Name Mode Size
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cell_type_labeling_files 040000
cnsl_2d_embedding_files 040000
cnsl_cellqc_files 040000
cnsl_dimensionality_reduction_files 040000
cnsl_dropletqc_files 040000
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cnsl_seurat_curated_workflow_files 040000
console_analysis_tutorial_files 040000
heatmap_files 040000
singleCellTK_files 040000
ui_screenshots 040000
visualization_files 040000
batch_correction.html 100644 42 kb
celda_curated_workflow.html 100644 37 kb
cell_type_labeling.html 100644 24 kb
clustering.html 100644 40 kb
cmd_import_scRNAseq_data_as_SCE.html 100644 12 kb
cmd_qc.html 100644 56 kb
cnsl_2d_embedding.html 100644 38 kb
cnsl_cellqc.html 100644 96 kb
cnsl_dimensionality_reduction.html 100644 33 kb
cnsl_dropletqc.html 100644 54 kb
cnsl_enrichR.html 100644 28 kb
cnsl_feature_selection.html 100644 25 kb
cnsl_normalization.html 100644 32 kb
cnsl_seurat_curated_workflow.html 100644 52 kb
console_analysis_tutorial.html 100644 69 kb
delete_data.html 100644 17 kb
differential_expression.html 100644 39 kb
export_data.html 100644 18 kb
filtering.html 100644 18 kb
find_marker.html 100644 27 kb
heatmap.html 100644 38 kb
import_annotation.html 100644 24 kb
import_data.html 100644 30 kb
import_genesets.html 100644 18 kb
index.html 100644 13 kb
installation.html 100644 29 kb
pathwayAnalysis.html 100644 24 kb
qc_inputConsole.png 100644 418 kb
qc_inputShell.png 100644 301 kb
qc_singleInput.png 100644 143 kb
qc_yamlParameters.png 100644 206 kb
singleCellTK.html 100644 18 kb
tabset.css 100644 1 kb
ui_qc.html 100644 17 kb
visualization.html 100644 27 kb
# Single Cell TK <!-- badges: start --> [![R-CMD-check](]( [![codecov](]( <!-- badges: end --> 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, and batch effect correction. 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]( ## Installation Detailed instruction on how to install SCTK and additional dependencies are available at our homepage: ## 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. #### Console Analysis Traditional analysis of scRNAseq data can be performed in the R console using wrapper functions for a multitude of tools and algorithms. #### Interactive Analysis The Shiny APP allows users without programming experience to easily analyze their scRNAseq data with a GUI. #### 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. ## 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]( on GitHub. Questions about how to best analyze your scRNA-seq data can be asked in the [Discussions]( page on GitHub.