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
bayNorm_1.31.0.zip(r-4.7-x86_64)bayNorm_1.31.0.zip(r-4.7-arm64)bayNorm_1.31.0.zip(r-4.6-x86_64)bayNorm_1.31.0.zip(r-4.6-arm64)
bayNorm_1.31.0.tgz(r-4.6-x86_64)bayNorm_1.31.0.tgz(r-4.6-arm64)
bayNorm_1.31.0.tar.gz(r-4.7-arm64)bayNorm_1.31.0.tar.gz(r-4.7-x86_64)bayNorm_1.31.0.tar.gz(r-4.6-arm64)bayNorm_1.31.0.tar.gz(r-4.6-x86_64)
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

6.64 score 11 stars 44 scripts 608 downloads 3 mentions 17 exports 40 dependencies

Last updated from:6b1aefc1f8. Checks:1 WARNING, 10 NOTE, 2 OK, 3 FAIL. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	341
linux-devel-arm64	NOTE	256
linux-devel-x86_64	NOTE	418
source / vignettes	OK	492
linux-release-arm64	NOTE	364
linux-release-x86_64	NOTE	506
macos-release-arm64	NOTE	219
macos-release-x86_64	NOTE	468
macos-oldrel-arm64	FAIL	106
macos-oldrel-x86_64	FAIL	236
windows-devel-arm64	NOTE	338
windows-devel-x86_64	NOTE	287
windows-release-arm64	NOTE	316
windows-release-x86_64	NOTE	487
windows-oldrel-x86_64	FAIL	68
wasm-release	OK	316

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