BEARscc_estimation: Estimate Parameters From Real Datasets by BEARscc

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

Estimate Parameters From Real Datasets by BEARscc

Description

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

Usage

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

Arguments

ref_data	A count matrix. Each row represents a gene and each column represents a cell.
verbose	Logical.
other_prior	A list with names of certain parameters. Some methods need extra parameters to execute the estimation step, so you must input them. When you use BEARscc, you must input the dilution.factor and the volume information to calculate the number of molecules of spike-ins.
seed	An integer of a random seed.

Details

In BEARscc, there are some important things that users should know before using it.

1. Make sure that there are spike-in genes in your count matrix. If not, an error may occur.

2. BEARscc needs ensembl gene id to execute estimation step, so it is better to transform the gene id previously. But users can also input official gene id and the procedure will convert them into ensembl gene id and note that this step may result in losing some genes when matching gene ids.

3. If users need the transformation of gene ids, users must input the species name: mouse or human. And we will match the according database to accomplish the conversion step.

4. Another important parameters: dilution.factor, volume

• dilution.factor: The dilution factor to dilute the ERCC spike-in mix liquid.

• volume: The volume (microliter) of spike-in mix used in sequencing step.

For more customed parameters in BEARscc, please check BEARscc::estimate_noiseparameters().

Value

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

References

Severson D T, Owen R P, White M J, et al. BEARscc determines robustness of single-cell clusters using simulated technical replicates. Nature communications, 2018, 9(1): 1-7. https://doi.org/10.1038/s41467-018-03608-y

Bioconductor URL: https://www.bioconductor.org/packages/release/bioc/html/BEARscc.html

Github URL: https://github.com/seversond12/BEARscc

Examples

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

other_prior = list(dilution.factor = 50000,
                   volume = 0.1,
                   species = "mouse")

estimate_result <- simmethods::BEARscc_estimation(
  ref_data = ref_data,
  other_prior = other_prior,
  verbose = TRUE,
  seed = 111
)

## End(Not run)

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