| Title: | Optimal p-value weighting with independent information |
|---|---|
| Description: | This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size. |
| Authors: | Mohamad Hasan [aut, cre], Paul Schliekelman [aut] |
| Maintainer: | Mohamad Hasan <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.27.0 |
| Built: | 2024-09-28 04:14:09 UTC |
| Source: | https://github.com/bioc/OPWeight |
A function to perform weighted pvalue multiple hypothesis test. This function compute the probabilities of the ranks of the filter statistics given the effect sizes, and consequently the weights if neighter the weights nor the probabilities are given. Then provides the number of rejected null hypothesis and the list of the rejected pvalues as well as the corresponing filter statistics.
opw(pvalue, filter, weight = NULL, ranksProb = NULL, mean_filterEffect = NULL, mean_testEffect = NULL, effectType = c("continuous", "binary"), alpha = 0.05, nrep = 10000, tail = 1L, delInterval = 0.001, method = c("BH", "BON"), ...)opw(pvalue, filter, weight = NULL, ranksProb = NULL, mean_filterEffect = NULL, mean_testEffect = NULL, effectType = c("continuous", "binary"), alpha = 0.05, nrep = 10000, tail = 1L, delInterval = 0.001, method = c("BH", "BON"), ...)
pvalue |
Numeric vector of pvalues of the test statistics |
filter |
Numeric vector of filter statistics |
weight |
An optional numeric weight vector not required |
ranksProb |
An optional numeric vector of the ranks probability of the filters given the mean effect |
mean_filterEffect |
Numeric, value of the mean filter effect of the true alternatives |
mean_testEffect |
Numeric, value of the mean test effect of the true alterantives |
effectType |
Character ("continuous" or "binary"), type of effect sizes |
alpha |
Numeric, significance level of the hypothesis test |
nrep |
Integer, number of replications for importance sampling, default value is 10,000, can be increased to obtain smoother probability curves |
tail |
Integer (1 or 2), right-tailed or two-tailed hypothesis test. default is right-tailed test. |
delInterval |
Numeric, interval between the |
method |
Character ("BH" or "BON"), type of methods is used to obtain the results; Benjemini-Hochberg or Bonferroni |
... |
Arguments passed to internal functions |
If one wants to test
then the mean_testEffect and mean_filterEffect should be mean
of the test and filter effect sizes, respectively. This is called hypothesis
testing for the continuous effect sizes.
If one wants to test
then mean_testEffect and mean_filterEffect should be median or
any discrete value of the test and filter effect sizes. This is called hypothesis
testing for the Binary effect sizes, where epsilon refers to a fixed value.
The main goal of the function is to compute the probabilities of the ranks from
the pvalues and the filter statistics, consequently the weights. Although weights
ranksProb are optional, opw has the options so that one can compute
the probabilities and the weights externally if necessary (see examples).
Internally, opw function compute the ranksProb and consequently
the weights, then uses the pvalues to make conclusions about hypotheses.
Therefore, if ranksProb is given then mean_filterEffect
and are redundant, and should not be provided to the funciton.
Although ranksProb is not required to the function,
One can compute ranksProb by using the function
prob_rank_givenEffect.
The function internally compute mean_filterEffect and mean_testEffect
from a simple linear regression with box-cox transformation between the test
and filter statistics, where the filters are regressed on the test statistics.
Thus, filters need to be positive to apply boxcox from the R
library MASS. Then the estimated mean_filterEffect and
mean_testEffect are used to obtian the ranksProb and the weights.
Thus, in order to apply the function properly, it is crucial to understand the
uses mean_filterEffect and mean_testEffect. If mean_filterEffect and
mean_testEffect are not provided then the test statistics computed from
the pvalues will be used to compute the relationship between the filter
statistics and the test statistics.
If one of the mean effects mean_filterEffect and mean_testEffect
are not provided then the missing mean effect will be computed internally.
totalTests Integer, total number of hypothesis tests evaluated
nullProp Numeric, estimated propotion of the true null
hypothesis
ranksProb Numeric vector of ranks probability given the
mean filter effect, p(rank | ey = mean_filterEffect)
weight Numeric vector of normalized weight
rejections Integer, total number of rejections
rejections_list data frame, list of rejected p-values and the
corresponding filter statistics and the adjusted p-values if method = "BH" used.
