Description Usage Arguments Value Examples

Add together two numbers.

1 2 3 | ```
bisect_semi_suprevised(methylation_unkown_samples, total_reads_unknown_samples,
methylation_known_samples, total_reads_known_samples,
cell_composition_known_samples, alpha = NA, iterations = 200)
``` |

`methylation_unkown_samples` |
a matrix of individuals (rows) on sites (columns), containing the number of methylated reads for each site, in each individual for the samples with unknown cell composition. |

`total_reads_unknown_samples` |
a matrix of individuals (rows) on sites (columns), containing the total number of reads for each site, in each individual for the samples with unknown cell composition. |

`methylation_known_samples` |
a matrix of individuals (rows) on sites (columns), containing the number of methylated reads for each site, in each individual for the samples with known cell composition. |

`total_reads_known_samples` |
a matrix of individuals (rows) on sites (columns), containing the total number of reads for each site, in each individual for the samples with known cell composition. |

`cell_composition_known_samples` |
a matrix of individuals (rows) on cell types (columns), containing the proportion of each cell type, in each known sample. |

`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 list containing P, a matrix of estimated cell proportions for the unknown samples, and Pi, an estimated reference (the probability of methylation in each cell type).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## Randomly choose samples to be used as known
n_known_samples <- 50
known_samples_indices <- sample.int(nrow(baseline_GSE40279), size = n_known_samples)
known_samples <- as.matrix(baseline_GSE40279[known_samples_indices, ])
## Fit a dirichlet distribution to known samples to use as prior
fit_dirichlet <- sirt::dirichlet.mle(as.matrix(known_samples))
alpha <- fit_dirichlet$alpha
## Prepare the 4 needed matrices
methylation_known <- methylation_GSE40279[known_samples_indices, ]
methylation_unknown <-methylation_GSE40279[-known_samples_indices, ]
total_known <- total_reads_GSE40279[known_samples_indices, ]
total_unknown <- total_reads_GSE40279[-known_samples_indices, ]
## Run Bisect. You should use around 200 iterations. I choose than to accelarate the example.
results <- bisect_semi_suprevised(methylation_unknown, total_unknown,
methylation_known, total_known,
known_samples, alpha, iterations = 10)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.