Package: DAseq 1.0.0

Jun Zhao

DAseq: Detecting regions of differential abundance between scRNA-seq datasets

DA-seq is a multiscale approach for detecting DA subpopulations. In contrast to clustering based approaches, our method can detect DA subpopulations that do not form well separated clusters. For each cell, we compute a multiscale differential abundance score measure. These scores are based on the k nearest neighbors in the transcriptome space for various values of k.

Authors:Jun Zhao <[email protected]>

DAseq_1.0.0.tar.gz

DAseq_1.0.0.tar.gz(r-4.4-noble)
DAseq_1.0.0.tgz(r-4.4-emscripten)
DAseq.pdf |DAseq.html✨
DAseq/json (API)

# Install 'DAseq' in R:

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

Bug tracker:https://github.com/klugerlab/daseq/issues

Datasets:

X.2d.melanoma - T-SNE embedding of the melanoma dataset
X.label.info - Sample label information
X.label.melanoma - Cell sample labels of the melanoma dataset
X.melanoma - Top 10 PCs of the melanoma dataset

On CRAN:

3.78 score 39 stars 31 scripts 12 exports 172 dependencies

Last updated 4 years agofrom:b1f58e0028. Checks:OK: 1. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 12 2024

Exports:addDAslot getDAcells getDAregion plotCellLabel plotCellScore plotDAsite runSTG SeuratLocalMarkers SeuratMarkerFinder STGlocalMarkers STGmarkerFinder updateDAcells

Dependencies:abind askpass base64enc BH bitops bslib cachem caret caTools class cli clock cluster codetools colorspace commonmark cowplot cpp11 crayon crosstalk curl data.table deldir diagram digest dotCall64 dplyr dqrng e1071 evaluate fansi farver fastDummies fastmap fitdistrplus FNN fontawesome foreach fs future future.apply generics ggplot2 ggrepel ggridges glmnet globals glue goftest gower gplots gridExtra gtable gtools hardhat here highr htmltools htmlwidgets httpuv httr ica igraph ipred irlba isoband iterators jquerylib jsonlite KernSmooth knitr labeling later lattice lava lazyeval leiden lifecycle listenv lmtest lubridate magrittr MASS Matrix matrixStats memoise mgcv mime miniUI ModelMetrics munsell nlme nnet numDeriv openssl parallelly patchwork pbapply pillar pkgconfig plotly plyr png polyclip pROC prodlim progressr promises proxy purrr R6 RANN rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppHNSW RcppProgress RcppTOML recipes reshape2 reticulate rlang rmarkdown ROCR rpart rprojroot RSpectra Rtsne sass scales scattermore sctransform Seurat SeuratObject shape shiny sitmo sourcetools sp spam spatstat.data spatstat.explore spatstat.geom spatstat.random spatstat.sparse spatstat.univar spatstat.utils SQUAREM stringi stringr survival sys tensor tibble tidyr tidyselect timechange timeDate tinytex tzdb utf8 uwot vctrs viridisLite withr xfun xtable yaml zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add DA slot	addDAslot
DAseq: Detecting regions of differential abundance between scRNA-seq datasets	DAseq-package DAseq
DA-seq Step 1 & Step 2: select DA cells	getDAcells
DA-seq Step 3: get DA regions	getDAregion
Plot cell labels	plotCellLabel
Plot a score for each cell	plotCellScore
Plot da site	plotDAsite
Run STG	runSTG
Find local markers	SeuratLocalMarkers
DA-seq Step 4: Seurat marker finder to characterize DA regions	SeuratMarkerFinder
STG local markers Run STG to find a set of genes that separate a given DA region from a local subset of cells.	STGlocalMarkers
DA-seq Step 4: STG feature selection	STGmarkerFinder
Update DA cells	updateDAcells
t-SNE embedding of the melanoma dataset	X.2d.melanoma
Sample label information	X.label.info
Cell sample labels of the melanoma dataset	X.label.melanoma
Top 10 PCs of the melanoma dataset	X.melanoma