Package: SPsimSeq 1.23.0

Joris Meys

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

SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.

Authors:Alemu Takele Assefa [aut], Olivier Thas [ths], Joris Meys [cre], Stijn Hawinkel [aut]

SPsimSeq_1.23.0.tar.gz
SPsimSeq_1.23.0.zip(r-4.7)SPsimSeq_1.23.0.zip(r-4.6)SPsimSeq_1.23.0.zip(r-4.5)
SPsimSeq_1.23.0.tgz(r-4.6-any)SPsimSeq_1.23.0.tgz(r-4.5-any)
SPsimSeq_1.23.0.tar.gz(r-4.7-any)SPsimSeq_1.23.0.tar.gz(r-4.6-any)
SPsimSeq_1.23.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SPsimSeq/json (API)

# Install 'SPsimSeq' in R:

install.packages('SPsimSeq', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/centerforstatistics-ugent/spsimseq/issues

Datasets:

scNGP.data - Neuroblastoma NGP cells single-cell RNA-seq.
zhang.data.sub - Neuroblastoma bulk RNA-seq data retrieved from Zhang et (2015).

On BioConductor:SPsimSeq-1.23.0(bioc 3.24)SPsimSeq-1.22.0(bioc 3.23)

geneexpression rnaseq singlecell sequencing dnaseq

7.17 score 10 stars 1 packages 31 scripts 4 mentions 3 exports 109 dependencies

Last updated from:72d88fe83e. Checks:1 NOTE, 9 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	228
linux-devel-x86_64	OK	461
source / vignettes	OK	419
linux-release-x86_64	OK	462
macos-release-arm64	OK	250
macos-oldrel-arm64	OK	235
windows-devel	OK	385
windows-release	OK	359
windows-oldrel	OK	352
wasm-release	OK	197

Exports:configExperiment evaluateDensities SPsimSeq

Dependencies:abind ade4 ape backports base64enc Biobase BiocGenerics biomformat Biostrings bslib cachem checkmate cli cluster codetools colorspace cpp11 crayon data.table DelayedArray digest doParallel dynamicTreeCut edgeR evaluate farver fastcluster fastmap fitdistrplus fontawesome foreach foreign Formula fs generics GenomicRanges ggplot2 glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets igraph impute IRanges isoband iterators jquerylib jsonlite knitr labeling lattice lifecycle limma locfit magrittr MASS Matrix MatrixGenerics matrixStats memoise mgcv mime multtest mvtnorm nlme nnet permute phyloseq pixmap pkgconfig plyr preprocessCore R6 rappdirs RColorBrewer Rcpp RcppArmadillo reshape2 rlang rmarkdown rpart rstudioapi S4Arrays S4Vectors S7 sass scales Seqinfo SingleCellExperiment sp SparseArray statmod stringi stringr SummarizedExperiment survival tinytex vctrs vegan viridisLite WGCNA withr xfun XVector yaml

Manual for the SPsimSeq package: semi-parametric simulation for bulk and single cell RNA-seq data

Rendered fromSPsimSeq.Rmdusingknitr::rmarkdown

Last update: 2020-05-15
Started: 2020-04-01

Citation

Development and contributors

Readme and manuals

Help Manual

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