Mohamad S. Hasan, [email protected]
prob_rank_givenEffect weight_binary
weight_continuous qvalue dnorm
# generate pvalues and filter statistics m = 1000 set.seed(3) filters = runif(m, min = 0, max = 2.5) # filter statistics H = rbinom(m, size = 1, prob = 0.1) # hypothesis true or false tests = rnorm(m, mean = H * filters) # Z-score pvals = 1 - pnorm(tests) # pvalue # general use results <- opw(pvalue = pvals, filter = filters, effectType = "continuous", method = "BH") # supply the mean effects for both the filters and the tests externally mod <- lm(log(filters) ~ tests) et = mean(tests) ey = mod$coef[[1]] + mod$coef[[2]]*et results2 <- opw(pvalue = pvals, filter = filters, mean_filterEffect = ey, mean_testEffect = et, tail = 2, effectType = "continuous", method = "BH") # supply the rank probabilities externally library(qvalue) ranks <- 1:m nullProp = qvalue(p = pvals, pi0.method = "bootstrap")$pi0 m0 = ceiling(nullProp*m) m1 = m - m0 probs <- sapply(ranks, prob_rank_givenEffect, et = ey, ey = ey, nrep = 10000, m0 = m0, m1 = m1) results3 <- opw(pvalue = pvals, filter = filters, ranksProb = probs, effectType = "continuous", tail = 2, method = "BH") # supply weight externally wgt <- weight_continuous(alpha = .05, et = et, m = m, ranksProb = probs) results4 <- opw(pvalue = pvals, filter = filters, weight = wgt, effectType = "continuous", alpha = .05, method = "BH")# generate pvalues and filter statistics m = 1000 set.seed(3) filters = runif(m, min = 0, max = 2.5) # filter statistics H = rbinom(m, size = 1, prob = 0.1) # hypothesis true or false tests = rnorm(m, mean = H * filters) # Z-score pvals = 1 - pnorm(tests) # pvalue # general use results <- opw(pvalue = pvals, filter = filters, effectType = "continuous", method = "BH") # supply the mean effects for both the filters and the tests externally mod <- lm(log(filters) ~ tests) et = mean(tests) ey = mod$coef[[1]] + mod$coef[[2]]*et results2 <- opw(pvalue = pvals, filter = filters, mean_filterEffect = ey, mean_testEffect = et, tail = 2, effectType = "continuous", method = "BH") # supply the rank probabilities externally library(qvalue) ranks <- 1:m nullProp = qvalue(p = pvals, pi0.method = "bootstrap")$pi0 m0 = ceiling(nullProp*m) m1 = m - m0 probs <- sapply(ranks, prob_rank_givenEffect, et = ey, ey = ey, nrep = 10000, m0 = m0, m1 = m1) results3 <- opw(pvalue = pvals, filter = filters, ranksProb = probs, effectType = "continuous", tail = 2, method = "BH") # supply weight externally wgt <- weight_continuous(alpha = .05, et = et, m = m, ranksProb = probs) results4 <- opw(pvalue = pvals, filter = filters, weight = wgt, effectType = "continuous", alpha = .05, method = "BH")
Comnpute the probability of rank of a test being higher than any other tests given the effect size from external information.
prob_rank_givenEffect(k, et, ey, nrep = 10000, m0, m1)prob_rank_givenEffect(k, et, ey, nrep = 10000, m0, m1)
k |
Integer, rank of a test |
et |
Numeric, effect of the targeted test for importance sampling |
ey |
Numeric, mean filter efffect from the external information |
nrep |
Integer, number of replications for importance sampling |
m0 |
Integer, number of true null hypothesis |
m1 |
Integer, number of true alternative hypothesis |
If one wants to test
then ey should be mean of the filter effect sizes,
This is called hypothesis testing for the continuous effect sizes.
If one wants to test
then ey should be median or any discrete value of the
filter effect sizes. This is called hypothesis testing for the Binary
effect sizes.
