mb.long {timecourse} | R Documentation |
Computes the tilde{T}^2 statistics and/or the MB-statistics of differential expression for longitudinal replicated developmental microarray time course data by multivariate empirical Bayes shrinkage of gene-specific sample variance-covariance matrices towards a common matrix.
mb.long(object, method = c("1D", "paired", "2D"), type = c("none", "robust"), times, reps, prior.df = NULL, prior.COV = NULL, prior.eta = NULL, condition.grp = NULL, rep.grp = NULL, time.grp = NULL, one.sample = FALSE, ref = NULL, p = 0.02, out.t = FALSE, tuning = 1.345, HotellingT2.only=TRUE)
object |
Required. An object of class matrix , MAList ,
marrayNorm , or exprSet containing log-ratios or
log-values of expression for a series of microarrays. |
method |
a character string, "1D" for the one-sample case where genes
of interest are those which change over time,
"paired" for the one-sample case where genes of interest are
those whose expected temporal profiles do not stay 0, for example, cDNA microarrays,
or the paired two-sample case where genes of interest are those with different
expected temporal profiles across 2 biological conditions,
"2D" for the independent two-sample case where genes of interest are those
with different expected temporal profiles across 2 biological conditions.
The default is "1D" . |
type |
a character string, indicating whether possible outliers should be down-weighted. |
times |
Required. A positive integer giving the number of time points. |
reps |
Required. A numeric vector or matrix corresponding to the sample sizes for all genes across different biological conditions, when biological conditions are sorted in ascending order. If a matrix, rows represent genes while columns represent biological conditions. |
prior.df |
an optional positive value giving the degrees of moderation. |
prior.COV |
an optional numeric matrix giving the common covariance matrix to which the gene-specific sample covariances are smoothed toward. |
prior.eta |
an optional numeric value giving the scale parameter for the covariance matrix for the expected time course profile. |
condition.grp |
a numeric or character vector with length equals to the number of arrays,
assigning the biological condition group of each array. Required if
method=2D . |
rep.grp |
an optional numeric or character vector with length equals to the number of arrays, assigning the replicate group of each array. |
time.grp |
an optional numeric vector with length equals to the number of arrays, assigning the time point group of each array. |
one.sample |
Is it a one-sample problem? Only specify this argument when method=paired .
The default is FALSE which means it is a paired two-sample problem. |
ref |
an optional numeric value or character specifying the name
of reference biological condition. The default uses the
first element of condition.grp . Only specify this argument when
method=paired and one.sample is FALSE . |
p |
a numeric value between 0 and 1, assumed proportion of genes which are differentially expressed. |
out.t |
logical. Should the moderated multivariate t-statistics be outputed? The default is
FALSE . |
tuning |
the tuning constant for the Huber weight function with a default 1.345. |
HotellingT2.only |
logical. Should only the HotellingT2 statistics be outputed? This should be
set as TRUE (default) when the sample size(s) are the same across genes, in order to reduce
computational time. |
This function implements the multivariate empirical Bayes statistics
described in Tai and Speed (2004), to rank genes in the order of
interest from longitudinal replicated developmental microarray time course
experiments. It calls one of the following functions,
depending
on which method
is used: mb.1D
,
mb.paired
, and mb.2D
.
The arguments condition.grp
, rep.grp
, and
time.grp
, if specified, should have lengths equal to the number
of arrays. The i_th elements of these three arguments should
correspond to the biological condition, replicate, and time for the i_th column (array) in the
expression value matrix of the input object, respectively.
The default assumes the columns of M
are in the ascending order of condition.grp
first,
and then rep.grp
, and finally time.grp
.
Arguments one.sample
and ref
are for method=paired
only.
When type=robust
, the numerator of the tilde{T}^2 statistic is calculated using
the weighted average time course vector(s), where the weight at each data point
is determined using Huber's weight function with the default tuning constant 1.345.
Warning: When there are only 2 replicates within conditions,
type="robust"
produces the same rankings as type="none"
since there is no concensus on gene expression values.
Check the output weights for these outliers.
