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:Alireza Khodadadi-Jamayran [aut, cre], Joseph Pucella [aut, ctb], Hua Zhou [aut, ctb], Nicole Doudican [aut, ctb], John Carucci [aut, ctb], Adriana Heguy [aut, ctb], Boris Reizis [aut, ctb], Aristotelis Tsirigos [aut, ctb]

iCellR_1.6.6.tar.gz
iCellR_1.6.6.zip(r-4.5)iCellR_1.6.6.zip(r-4.4)iCellR_1.6.6.zip(r-4.3)
iCellR_1.6.6.tgz(r-4.4-x86_64)iCellR_1.6.6.tgz(r-4.4-arm64)iCellR_1.6.6.tgz(r-4.3-x86_64)iCellR_1.6.6.tgz(r-4.3-arm64)
iCellR_1.6.6.tar.gz(r-4.5-noble)iCellR_1.6.6.tar.gz(r-4.4-noble)
iCellR_1.6.6.tgz(r-4.4-emscripten)iCellR_1.6.6.tgz(r-4.3-emscripten)
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'))

Peer review:

Bug tracker:https://github.com/rezakj/icellr/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • g2m.phase - A dataset of G2 and M phase genes
  • s.phase - A dataset of S phase genes

On CRAN:

10xgenomics3dbatch-normalizationcell-type-classificationcite-seqclusteringclustering-algorithmdiffusion-mapsdropouticellrimputationintractive-graphnormalizationpseudotimescrna-seqscvdj-seqsingel-cell-sequencingumap

5.95 score 120 stars 1 packages 7 scripts 699 downloads 66 exports 141 dependencies

Last updated 4 months agofrom:3134c866bc. Checks:OK: 7 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 08 2024
R-4.5-win-x86_64NOTEOct 08 2024
R-4.5-linux-x86_64NOTEOct 08 2024
R-4.4-win-x86_64OKOct 08 2024
R-4.4-mac-x86_64OKOct 08 2024
R-4.4-mac-aarch64OKOct 08 2024
R-4.3-win-x86_64OKOct 08 2024
R-4.3-mac-x86_64OKOct 08 2024
R-4.3-mac-aarch64OKOct 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

Readme and manuals

Help Manual

Help pageTopics
Add image data to iCellR objectadd.10x.image
Add CITE-seq antibody-derived tags (ADT)add.adt
Add V(D)J recombination dataadd.vdj
Merge RNA and ADT dataadt.rna.merge
Read 10X image datacapture.image.10x
Calculate Cell cycle phase predictioncc
Cell cycle phase predictioncell.cycle
Filter cellscell.filter
Cell gatingcell.gating
Create heatmaps or dot plots for genes in clusters to find thier cell types using ImmGen data.cell.type.pred
Change the cluster number or re-name themchange.clust
Make 2D and 3D scatter plots for clonotypes.clono.plot
Create a data frame of mean expression of genes per clusterclust.avg.exp
Calculate cluster and conditions frequenciesclust.cond.info
Sort and relabel the clusters randomly or based on pseudotimeclust.ord
Remove the cells that are in a clusterclust.rm
Plotting tSNE, PCA, UMAP, Diffmap and other dim reductionsclust.stats.plot
Plot nGenes, UMIs and perecent mitocluster.plot
Merge multiple data frames and add the condition names to their cell idsdata.aggregation
Scale datadata.scale
Down sample conditionsdown.sample
K Nearest Neighbour Searchfind_neighbors
Find model genes from PCA datafind.dim.genes
Find marker genes for each clusterfindMarkers
A dataset of G2 and M phase genesg2m.phase
Assign cluster number to cell idsgate.to.clust
Make scatter, box and bar plots for genesgene.plot
Make statistical information for each gene across all the cells (SD, mean, expression, etc.)gene.stats
Gene-gene correlation. This function helps to visulaize and calculate gene-gene correlations.gg.cor
Create heatmaps for genes in clusters or conditions.heatmap.gg.plot
Demultiplexing HTOshto.anno
Cell cycle phase predictioni.score
iCellR Batch Alignment (IBA)iba
iCellR Clusteringiclust
Load h5 data as data.frameload.h5
Load 10X data as data.frameload10x
Make BED Filesmake.bed
Make a gene model for clusteringmake.gene.model
Create an object of class iCellR.make.obj
Impute datamyImp
Normalize ADT data. This function takes data frame and Normalizes ADT data.norm.adt
Normalize datanorm.data
Find optimal number of PCs for clusteringopt.pcs.plot
Prepare VDJ dataprep.vdj
Pseudotimepseudotime
iCellR KNN Networkpseudotime.knetl
Pseudotime Treepseudotime.tree
Calculate the number of UMIs and genes per cell and percentage of mitochondrial genes per cell and cell cycle genes.qc.stats
RphenoGraph clusteringRphenograph
Run anchor alignment on the main data.run.anchor
Run CCA on the main datarun.cca
Clustering the datarun.clustering
Differential expression (DE) analysisrun.diff.exp
Run diffusion map on PCA data (PHATE - Potential of Heat-Diffusion for Affinity-Based Transition Embedding)run.diffusion.map
Impute the main datarun.impute
iCellR KNN Networkrun.knetl
Run MNN alignment on the main data.run.mnn
Run tSNE on PCA Data. Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embeddingrun.pc.tsne
Run PCA on the main datarun.pca
Clustering the datarun.phenograph
Run tSNE on the Main Data. Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embeddingrun.tsne
Run UMAP on PCA Data (Computes a manifold approximation and projection)run.umap
A dataset of S phase geness.phase
Plot nGenes, UMIs and perecent mito, genes, clusters and more on spatial imagespatial.plot
Plot nGenes, UMIs and percent mitostats.plot
Choose top marker genestop.markers
VDJ statsvdj.stats
Create MA and Volcano plots.volcano.ma.plot