estimate_dispersion.RdIt estimates and regularizes the genes (or features) dispersion parameter
of decal negative binomial model using the strategy developed by
Hafemeister & Satija (2019).
estimate_dispersion(count, n = 2000, min_mu = 0.05)
| count | UMI count matrix with cells as columns and genes (or features) as rows. |
|---|---|
| n | number of genes sampled to preliminary estimation. |
| min_mu | minimal overall average expression ( |
a numeric vector of the estimated dispersion for each row of count
First, for a subset of genes it fits a Poisson regression offseted by
log(depth) and estimate a crude theta using a maximum likelihood
estimator with the observed counts and regression results. Next, it
regularize and expands theta estimates with a kernel smoothing function
as a function of average count (mu).