Object of MArrayTC
.
Yu Chuan Tai yuchuan@stat.berkeley.edu
Yu Chuan Tai and Terence P. Speed (2004). A multivariate empirical Bayes statistic for replicated microarray time course data. Technical Report no. 667, Department of Statistics, University of California, Berkeley.
Yu Chuan Tai and Terence P. Speed (2005). Statistical analysis of microarray time course data. In: DNA Microarrays, U. Nuber (ed.), BIOS Scientific Publishers Limited, Taylor & Francis, 4 Park Square, Milton Park, Abingdon OX14 4RN, Chapter 20. In press.
P. J. Huber (2004). Robust Statistics. Wiley series in probability and mathematical statistics.
timecourse Vignette.
data(fruitfly) colnames(fruitfly) ## check if arrays are arranged in the default order gnames <- rownames(fruitfly) assay <- rep(c("A", "B", "C"), each = 12) time.grp <- rep(c(1:12), 3) size <- rep(3, nrow(fruitfly)) out1 <- mb.long(fruitfly, times=12, reps=size, rep.grp = assay, time.grp = time.grp) summary(out1) plotProfile(out1, type="b", gnames=gnames, legloc=c(2,15), pch=c("A","B","C"), xlab="Hour") ## Simulate gene expression data ## Note: this simulation is for demonstration purpose only, ## and does not necessarily reflect the real ## features of longitudinal time course data ## one biological condition, 5 time points, 3 replicates ## 500 genes, 10 genes change over time SS <- matrix(c( 0.01, -0.0008, -0.003, 0.007, 0.002, -0.0008, 0.02, 0.002, -0.0004, -0.001, -0.003, 0.002, 0.03, -0.0054, -0.009, 0.007, -0.0004, -0.00538, 0.02, 0.0008, 0.002, -0.001, -0.009, 0.0008, 0.07), ncol=5) sim.Sigma <- function() { S <- matrix(rep(0,25),ncol=5) x <- mvrnorm(n=10, mu=rep(0,5), Sigma=10*SS) for(i in 1:10) S <- S+crossprod(t(x[i,])) solve(S) } sim.data1 <- function(x, indx=1) { mu <- rep(runif(1,8,x[1]),5) if(indx==1) res <- as.numeric(t(mvrnorm(n=3, mu=mu+rnorm(5,sd=4), Sigma=sim.Sigma()))) if(indx==0) res <- as.numeric(t(mvrnorm(n=3, mu=mu, Sigma=sim.Sigma()))) res } M1 <- matrix(rep(14,500*15), ncol=15) M1[1:10,] <- t(apply(M1[1:10,],1,sim.data1)) M1[11:500,] <- t(apply(M1[11:500,],1,sim.data1, 0)) ## Which genes are nonconstant? MB.1D1 <- mb.long(M1, times=5, reps=rep(3, 500)) MB.1D1$percent # check the percent of moderation plotProfile(MB.1D1,type="b") # plots the no. 1 gene plotProfile(MB.1D1,type="b",ranking=10) # plots the no. 10 gene genenames <- as.character(1:500) plotProfile(MB.1D1, type="b", gid="8", gnames=genenames) #plots the gene with ID "8" ## MB.1D1.r <- mb.long(M1, type="r", times=5, reps=rep(3, 500)) plotProfile(MB.1D1.r,type="b",gnames=genenames) plotProfile(MB.1D1.r,type="b", gid="1", gnames=genenames) #plots the gene with ID "1" ## assign the following labellings to columns of M1 ## which is actually the same as the default ## Not Run trt <- rep("wildtype", 15) assay <- rep(c("A","B","C"), rep(5,3)) time.grp <- rep(c(0, 1, 3, 4, 6), 3) ## MB.1D2 should give the same results as MB.1D1 #MB.1D2 <- mb.long(M1, times=5, reps=rep(3, 500), condition.grp = trt, rep.grp = assay, #time.grp=time.grp) ## suppose now the replicates are in this order instead assay <- rep(c("A","C","B"), rep(5,3)) ## then MB.1D3 <- mb.long(M1, times=5, reps=rep(3, 500), condition.grp = trt, rep.grp = assay, time.grp=time.grp) MB.1D3$rep.group #check the replicate and time group MB.1D3$time.group ## Now let's simulate another dataset with two biological conditions ## 500 genes also, 10 of them have different expected time course profiles ## between these two biological conditions ## 3 replicates, 5 time points for each condition sim.data2 <- function(x, indx=1) { mu <- rep(runif(1,8,x[1]),5) if(indx==1) res <- c(as.numeric(t(mvrnorm(n=3, mu=mu+rnorm(5,sd=5), Sigma=sim.Sigma()))), as.numeric(t(mvrnorm(n=3, mu=mu+rnorm(5,sd=3.2), Sigma=sim.Sigma())))) if(indx==0) res <- as.numeric(t(mvrnorm(n=6, mu=mu+rnorm(5,sd=3), Sigma=sim.Sigma()))) res } M2 <- matrix(rep(14,500*30), ncol=30) M2[1:10,] <- t(apply(M2[1:10,],1,sim.data2)) M2[11:500,] <- t(apply(M2[11:500,],1,sim.data2, 0)) ## assume it is a paired two-sample problem trt <- rep(c("wt","mt"),each=15) assay <- rep(rep(c("1.2.04","2.4.04","3.5.04"),each=5),2) size <- matrix(3, nrow=500, ncol=2) MB.paired <- mb.long(M2, method="paired", times=5, reps=size, condition.grp=trt, rep.grp=assay) MB.paired$con.group # check the condition, replicate and time groups MB.paired$rep.group MB.paired$time.group plotProfile(MB.paired, type="b") genenames <- as.character(1:500) plotProfile(MB.paired, gid="12", type="b", gnames=genenames) #plots the gene with ID "12" ### assume it is a unpaired two-sample problem assay <- rep(c("1.2.04","2.4.04","3.5.04","5.21.04","7.17.04","8.4.04"),each=5) MB.2D <- mb.long(M2, method="2", times=5, reps=size, condition.grp=trt, rep.grp=assay) MB.2D$con.group # check the condition, replicate and time groups MB.2D$rep.group MB.2D$time.group plotProfile(MB.2D,type="b", gnames=genenames) # plot the no. 1 gene ## Now let's simulate another dataset with two biological conditions ## 500 genes also, 10 of them have different expected time course profiles ## between these two biological conditions ## the first condition has 3 replicates, while the second condition has 4 replicates, ## 5 time points for each condition sim.data3 <- function(x, indx=1) { mu <- rep(runif(1,8,x[1]),5) if(indx==1) res <- c(as.numeric(t(mvrnorm(n=3, mu=mu+rnorm(5,sd=5), Sigma=sim.Sigma()))), as.numeric(t(mvrnorm(n=4, mu=mu+rnorm(5,sd=3.2), Sigma=sim.Sigma())))) if(indx==0) res <- as.numeric(t(mvrnorm(n=7, mu=mu+rnorm(5,sd=3), Sigma=sim.Sigma()))) res } M3 <- matrix(rep(14,500*35), ncol=35) M3[1:10,] <- t(apply(M3[1:10,],1,sim.data3)) M3[11:500,] <- t(apply(M3[11:500,],1,sim.data3, 0)) assay <- rep(c("1.2.04","2.4.04","3.5.04","5.21.04","7.17.04","9.10.04","12.1.04"),each=5) trt <- c(rep(c("wildtype","mutant"),each=15),rep("mutant",5)) ## Note that "mutant" < "wildtype", the sample sizes are (4, 3) size <- matrix(c(4,3), nrow=500, ncol=2, byrow=TRUE) MB.2D.2 <- mb.long(M3, method="2", times=5, reps=size, rep.grp=assay, condition.grp=trt) MB.2D.2$con.group # check the condition, replicate and time groups MB.2D.2$rep.group MB.2D.2$time.group plotProfile(MB.2D.2, type="b") # plot the no. 1 gene