srtsim_fit: Fit the marginal distributions for each row of a count matrix

In SRTsim: Simulator for Spatially Resolved Transcriptomics

Fit the marginal distributions for each row of a count matrix

Description

Fit the marginal distributions for each row of a count matrix

Usage

srtsim_fit(
  simsrt,
  marginal = c("auto_choose", "zinb", "nb", "poisson", "zip"),
  sim_scheme = c("tissue", "domain"),
  min_nonzero_num = 2,
  maxiter = 500
)

Arguments

simsrt	A SRTsim object
marginal	Specification of the types of marginal distribution.Default value is 'auto_choose' which chooses between ZINB, NB, ZIP, and Poisson by a likelihood ratio test (lrt),AIC and whether there is underdispersion.'zinb' will fit the ZINB model. If there is underdispersion, it will fit the Poisson model. If there is no zero at all or an error occurs, it will fit an NB model instead.'nb' fits the NB model and chooses between NB and Poisson depending on whether there is underdispersion. 'poisson' simply fits the Poisson model.'zip' fits the ZIP model and chooses between ZIP and Poisson by a likelihood ratio test
sim_scheme	a character string specifying simulation scheme. "tissue" stands for tissue-based simulation; "domain" stands for domain-specific simulation. Default is "tissue".
min_nonzero_num	The minimum number of non-zero values required for a gene to be fitted. Default is 2.
maxiter	The number of iterations for the model-fitting. Default is 500.

Value

Returns an object with estimated parameters

Examples

## Create a simSRT object
toySRT  <- createSRT(count_in=toyData$toyCount,loc_in = toyData$toyInfo)
set.seed(1)
## Estimate model parameters for data generation
toySRT <- srtsim_fit(toySRT,sim_schem="tissue")

SRTsim documentation built on Jan. 13, 2023, 5:12 p.m.