--- /dev/null
+\name{EBMultiTest}
+\alias{EBMultiTest}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{
+Using EM algorithm to calculate the posterior probabilities of interested patterns in multiple condition study
+}
+\description{
+Base on the assumption of NB-Beta Empirical Bayes model, the EM algorithm is used to get the posterior probability of interested patterns.
+}
+\usage{
+EBMultiTest(Data,NgVector=NULL,Conditions,AllParti=NULL, sizeFactors, maxround, tau=NULL,CI=NULL,CIthre=NULL, Pool=F, NumBin=1000, Approx=10^-10,PoolLower=.25, PoolUpper=.75)
+}
+%- maybe also 'usage' for other objects documented here.
+\arguments{
+
+ \item{Data}{
+A data matrix contains expression values for each transcript .(Gene level or Isoform level.). In which rows should be transcripts and columns should be samples.
+}
+ \item{NgVector}{
+A vector contains the Ng value of each isoform. If the isoform is in a gene with 2 isoforms, Ng should be 2. Ng could be only 1, 2 or 3. If it's gene level data, Ngvector should all be 1. The vector length should be the same as the number of rows in Data.
+}
+ \item{Conditions}{
+A vector indicates the condition each sample belongs to.
+}
+
+\item{AllParti}{
+ A matrix indicates the interested patterns. Columns shoule be conditions and rows should be patterns.
+ The matrix could be obtained by the GetPatterns function. If AllParti=NULL, all possible patterns will be used.
+}
+
+ \item{sizeFactors}{
+The normalization factors.
+The normalization factors could be a vector with lane specitic numbers.
+Or it could be a matrix with lane and transcript specific numbers.
+}
+ \item{maxround}{
+Number of iterations. The suggested value is 5.
+}
+
+\item{tau}{
+The tau value from RSEM output. If the data has no replicates within condition,
+EBSeq will use the CI of tau to capture the variation from mapping
+uncertainty and estimate the variance.
+ }
+\item{CI}{
+The CI of each tau from RSEM output
+ }
+\item{CIthre}{
+The threshold of CI RSEM used.
+ }
+\item{Pool, NumBin}{
+Working without replicates, we should define the Pool=T in the
+ EBTest function to enable pooling.
+By defining NumBin = 1000, EBSeq will group the genes with similar means
+together into 1,000 bins.
+With the assumption that no more than 50\% genes are DE in the data set,
+We take genes whose FC are in the 25\% - 75\% quantile of the FC's as the
+candidate genes.
+For each bin, the bin-wise variance estimation would be the median of the
+cross condition variance estimations of the candidate genes within that bin.
+We use the cross condition variance estimations for the candidate genes
+and the bin-wise variance estimations of the host bin for the non-candidate genes.
+}
+
+}
+
+\details{
+For each transcript gi within condition, the model assumes:
+X_gis|mu_gi ~ NB (r_gi0 * l_s, q_gi)
+q_gi|alpha, beta^N_g,b_gi ~ Beta (alpha, neta^N_g,b_gi)
+In which the l_s is the sizeFactors of sample s.
+
+The function will test:
+H0: q_giC1 = q_giC2
+H1: q_giC1 != q_giC2
+
+
+}
+\value{
+\item{Alpha }{Fitted parameter alpha of the prior beta distribution. Rows are the values for each iteration.}
+\item{Beta }{Fitted parameter beta of the prior beta distribution. Rows are the values for each iteration.}
+\item{P, PFromZ }{ The bayes estimator of being DE.Rows are the values for each iteration.}
+\item{Z, PoissonZ}{ The Posterior Probability of being DE for each transcript. (Maybe not in the same order of input)}
+\item{RList}{ The fitted values of r for each transcript.}
+\item{MeanList}{The mean of each transcript. (Cross conditions)}
+\item{VarList}{The variance of each transcript. (Cross conditions, using the expression values devided by it's sizeFactors)}
+\item{QListi1}{The fitted q values of each transcript within condition 1.}
+\item{QListi2}{The fitted q values of each transcript within condition 2.}
+\item{C1Mean}{The mean of each transcript within Condition 1}
+\item{C2Mean}{The mean of each transcript within Condition 2}
+\item{C1EstVar}{The estimated variance of each transcript within Condition 1}
+\item{C2EstVar}{The estimated variance of each transcript within Condition 2}
+\item{PoolVar}{The variance of each transcript. (The pooled value of within condition EstVar)}
+\item{DataList}{A List of data that grouped with Ng and bias.}
+\item{PPDE}{The Posterior Probability of being each pattern for each transcript. (The same order of input)}
+\item{f}{The likelihood of predictive distribution of being each pattern for each transcript. }
+\item{AllParti}{The matrix describe the patterns}
+}
+\references{
+}
+\author{
+Ning Leng
+}
+\note{
+}
+
+
+\seealso{
+}
+\examples{
+Conditions=c("C1","C1","C2","C2","C3","C3")
+PosParti=GetPatterns(Conditions)
+AllParti=PosParti[-3,]
+
+MultiData=GeneMultiSimu(Conditions=Conditions,AllParti=AllParti,
+ NumofSample=6,NumofGene=1000,DEGeneProp=c(.7,.1,.1,.1),
+ DVDqt1=.98,DVDqt2=.99,Phi.qt1=.25,Phi.qt2=.75)
+
+MultiRes=EBMultiTest(MultiData[[1]],NgVector=NULL,Conditions=Conditions,
+ AllParti=AllParti, sizeFactors=rep(1,6), maxround=5, tau=NULL,CI=NULL,
+ CIthre=NULL, Pool=F, NumBin=1000, Approx=10^-10,PoolLower=.25, PoolUpper=.75)
+MultiPP=GetMultiPP(MultiRes)
+
+sum(MultiPP$MAP==MultiData[[2]])
+
+}
+% Add one or more standard keywords, see file 'KEYWORDS' in the
+% R documentation directory.
+\keyword{ ~kwd1 }
+\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
projects / rsem.git / blobdiff