EBMultiTest: Using EM algorithm to calculate the posterior probabilities...

In wiscstatman/EBSeq: An R package for gene and isoform differential expression analysis of RNA-seq data

Using EM algorithm to calculate the posterior probabilities of interested patterns in a multiple condition study

Description

'EBMultiTest' is built based on the assumption of NB-Beta Empirical Bayes model. It utilizes the EM algorithm to give the posterior probability of the interested patterns.

Usage

EBMultiTest(Data, NgVector = NULL, Conditions, sizeFactors, uc = 0, AllParti = NULL, fast = T,
    Alpha = NULL, Beta = NULL, Qtrm = 1, QtrmCut = 0, maxround = 50, 
    step1 = 1e-06, step2 = 0.01, thre = log(2), sthre = 0, 
    filter = 10, stopthre = 1e-04, nequal = 2)

Arguments

Data	A data matrix contains expression values for each transcript (gene or isoform level). In which rows should be transcripts and columns should be samples.
NgVector	A vector indicates the uncertainty group assignment of each isoform. e.g. if we use number of isoforms in the host gene to define the uncertainty groups, suppose the isoform is in a gene with 2 isoforms, Ng of this isoform should be 2. The length of this vector should be the same as the number of rows in Data. If it's gene level data, Ngvector could be left as NULL.
Conditions	A vector indicates the condition in which each sample belongs to.
sizeFactors	The normalization factors. It should be a vector with lane specific numbers (the length of the vector should be the same as the number of samples, with the same order as the columns of Data).
uc	number of unceratin positions, unit levels
AllParti	user specified set of partitions, a matrix, with each row represent a partition
fast	boolean indicator whether to use fast EBSeq or full EBSeq
Alpha	start value of hyper parameter alpha
Beta	start value of hyper parameter beta
Qtrm, QtrmCut	Transcripts with Qtrm th quantile < = QtrmCut will be removed before testing. The default value is Qtrm = 1 and QtrmCut=0. By default setting, transcripts with all 0's won't be tested.
maxround	Number of iterations. The default value is 50. Users should always check the convergency by looking at the Alpha and Beta in output. If the hyper-parameter estimations are not converged in 50 iterations, larger number is suggested.
step1	stepsize for gradient ascent of alpha
step2	stepsize for gradietn ascent of beta
thre	threshold for determining the state of a position
sthre	shrinkage threshold for iterative pruning during the EM updates
filter	filterthreshold for low expression units
stopthre	stopping threshold for EM
nequal	when there is a chain of equal states with the number of equal states bigger than nequal, equalhandle algorithm will be used to further checking the homogeneity between the group means

Value

Alpha	Fitted parameter alpha of the prior beta distribution.
Beta	Fitted parameter beta of the prior beta distribution.
P	Global proportion of DE patterns.
RList	The fitted values of r for each transcript.
MeanList	The mean of each transcript (across conditions).
VarList	The variance of each transcript (across conditions).
QList	The fitted q values of each transcript within the two conditions
Mean	The mean of each transcript within the two conditions (adjusted by normalization factors).
Var	The estimated variance of each transcript within the two conditions (adjusted by normalization factors).
PoolVar	The variance of each transcript (The pooled value of within condition EstVar).
DataNorm	Normalized expression matrix.
Iso	same as NgVector
AllZeroIndex	The transcript with expression 0 for all samples (which are not tested).
PPMat	The Posterior Probability of following each pattern (columns) for each transcript (rows). Transcripts with expression 0 for all samples are not shown in this matrix.
AllParti	selected patterns
PPMatWith0	The Posterior Probability of following each pattern (columns) for each transcript (rows). Transcripts with expression 0 for all samples are shown in this matrix with PP(any_pattrn)=NA. The transcript order is exactly the same as the order of the input data.
Conditions	The input conditions.
NumUC	The number of uncertain positions at each unit

Author(s)

Ning Leng, Xiuyu Ma

References

Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013)

See Also

EBTest, GetMultiPP, GetMultiFC

Examples

data(MultiGeneMat)
Conditions = c("C1","C1","C2","C2","C3","C3")
MultiSize = MedianNorm(MultiGeneMat)
MultiOut = EBMultiTest(MultiGeneMat,Conditions=Conditions,uc = 2,
                     sizeFactors=MultiSize)
MultiPP = GetMultiPP(MultiOut)

wiscstatman/EBSeq documentation built on June 3, 2023, 7:34 a.m.