Package: nebula 1.5.6

nebula: Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data

A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.

Authors:Liang He [aut, cre], Raghav Sharma [ctb]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
nebula/json (API)

# Install 'nebula' in R:

install.packages('nebula', repos = c('https://lhe17.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lhe17/nebula/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

sample_data - An example data set for testing nebula
sample_seurat - An example data set for testing scToNeb

On CRAN:

cpp

7.07 score 59 stars 395 scripts 641 downloads 4 exports 165 dependencies

Last updated from:f166b5158b. Checks:11 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	331
linux-devel-x86_64	NOTE	444
source / vignettes	OK	376
linux-release-arm64	NOTE	375
linux-release-x86_64	NOTE	377
macos-release-arm64	NOTE	231
macos-release-x86_64	NOTE	620
macos-oldrel-arm64	NOTE	240
macos-oldrel-x86_64	NOTE	512
windows-devel	NOTE	357
windows-release	NOTE	356
windows-oldrel	NOTE	395
wasm-release	OK	222

Exports:group_cell nbresidual nebula scToNeb

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

A fast negative binomial mixed model for analyzing multi-subject single-cell data

Rendered fromnebula_example.Rmdusingknitr::rmarkdown

Last update: 2025-11-28
Started: 2020-09-23

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Negative Binomial Mixed Models Using Large-Sample Approximation for Differential Expression Analysis of ScRNA-Seq Data	nebula-package
Group cells according to subject IDs	group_cell
Extract Pearson residuals from the results of NEBULA	nbresidual
Association analysis of a multi-subject single-cell data set using a fast negative binomial mixed model	nebula
An example data set for testing nebula	sample_data
An example data set for testing scToNeb	sample_seurat
Retrieve data from Seurat or SingleCellExperiment object to prepare for use in nebula	scToNeb