PrecTpFpMatrix {qtlhot} | R Documentation |
Determine false positive and true positive rates for known targets.
Description
Determine how well different tests do to predict candidates of regulation.
Usage
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")
Arguments
cross |
object of class |
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 |
Q.chr |
QTL chromosome (number or label) |
Q.pos |
QTL position in cM |
verbose |
verbose printout if |
comap |
list result of |
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 |
file |
prefix for file names when running |
cand.reg |
object from |
cis.cand.reg |
object from |
method |
method for p-value adjustment; see |
Details
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
.
Value
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 |
Author(s)
Elias Chaibub Neto
See Also
GetCandReg
, CMSTtests
, p.adjust
Examples
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)