bisect_supervised: Add together two numbers.
In bisect: Estimating Cell Type Composition from Methylation Sequencing Data

Description Usage Arguments Value Examples

Add together two numbers.

1 2	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)