Semi-parametric simulation tool for bulk and single-cell RNA sequencing data

Documentation for package ‘SPsimSeq’ version 1.3.0

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SPsimSeq-package	SPsimSeq package
buildXmat	An auxialiary function to quickly construct the polyomial matrix, using Horner's rule
calculateCPM	Calculates counts per millions of reads, possibly with log-transform
checkInputValidity	Check for data validity
chooseCandGenes	Select candidate genes
configExperiment	Configure experiment
constructDens	Construct the cumulative density
estLibSizeDistr	Estimate log-normal distribution for the library sizes
evaluateDensities	Evaluate the densities in the estimated SPsimSeq object
expit	Evaluate the expit function
extractMat	A function with S4 dispatching to extract the count matrix
extractMat-method	A function with S4 dispatching to extract the count matrix
fitLLmodel	Fit log linear model for each gene
fitPoisGlm	Fast fit Poisson regression
fracZeroLogitModel	Extract data and iterate over batches to estimate zero probability models
genCopula	Generate a copula instance
geneParmEst	Gene level param estimates for density estimation
genLibSizes	Generate library sizes from log-normal
matchCopula	Match copulas to estimated SP distribution
obtCorMatsBatch	A function to obtain copulas or uniform random variables
obtCount	Calculates height and mid points of a distribution
parmEstDensVec	Density estimation on a single vector
prepareSPsimOutputs	A function to prepare outputs
samZeroID	Return ID for observations to be set to zero
scNGP.data	Neuroblastoma NGP cells single-cell RNA-seq.
selectGenes	Sample genes from candidate genes
SPsimPerGene	A function that generates the simulated data for a single gene
SPsimSeq	A function to simulate bulk or single cell RNA sequencing data
zeroProbModel	Predict zero probability using logistic rgression
zhang.data.sub	Neuroblastoma bulk RNA-seq data retrieved from Zhang et (2015).