Package: iCellR 1.6.6
Alireza Khodadadi-Jamayran
iCellR: Analyzing High-Throughput Single Cell Sequencing Data
A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.
Authors:
iCellR_1.6.6.tar.gz
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iCellR.pdf |iCellR.html✨
iCellR/json (API)
# Install 'iCellR' in R: |
install.packages('iCellR', repos = c('https://rezakj.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/rezakj/icellr/issues
10xgenomics3dbatch-normalizationcell-type-classificationcite-seqclusteringclustering-algorithmdiffusion-mapsdropouticellrimputationintractive-graphnormalizationpseudotimescrna-seqscvdj-seqsingel-cell-sequencingumap
Last updated 4 months agofrom:3134c866bc. Checks:OK: 7 NOTE: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 08 2024 |
R-4.5-win-x86_64 | NOTE | Oct 08 2024 |
R-4.5-linux-x86_64 | NOTE | Oct 08 2024 |
R-4.4-win-x86_64 | OK | Oct 08 2024 |
R-4.4-mac-x86_64 | OK | Oct 08 2024 |
R-4.4-mac-aarch64 | OK | Oct 08 2024 |
R-4.3-win-x86_64 | OK | Oct 08 2024 |
R-4.3-mac-x86_64 | OK | Oct 08 2024 |
R-4.3-mac-aarch64 | OK | Oct 08 2024 |
Exports:add.10x.imageadd.adtadd.vdjadt.rna.mergecapture.image.10xcccell.cyclecell.filtercell.gatingcell.type.predchange.clustclono.plotclust.avg.expclust.cond.infoclust.ordclust.rmclust.stats.plotcluster.plotdata.aggregationdata.scaledown.samplefind_neighborsfind.dim.genesfindMarkersgate.to.clustgene.plotgene.statsgg.corheatmap.gg.plothto.annoi.scoreibaiclustload.h5load10xmake.bedmake.gene.modelmake.objmyImpnorm.adtnorm.dataopt.pcs.plotprep.vdjpseudotimepseudotime.knetlpseudotime.treeqc.statsRphenographrun.anchorrun.ccarun.clusteringrun.diff.exprun.diffusion.maprun.imputerun.knetlrun.mnnrun.pc.tsnerun.pcarun.phenographrun.tsnerun.umapspatial.plotstats.plottop.markersvdj.statsvolcano.ma.plot
Dependencies:abindapeaskpassbackportsbase64encBHbitbit64bootbroombslibcachemcarcarDatacheckmatecliclustercolorspacecommonmarkcorrplotcowplotcpp11crayoncrosstalkcurldata.tableDerivdigestdoBydplyrdqrngevaluatefansifarverfastmapFNNfontawesomeforeignFormulafsgenericsggdendroggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehdf5rhighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvhttrigraphirlbaisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimeminqamodelrmunsellNbClustnlmenloptrnnetnumDerivopensslpbkrtestpheatmappillarpkgconfigplotlyplyrpngpolynomprettyunitsprogresspromisespurrrquantregR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppEigenRcppProgressreshaperlangrmarkdownrpartRSpectrarstatixrstudioapiRtsnesassscalesscatterplot3dshinysitmosourcetoolsSparseMstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8uwotvctrsviridisviridisLitewithrxfunxtableyaml