simuDM: simuDM

In scDD: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions

Description

Simulation for Differential Modalities Case

Usage

simuDM(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC,
  coeff, RP, modeFC, generateZero, constantZero, varInflation)

Arguments

Dataset1	Numeric matrix of expression values with genes in rows and samples in columns.
Simulated_Data	Required input empty matrix to provide structure information of output matrix with simulated data
DEIndex	Index for DE genes
samplename	The name for genes that chosen for simulation
Zeropercent_Base	Zero percentage for corresponding gene expression values
f	Fold change values (number of SDs) for each gene
FC	Fold Change values for DE Simulation
coeff	Relationship coefficients for Mean and Variance
RP	matrix for NB parameters for genes in samplename
modeFC	Vector of values to use for fold changes between modes for DP, DM, and DB.
generateZero	Specification of how to generate the zero values. If "empirical" (default), the observed proportion of zeroes in each gene is used for the simuated data, and the nonzeroes are simulated from a truncated negative binomial distribution. If "simulated", all values are simulated out of a negative binomial distribution, includling the zeroes. If "constant", then each gene has a fixed proportion of zeroes equal to constantZero.
constantZero	Numeric value between 0 and 1 that indicates the fixed proportion of zeroes for every gene. Ignored if generateZero method is not equal to "constant".
varInflation	Optional numeric vector with one element for each condition that corresponds to the multiplicative variance inflation factor to use when simulating data. Useful for sensitivity studies to assess the impact of confounding effects on differential variance across conditions. Currently assumes all samples within a condition are subject to the same variance inflation factor.

Value

Simulated_Data Simulated dataset for DM

References

Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y

scDD documentation built on Nov. 8, 2020, 7:10 p.m.