1. singleCellTK
  2. README.md
Browse code

Formatted tutorial section of README

Joshua D. Campbell authored on 19/01/2023 02:28:21
Showing 1 changed files
README.md
History View file @ 57407bc28
... ... 
@@ -24,7 +24,8 @@ SCTK offers multiple ways to analyze your scRNAseq data through the R console, t
24 24
25 25 
 -   [Import and QC:](https://camplab.net/sctk/current/articles/import_data.html) The Import and QC workflows allow users to import data from multiple formats and perform comprehensive quality control and filtering.
26 26 
 -   ["*A la carte*" workflow:](https://camplab.net/sctk/current/articles/02_a_la_carte_workflow.html) The "A la carte" workflow lets users choose from a variety of options during each step of the analysis workflow including normalization, batch correction (optional), dimensionality reduction, 2-D embedding, and clustering.
27 
--   [Seurat curated workflow:](https://camplab.net/sctk/current/articles/seurat_curated_workflow.html) The curated workflows recapitulates the steps for clustering and integration from the Seurat package.
27 
+-   [Seurat curated workflow:](https://camplab.net/sctk/current/articles/seurat_curated_workflow.html) This curated workflows recapitulates the steps for clustering and integration from the Seurat package (R).
28 
+-   [Scanpy curated workflow:](https://camplab.net/sctk/current/articles/scanpy_curated_workflow.html) This curated workflows recapitulates the steps for clustering from the Scanpy package (Python).
28 29 
 -   [Celda curated workflow:](https://camplab.net/sctk/current/articles/celda_curated_workflow.html) The curated Celda workflow performs matrix factorization by clustering genes into co-expression modules, cells into subpopulations, and estimating the amount of each module in each cell population.
29 30
30 31 
 ## Installation
Browse code

Removed images and finalized README

Joshua D. Campbell authored on 18/01/2023 19:05:37
Showing 1 changed files
README.md
History View file @ 6002b70e4
... ... 
@@ -4,7 +4,7 @@
4 4
5 5 
 The Single Cell Toolkit (SCTK) in the *singleCellTK* R package 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.
6 6
7 
-![](images/image-516723231.png)
7 
+![](https://camplab.net/sctk/img/interior-2.png)
8 8
9 9 
 ## Features
10 10
... ... 
@@ -22,23 +22,10 @@ SCTK offers multiple ways to analyze your scRNAseq data through the R console, t
22 22
23 23 
 ## Tutorials
24 24
25 
-#### The Import and QC workflows allow users to import data from multiple formats and perform comprehensive quality control and filtering:
26 
-
27 
-[![](images/image-2029858439.png)](https://camplab.net/sctk/current/articles/import_data.html)
28 
-
29 
-#### The "A la carte" workflow lets users choose from a variety of options during each step of the clustering workflow including normalization, batch correction (optional), dimensionality reduction, 2-D embedding, and clustering:
30 
-
31 
-[![](images/image-295296270.png)](https://camplab.net/sctk/current/articles/02_a_la_carte_workflow.html)
32 
-
33 
-<https://camplab.net/sctk/current/articles/02_a_la_carte_workflow.html>
34 
-
35 
-#### The curated Seurat workflow recapitulates the steps for clustering and integration from the Seurat package:
36 
-
37 
-<https://camplab.net/sctk/current/articles/seurat_curated_workflow.html>
38 
-
39 
-#### The curated Celda workflow performs matrix factorization and clusters genes into co-expression modules:
40 
-
41 
-<https://camplab.net/sctk/current/articles/celda_curated_workflow.html>
25 
+-   [Import and QC:](https://camplab.net/sctk/current/articles/import_data.html) The Import and QC workflows allow users to import data from multiple formats and perform comprehensive quality control and filtering.
26 
+-   ["*A la carte*" workflow:](https://camplab.net/sctk/current/articles/02_a_la_carte_workflow.html) The "A la carte" workflow lets users choose from a variety of options during each step of the analysis workflow including normalization, batch correction (optional), dimensionality reduction, 2-D embedding, and clustering.
27 
+-   [Seurat curated workflow:](https://camplab.net/sctk/current/articles/seurat_curated_workflow.html) The curated workflows recapitulates the steps for clustering and integration from the Seurat package.
