Package: scCB2 1.23.0

Zijian Ni

scCB2: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data

scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled.

Authors:Zijian Ni [aut, cre], Shuyang Chen [ctb], Christina Kendziorski [ctb]

scCB2_1.23.0.tar.gz
scCB2_1.23.0.zip(r-4.7)scCB2_1.23.0.zip(r-4.6)scCB2_1.23.0.zip(r-4.5)
scCB2_1.23.0.tgz(r-4.6-any)scCB2_1.23.0.tgz(r-4.5-any)
scCB2_1.23.0.tar.gz(r-4.7-any)scCB2_1.23.0.tar.gz(r-4.6-any)
scCB2_1.23.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
scCB2/json (API)

# Install 'scCB2' in R:

install.packages('scCB2', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zijianni/sccb2/issues

Datasets:

mbrainSub - Subset of 1k Brain Cells from an E18 Mouse

On BioConductor:scCB2-1.23.0(bioc 3.24)scCB2-1.22.0(bioc 3.23)

dataimport rnaseq singlecell sequencing geneexpression transcriptomics preprocessing clustering

5.42 score 11 stars 12 scripts 296 downloads 7 exports 185 dependencies

Last updated from:77e6f0162c. Checks:6 NOTE, 4 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	236
linux-devel-x86_64	NOTE	576
source / vignettes	OK	438
linux-release-x86_64	NOTE	531
macos-release-arm64	NOTE	337
macos-oldrel-arm64	OK	328
windows-devel	NOTE	503
windows-release	NOTE	448
windows-oldrel	OK	401
wasm-release	OK	190

Exports:CB2FindCell CheckBackgroundCutoff FilterGB GetCellMat QuickCB2 Read10xRaw Read10xRawH5

Dependencies:abind askpass assorthead base64enc beachmat BH Biobase BiocGenerics biocmake BiocParallel bitops bslib cachem caTools cli cluster codetools commonmark cowplot cpp11 crosstalk curl data.table DelayedArray DelayedMatrixStats deldir digest dir.expiry doParallel dotCall64 dplyr dqrng DropletUtils edgeR evaluate farver fastDummies fastmap filelock fitdistrplus FNN fontawesome foreach formatR fs futile.logger futile.options future future.apply generics GenomicRanges ggplot2 ggrepel ggridges globals glue goftest gplots gridExtra gtable gtools h5mread HDF5Array here highr htmltools htmlwidgets httpuv httr ica igraph IRanges irlba isoband iterators jquerylib jsonlite KernSmooth knitr labeling lambda.r later lattice lazyeval lifecycle limma listenv lmtest locfit magrittr MASS Matrix MatrixGenerics matrixStats memoise mime miniUI nlme openssl otel parallelly patchwork pbapply pillar pkgconfig plotly plyr png polyclip progressr promises purrr R.methodsS3 R.oo R.utils R6 RANN rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML reshape2 reticulate rhdf5 rhdf5filters Rhdf5lib rlang rmarkdown ROCR rprojroot RSpectra Rtsne S4Arrays S4Vectors S7 sass scales scattermore sctransform scuttle Seqinfo Seurat SeuratObject shiny SingleCellExperiment sitmo snow sourcetools sp spam SparseArray sparseMatrixStats spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils statmod stringi stringr SummarizedExperiment survival sys tensor tibble tidyr tidyselect tinytex utf8 uwot vctrs viridisLite withr xfun xtable XVector yaml zoo

CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data

Rendered fromscCB2.Rmdusingknitr::rmarkdown

Last update: 2022-01-18
Started: 2019-10-06

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Main function of distinguish real cells from empty droplets using clustering-based Monte-Carlo test	CB2FindCell
Check different background cutoffs and recommend an appropriate one	CheckBackgroundCutoff
Filter out low count genes and barcodes from count matrix	FilterGB
Extract real cell matrix from 'CB2FindCell' output and optionally filter out low-quality cells	GetCellMat
Subset of 1k Brain Cells from an E18 Mouse	mbrainSub
All-in-one function from raw data to filtered cell matrix	QuickCB2
Read 10x output data	Read10xRaw Read10xRawH5