Imputation {M3Drop} | R Documentation

## Normalized Data using the DANB model

### Description

Normalizes data to a common library size, imputing zeros as needed.

### Usage

    	NBumiImputeNorm(counts, fit, total_counts_per_cell=median(fit$vals$tis))

### Arguments

`counts` | raw count matrix, rows=genes, cols=cells
`fit` | Output from `NBumiFitModel`
`total_counts_per_cell` | reference library size to normalize all cells to.

### Details

Converts raw counts into positions in the CDF for the depth-adjusted negative binomial model fit to each observation. Adjusts the DANB parameters (mean and size) for the new library size. Then calculate the normalized counts for the equivalent position in the CDF for the NB using the new parameters.

### Value

Normalized count matrix.

### Examples

    library(M3DExampleData)
    counts <- as.matrix(Mmus_example_list$data);
    counts <- counts[rowSums(counts) > 0,];
    fit <- NBumiFitModel(counts);
    normed_counts <- NBumiImputeNorm(counts, fit, 1000000)

[Package M3Drop version 1.16.0 Index]