Package: scBubbletree 1.9.0

Simo Kitanovski

scBubbletree: Quantitative visual exploration of scRNA-seq data

scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation.

Authors:Simo Kitanovski [aut, cre]

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NEWS

# Install 'scBubbletree' in R:

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

Bug tracker:https://github.com/snaketron/scbubbletree/issues

Datasets:

d_500 - Dataset: 500 PBMCs
d_ccl - Dataset: scRNA-seq data of 3,918 cells from 5 adenocarcinoma cell lines

On BioConductor:scBubbletree-1.9.0(bioc 3.21)scBubbletree-1.8.0(bioc 3.20)

visualization clustering singlecell transcriptomics rnaseq big-data bigdata scrna-seq scrna-seq-analysis visual visual-exploration

5.82 score 6 stars 8 scripts 198 downloads 12 exports 162 dependencies

Last updated 4 months agofrom:2950075e4c. Checks:1 OK, 7 NOTE. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Jan 29 2025
R-4.5-win	NOTE	Jan 29 2025
R-4.5-mac	NOTE	Jan 29 2025
R-4.5-linux	NOTE	Jan 29 2025
R-4.4-win	NOTE	Jan 29 2025
R-4.4-mac	NOTE	Jan 29 2025
R-4.3-win	NOTE	Jan 29 2025
R-4.3-mac	NOTE	Jan 29 2025

Exports:compare_bubbletrees get_bubbletree_dummy get_bubbletree_graph get_bubbletree_kmeans get_cat_tiles get_gini get_gini_k get_k get_num_cell_tiles get_num_tiles get_num_violins get_r

Dependencies:abind ape aplot askpass base64enc BH BiocParallel bitops bslib cachem caTools cli cluster codetools colorspace commonmark cowplot cpp11 crayon crosstalk curl data.table deldir digest dotCall64 dplyr dqrng evaluate fansi farver fastDummies fastmap fitdistrplus FNN fontawesome formatR fs futile.logger futile.options future future.apply generics ggfun ggplot2 ggplotify ggrepel ggridges ggtree globals glue goftest gplots gridExtra gridGraphics gtable gtools here highr htmltools htmlwidgets httpuv httr ica igraph irlba isoband jquerylib jsonlite KernSmooth knitr labeling lambda.r later lattice lazyeval leidenbase lifecycle listenv lmtest magrittr MASS Matrix matrixStats memoise mgcv mime miniUI munsell nlme openssl parallelly patchwork pbapply pillar pkgconfig plotly plyr png polyclip progressr promises proxy purrr R6 RANN rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML reshape2 reticulate rlang rmarkdown ROCR rprojroot RSpectra Rtsne sass scales scattermore sctransform Seurat SeuratObject shiny sitmo snow sourcetools sp spam spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils stringi stringr survival sys tensor tibble tidyr tidyselect tidytree tinytex treeio utf8 uwot vctrs viridisLite withr xfun xtable yaml yulab.utils zoo

Exploring scRNA-seq data from 5 cancer cell lines with scBubbletree

Simo Kitanovski ([email protected])

Rendered fromUser_manual.Rmdusingknitr::rmarkdownon Jan 29 2025.

Last update: 2024-09-20
Started: 2022-09-22

Help page	Topics
The R package scBubbletree	scBubbletree-package scBubbletree
Comparison of two bubbletrees generated from the same scRNA-seq data	compare_bubbletrees
Dataset: 500 PBMCs	d_500
Dataset: scRNA-seq data of 3,918 cells from 5 adenocarcinoma cell lines	d_ccl
Build bubbletree from matrix A of low-dimensional projections and vector cs of externally generated cluster IDs	get_bubbletree_dummy
Louvain clustering and hierarchical grouping of k' clusters (bubbles)	get_bubbletree_graph
k-means clustering and hierarchical grouping of k clusters (bubbles)	get_bubbletree_kmeans
Visualization of categorical cell features using tile plots	get_cat_tiles
Gini impurity index computed for a clustering solution and a vector of categorical cell feature labels	get_gini
Gini impurity index computed for a list of clustering solutions obtained by functions 'get_k' or 'get_r' and a vector of categorical cell feature labels	get_gini_k
Finding optimal number k of clusters	get_k
Visualization of numeric features of individual cells using tile plots	get_num_cell_tiles
Visualization of numeric cell features using tile plots	get_num_tiles
Visualization of numeric cell features using violin plots	get_num_violins
Finding optimal clustering resulution r and number of communities k'	get_r

Package: scBubbletree 1.9.0

scBubbletree: Quantitative visual exploration of scRNA-seq data

Exploring scRNA-seq data from 5 cancer cell lines with scBubbletree

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Help Manual

Usage by other packages (reverse dependencies)