28 
+-   [Celda curated workflow:](https://camplab.net/sctk/current/articles/celda_curated_workflow.html) The curated Celda workflow performs matrix factorization by clustering genes into co-expression modules, cells into subpopulations, and estimating the amount of each module in each cell population.
42 29
43 30 
 ## Installation
44 31
... ... 
@@ -55,10 +42,14 @@ Additional information on how to install from GitHub, install Python dependencie
55 42
56 43 
 ## Citation
57 44
58 
-If you use SCTK, please cite our *Nature Communication* paper
45 
+If you use SCTK for quality control, please cite our *Nature Communication* paper
59 46
60 47 
 > 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.
61 48
49 
+If you use SCTK for analysis in the Rconsole or the interactive graphical user interface, please cite our bioRxiv paper:
50 
+
51 
+> Yichen Wang, Irzam Sarfraz, Rui Hong, Yusuke Koga, Vidya Akavoor, Xinyun Cao, Salam Alabdullatif, Nida Pervaiz, Syed Ali Zaib, Zhe Wang, Frederick Jansen, Masanao Yajima, W Evan Johnson, Joshua D Campbell, "Interactive analysis of single-cell data using flexible workflows with SCTK2.0", *bioRxiv*, 2022.07.13.499900; doi: <https://doi.org/10.1101/2022.07.13.499900>.
52 
+
62 53 
 ## Report Issues
63 54
64 55 
 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.
Browse code

Updated README text and thumbnails

Joshua D. Campbell authored on 29/12/2022 15:00:47
Showing 1 changed files
README.md
History View file @ fbeeabe8f
... ... 
@@ -1,50 +1,64 @@
1 
-# Single Cell TK
1 
+# The Single Cell Tool Kit
2 2
3 
-[![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)
4 
-[![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)
5 
-[![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/devel/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
3 
+[![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)
6 4
7 
-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/).
5 
+The Single Cell Toolkit (SCTK) in the *singleCellTK* R package 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.
6 
+
7 
+![](images/image-516723231.png)
8 8
9 9 
 ## Features
10 10
11 
-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.
11 
+SCTK offers multiple ways to analyze your scRNAseq data through the R console, the command line, and a graphical user interface (GUI) with the ability to use a large number of algorithms from both R & Python integrated within the toolkit.
12 
+
13 
+-   **Interactive Analysis:** The Shiny APP allows users without programming experience to easily analyze their scRNAseq data with a GUI. Try it out at <https://sctk.bu.edu/>.
14 
+
15 
+-   **Console Analysis:** Traditional analysis of scRNAseq data can be performed in the R console using wrapper functions for a multitude of tools and algorithms.
16 
+
17 
+-   **Reports:** Comprehensive HTML reports developed with RMarkdown allows users to document, explore, and share their analyses.
18 
+
19 
+-   **Interoperability:** Tools from both R and Python package can be seamlessly integrated within the same analysis workflow without the need for manual conversion between different data objects and file formats.
20 
+
21 
+-   **Number of tools:** SCTK provides access to the largest number of tools within the same platform streamlining end-to-end analysis workflows. Curated workflows include those from Seurat, Scanpy, Scater/Scran (Bioconductor), and Celda.
22 
+
23 
+## Tutorials
24 
+
25 
+#### The Import and QC workflows allow users to import data from multiple formats and perform comprehensive quality control and filtering:
12 26
13 
-#### Interactive Analysis
27 
+[![](images/image-2029858439.png)](https://camplab.net/sctk/current/articles/import_data.html)
14 28
15 
-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/
29 
+#### The "A la carte" workflow lets users choose from a variety of options during each step of the clustering workflow including normalization, batch correction (optional), dimensionality reduction, 2-D embedding, and clustering:
16 30
17 
-#### Console Analysis
31 
+[![](images/image-295296270.png)](https://camplab.net/sctk/current/articles/02_a_la_carte_workflow.html)
18 32
19 
-Traditional analysis of scRNAseq data can be performed in the R console using wrapper functions for a multitude of tools and algorithms.
33 
+<https://camplab.net/sctk/current/articles/02_a_la_carte_workflow.html>
20 34
21 
-#### Reports
35 
+#### The curated Seurat workflow recapitulates the steps for clustering and integration from the Seurat package:
22 36
23 
-Comprehensive HTML reports developed with RMarkdown allows users to document, explore, and share their analyses.
37 
+<https://camplab.net/sctk/current/articles/seurat_curated_workflow.html>
24 38
25 
-#### Interoperability
39 
+#### The curated Celda workflow performs matrix factorization and clusters genes into co-expression modules:
26 40
27 
-Tools from both R and Python can be seamlessly integrated within the same analysis workflow.
41 
+<https://camplab.net/sctk/current/articles/celda_curated_workflow.html>
28 42
29 43 
 ## Installation
30 44
31 
-R package `singleCellTK` is available on [Bioconductor](https://bioconductor.org/packages/release/bioc/html/singleCellTK.html).
45 
+R package `singleCellTK` is available on [Bioconductor](https://bioconductor.org/packages/release/bioc/html/singleCellTK.html) and can be installed with the following commands:
32 46
33 
-```R
47 
+``` r
34 48 
 if (!require("BiocManager", quietly = TRUE))
35 49 
     install.packages("BiocManager")
36 50
37 51 
 BiocManager::install("singleCellTK")
38 52 
 ```
39 53
40 
-Detailed instruction on how to install SCTK and additional dependencies are available at our [Homepage](https://camplab.net/sctk/).
54 
+Additional information on how to install from GitHub, install Python dependencies, and for troubleshooting is available on the [Installation](https://camplab.net/sctk/current/articles/installation.html) page.
41 55
42 56 
 ## Citation
43 57
44 58 
 If you use SCTK, please cite our *Nature Communication* paper
45 59
46 
-> 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.
60 
+> 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.
47 61
48 62 
 ## Report Issues
49 63
50 
-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.
64 
+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.
Browse code

new version 2.7.1

Yichen Wang authored on 29/06/2022 23:30:51
Showing 1 changed files
README.md
History View file @ 7d46c6eb7
... ... 
@@ -43,7 +43,7 @@ Detailed instruction on how to install SCTK and additional dependencies are avai
43 43
44 44 
 If you use SCTK, please cite our *Nature Communication* paper
45 45
46 
-> 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. *Nat Commun* **13**, 1688 (2022). https://doi.org/10.1038/s41467-022-29212-9
46 
+> 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.
47 47
48 48 
 ## Report Issues
49 49
Browse code

Fix README

Yichen Wang authored on 28/06/2022 20:52:12
Showing 1 changed files
README.md
History View file @ fc7a1024b
... ... 
@@ -1,27 +1,50 @@
1 1 
 # Single Cell TK
2 
-  <!-- badges: start -->
3 
-[![R-CMD-check](https://github.com/compbiomed/singleCellTK/workflows/R-CMD-check/badge.svg)](https://github.com/compbiomed/singleCellTK/actions)
4 
-[![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/devel/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
5 
-<!-- badges: end -->
6 
-
7 
-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](https://camplab.net/sctk/).
8 2
9 
-## Installation
3 
+[![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)
4 
+[![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)
5 
+[![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/devel/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
10 6
11 
-Detailed instruction on how to install SCTK and additional dependencies are available at our homepage:
12 
-https://camplab.net/sctk/
7 
+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/).
13 8
14 9 
 ## Features
10 
+
15 11 
 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.
12 
+
13 
+#### Interactive Analysis
14 
+
15 
+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/
16 
+
16 17 
 #### Console Analysis
18 
+
17 19 
 Traditional analysis of scRNAseq data can be performed in the R console using wrapper functions for a multitude of tools and algorithms.
18 
-#### Interactive Analysis
19 
-The Shiny APP allows users without programming experience to easily analyze their scRNAseq data with a GUI.
20 
+
20 21 
 #### Reports
22 
+
21 23 
 Comprehensive HTML reports developed with RMarkdown allows users to document, explore, and share their analyses.
24 
+
22 25 
 #### Interoperability
26 
+
23 27 
 Tools from both R and Python can be seamlessly integrated within the same analysis workflow.
24 28
29 
+## Installation
30 
+
31 
+R package `singleCellTK` is available on [Bioconductor](https://bioconductor.org/packages/release/bioc/html/singleCellTK.html).
