Package: bayNorm 1.23.0

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]

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bayNorm/json (API)
NEWS

# 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-1.23.0(bioc 3.20)bayNorm-1.22.0(bioc 3.19)

bioconductor-package

17 exports 0.91 score 50 dependencies 3 mentions

Last updated 2 months agofrom:c178c52ac8

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 askpass BB BH Biobase BiocGenerics BiocParallel codetools cpp11 crayon curl DelayedArray doSNOW fitdistrplus foreach formatR futile.logger futile.options GenomeInfoDb GenomeInfoDbData GenomicRanges httr IRanges iterators jsonlite lambda.r lattice locfit MASS Matrix MatrixGenerics matrixStats mime openssl quadprog R6 Rcpp RcppArmadillo RcppProgress S4Arrays S4Vectors SingleCellExperiment snow SparseArray SummarizedExperiment survival sys UCSC.utils XVector zlibbioc

Introduction to bayNorm

Wenhao Tang, Fran<U+00E7>ois Bertaux, Philipp Thomas, Claire Stefanelli, Malika Saint, Samuel Marguerat, Vahid Shahrezaei

Rendered frombayNorm.Rmdusingknitr::rmarkdownon Jun 30 2024.

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