Package: SignacX 2.2.0
SignacX: Cell Type Identification and Discovery from Single Cell Gene Expression Data
An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.
Authors:
SignacX_2.2.0.tar.gz
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SignacX_2.2.0.tgz(r-4.4-any)SignacX_2.2.0.tgz(r-4.3-any)
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SignacX.pdf |SignacX.html✨
SignacX/json (API)
NEWS
| # Install 'SignacX' in R: |
| install.packages('SignacX', repos = c('https://mathewchamberlain.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mathewchamberlain/signacx/issues
- Genes_Of_Interest - Genes of interest for drug discovery / disease biology research
cellular-phenotypesseuratsingle-cell-rna-seq
Last updated 2 years agofrom:11e872c292. Checks:OK: 1 WARNING: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 01 2024 |
| R-4.5-win | WARNING | Nov 01 2024 |
| R-4.5-linux | WARNING | Nov 01 2024 |
| R-4.4-win | WARNING | Nov 01 2024 |
| R-4.4-mac | WARNING | Nov 01 2024 |
| R-4.3-win | WARNING | Nov 01 2024 |
| R-4.3-mac | WARNING | Nov 01 2024 |
Exports:CID.entropyCID.GetDistMatCID.IsUniqueCID.LoadDataCID.LoadEdgesCID.LouvainCID.NormalizeCID.smoothCID.writeJSONGenerateLabelsget_colorsGetModels_HPCAGetTrainingData_HPCAKSoftImputeMASCModelGeneratorSaveCountsToH5SignacSignacBootSignacFast
Dependencies:abindaskpassbase64encBHbitopsbootbslibcachemcaToolscliclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tabledeldirDerivdigestdotCall64dplyrdqrngevaluatefansifarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfuturefuture.applygenericsggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalleidenlifecyclelistenvlme4lmtestmagrittrMASSMatrixmatrixStatsmemoisemgcvmimeminiUIminqamunsellneuralnetnlmenloptropensslparallellypatchworkpbapplypbmcapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLreshape2reticulateRJSONIOrlangrmarkdownROCRrprojrootRSpectraRtsnesassscalesscattermoresctransformSeuratSeuratObjectshinysitmosourcetoolsspspamspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrsurvivalsystensortibbletidyrtidyselecttinytexutf8uwotvctrsviridisLitewithrxfunxtableyamlzoo
Cell type classification with SignacX: CITE-seq PBMCs from 10X Genomics
Rendered fromsignac-Seurat_CITE-seq.Rmdusingknitr::rmarkdown_notangleon Nov 01 2024.Last update: 2021-03-01
Started: 2021-02-25
SignacX, Seurat and MASC: Analysis of kidney cells from AMP
Rendered fromsignac-Seurat_AMP.Rmdusingknitr::rmarkdown_notangleon Nov 01 2024.Last update: 2021-03-01
Started: 2021-02-04
Cell type classification with SignacX: 1k PBMCs from 10X Genomics
Rendered fromsignac-Seurat_pbmcs.Rmdusingknitr::rmarkdown_notangleon Nov 01 2024.Last update: 2021-03-01
Started: 2021-02-25
Benchmarking SignacFast with single cell flow-sorted data
Rendered fromSignacFast-Seurat_AMP_RA.Rmdusingknitr::rmarkdown_notangleon Nov 01 2024.Last update: 2021-03-01
Started: 2021-02-18
Benchmarking SignacX and SingleR with synovial flow cytometry data
Rendered fromsignac-Seurat_AMP_RA.Rmdusingknitr::rmarkdown_notangleon Nov 01 2024.Last update: 2021-03-01
Started: 2021-02-11
Learning CD56 NK cells from CITE-seq PBMCs
Rendered fromsignac-SPRING_Learning.Rmdusingknitr::rmarkdown_notangleon Nov 01 2024.Last update: 2021-03-01
Started: 2021-02-11
Mapping homologous gene symbols
Rendered fromCrabeating_vignette.Rmdusingknitr::rmarkdown_notangleon Nov 01 2024.Last update: 2021-03-01
Started: 2021-02-10
