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.
- Version1.6.7
- R versionunknown
- LicenseGPL-2
- Needs compilation?Yes
- Last release01/29/2024
Documentation
Team
Alireza Khodadadi-Jamayran
Hua Zhou
Show author detailsRolesAuthor, ContributorJoseph Pucella
Nicole Doudican
John Carucci
Adriana Heguy
Show author detailsRolesAuthor, ContributorBoris Reizis
Aristotelis Tsirigos
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends2 packages
- Imports26 packages
- Linking To1 package