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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.zip(r-4.7-any)DAseq_1.0.0.zip(r-4.6-any)DAseq_1.0.0.zip(r-4.5-any)
DAseq_1.0.0.tgz(r-4.6-any)DAseq_1.0.0.tgz(r-4.5-any)
DAseq_1.0.0.tar.gz(r-4.7-any)DAseq_1.0.0.tar.gz(r-4.6-any)
DAseq_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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:

4.13 score 44 stars 61 scripts 12 exports 170 dependencies

Last updated from:b1f58e0028. Checks:7 ERROR, 2 OK. Indexed: no.

Target	Result	Time
linux-devel	ERROR	214
source / vignettes	OK	263
linux-release	ERROR	210
macos-release	ERROR	116
macos-oldrel	ERROR	100
windows-devel	ERROR	130
windows-release	ERROR	98
windows-oldrel	ERROR	102
wasm-release	OK	162

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 commonmark cowplot cpp11 crosstalk curl data.table deldir diagram digest dotCall64 dplyr dqrng e1071 evaluate 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 lifecycle listenv lmtest lubridate magrittr MASS Matrix matrixStats memoise mime miniUI ModelMetrics nlme nnet numDeriv openssl otel 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 RcppThread RcppTOML recipes reshape2 reticulate rlang rmarkdown ROCR rpart rprojroot RSpectra Rtsne S7 sass scales scattermore sctransform Seurat SeuratObject shape shiny sitmo sourcetools sp spam sparsevctrs 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

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

Package: DAseq 1.0.0

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

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