Description Usage Arguments Details Value Author(s) Examples
Function will estimate the Poisson-Gamma-Beta model parameters using Method of Moments
(MOM).In pathDESeq package this function uses as a internal function for the pgbmrfICM
function.
1 |
data |
A data frame which consists the gene expression data. First m columns correspond to the control group and next n columns correspond to the treatment group. Each row represents the gene expression data for a particular gene. |
state |
A data frame of differential expression states for given genes. Should be in the same gene order as in gene the expression data and the neighbourhood matrix. |
m |
Number of replicates for the control group. |
n |
Number of replicates for the treatment group. |
The Gamma distribution with parameters shape = S and rate = R has density,
f(x)= [R^S x^(S-1) e^(-Rx)]/Gamma(S)
The mean and variance can be written as, E(X) = S/R and Var(X) = S/R^2.
for x > 0, S > 0 and R > 0. Here Gamma(S) is the function implemented by R's gamma()
and defined in its help file.
The Beta distribution with parameters A and B has density, f(x)=[ x^(A-1) (1-x)^(B-1)]/ Beta(A,B)
The mean and variance can be written as, E(X) = A/(A+B) and Var(X)=AB/[(A+B)^2 (A+B+1)]
for x > 0, A > 0 and B > 0. Here Beta(A,B) is the function implemented by R's beta()
and defined in its help file.
This function will result in a vector of Poisson-Gamma-Beta parameter estimates with 4 elements; the Gamma Shape parameter, Gamma Rate parameter, Beta Shape1 parameter and Beta Shape2 parameter respectively. Invalid arguments will result in return value NaN, with a warning.
Malathi S.I. Dona
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | #load a data frame of simulated DE states for 1065 Reactome pathway genes
data("state1065")
#load a data frame with 1065 observations for 21 variables.
#First column consists gene names. The next 10 columns correspond to the
#control group and the last 10 columns correspond to the treatment group.
#Each row represents gene expression data for a particular gene.
data("data1065")
#store gene expression data
data<-data1065[,-1]
#take m=10 and n=10 as replicates for groups
#estimate PGB model parameters
pgbEst(data=data,state=state1065,m=10,n=10)
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