Description Usage Arguments Value
Run the MRF model to estimate posterior probabilities of differential expression for each gene across each cell type
1 2 3 4 5 6 7 8 9 10 | get_DE_MRF(
data,
g_g,
c_c,
nulltype = 1,
df = 15,
iterEM = 200,
iterGibbsPost = 20000,
brPost = 10000
)
|
g_g |
Gene to gene network matrix |
c_c |
Cell to cell dependency matrix |
nulltype |
Type of null hypothesis assumed in estimating f0, see locfdr package.Default is the MLE (nulltype=1) |
df |
Degrees of freedom for fitting the estimated density, see locfdr package. Default df=15 |
iterEM |
Max number of iterations for the EM algorithm. Default=200 |
iterGibbsPost |
Number of Gibbs posterior samples. Default=20,000 |
brPost |
Number of burn-in for the posterior samples. Default=10,000 |
zz |
Summary statistics matrix, rows are genes, columns are cell types |
The estimated model parameters and the posterior probabilities of differential expression
postDE |
Posterior probabilities of differential expression. A 2-dimensional array: (num of genes)*(num of cell types) |
paraMRF |
Estimated model parameters |
paraMRFTrace |
Trace of the estimated model parameters in the EM algorithm |
paraVar |
Variance-covariance matrix of the estimated model parameters in the EM algorithm |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.