View source: R/betaparametVP.R
betaparametVP | R Documentation |
This function is used to estimate parameters P and V by optimalizing estimates of parameters: alpha and beta.
betaparametVP(X, NX)
X |
count dataset derived from |
NX |
vector of m library sizes. Library size is sum of counts over the whole library. |
Count data of RNA sequence reads are assumed
to follow binomial distribution with parameters (P
)
and (n
) or negative binomial distribution with
parameters (P
) and (r
) , while P
is frequency of a gene or an isoform in RNA sequence
population and assumed to follow beta distribution with
parameters alpha
(a
) and beta
(b
).
Parameters P and V
are estimated by using optimal
estimation of parameters a and b. The optimal method is an
iteration algorithm drived by weighting proportion of gene
or isoform in each replicate library. This is a large-scale
method for estimating these parameters. Estimation of
parameters P and V
is core of the multiple beta
t-test method because P
and V
will be
used to calculate t-value.
return a list:
P |
N proportions estimated. |
V |
N variances estimated. |
betaparametVP requres functions betaparametab and betaparametw.
Yuan-DE Tan tanyuande@gmail.com
Baggerly KA, Deng L, Morris JS, Aldaz CM (2003)
Differential expression in SAGE: accounting for normal
between-library variation. Bioinformatics,
19: 1477-1483.
Yuan-De Tan, Anita M. Chandler, Arindam Chaudhury,
and Joel R. Neilson(2015) A Powerful Statistical
Approach for Large-scale Differential Transcription Analysis.
Plos One,10.1371/journal.pone.0123658.
betaparametab
, betaparametw
data(jkttcell) X<-jkttcell[1:100,] na<-3 nb<-3 cn<-length(X[1,]) rn<-length(X[,1]) XC<-X[,1:(cn-na-nb)] XX<-X[,(cn-na-nb+1):cn] n<-na+nb XA<-XX[,1:na] SA<-apply(XA,2,sum) PA<-betaparametVP(XA,SA)
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