Description Usage Arguments Value Examples
the MGN distribution model the joint distribution, pi(lambda,delta), by a K-component MGN distribution, and allows degenerate normal for delta when the null hypothesis is simple.
1 |
data |
the RNA-seq data, should be the output from RNASeq.Data |
nK |
the number of components in MGN. When testing for fold-changes (FC), nK includes all components, when testing for differential expression (DE), nK only includes the components that are NOT degenerated. |
p0 |
the proportion of null genes when testing for DE genes. |
d0 |
the point where 'delta' is degerated, default is 0 when testing for DE genes. |
nK0 |
the number of components that are degenerated when testing for DE genes. |
iter.max |
maximium number of interations in the EM algorithm |
print.steps |
print the esimates of MGN in each iteration step, if TRUE. Default is FALSE |
MGN0 |
The initialization of the MGN. It should be a data.frame with 5 columns: pr, alpha, beta, mu, sigma. The methods of moment estimation will be used if not provided. |
model |
data model, can be 'nbinom' or 'poisson'. the default will be the same as in 'data' |
nMC |
the number of random samples from Gamma and Normal distrubitons in the Monte-Carlo simulation. |
MGN |
The estimated MGN distribution, as a data.frame with 5 columns: pr, alpha,beta,mu,sigma. pr: the proportion (weight) of each component \ alpha: alpha in the Gamma distribution \ beta: beta in the Gamma distribution \ mu: mu (mean) of the Normal distribution\ sigma: sigma (standard deviation) of the Normal distribution. sigma=0 is allowed for degenerated Normal |
lam |
the shrinked estimation of lambda (mean expression for each gene) |
del |
the shrinked estimation of delta (log-fold change) for each gene |
1 | #### see examples by typing 'help(test.AMAP)'
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