| Title: | Irreproducible Discovery Rate |
|---|---|
| Description: | This is a package for estimating the copula mixture model and plotting correspondence curves. Details are in "Measuring reproducibility of high-throughput experiments" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel. |
| Authors: | Qunhua Li |
| Maintainer: | Qunhua Li <[email protected]> |
| License: | GPL (>= 2.0) |
| Version: | 1.3 |
| Built: | 2024-11-10 06:39:46 UTC |
| Source: | CRAN |
This package estimates the reproducibility of observations on a pair of replicate rank lists. It consists of three components: (1) plotting the correspondence curve to visualize reproducibility, (2) quantifying reproducibility using a copula mixture model and estimating the posterior probability for each obsrvation to be irreproducible (local irreproducible discovery rate), and (3) ranking and selecting observations by their irreproducibility.
| Package: | idr |
| Type: | Package |
| Version: | 1.2 |
| Date: | 2012-10-26 |
| Updates: | Improve the convergence of est.IDR (2014-08-15) |
| License: | GPL-2 |
| LazyLoad: | yes |
The main functions are est.IDR(), get.correspondence() and select.IDR(). est.IDR estimates the copula mixture model and the posterior probability for each observation to be irreproducible. get.correspondence generates the values for plotting the correspondence curve. select.IDR ranks obervations by their reproducibility and reports the number of observations passing the specified IDR thresholds.
Qunhua Li
Maintainer: Qunhua Li <[email protected]>
Q. Li, J. B. Brown, H. Huang and P. J. Bickel. (2011) Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779.
data("simu.idr") x <- cbind(-simu.idr$x, -simu.idr$y) mu <- 2.6 sigma <- 1.3 rho <- 0.8 p <- 0.7 idr.out <- est.IDR(x, mu, sigma, rho, p, eps=0.001, max.ite=20) names(idr.out)data("simu.idr") x <- cbind(-simu.idr$x, -simu.idr$y) mu <- 2.6 sigma <- 1.3 rho <- 0.8 p <- 0.7 idr.out <- est.IDR(x, mu, sigma, rho, p, eps=0.001, max.ite=20) names(idr.out)
Fit a Gaussian copula mixture model.
est.IDR(x, mu, sigma, rho, p, eps=0.001, max.ite=30)est.IDR(x, mu, sigma, rho, p, eps=0.001, max.ite=30)
x |
an n by m numeric matrix, where m= num of replicates, n=num of observations. Numerical values representing the significance of the observations. Note that significant signals are expected to have large values of x. In case that smaller values represent higher significance (e.g. p-value), a monotonic transformation needs to be applied to reverse the order before using this function, for example, -log(p-value). Currently, m=2. |
mu |
a starting value for the mean of the reproducible component. |
sigma |
a starting value for the standard deviation of the reproducible component. |
rho |
a starting value for the correlation coefficient of the reproducible component. |
p |
a starting value for the proportion of reproducible component. |
eps |
Stopping criterion. Iterations stop when the increment of log-likelihood is < eps*log-likelihood, Default=0.001. |
max.ite |
Maximum number of iterations. Default=30. |
para |
estimated parameters: p, rho, mu, sigma. |
idr |
a numeric vector of the local idr for each observation (i.e. estimated conditional probablility for each observation to belong to the irreproducible component. |
IDR |
a numerical vector of the expected irreproducible discovery rate for observations that are as irreproducible or more irreproducible than the given observations. |
loglik |
log-likelihood at the end of iterations. |
loglik.trace |
trajectory of log-likelihood. |
Qunhua Li
Q. Li, J. B. Brown, H. Huang and P. J. Bickel. (2011) Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779.
data("simu.idr") # simu.idr$x and simu.idr$y are p-values # Transfer them such that large values represent significant ones x <- cbind(-simu.idr$x, -simu.idr$y) mu <- 2.6 sigma <- 1.3 rho <- 0.8 p <- 0.7 idr.out <- est.IDR(x, mu, sigma, rho, p, eps=0.001, max.ite=20) names(idr.out)data("simu.idr") # simu.idr$x and simu.idr$y are p-values # Transfer them such that large values represent significant ones x <- cbind(-simu.idr$x, -simu.idr$y) mu <- 2.6 sigma <- 1.3 rho <- 0.8 p <- 0.7 idr.out <- est.IDR(x, mu, sigma, rho, p, eps=0.001, max.ite=20) names(idr.out)
Compute the correspondence profiles (Psi and Psi') and the corresponding smoothed curve using spline
get.correspondence(x1, x2, t, spline.df = NULL)get.correspondence(x1, x2, t, spline.df = NULL)
x1 |
Data values or ranks of the data values on list 1, a vector of numeric values. Large values need to be significant signals. If small values represent significant signals, rank the signals reversely (e.g. by ranking negative values) and use the rank as x1. |
x2 |
Data values or ranks of the data values on list 2, a vector of numeric values. Large values need to be significant signals. If small values represent significant signals, rank the signals reversely (e.g. by ranking negative values) and use the rank as x1. |
t |
A numeric vector between 0 and 1 in ascending order. t is the right-tail percentage. |
spline.df |
Degree of freedom for spline, to control the smoothness of the smoothed curve. |
psi |
the correspondence profile in terms of the scale of percentage, i.e. between (0, 1) |
dpsi |
the derivative of the correspondence profile in terms of the scale of percentage, i.e. between (0, 1) |
psi.n |
the correspondence profile in terms of the scale of the number of observations |
dpsi.n |
the derivative of the correspondence profile in terms of the scale of the number of observations |
Each object above is a list consisting of the following items: t: upper percentage (for psi and dpsi) or number of top ranked observations (for psi.n and dpsi.n) value: psi or dpsi smoothed.line: smoothing spline ntotal: the number of observations jump.point: the index of the vector of t such that psi(t[jump.point]) jumps up due to ties at the low values. This only happends when data consists of a large number of discrete values, e.g. values imputed for observations appearing on only one replicate.
