+++ /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