Package: bayNorm 1.31.0

Wenhao Tang

bayNorm: Single-cell RNA sequencing data normalization

bayNorm is used for normalizing single-cell RNA-seq data.

Authors:Wenhao Tang [aut, cre], Fran<U+00E7>ois Bertaux [aut], Philipp Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut], Samuel Marguerat [aut], Vahid Shahrezaei [aut]

bayNorm_1.31.0.tar.gz
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bayNorm_1.31.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bayNorm/json (API)

# Install 'bayNorm' in R:

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

Bug tracker:https://github.com/wt215/baynorm/issues

Uses libs:

c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

EXAMPLE_DATA_list - A subset of Grun et al (2014) data: 2i samples

On BioConductor:bayNorm-1.31.0(bioc 3.24)bayNorm-1.30.0(bioc 3.23)

immunooncology normalization rnaseq singlecell sequencing scrnaseq cpp openmp

6.55 score 10 stars 39 scripts 3 mentions 17 exports 40 dependencies

Last updated from:6b1aefc1f8. Checks:9 NOTE, 2 OK, 3 FAIL. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	217
linux-devel-arm64	NOTE	276
linux-devel-x86_64	NOTE	370
source / vignettes	OK	319
linux-release-arm64	NOTE	275
linux-release-x86_64	NOTE	405
macos-release-arm64	NOTE	233
macos-release-x86_64	NOTE	355
macos-oldrel-arm64	FAIL	98
macos-oldrel-x86_64	FAIL	195
windows-devel	NOTE	259
windows-release	NOTE	266
windows-oldrel	FAIL	95
wasm-release	OK	199

Exports:AdjustSIZE_fun as_matrix asMatrix bayNorm bayNorm_sup BB_Fun BetaFun Check_input DownSampling EstPrior EstPrior_rcpp EstPrior_sprcpp NOISY_FUN noisy_gene_detection Prior_fun SyntheticControl t_sp

Dependencies:abind BB BH Biobase BiocGenerics BiocParallel codetools cpp11 DelayedArray doSNOW fitdistrplus foreach formatR futile.logger futile.options generics GenomicRanges IRanges iterators lambda.r lattice locfit MASS Matrix MatrixGenerics matrixStats quadprog Rcpp RcppArmadillo RcppProgress rlang S4Arrays S4Vectors Seqinfo SingleCellExperiment snow SparseArray SummarizedExperiment survival XVector

Introduction to bayNorm

Rendered frombayNorm.Rmdusingknitr::rmarkdown

Last update: 2019-03-26
Started: 2017-12-11

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Adjust MME size estimate	AdjustSIZE_fun
Rcpp version's as.matrix	as_matrix
Rcpp version: as.matrix	asMatrix
A wrapper function of prior estimation and bayNorm function	bayNorm
bayNorm with estimated parameters as input	bayNorm_sup
Estimating size for each gene by either 1D or 2D maximization of marginal distribution	BB_Fun
Estimate capture efficiency for cells	BetaFun
Check input	Check_input
Binomial downsampling	DownSampling
Estimate size and mu for Negative Binomial distribution for each gene using MME method	EstPrior
Estimate size and mu for Negative Binomial distribution for each gene using MME method (Rcpp version)	EstPrior_rcpp
Estimate size and mu for Negative Binomial distribution for each gene using MME method (Rcpp version, sp_mat)	EstPrior_sprcpp
A subset of Grun et al (2014) data: 2i samples	EXAMPLE_DATA_list
Noisy gene detection	NOISY_FUN
A wrapper function for noisy gene detection from raw data. his produces synthetic control, performs bayNorm on both real cell data and synthetic controls and does noisy gene detection.	noisy_gene_detection
A wrapper function of 'EstPrior' and 'AdjustSIZE_fun'	Prior_fun
Generate synthetic control	SyntheticControl
Transpose of sparse matrix	t_sp