. The hidden routine CitTests is invoked by call to FitAllTests; this is hidden because we do not recommend its use.

JoinTestOutputs joins results of FitAllTests, either from a list tests or from a collection of files prefixed by file. The joined tests from JoinTestOutputs are summarized with PrecTpFpMatrix using the biologically validated true positives, false positives and precision, for the inferred causal relations. We define a true positive as a statistically significant causal relation between a gene and a putative target gene when the putative target gene belongs to the known signature of the gene. Similarly, we define a false positive as a statistically significant causal relation between a gene and a putative target gene when the target gene does not belong to the signature. (For the AIC and BIC methods that do not provide a p-value measuring the significance of the causal call, we simply use the detected causal relations in the computation of true and false positives). The validated precision is computed as the ratio of true positives by the sum of true and false positives. The PrecTpFpMatrix computes these measures to both all genes, and to cis genes only. Simulations suggest only non-parametric tests need to be adjusted using Benjamini-Hochberg via p.adjust.np.

List containing

`Prec1,Prec2`	matrix of precision with rows for significance level and columns for test; first is for all, second is for cis candidates only
`Tp1,Tp2`	matrix of true positive rate with rows for significance level and columns for test; first is for all, second is for cis candidates only
`Fp1,Fp2`	matrix of false positive rate with rows for significance level and columns for test; first is for all, second is for cis candidates only

Elias Chaibub Neto

GetCandReg, CMSTtests, p.adjust

example(GetCandReg)
## Suppose y1 is causal with targets y2 and y3.
targets <- list(y1 = c("y2","y3"))

tests <- list()
for(k in seq(names(comap.targets))) {
  tests[[k]] <- FitAllTests(CMSTCross, pheno1 = names(comap.targets)[k],
                      pheno2 = comap.targets[[k]],
                      Q.chr = cand.reg[k, 4],
                      Q.pos = cand.reg[k, 5])
}
names(tests) <- names(comap.targets)
tests <- JoinTestOutputs(comap.targets, tests)

PrecTpFpMatrix(alpha = seq(0.01, 0.10, by = 0.01),
  val.targets = targets, all.orfs = CMSThigh$names, tests = tests,
  cand.reg = cand.reg, cis.cand.reg = cis.cand.reg)