simuDP: simuDP

Description Usage Arguments Value References

View source: R/SimuDP.R

Description

Simulation for Differential Proportion Case

Usage

1
2
simuDP(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC,
  coeff, RP, modeFC, DP, 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.

DP

Differetial Proportion vector

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 DP

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


kdkorthauer/scDD documentation built on Nov. 19, 2019, 3:41 p.m.