Qunhua Li
Q. Li, J. B. Brown, H. Huang and P. J. Bickel. (2011) Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779.
# salmon data data(salmon) # get.correspondence() needs the observations with high ranks have # high correlation and the observations with low ranks have low correlation. # In this dataset, small values have high correlation and large values # have low correlation. # Ranking negative values makes the data follow the structure required # by get.correspondence(). # There are 28 observations in this data set. rank.x <- rank(-salmon$spawners) rank.y <- rank(-salmon$recruits) uv <- get.correspondence(rank.x, rank.y, seq(0.01, 0.99, by=1/28)) # plot correspondence curve on the scale of percentage plot(uv$psi$t, uv$psi$value, xlab="t", ylab="psi", xlim=c(0, max(uv$psi$t)), ylim=c(0, max(uv$psi$value)), cex.lab=2) lines(uv$psi$smoothed.line, lwd=4) abline(coef=c(0,1), lty=3) # plot the derivative of correspondence curve on the scale of percentage plot(uv$dpsi$t, uv$dpsi$value, xlab="t", ylab="psi'", xlim=c(0, max(uv$dpsi$t)), ylim=c(0, max(uv$dpsi$value)), cex.lab=2) lines(uv$dpsi$smoothed.line, lwd=4) abline(h=1, lty=3) # plot correspondence curve on the scale of the number of observations plot(uv$psi.n$t, uv$psi.n$value, xlab="t", ylab="psi", xlim=c(0, max(uv$psi.n$t)), ylim=c(0, max(uv$psi.n$value)), cex.lab=2) lines(uv$psi.n$smoothed.line, lwd=4) abline(coef=c(0,1), lty=3) # plot the derivative of correspondence curve on the scale of the number # of observations plot(uv$dpsi.n$t, uv$dpsi.n$value, xlab="t", ylab="psi'", xlim=c(0, max(uv$dpsi.n$t)), ylim=c(0, max(uv$dpsi.n$value)), cex.lab=2) lines(uv$dpsi.n$smoothed.line, lwd=4) abline(h=1, lty=3) # If the rank lists consist of a large number of ties at the bottom # (e.g. the same low value is imputed to the list for the observations # that appear on only one list), it may be desirable to plot only # observations before hitting the ties. Then it can be plotted using the # following plot(uv$psi$t[1:uv$psi$jump.point], uv$psi$value[1:uv$psi$jump.point], xlab="t", ylab="psi", xlim=c(0, max(uv$psi$t[1:uv$psi$jump.point])), ylim=c(0, max(uv$psi$value[1:uv$psi$jump.point])), cex.lab=2) lines(uv$psi$smoothed.line, lwd=4) abline(coef=c(0,1), lty=3)# salmon data data(salmon) # get.correspondence() needs the observations with high ranks have # high correlation and the observations with low ranks have low correlation. # In this dataset, small values have high correlation and large values # have low correlation. # Ranking negative values makes the data follow the structure required # by get.correspondence(). # There are 28 observations in this data set. rank.x <- rank(-salmon$spawners) rank.y <- rank(-salmon$recruits) uv <- get.correspondence(rank.x, rank.y, seq(0.01, 0.99, by=1/28)) # plot correspondence curve on the scale of percentage plot(uv$psi$t, uv$psi$value, xlab="t", ylab="psi", xlim=c(0, max(uv$psi$t)), ylim=c(0, max(uv$psi$value)), cex.lab=2) lines(uv$psi$smoothed.line, lwd=4) abline(coef=c(0,1), lty=3) # plot the derivative of correspondence curve on the scale of percentage plot(uv$dpsi$t, uv$dpsi$value, xlab="t", ylab="psi'", xlim=c(0, max(uv$dpsi$t)), ylim=c(0, max(uv$dpsi$value)), cex.lab=2) lines(uv$dpsi$smoothed.line, lwd=4) abline(h=1, lty=3) # plot correspondence curve on the scale of the number of observations plot(uv$psi.n$t, uv$psi.n$value, xlab="t", ylab="psi", xlim=c(0, max(uv$psi.n$t)), ylim=c(0, max(uv$psi.n$value)), cex.lab=2) lines(uv$psi.n$smoothed.line, lwd=4) abline(coef=c(0,1), lty=3) # plot the derivative of correspondence curve on the scale of the number # of observations plot(uv$dpsi.n$t, uv$dpsi.n$value, xlab="t", ylab="psi'", xlim=c(0, max(uv$dpsi.n$t)), ylim=c(0, max(uv$dpsi.n$value)), cex.lab=2) lines(uv$dpsi.n$smoothed.line, lwd=4) abline(h=1, lty=3) # If