If monitor = TRUE then a window will open to see the progress of the
computation. It is useful for a large number of tests
m1 and m0 can be estimated using qvalue from
a bioconductor package qvalue.
prob Numeric, probability of the rank of a test
Mohamad S. Hasan, [email protected]
# compute the probability of the rank of a test being third if all tests are # from the true null prob <- prob_rank_givenEffect(k = 3, et = 0, ey = 0, nrep = 10000, m0 = 50, m1 = 50) # compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot plot(ranks,prob)# compute the probability of the rank of a test being third if all tests are # from the true null prob <- prob_rank_givenEffect(k = 3, et = 0, ey = 0, nrep = 10000, m0 = 50, m1 = 50) # compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot plot(ranks,prob)
A normal approximation to comnpute the probability of rank of a test being higher than any other test given the effect size from external information.
prob_rank_givenEffect_approx(k, et, ey, nrep = 10000, m0, m1, effectType = c("binary", "continuous"))prob_rank_givenEffect_approx(k, et, ey, nrep = 10000, m0, m1, effectType = c("binary", "continuous"))
k |
Integer, rank of a test |
et |
Numeric, effect of the targeted test for importance sampling |
ey |
Numeric, mean/median filter efffect from external information |
nrep |
Integer, number of replications for importance sampling |
m0 |
Integer, number of true null hypothesis |
m1 |
Integer, number of true alternative hypothesis |
effectType |
Character ("continuous" or "binary"), type of effect sizes |
If one wants to test
then ey should be mean of the filter effect sizes,
This is called hypothesis testing for the continuous effect sizes.
If one wants to test
then ey should be median or any discrete value of the
filter effect sizes. This is called hypothesis testing for the Binary
effect sizes.
m1 and m0 can be estimated using qvalue from
a bioconductor package qvalue.
prob Numeric, probability of the rank of a test
Mohamad S. Hasan, [email protected]
# compute the probability of the rank of a test being third if all tests are # from the true null prob <- prob_rank_givenEffect(k = 3, et = 0, ey = 0, nrep = 10000, m0 = 50, m1 = 50) # compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot plot(ranks,prob)# compute the probability of the rank of a test being third if all tests are # from the true null prob <- prob_rank_givenEffect(k = 3, et = 0, ey = 0, nrep = 10000, m0 = 50, m1 = 50) # compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot plot(ranks,prob)
An exact method to comnpute the probability of rank of a test being higher than any other test given the effect size from external information.
prob_rank_givenEffect_exact(k, et, ey, nrep = 10000, m0, m1, effectType = c("binary", "continuous"))prob_rank_givenEffect_exact(k, et, ey, nrep = 10000, m0, m1, effectType = c("binary", "continuous"))
k |
Integer, rank of a test |
et |
Numeric, effect of the targeted test for importance sampling |
ey |
Numeric, mean/median filter efffect from external information |
nrep |
Integer, number of replications for importance sampling |
m0 |
Integer, number of true null hypothesis |
m1 |
Integer, number of true alternative hypothesis |
effectType |
Character ("continuous" or "binary"), type of effect sizes |
If one wants to test
then ey should be mean of the filter effect sizes,
This is called hypothesis testing for the continuous effect sizes.
If one wants to test
then ey should be median or any discrete value of the
filter effect sizes. This is called hypothesis testing for the Binary
effect sizes.
m1 and m0 can be estimated using qvalue from
a bioconductor package qvalue.
prob Numeric, probability of the rank of a test
Mohamad S. Hasan, [email protected]
# compute the probability of the rank of a test being third if all tests are # from the true null prob <- prob_rank_givenEffect_exact(k=3, et=0, ey=0, nrep=10000, m0=50, m1=50, effectType= "continuous") # compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot plot(ranks, prob)# compute the probability of the rank of a test being third if all tests are # from the true null prob <- prob_rank_givenEffect_exact(k=3, et=0, ey=0, nrep=10000, m0=50, m1=50, effectType= "continuous") # compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot plot(ranks, prob)
A simulation approach to comnpute the probability of rank of a test being higher than any other test given the effect size from the external information.
prob_rank_givenEffect_simu(s, ey, e.one, m0, m1, effectType = c("binary", "continuous"))prob_rank_givenEffect_simu(s, ey, e.one, m0, m1, effectType = c("binary", "continuous"))
s |
number of samples of test statistics composed of null and alternative tests |
ey |
Numeric, filter test efffect from the external information |
e.one |
Numeric, one test effect that will vary across all tests |
m0 |
Integer, number of true null hypothesis |
m1 |
Integer, number of true alternative hypothesis |
effectType |
Character ("continuous" or "binary"), type of effect sizes |
If one wants to test
then ey should be mean of the filter effect sizes,
This is called hypothesis testing for the continuous effect sizes.
