bisect_supervised {bisect} | R Documentation |
Add together two numbers.
bisect_supervised(methylation, total_reads, reference, alpha = NA, iterations = 200)
methylation |
a matrix of individuals (rows) on sites (columns), containing the number of methylated reads for each site, in each individual. |
total_reads |
a matrix of individuals (rows) on sites (columns), containing the total number of reads for each site, in each individual. |
reference |
a matrix of sites (rows) on cell types (columns), containing the probability for methylation in each site, in each cell type. |
alpha |
a vector containing the hyper-parameters for the dirichelt prior. One value for each cell type. If NA, it is initiallized to 1/(number of cell types). |
iterations |
the number of iterations to use in the EM algorithm. |
A matrix of individuals (rows) on cell types (columns) containing the estimated proportion of each cell type, in each individual.
## Prepare the methylation and total reads matrices methylation <- as.matrix(methylation_GSE40279) total_reads <- as.matrix(total_reads_GSE40279) ## Remove the IDs column from the reference Pi <- as.matrix(reference_blood[,-1]) ## Run Bisect. You should use around 200 iterations. I choose than to accelarate the example. results <- bisect_supervised(methylation, total_reads, Pi, alpha_blood, iterations = 10)