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.

1 2 3 4 5 6 7 8 | ```
## 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)
``` |

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