RCTIcollection: an easy-to-use wrapper function to run celltype identifiers in R

• The package provides an easy-to-use R wrapper function to run various celltype identifiers for single-cell RNA-seq data.

• It was used to obtain the results in the following papers.

  • "Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing", Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbad006, https://doi.org/10.1101/2022.07.27.501701.
  • "MarkerCount: A stable, count-based cell type identifier for single cell RNA-Seq experiments", Computational and Structural Biotechnology Journal, June 2022. https://doi.org/10.1016/j.csbj.2022.06.010.

Supported celltype identifiers

• Marker-based identifiers (require HiCAT marker file)

  1. HiCAT (https://github.com/combio-dku/HiCAT)
  2. Garnett (https://cole-trapnell-lab.github.io/garnett/)
  3. SCINA (https://github.com/jcao89757/SCINA)
  4. scSorter (https://cran.r-project.org/web/packages/scSorter/vignettes/scSorter.html)
  5. scType (https://github.com/IanevskiAleksandr/sc-type)
  6. scCatch (https://github.com/ZJUFanLab/scCATCH)

• Reference-based identifiers (require reference gene expression data with pre-annotation)

  1. MarkerCount_Ref (https://github.com/combio-dku/MarkerCount)
  2. SingleR (https://github.com/dviraran/SingleR)
  3. CaSTLe (https://github.com/yuvallb/CaSTLe)
  4. CHETAH (https://github.com/jdekanter/CHETAH)
  5. scmap-cell (https://github.com/hemberg-lab/scmap)
  6. scmap-cluster (https://github.com/hemberg-lab/scmap)

Requirements & Installation

• Python 3.8 or later

• R 4.0.2 or later

• Required python packages: numpy, pandas, scikit-learn, scipy, scikit-network, MarkerCount (HiCAT is bundled with MarkerCount) (can be installed using pip install <package name>) (see git repo. https://github.com/combio-dku/HiCAT and https://github.com/combio-dku/MarkerCount)

• Required R packages: igraph, scater, xgboost, SingleCellExperiment, dplyr, stringr, preprocessCore, Seurat, org.Hs.eg.db, scuttle, SingleR, CHETAH, scmap, SCINA, scSorter, garnett, scCATCH, reticulate

• Once requirements are met, RCTIcollection can be installed using the following command in R:

  1. run R
  2. run devtools::install_github("combio-dku/RCTIcollection") in R

Using the package

See the jupyter notebook provided in this repo. (RCTIcollection_example.ipynb)

Contact

Send email to syoon@dku.edu for any inquiry on the usages.