| variance_adjust {ruv} | R Documentation |
Calculate rescaled variances, empirical variances, etc. For use with RUV model fits.
variance_adjust(fit, ebayes = TRUE, evar = TRUE, rsvar = TRUE,
bin = 10, rescaleconst = NULL)
fit |
A RUV model fit (a list), as returned by RUV2 / RUV4 / RUVinv / RUVrinv |
ebayes |
A logical variable. Should empirical bayes estimates be calculated? |
evar |
A logical variable. Should empirical variance estimates be calculated? |
rsvar |
A logical variable. Should rescaled variance estimates be calculated? |
bin |
The bin size to use when calculating empirical variances. |
rescaleconst |
Can be used to speed up execution. See |
An RUV model fit (a list). In addition to the elements of the list returned by RUV2 / RUV4 / RUVinv / RUVrinv, the list will now contain:
sigma2.ebayes |
Estimates of sigma^2 using the empirical bayes shrinkage method of Smyth (2004) |
df.ebayes |
Estimate of degrees of freedom using the empirical bayes shrinkage method of Smyth (2004) |
varbetahat |
"Standard" estimate of the variance of |
varbetahat.rsvar |
"Rescaled Variances" estimate of the variance of |
varbetahat.evar |
"Empirical Variances" estimate of the variance of |
varbetahat.ebayes |
"Empirical Bayes" estimate of the variance of |
varbetahat.rsvar.ebayes |
"Rescaled Empirical Bayes" estimate of the variance of |
p.rsvar |
P-values, after applying the method of rescaled variances |
p.evar |
P-values, after applying the method of empirical variances |
p.ebayes |
P-values, after applying the empirical bayes method of Smyth (2004) |
p.rsvar.ebayes |
P-values, after applying the empirical bayes method of Smyth (2004) and the method of rescaled variances |
p.BH |
FDR-adjusted p-values |
p.rsvar.BH |
FDR-adjusted p-values, after applying the method of rescaled variances |
p.evar.BH |
FDR-adjusted p-values, after applying the method of empirical variances |
p.ebayes.BH |
FDR-adjusted p-values, after applying the empirical bayes method of Smyth (2004) |
p.rsvar.ebayes.BH |
FDR-adjusted p-values, after applying the empirical bayes method of Smyth (2004) and the method of rescaled variances |
Johann Gagnon-Bartsch
Using control genes to correct for unwanted variation in microarray data. Gagnon-Bartsch and Speed, 2012. Available at: http://biostatistics.oxfordjournals.org/content/13/3/539.full.
Removing Unwanted Variation from High Dimensional Data with Negative Controls. Gagnon-Bartsch, Jacob, and Speed, 2013. Available at: http://statistics.berkeley.edu/tech-reports/820.
Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Smyth, 2004.
RUV2, RUV4, RUVinv, RUVrinv, get_empirical_variances, sigmashrink
## Create some simulated data m = 50 n = 10000 nc = 1000 p = 1 k = 20 ctl = rep(FALSE, n) ctl[1:nc] = TRUE X = matrix(c(rep(0,floor(m/2)), rep(1,ceiling(m/2))), m, p) beta = matrix(rnorm(p*n), p, n) beta[,ctl] = 0 W = matrix(rnorm(m*k),m,k) alpha = matrix(rnorm(k*n),k,n) epsilon = matrix(rnorm(m*n),m,n) Y = X%*%beta + W%*%alpha + epsilon ## Run RUV-inv fit = RUVinv(Y, X, ctl) ## Get adjusted variances and p-values fit = variance_adjust(fit)