Name Mode Size
..
DownsampleMatrix.R 100644 17 kb
abundance.R 100644 10 kb
allGenerics.R 100644 3 kb
celda_decontX.R 100644 13 kb
combineSCE.R 100644 9 kb
computeHeatmap.R 100644 6 kb
computeZScore.R 100644 1 kb
data.R 100644 3 kb
decorate.R 100644 2 kb
descriptions.R 100644 17 kb
detectCellOutlier.R 100644 4 kb
doubletFinder_doubletDetection.R 100644 26 kb
dropletUtils_barcodeRank.R 100644 6 kb
dropletUtils_emptyDrops.R 100644 6 kb
enrichRSCE.R 100644 8 kb
exportSCEtoAnndata.R 100644 4 kb
exportSCEtoTXT.R 100644 6 kb
featureIndex.R 100644 5 kb
findMarker.R 100644 11 kb
getBiomarker.R 100644 2 kb
getTSNE.R 100644 7 kb
getTopHVG.R 100644 13 kb
getUMAP.R 100644 9 kb
ggPerQCWrapper.R 100644 102 kb
ggPlotting.R 100644 117 kb
htmlReports.R 100644 61 kb
importAlevin.R 100644 3 kb
importAnnData.R 100644 7 kb
importBUStools.R 100644 6 kb
importCellRanger.R 100644 31 kb
importDropEst.R 100644 7 kb
importExampleData.R 100644 5 kb
importFromFiles.R 100644 6 kb
importGeneSets.R 100644 27 kb
importMultipleSources.R 100644 6 kb
importOptimus.R 100644 10 kb
importSTARSolo.R 100644 8 kb
importSeqc.R 100644 9 kb
mergeSCEColData.R 100644 3 kb
miscFunctions.R 100644 17 kb
plotBatchVariance.R 100644 18 kb
plotDEAnalysis.R 100644 33 kb
plotDimRed.R 100644 2 kb
plotHeatmapMulti.R 100644 0 kb
plotMarkerDiffExp.R 100644 10 kb
plotPCA.R 100644 2 kb
plotPathway.R 100644 6 kb
plotSCEHeatmap.R 100644 26 kb
plotTSNE.R 100644 2 kb
plotTopHVG.R 100644 4 kb
plotUMAP.R 100644 2 kb
readSingleCellMatrix.R 100644 3 kb
reticulate_setup.R 100644 10 kb
runBatchCorrection.R 100644 38 kb
runCluster.R 100644 14 kb
runDEAnalysis.R 100644 33 kb
runDimReduce.R 100644 11 kb
runFeatureSelection.R 100644 2 kb
runGSVA.R 100644 2 kb
runNormalization.R 100644 7 kb
runQC.R 100644 10 kb
runSingleR.R 100644 7 kb
runSoupX.R 100644 33 kb
runTSCAN.R 100644 40 kb
runVAM.R 100644 5 kb
sampleSummaryStats.R 100644 9 kb
scDblFinder_doubletDetection.R 100644 4 kb
scater_CPM.R 100644 1 kb
scater_PCA.R 100644 3 kb
scater_addPerCellQC.R 100644 15 kb
scater_logNormCounts.R 100644 1 kb
scds_doubletdetection.R 100644 15 kb
sce2adata.R 100644 2 kb
scran_modelGeneVar.R 100644 2 kb
scrublet_doubletDetection.R 100644 11 kb
sctkQCUtils.R 100644 40 kb
sctkTagging.R 100644 11 kb
seuratFunctions.R 100644 74 kb
singleCellTK.R 100644 1 kb
subsetSCE.R 100644 8 kb
sysdata.rda 100644 45 kb
trimCounts.R 100644 1 kb
validityFunctions.R 100644 0 kb
README.md
# Single Cell TK [![BioC-check](https://github.com/compbiomed/singleCellTK/actions/workflows/BioC-check.yaml/badge.svg?branch=master)](https://github.com/compbiomed/singleCellTK/actions/workflows/BioC-check.yaml) [![R-CMD-check](https://github.com/compbiomed/singleCellTK/actions/workflows/R-CMD-check.yaml/badge.svg?branch=master)](https://github.com/compbiomed/singleCellTK/actions/workflows/R-CMD-check.yaml) [![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/devel/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK) The Single Cell ToolKit (SCTK) is an analysis platform that provides an **R interface to several popular single-cell RNA-sequencing (scRNAseq) data preprocessing, quality control, analysis, and visualization tools**. SCTK imports raw or filtered counts from various scRNAseq preprocessing tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R or Python, SCTK can be used to perform extensive quality control including doublet detection, ambient RNA removal. SCTK integrates analysis workflows from popular tools such as Seurat and Bioconductor/OSCA into a single unified framework. Results from various workflows can be summarized and easily shared using comprehensive HTML reports. Lastly, data can be exported to Seurat or AnnData object to allow for seamless integration with other downstream analysis workflows. More information about the toolkit can be found at the toolkit [Homepage](https://camplab.net/sctk/). ## Features SCTK offers multiple ways to analyze your scRNAseq data both through the R console, commandline (QC) and graphical user interface (GUI) with the ability to use a large number of algorithms from both R & Python integrated within the toolkit. #### Interactive Analysis The Shiny APP allows users without programming experience to easily analyze their scRNAseq data with a GUI. Try the instance at https://sctk.bu.edu/ #### Console Analysis Traditional analysis of scRNAseq data can be performed in the R console using wrapper functions for a multitude of tools and algorithms. #### Reports Comprehensive HTML reports developed with RMarkdown allows users to document, explore, and share their analyses. #### Interoperability Tools from both R and Python can be seamlessly integrated within the same analysis workflow. ## Installation R package `singleCellTK` is available on [Bioconductor](https://bioconductor.org/packages/release/bioc/html/singleCellTK.html). ```R if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("singleCellTK") ``` Detailed instruction on how to install SCTK and additional dependencies are available at our [Homepage](https://camplab.net/sctk/). ## Citation If you use SCTK, please cite our *Nature Communication* paper > Rui Hong, Yusuke Koga, Shruthi Bandyadka, Anastasia Leshchyk, Yichen Wang, Vidya Akavoor, Xinyun Cao, Irzam Sarfraz, Zhe Wang, Salam Alabdullatif, Frederick Jansen, Masanao Yajima, W. Evan Johnson & Joshua D. Campbell, “Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data,” *Nature Communications*, vol. 13, no. 1688, 2022, doi: 10.1038/s41467-022-29212-9. ## Report Issues If you face any difficulty in installing or have identified a bug in the toolkit, please feel free to open up an [Issue](https://github.com/compbiomed/singleCellTK/issues) on GitHub. Questions about how to best analyze your scRNA-seq data can be asked in the [Discussions](https://github.com/compbiomed/singleCellTK/discussions) page on GitHub.