Description Usage Arguments Details Value Author(s) Examples
View source: R/EBHMMNBMultiEM_2chain.R
Run EBSeqHMM model with a fixed expected FC
1 2 3 4 5 6 7 8 | EBHMMNBMultiEM_2chain(Data,
NgVector=NULL, Conditions, AllTran=NULL,
AllPi0=NULL, Terms=NULL,
sizeFactors, NumTranStage=c(3,2),PriorFC=2,
StateNames=c("Up","Down"),homo=FALSE,
UpdateRd=5, PIBound=.9, UpdatePI=FALSE,Print=FALSE,
WithinCondR=TRUE,
PenalizeLowMed=TRUE, PenalizeLowMedQt=.1,PenalizeLowMedVal=10)
|
Data |
input data, rows are genes and columns are samples |
NgVector |
Ng vector; NULL for gene level data |
Conditions |
A factor indicates the condition (time/spatial point) which each sample belongs to. |
AllTran |
initial values for transition matrices |
AllPi0 |
initial values for starting probabilities |
Terms |
Terms |
sizeFactors |
a vector indicates library size factors |
StateNames |
names of the hidden states |
NumTranStage |
number of states in two chains |
PriorFC |
target FC for gridient change |
homo |
whether the chain is assumed to be homogenious |
UpdateRd |
max number of iteration |
UpdatePI |
whether update the mixture proportion of two chains |
PIBound |
upper bound of the mixture proportion of the two chains |
Print |
Whether print the elapsed-time while running the test. |
WithinCondR |
By defining WithinCondR=TRUE, estimation of r's are obtained within each condition. (WithinCondR=FALSE is not suggested here) |
PenalizeLowMed,PenalizeLowMedQt,PenalizeLowMedVal |
Transcripts with median quantile < = PenalizeLowMedQt will be penalized |
EBHMMNBMultiEM_2chain() function implements the EBSeqHMM model to perform statistical analysis in an RNA-seq experiment with ordered conditions. EBHMMNBMultiEM_2chain() calls EBHMMNBfunForMulti() function to perform Balm-Welch algorithm that estimates the starting probabilities and transition probabilities. Here the emission distribution of each gene is assumed to be a Beta-Negative Binomial distribution with parameters (r_g, alpha, beta) , in which alpha and beta are shared by all the genes and r_g is gene specific. If not specified, r_g, alpha and beta will be estimated using method of moments. For isoform data, we assume isoforms from the same Ig group share the same beta^Ig. alpha is shared by all the isoforms and r_gi is isoform specific. The user also needs to specify an expected FC. Function EBSeqHMMTest() runs several models with varying FCs and returns the model with maximum likelihood.
Pi0Out: estimated starting probabilities of each iteration.
TranOut: estimated transition probabilities of each iteration.
Pi: estimated probability of being each chain.
Alpha, Beta: estimated alpha and beta(s).
LLSum: log likelihood of the model.
QList: estimated q's.
MgAllPP: marginal PP for all paths.
MgAllMAPChar: Most likely path based on MgAllPP.
MgAllMaxVal: Highest PP based on MgAllPP.
PPMatW: Posterior probabilities of being each of the chains.
Ning Leng
1 2 3 4 5 6 | data(GeneExampleData)
CondVector <- rep(paste("t",1:5,sep=""),each=3)
Conditions <- factor(CondVector, levels=c("t1","t2","t3","t4","t5"))
Sizes <- MedianNorm(GeneExampleData)
tmp <- EBHMMNBMultiEM_2chain(Data=GeneExampleData,sizeFactors=Sizes, Conditions=Conditions,
UpdateRd=2)
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