Other packages > Find by keyword >

iCellR  

Analyzing High-Throughput Single Cell Sequencing Data
View on CRAN: Click here


Download and install iCellR package within the R console
Install from CRAN:
install.packages("iCellR")

Install from Github:
library("remotes")
install_github("cran/iCellR")

Install by package version:
library("remotes")
install_version("iCellR", "1.7.0")



Attach the package and use:
library("iCellR")
Maintained by
Alireza Khodadadi-Jamayran
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-08-02
Latest Update: 2025-09-03
Description:
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) and Khodadadi-Jamayran, et al (2020) for more details.
How to cite:
Alireza Khodadadi-Jamayran (2019). iCellR: Analyzing High-Throughput Single Cell Sequencing Data. R package version 1.7.0, https://cran.r-project.org/web/packages/iCellR. Accessed 06 Mar. 2026.
Previous versions and publish date:
1.0.0 (2019-08-02 12:50), 1.1.2 (2019-09-10 21:20), 1.1.4 (2019-09-26 19:30), 1.2.0 (2019-10-15 07:20), 1.2.2 (2019-10-22 23:00), 1.2.5 (2019-11-04 23:00), 1.2.7 (2019-12-04 20:50), 1.2.9 (2020-01-17 00:10), 1.3.0 (2020-01-24 22:50), 1.3.1 (2020-02-26 13:50), 1.3.3 (2020-03-14 18:00), 1.4.0 (2020-04-03 16:10), 1.4.5 (2020-04-10 09:20), 1.5.0 (2020-05-08 10:40), 1.5.1 (2020-06-17 11:40), 1.5.4 (2020-07-03 18:30), 1.5.5 (2020-07-16 23:20), 1.5.8 (2020-10-09 06:40), 1.5.9 (2021-01-21 06:30), 1.6.0 (2021-01-30 07:00), 1.6.1 (2021-03-04 06:20), 1.6.4 (2021-04-27 19:10), 1.6.5 (2021-10-09 17:00), 1.6.7 (2024-01-29 21:20)
Other packages that cited iCellR R package
View iCellR citation profile
Other R packages that iCellR depends, imports, suggests or enhances
Complete documentation for iCellR
Functions, R codes and Examples using the iCellR R package
Some associated functions: Rphenograph . add.10x.image . add.adt . add.vdj . adt.rna.merge . bubble.gg.plot . capture.image.10x . cc . cell.cycle . cell.filter . cell.gating . cell.type.pred . change.clust . clono.plot . clust.avg.exp . clust.cond.info . clust.ord . clust.rm . clust.stats.plot . cluster.plot . data.aggregation . data.scale . down.sample . find.dim.genes . findMarkers . find_neighbors . g2m.phase . gate.to.clust . gene.plot . gene.stats . gg.cor . heatmap.gg.plot . hto.anno . i.score . iba . iclust . load.h5 . load10x . make.bed . make.gene.model . makej . myImp . norm.adt . norm.data . opt.pcs.plot . prep.vdj . pseudotime.knetl . pseudotime . pseudotime.tree . qc.stats . run.anchor . run.cca . run.clustering . run.diff.exp . run.diffusion.map . run.impute . run.knetl . run.mnn . run.pc.tsne . run.pca . run.phenograph . run.tsne . run.umap . s.phase . spatial.plot . stats.plot . top.markers . vdj.stats . volcano.ma.plot . 
Some associated R codes: F0001.R . F0002.R . F0003.R . F0004.R . F0005.R . F0006.R . F0007.R . F0008.R . F0009.R . F0010.R . F0011.R . F0012.R . F0013.R . F0014.R . F0015.R . F0016.R . F0017.R . F0018.R . F0019.R . F0020.R . F0021.R . F0022.R . F0023.R . F0024.R . F0025.R . F0026.R . F0027.R . F0028.R . F0029.R . F0030.R . F0031.R . F0032.R . F0033.R . F0034.R . F0035.R . F0036.R . F0037.R . F0038.R . F0039.R . F0040.R . F0041.R . F0042.R . F0043.R . F0044.R . F0045.R . F0046.R . F0047.R . F0048.R . F0049.R . F0050.R . F0051.R . F0052.R . F0053.R . F0054.R . F0055.R . F0057.R . F0058.R . F0059.R . F0060.R . F0061.R . F0062.R . F0063.R . F0064.R . F0065.R . F0066.R . F0067.R . F0068.R . F0069.R . F0070.R . F0071.R . F0072.R . F0100.R . RcppExports.R .  Full iCellR package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

DatabionicSwarm  
Swarm Intelligence for Self-Organized Clustering
Algorithms implementing populations of agents that interact with one another and sense their environ ...
Download / Learn more Package Citations See dependency  
mlr3viz  
Visualizations for 'mlr3'
Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, le ...
Download / Learn more Package Citations See dependency  
EMVS  
The Expectation-Maximization Approach to Bayesian Variable Selection
An efficient expectation-maximization algorithm for fitting Bayesian spike-and-slab regularization p ...
Download / Learn more Package Citations See dependency  
solitude  
An Implementation of Isolation Forest
Isolation forest is anomaly detection method introduced by the paper Isolation based Anomaly Detecti ...
Download / Learn more Package Citations See dependency  
lbfgs  
Limited-memory BFGS Optimization
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implem ...
Download / Learn more Package Citations See dependency  
openxlsx  
Read, Write and Edit xlsx Files
Simplifies the creation of Excel .xlsx files by providing a high level interface to writing, stylin ...
Download / Learn more Package Citations See dependency  

26,264

R Packages

223,360

Dependencies

70,376

Author Associations

26,265

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA