ESCO_estimation: Estimate Parameters From Real Datasets by ESCO

In duohongrui/simmethods: A collection of 40+ simulation methods for singlce-cell RNA seqencing data

Estimate Parameters From Real Datasets by ESCO

Description

This function is used to estimate useful parameters from a real dataset by using escoEstimate function in ESCO package.

Usage

ESCO_estimation(ref_data, other_prior = NULL, verbose = FALSE, seed)

Arguments

ref_data	A count matrix. Each row represents a gene and each column represents a cell.
other_prior	A list with names of certain parameters. Some methods need extra parameters to execute the estimation step, so you must input them. In simulation step, the number of cells, genes, groups, batches, the percent of DEGs are usually customed, so before simulating a dataset you must point it out. See Details below for more information.
verbose	Logical.
seed	An integer of a random seed.

Details

In ESCO, users can input cell group information when it is available but in this case ESCO is not stable and may fail to estimate suitable distribution parameters from real data. If users want to estimate tree structured parameters, set other_prior = list(tree = TRUE). For more instructions, see Examples.

Value

A list contains the estimated parameters and the results of execution detection.

References

Tian J, Wang J, Roeder K. ESCO: single cell expression simulation incorporating gene co-expression. Bioinformatics, 2021, 37(16): 2374-2381. https://doi.org/10.1093/bioinformatics/btab116

Github URL: https://github.com/JINJINT/ESCO

Examples

## Not run: 
ref_data <- simmethods::data

estimate_result <- simmethods::ESCO_estimation(ref_data = ref_data,
                                               other_prior = NULL,
                                               verbose = TRUE,
                                               seed = 111)
If cell group information is available, it can be another prior information.
But there is a bug in ESCO, and some datasets can not be estimated due to the
failing estimation of distribution parameters.
group_condition <- as.numeric(simmethods::group_condition)
estimate_result <- simmethods::ESCO_estimation(
  ref_data = ref_data,
  other_prior = list(group.condition = group_condition),
  verbose = TRUE,
  seed = 111
)

# ----------------- Estimate tree or trajectory structured data -------------
# Load data
ref_data <- simmethods::data
# Estimate parameters
estimate_result <- simmethods::ESCO_estimation(ref_data = ref_data,
                                               other_prior = list(tree = TRUE),
                                               verbose = TRUE,
                                               seed = 10)

## End(Not run)

duohongrui/simmethods documentation built on June 17, 2024, 10:49 a.m.