Package: DAseq 1.0.0
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:
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
- 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
Last updated 3 years agofrom:b1f58e0028. Checks:OK: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Aug 14 2024 |
Exports:addDAslotgetDAcellsgetDAregionplotCellLabelplotCellScoreplotDAsiterunSTGSeuratLocalMarkersSeuratMarkerFinderSTGlocalMarkersSTGmarkerFinderupdateDAcells
Dependencies:abindaskpassbase64encBHbitopsbslibcachemcaretcaToolsclasscliclockclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tabledeldirdiagramdigestdotCall64dplyrdqrnge1071evaluatefansifarverfastDummiesfastmapfitdistrplusFNNfontawesomeforeachfsfuturefuture.applygenericsggplot2ggrepelggridgesglmnetglobalsgluegoftestgowergplotsgridExtragtablegtoolshardhatherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphipredirlbaisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevalleidenlifecyclelistenvlmtestlubridatemagrittrMASSMatrixmatrixStatsmemoisemgcvmimeminiUIModelMetricsmunsellnlmennetnumDerivopensslparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclippROCprodlimprogressrpromisesproxypurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLrecipesreshape2reticulaterlangrmarkdownROCRrpartrprojrootRSpectraRtsnesassscalesscattermoresctransformSeuratSeuratObjectshapeshinysitmosourcetoolsspspamspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsSQUAREMstringistringrsurvivalsystensortibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8uwotvctrsviridisLitewithrxfunxtableyamlzoo
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 |