Package: scCB2 1.15.0

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]

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scCB2.pdf |scCB2.html✨
scCB2/json (API)
NEWS

# 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:scCB2-1.15.0(bioc 3.20)scCB2-1.14.0(bioc 3.19)

bioconductor-package

7 exports 0.49 score 186 dependencies

Last updated 2 months agofrom:35426baf52

Exports:CB2FindCell CheckBackgroundCutoff FilterGB GetCellMat QuickCB2 Read10xRaw Read10xRawH5

Dependencies:abind askpass base64enc beachmat BH Biobase BiocGenerics BiocParallel bitops bslib cachem caTools cli cluster codetools colorspace commonmark cowplot cpp11 crayon crosstalk curl data.table DelayedArray DelayedMatrixStats deldir digest doParallel dotCall64 dplyr dqrng DropletUtils edgeR evaluate fansi farver fastDummies fastmap fitdistrplus FNN fontawesome foreach formatR fs futile.logger futile.options future future.apply generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 ggrepel ggridges globals glue goftest gplots gridExtra gtable gtools HDF5Array here highr htmltools htmlwidgets httpuv httr ica igraph IRanges irlba isoband iterators jquerylib jsonlite KernSmooth knitr labeling lambda.r later lattice lazyeval leiden lifecycle limma listenv lmtest locfit magrittr MASS Matrix MatrixGenerics matrixStats memoise mgcv mime miniUI munsell nlme openssl 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 sass scales scattermore sctransform scuttle Seurat SeuratObject shiny SingleCellExperiment sitmo snow sourcetools sp spam SparseArray sparseMatrixStats spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.utils statmod stringi stringr SummarizedExperiment survival sys tensor tibble tidyr tidyselect tinytex UCSC.utils utf8 uwot vctrs viridisLite withr xfun xtable XVector yaml zlibbioc zoo

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

Zijian Ni and Christina Kendziorski

Rendered fromscCB2.Rmdusingknitr::rmarkdownon Jun 30 2024.

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