Usage
EBMultiTest(Data, NgVector = NULL, Conditions, AllParti = NULL,
sizeFactors, maxround, Pool = F, NumBin = 1000,
ApproxVal=10^-10, PoolLower=.25, PoolUpper = .75, Print=T,Qtrm=1,QtrmCut=0)
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.
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.
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).
maxround
Number of iterations. The default value is 5.
Users should always check the convergency by looking at the Alpha and
Beta in output. If the hyper-parameter estimations are not converged
in 5 iterations, larger number is suggested.
Pool
While working without replicates, user could define the Pool = TRUE in the EBTest function to enable pooling.
NumBin
By defining NumBin = 1000, EBSeq will group the genes with similar means together into 1,000 bins.
PoolLower, PoolUpper
With the assumption that only subset of the genes are DE in the data set, we take genes whose FC are in the PoolLower - PoolUpper quantile of the FC's as the candidate genes (default is 25%-75%). For each bin, the bin-wise variance estimation is defined as 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.
ApproxVal
The variances of the transcripts with mean < var will be approximated as mean/(1-ApproxVal).
Print
Whether print the elapsed-time while running the test.
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.