If one wants to test
then ey should be median or any discrete value of the
filter effect sizes. This is called hypothesis testing for the Binary
effect sizes.
This is a simulation approach to compute the probability of the rank,
P(rank | effect = ey) to verify the actual P(rank | effect = ey).
Suppose, we have a vector of m = m1+m0 observations, where the first m1
observations are
from the true alternative and second m0 are from the true null models. If we
pick two tests one from the first position and the other from the (m0+1)-th position,
then we would expect that the first observation's rank is greater than m0,
and (m1+1)-th observation's rank is less than or equal to m1.
However, this is not always true, especially when the effect size of the test
statistics is low, but the above scenerio become obvious as the the effect
size increases. m1 and m0 can be estimated using qvalue from
a bioconductor package qvalue.
r0 Integer, rank of the null test statisticr1 Integer, rank of the alternative test statistic
Mohamad S. Hasan, [email protected]
# total number of sample generated (use sample size at least 1,000,000) sampleSize = 10000 m0 = 50 m1 = 50 m = m0 +m1 # compute rank of the tests rank <- sapply(1:sampleSize, prob_rank_givenEffect_simu, ey = 1, e.one = 1, m0 = m0, m1 = m1, effectType = "continuous") # rank may generate missing valuse because of the large effcet size, # therefore, to make a matplot one needs vector of equal size. This procedure # will replace the missing value to make the equal sized vectors # probability of the rank of a null test prob0 <- rep(NA, m) prob0_x <- tapply(rank[1,], rank[1,], length)/sampleSize prob0[as.numeric(names(prob0_x))] <- as.vector(prob0_x) # probability of the rank of an alternative test prob1 <- rep(NA, m) prob1_x <- tapply(rank[2,], rank[2,], length)/sampleSize prob1[as.numeric(names(prob1_x))] <- as.vector(prob1_x) # plot matplot(1:m, cbind(prob0, prob1), type = "l")# total number of sample generated (use sample size at least 1,000,000) sampleSize = 10000 m0 = 50 m1 = 50 m = m0 +m1 # compute rank of the tests rank <- sapply(1:sampleSize, prob_rank_givenEffect_simu, ey = 1, e.one = 1, m0 = m0, m1 = m1, effectType = "continuous") # rank may generate missing valuse because of the large effcet size, # therefore, to make a matplot one needs vector of equal size. This procedure # will replace the missing value to make the equal sized vectors # probability of the rank of a null test prob0 <- rep(NA, m) prob0_x <- tapply(rank[1,], rank[1,], length)/sampleSize prob0[as.numeric(names(prob0_x))] <- as.vector(prob0_x) # probability of the rank of an alternative test prob1 <- rep(NA, m) prob1_x <- tapply(rank[2,], rank[2,], length)/sampleSize prob1[as.numeric(names(prob1_x))] <- as.vector(prob1_x) # plot matplot(1:m, cbind(prob0, prob1), type = "l")
Compute weight from the probability of the rank given the effect size for the binary effect size
weight_binary(alpha, et, m, m1, tail = 1L, delInterval = 0.001, ranksProb)weight_binary(alpha, et, m, m1, tail = 1L, delInterval = 0.001, ranksProb)
alpha |
Numeric, significance level of the hypothesis test |
et |
Numeric, mean effect size of the test statistics |
m |
Integer, totoal number of hypothesis test |
m1 |
Integer, number of true alternative hypothesis |
tail |
Integer (1 or 2), right-tailed or two-tailed hypothesis test. default is right-tailed test. |
delInterval |
Numeric, interval between the |
ranksProb |
Numeric vector of the ranks probability of the tests given the effect size |
If one wants to test
then et and ey should be median or any discrete value of the test
and filter effect sizes, respectively. This is called hypothesis testing for
the Binary effect sizes. m1 can be estimated using qvalue from
a bioconductor package qvalue.
