simulateCellMix: A function to generate a dataframe of mixed cell-type origin...

In BIMSBbioinfo/deconvR: Simulation and Deconvolution of Omic Profiles

A function to generate a dataframe of mixed cell-type origin simulated samples using given reference atlas.

Description

A function to generate a dataframe of mixed cell-type origin simulated samples using given reference atlas.

Usage

simulateCellMix(numberOfSamples, mixingVector = NULL, reference)

Arguments

numberOfSamples	The number of simulated samples to be generated in the dataframe.
mixingVector	Specify the cell origin proportions.If numberOfSamples = 1, this can be a vector of length = number of cell types in reference.Otherwise, this is a dataframe with rows for cell types (must be equal to cell types in reference) and columns for samples. Cells contain the proportion of the sample from the cell type. Use zeros for any unused cell type. If this object is not given,will use random values for the simulation.
reference	A dataframe containing signatures of different cell types used to generate the simulation. The first column should contain a unique ID (e.g. CpG target ID) which can be used in deconvolution to match rows of the reference to rows of the bulk. All subsequent columns are cell types. Rows are units of the signature. Each cell contains the value for the cell type and signature unit (e.g. methylation value at this CpG).

Value

A list containing two data frames. simulated: A dataframe which contains mixed cell-type origin simulated samples. The first column contains a unique ID (used from reference) which can be used in deconvolution to match rows of the reference to rows of the bulk.All subsequent columns are cell types. Rows are units of signature (e.g. CpGs). Each cell contains the value for the cell type and unit (e.g. methylation value at this CpG) proportions: A dataframe with the cell proportions of the generated samples. Each row is a sample. Columns are cell types.

References

Moss, J. et al. (2018). Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nature communications, 9(1), 1-12. https://doi.org/10.1038/s41467-018-07466-6

Examples

data("HumanCellTypeMethAtlas")
bulk_mix50 <- simulateCellMix(50, reference = HumanCellTypeMethAtlas)

bulk_mixVec <- simulateCellMix(1, mixingVector = c(
  0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), reference = HumanCellTypeMethAtlas)

BIMSBbioinfo/deconvR documentation built on April 26, 2023, 7:05 a.m.