powsimR_estimation: Estimate Parameters From Real Datasets by powsimR

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

Estimate Parameters From Real Datasets by powsimR

Description

This function is used to estimate useful parameters from a real dataset by using estimateParam or estimateSpike function in powsimR package.

Usage

powsimR_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. 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.
seed	An integer of a random seed.

Details

powsimR provides some choices for users to select suitable parameters according to different types of data, platforms, normalization methods, distributions and so on.

1. RNAseq. "bulk" or "singlecell" (default).

2. Protocol. Options are "UMI" (default) (e.g. 10X Genomics, CEL-seq2) or "Read" (e.g. Smart-seq2).

3. Distribution. "NB" (default) for negative binomial or "ZINB" for zero-inflated negative binomial distribution fitting.

4. Normalisation. "TMM" (default), "MR", "PosCounts", "UQ", "scran", "Linnorm", "SCnorm", "Census", "depth", "none".

powsimR also provides an another choice to estimate parameters (not neccessary) via spike-ins. If users want to use this, make sure that the reference data must contain ERCC spike-in counts. In addtion, users must set dilution.factor and volume information by other_prior = list(dilution.factor = xxx, volume = xxx). For more instructions, see Examples.

Value

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

References

Vieth B, Ziegenhain C, Parekh S, et al. powsimR: power analysis for bulk and single cell RNA-seq experiments. Bioinformatics, 2017, 33(21): 3486-3488. https://doi.org/10.1093/bioinformatics/btx435

Github URL: https://github.com/bvieth/powsimR

Examples

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

## Estimate parameters without ERCC spike-in
estimate_result <- powsimR_estimation(
  ref_data = ref_data,
  other_prior = list(RNAseq = "singlecell",
                     Protocol = "UMI",
                     Normalisation = "scran"),
  verbose = TRUE,
  seed = 111)

## Estimate parameters with ERCC spike-in
### Make sure there are ERCC names in reference data
rownames(ref_data)[grep(rownames(ref_data), pattern = "^ERCC")]
### Users must input the dilution.factor and volume (microliter) to determine the ERCC molecules
estimate_result <- powsimR_estimation(
  ref_data = ref_data,
  other_prior = list(RNAseq = "singlecell",
                     Protocol = "UMI",
                     Normalisation = "scran",
                     dilution.factor = 50000,
                     volume = 1),
  verbose = TRUE,
  seed = 111)

## End(Not run)

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