weight Numeric vector of normalized weight of the tests for
the binary case
Mohamad S. Hasan, [email protected]
prob_rank_givenEffect weight_continuous
qvalue
# compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob2 <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot the prooabbility plot(ranks, prob2) # compute weight for the binary case weight_bin <- weight_binary(alpha = .05, et = 1, m = 100, m1 = 50, tail=1, delInterval = .0001, ranksProb = prob2) # plot the weight plot(ranks, weight_bin)# compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob2 <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot the prooabbility plot(ranks, prob2) # compute weight for the binary case weight_bin <- weight_binary(alpha = .05, et = 1, m = 100, m1 = 50, tail=1, delInterval = .0001, ranksProb = prob2) # plot the weight plot(ranks, weight_bin)
Compute sum of weights for a given value of the LaGrange multiplier
weight_by_delta(delta, alpha = 0.05, et, m, m1, tail = 1L, ranksProb, effectType = c("continuous", "binary"))weight_by_delta(delta, alpha = 0.05, et, m, m1, tail = 1L, ranksProb, effectType = c("continuous", "binary"))
delta |
Numeric value of the LagRange multiplier |
alpha |
Numeric, significance level of the hypothesis test |
et |
Numeric, mean effect size of the test statistics |
m |
Integer, totoal number of hypothesis test |
m1 |
Integer, number of true alternative tests |
tail |
Integer (1 or 2), right-tailed or two-tailed hypothesis test. default is right-tailed test. |
ranksProb |
Numeric vector of the ranks probability of the filter statistics given the effect size |
effectType |
Character ("continuous" or "binary"), type of effect sizes |
To obtain the normalized weight, and to make sure that the sum of the weights is
equal to the number of tests and the weights are positive, an optimal value of the
LaGrange multiplier delta needed. This function will compute the weights for a given
value of the LaGrange multiplier and provide the sum of the weights in return.
sumWeight_per_delta sum of weights per delta value
Mohamad S. Hasan, [email protected]
# generate a sequence of delta delta <- seq(0, 1, .0001) # compute probability fiven effect filters = runif(100, min = 0, max = 2.5) probs <- dnorm(filters, mean = 0, sd = 1) # compute the sum of weights for each delta weightSum_by_delta <- sapply(delta, weight_by_delta, m = 100, m1 = 50, et = 2, ranksProb = probs, effectType = "continuous")# generate a sequence of delta delta <- seq(0, 1, .0001) # compute probability fiven effect filters = runif(100, min = 0, max = 2.5) probs <- dnorm(filters, mean = 0, sd = 1) # compute the sum of weights for each delta weightSum_by_delta <- sapply(delta, weight_by_delta, m = 100, m1 = 50, et = 2, ranksProb = probs, effectType = "continuous")
Compute weight from the probability of the rank given the effect size for the continuous effect size
weight_continuous(alpha, et, m, tail = 1L, delInterval = 0.001, ranksProb)weight_continuous(alpha, et, m, tail = 1L, delInterval = 0.001, ranksProb)
alpha |
Numeric, significance level of the hypothesis test |
et |
Numeric, mean effect size of the test statistics |
m |
Integer, totoal number of hypothesis test |
tail |
Integer (1 or 2), right-tailed or two-tailed hypothesis test. default is right-tailed test. |
delInterval |
Numeric, interval between the |
ranksProb |
Numeric vector of ranks probability of the tests given the effect size |
If one wants to test
then et and ey should be mean value of the test and filter
effect sizes, respectively. This is called hypothesis testing for the continuous
effect sizes.
weight Numeric vector of normalized weight of the tests
for the continuous case
Mohamad S. Hasan, [email protected]
prob_rank_givenEffect weight_binary
# compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob2 <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot the prooabbility plot(ranks, prob2) # compute weight for the continuous case weight_cont <- weight_continuous(alpha = .05, et = 1, m = 100, tail = 1, delInterval = .0001, ranksProb = prob2) # plot the weight plot(ranks, weight_cont)# compute the probabilities of the ranks of a test being rank 1 to 100 if the # targeted test effect is 2 and the overall mean filter effect is 1. ranks <- 1:100 prob2 <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000, m0 = 50, m1 = 50) # plot the prooabbility plot(ranks, prob2) # compute weight for the continuous case weight_cont <- weight_continuous(alpha = .05, et = 1, m = 100, tail = 1, delInterval = .0001, ranksProb = prob2) # plot the weight plot(ranks, weight_cont)