the rank lists consist of a large number of ties at the bottom # (e.g. the same low value is imputed to the list for the observations # that appear on only one list), it may be desirable to plot only # observations before hitting the ties. Then it can be plotted using the # following plot(uv$psi$t[1:uv$psi$jump.point], uv$psi$value[1:uv$psi$jump.point], xlab="t", ylab="psi", xlim=c(0, max(uv$psi$t[1:uv$psi$jump.point])), ylim=c(0, max(uv$psi$value[1:uv$psi$jump.point])), cex.lab=2) lines(uv$psi$smoothed.line, lwd=4) abline(coef=c(0,1), lty=3)
This data is from Simonoff (1990, p 161). It concerns the size of the annual spawning stock and its production of new catchable-sized fish for 1940 through 1967 for the Skeena river sockeye salmon stock (in thousands of fish). It has three columns, year, spawners and recruits. It was speculated to consist of two different populations.
data(salmon)data(salmon)
A data frame with 28 observations on the following 3 variables.
yeara numeric vector of the year
spawnersa numeric vector of the annual spawning stock
recruitsa numeric vector of the production of new catchable-sized fish
Data is salmon.dat in Simonoff (1990). It can be downloaded from the book's website.
Simonoff, J. S. (1990), Smoothing Methods in Statistics, New York: Spinger-Verlag.
data(salmon) plot(rank(salmon$spawners), rank(salmon$recruits))data(salmon) plot(rank(salmon$spawners), rank(salmon$recruits))
Select observations that exceeding a given IDR level
select.IDR(x, IDR.x, IDR.level)select.IDR(x, IDR.x, IDR.level)
x |
a n by m numeric matrix, where m= num of replicates, n=num of observations. Numerical values representing the significance of the observations, where larger values represent higher significance, for example, -log(p-value). Currently, m=2. |
IDR.x |
Irreproducibile discovery rate for each entry of x. It is computed from est.IDR(). |
IDR.level |
IDR cutoff, a numerical value between [0,1]. |
x |
Observations that are selected. |
n |
Number of observations that are selected. |
IDR.level |
IDR cutoff, a numerical value between [0,1]. |
Qunhua Li
Q. Li, J. B. Brown, H. Huang and P. J. Bickel. (2011) Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779.
data("simu.idr") x <- cbind(-simu.idr$x, -simu.idr$y) mu <- 2.6 sigma <- 1.3 rho <- 0.8 p <- 0.7 idr.out <- est.IDR(x, mu, sigma, rho, p, eps=0.001, max.ite=20) # select observations exceeding IDR threshold=0.01 IDR.level <- 0.01 x.selected <- select.IDR(x, idr.out$IDR, IDR.level)data("simu.idr") x <- cbind(-simu.idr$x, -simu.idr$y) mu <- 2.6 sigma <- 1.3 rho <- 0.8 p <- 0.7 idr.out <- est.IDR(x, mu, sigma, rho, p, eps=0.001, max.ite=20) # select observations exceeding IDR threshold=0.01 IDR.level <- 0.01 x.selected <- select.IDR(x, idr.out$IDR, IDR.level)
This is a simulated dataset for testing the program. Data is first simulated from the copula mixture model with latent structure of 0.65 N(mu, mu, sigma, sigma, rho) + 0.95 N(0, 0, 1, 1, 0), where mu=2.5, sigma=1, rho=0.84. The observations in the dataset are then generated by taking the p-values from a z-test H0: mu=0.
data(simu.idr)data(simu.idr)
A data frame with 1000 observations on the following 3 variables.
xa numeric vector, representing p-values on replicate 1
ya numeric vector, representing p-values on replicate 2
labelsa binary vector, where 1 represents the reproducible component and 0 represents the irreproducible component.
Q. Li, J. B. Brown, H. Huang and P. J. Bickel. (2011) Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779.
data(simu.idr) plot(rank(simu.idr$x), rank(simu.idr$y))data(simu.idr) plot(rank(simu.idr$x), rank(simu.idr$y))