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", "1.6.7")



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: 2021-10-09
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 1.6.7, https://cran.r-project.org/web/packages/iCellR
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)
Other packages that cited iCellR R package
View iCellR citation profile
Other R packages that iCellR depends, imports, suggests or enhances
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
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
mistral  
Methods in Structural Reliability
Various reliability analysis methods for rare event inference (computing failure probability and qua ...
Download / Learn more Package Citations See dependency  
steepness  
Testing Steepness of Dominance Hierarchies
The steepness package computes steepness as a property of dominance hierarchies. Steepness is define ...
Download / Learn more Package Citations See dependency  
ftaproxim  
Fault Tree Analysis Based on Proxel Simulation
Calculation and plotting of instantaneous unavailabilities of basic events along with the top event ...
Download / Learn more Package Citations See dependency  
critpath  
Setting the Critical Path in Project Management
Solving the problem of project management using CPM (Critical Path Method), PERT (Program Evaluation ...
Download / Learn more Package Citations See dependency  
rdbnomics  
Download DBnomics Data
R access to hundreds of millions data series from DBnomics API (). ...
Download / Learn more Package Citations See dependency  

22,114

R Packages

188,080

Dependencies

55,244

Author Associations

22,115

Publication Badges

© Copyright 2022 - present. All right reserved, rpkg.net. Contact Us / Suggestions / Concerns