Description Usage Arguments Details Value Examples

Fits means and dispersions for depth-adjusted or basic negative binomial models to a read/UMI count matrix.

1 2 3 | ```
NBumiFitModel(counts)
NBumiFitBasicModel(counts)
``` |

`counts` |
a numeric matrix of raw UMI counts, columns = samples, rows = genes. |

`NBumiFitModel`

Fits a depth-adjusted negative binomial model for each expression observation. Each expression is modelled using a negative binomial distribution with mean equal to t_i*t_j/T, where t_i is the total counts for sample i, t_j is the total counts for gene j and T is the total counts. Dispersions (R size parameter) are fit such that: var_j(counts_ij-mu_ij) = sum(mu_ij+mu_ij^2/size_j). Cases where genes exhibit poissonian behavior (size->infinity) are assigned a size of 10^10.
`NBumiFitBasicModel`

Fits a basic negative binomial model for each expression observation. Each expression is modelled using a negative binomial distribution with mean equal to t_j/n, where t_j is the total counts for gene j and n is the number of cells. Dispersions (R size parameter) are fit such that: var_j(counts_ij) = sum(mu_j+mu_j^2/size_j). Cases where genes exhibit poissonian behavior (size->infinity) are assigned a size of 10^10.

A named list of: mus = a genes by samples matrix of means for the observation-specific negative binomial sizes = a vector of dispersions for each gene. vals = named list of summary statistics of the original count matrix: tis = total molecules per cell, tjs = total molecules per gene, dis = total dropouts per cell, djs = total dropouts per genes, total = total molecules in dataset, nc = number of cells, ng = number of genes

1 2 3 | ```
library(M3DExampleData)
counts <- NBumiConvertData(Mmus_example_list$data)
fit <- NBumiFitModel(counts);
``` |

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