escoParams: The escoParams class

In JINJINT/ESCO: Simple Simulation of Single-cell RNA Sequencing Data

Description

S4 class that holds parameters for the ESCO simulation.

Parameters

The ESCO simulation requires the following parameters:

nGenes

The number of genes to simulate.

nCells

The number of cells to simulate.

[seed]

Seed to use for generating random numbers.

Mean parameters

[mean.method]

Whether to use a gamma distribution or a given density.

mean.dens

A density object.

mean.shape

Shape parameter for the mean gamma distribution.

mean.rate

Rate parameter for the mean gamma distribution.

Library size parameters

[lib.method]

Whether to use a gamma distribution or a given density.

lib.dens

A density object.

lib.loc

Location (meanlog) parameter for the library size log-normal distribution, or mean parameter if a normal distribution is used.

lib.scale

Scale (sdlog) parameter for the library size log-normal distribution, or sd parameter if a normal distribution is used.

lib.norm

Logical. Whether to use a normal distribution for library sizes instead of a log-normal.

Expression outlier parameters

out.prob

Probability that a gene is an expression outlier.

out.facLoc

Location (meanlog) parameter for the expression outlier factor log-normal distribution.

out.facScale

Scale (sdlog) parameter for the expression outlier factor log-normal distribution.

Group parameters

[nGroups]

The number of groups to simulate.

[group.prob]

Probability that a cell comes from a group.

Tree parameters

[tree]

The tree structure to simulate.

Differential expression parameters

[de.center]

The mean of the tree DE factors.

[de.prob]

Probability that a gene is differentially expressed in a group. Can be a vector.

[de.loProb]

Probability that a differentially expressed gene is down-regulated. Can be a vector.

[de.facLoc]

Location (meanlog) parameter for the differential expression factor log-normal distribution. Can be a vector.

[de.facScale]

Scale (sdlog) parameter for the differential expression factor log-normal distribution. Can be a vector.

Biological Coefficient of Variation parameters

bcv.common

Underlying common dispersion across all genes.

bcv.df

Degrees of Freedom for the BCV inverse chi-squared distribution.

Dropout parameters

[dropout.type]

The type of dropout to simulate. "none" indicates no dropout, "zeroinflate" uses zero inflation model to add dropouts, "downsampling" uses similar procedure in SymSim to mimic the experimental steps for adding dropouts.

dropout.mid

Midpoint parameter for the dropout logistic function.

dropout.shape

Shape parameter for the dropout logistic function.

[alpha_mean]

Mean parameter for the dwonsampling gamma function.

[alpha_sd]

Standard variance parameter for the downsampling gamma function.

[lenslope]

Shape parameter for the dropout logistic function.

[nbins]

Shape parameter for the dropout logistic function.

[amp_bias_limt]

Shape parameter for the dropout logistic function.

[rate_2PCR]

PCR efficiency, usually very high

[LinearAmp]

if linear amplification is used for pre-amplification step, default is FALSE

[LinearAmp_coef]

the coeficient of linear amplification, that is, how many times each molecule is amplified by

[depth_mean]

Mean parameter of the sequencing depths.

[depth_sd]

Standard variance parameter of sequencing depths.

The parameters not shown in brackets can be estimated from real data using escoEstimate. For details of the Splatter simulation see escoSimulate.

JINJINT/ESCO documentation built on May 13, 2021, 7:25 p.m.