32 
+
33 
+```R
34 
+if (!require("BiocManager", quietly = TRUE))
35 
+    install.packages("BiocManager")
36 
+
37 
+BiocManager::install("singleCellTK")
38 
+```
39 
+
40 
+Detailed instruction on how to install SCTK and additional dependencies are available at our [Homepage](https://camplab.net/sctk/).
41 
+
42 
+## Citation
43 
+
44 
+If you use SCTK, please cite our *Nature Communication* paper
45 
+
46 
+> 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. *Nat Commun* **13**, 1688 (2022). https://doi.org/10.1038/s41467-022-29212-9
47 
+
25 48 
 ## Report Issues
26 49
27 50 
 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.
Browse code

Updated codecov badge branch to devel

Irzam Sarfraz authored on 20/10/2021 11:10:42 • GitHub committed on 20/10/2021 11:10:42
Showing 1 changed files
README.md
History View file @ 9bb36386b
... ... 
@@ -1,7 +1,7 @@
1 1 
 # Single Cell TK
2 2 
   <!-- badges: start -->
3 3 
 [![R-CMD-check](https://github.com/compbiomed/singleCellTK/workflows/R-CMD-check/badge.svg)](https://github.com/compbiomed/singleCellTK/actions)
4 
-[![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/master/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
4 
+[![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/devel/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
5 5 
 <!-- badges: end -->
6 6
7 7 
 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](https://camplab.net/sctk/).
Browse code

version bump, rebuild docs, and NEWS update

Joshua D. Campbell authored on 10/10/2021 16:15:14
Showing 1 changed files
README.md
History View file @ f0cb48b38
... ... 
@@ -4,9 +4,7 @@
4 4 
 [![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/master/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
5 5 
 <!-- badges: end -->
6 6
7 
-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, and visualization tools**. SCTK imports raw or filtered counts from various scRNAseq technologies and upstream tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R as well as Python, SCTK performs extensive quality control measures including doublet detection and batch effect correction. Additionally, SCTK summarizes results and related visualizations in a comprehensive R markdown and/or HTML report. SCTK provides a standardized single cell analysis workflow by representing the counts data and the results using the [SingleCellExperiment](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) R object. Furthermore, SCTK enables seamless downstream analysis by exporting data and results in flat .txt and Python Anndata formats.
8 
-
9 
-A comprehensive list of available functions is listed in the Reference section. More information about the toolkit can be found at the toolkit [homepage](https://camplab.net/sctk/).
7 
+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](https://camplab.net/sctk/).
10 8
11 9 
 ## Installation
12 10
... ... 
@@ -26,4 +24,4 @@ Tools from both R and Python can be seamlessly integrated within the same analys
26 24
27 25 
 ## Report Issues
28 26
29 
-If you face any difficulty in installing or using the toolkit or have identified a bug in the toolkit, please feel free to open up a [GitHub Issue](https://github.com/compbiomed/singleCellTK/issues).
27 
+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.
Browse code

Minor updates and rebuild site

Yichen Wang authored on 09/10/2021 17:53:38
Showing 1 changed files
README.md
History View file @ 2e0cd0ac1
... ... 
@@ -4,28 +4,26 @@
4 4 
 [![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/master/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
5 5 
 <!-- badges: end -->
6 6
7 
-The Single Cell ToolKit (SCTK) is an analysis platform that provides an **R interface to several popular scRNA-seq preprocessing, quality control, and visualization tools**. SCTK imports raw or filtered counts from various single cell sequencing technologies and upstream tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R as well as Python, SCTK performs extensive quality control measures including doublet detection and batch effect correction. Additionally, SCTK summarizes results and related visualizations in a comprehensive R markdown and/or HTML report. SCTK provides a standardized single cell analysis workflow by representing the counts data and the results using the [SingleCellExperiment](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) R object. Furthermore, SCTK enables seamless downstream analysis by exporting data and results in flat .txt and Python Anndata formats.
7 
+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, and visualization tools**. SCTK imports raw or filtered counts from various scRNAseq technologies and upstream tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R as well as Python, SCTK performs extensive quality control measures including doublet detection and batch effect correction. Additionally, SCTK summarizes results and related visualizations in a comprehensive R markdown and/or HTML report. SCTK provides a standardized single cell analysis workflow by representing the counts data and the results using the [SingleCellExperiment](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) R object. Furthermore, SCTK enables seamless downstream analysis by exporting data and results in flat .txt and Python Anndata formats.
8 8
9 9 
 A comprehensive list of available functions is listed in the Reference section. More information about the toolkit can be found at the toolkit [homepage](https://camplab.net/sctk/).
10 10
11 11 
 ## Installation
12 12
13 
-Detailed intstructions on how to install singleCellTK are available at our homepage:
13 
+Detailed instruction on how to install SCTK and additional dependencies are available at our homepage:
14 14 
 https://camplab.net/sctk/
15 15
16 16 
 ## Features
17 
-The toolkit offers mulitple ways to analyze your single cell data both through the R console, commandline (QC) and graphical user interface with the ability to use a large number of algorithms from both R & Python integrated within the toolkit.
17 
+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.
18 18 
 #### Console Analysis
19 
-Traditional analysis of single-cell RNA-seq data can be performed in the R console using wrapper functions for a multitude of tools and algorithms.
19 
+Traditional analysis of scRNAseq data can be performed in the R console using wrapper functions for a multitude of tools and algorithms.
20 20 
 #### Interactive Analysis
21 
-The shiny app allows users without programming experience to easily analyze their single cell RNA-seq data with a graphical user interface.
21 
+The Shiny APP allows users without programming experience to easily analyze their scRNAseq data with a GUI.
22 22 
 #### Reports
23 
-Comprehensive html reports developed with rmarkdown allows users to document, explore, and share their analyses.
23 
+Comprehensive HTML reports developed with RMarkdown allows users to document, explore, and share their analyses.
24 24 
 #### Interoperability
25 25 
 Tools from both R and Python can be seamlessly integrated within the same analysis workflow.
26 26
27 27 
 ## Report Issues
28 28
29 
-If you face any difficulty in installing or using the toolkit or have identified a bug in the toolkit, please feel free to open up a [GitHub issue](https://github.com/compbiomed/singleCellTK/issues).
30 
-
31 
-
29 
+If you face any difficulty in installing or using the toolkit or have identified a bug in the toolkit, please feel free to open up a [GitHub Issue](https://github.com/compbiomed/singleCellTK/issues).
Browse code

Update README.md

Irzam Sarfraz authored on 08/10/2021 15:30:59 • GitHub committed on 08/10/2021 15:30:59
Showing 1 changed files
README.md
History View file @ 9f17b84c5
... ... 
@@ -6,15 +6,12 @@
6 6
7 7 
 The Single Cell ToolKit (SCTK) is an analysis platform that provides an **R interface to several popular scRNA-seq preprocessing, quality control, and visualization tools**. SCTK imports raw or filtered counts from various single cell sequencing technologies and upstream tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R as well as Python, SCTK performs extensive quality control measures including doublet detection and batch effect correction. Additionally, SCTK summarizes results and related visualizations in a comprehensive R markdown and/or HTML report. SCTK provides a standardized single cell analysis workflow by representing the counts data and the results using the [SingleCellExperiment](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) R object. Furthermore, SCTK enables seamless downstream analysis by exporting data and results in flat .txt and Python Anndata formats.
8 8
9 
-A comprehensive list of available functions is listed in the Reference section.  (add link here)
10 
-
11 
-More information about the toolkit can be found at the toolkit [homepage](https://camplab.net/sctk/).
9 
+A comprehensive list of available functions is listed in the Reference section. More information about the toolkit can be found at the toolkit [homepage](https://camplab.net/sctk/).
12 10
13 11 
 ## Installation
14 12
15 
-Detailed intstructions on how to install singleCellTK are available at the link below:
16 
-
17 
-(add link here)
13 
+Detailed intstructions on how to install singleCellTK are available at our homepage:
14 
+https://camplab.net/sctk/
18 15
19 16 
 ## Features
20 17 
 The toolkit offers mulitple ways to analyze your single cell data both through the R console, commandline (QC) and graphical user interface with the ability to use a large number of algorithms from both R & Python integrated within the toolkit.
... ... 
@@ -27,8 +24,6 @@ Comprehensive html reports developed with rmarkdown allows users to document, ex
27 24 
 #### Interoperability
28 25 
 Tools from both R and Python can be seamlessly integrated within the same analysis workflow.
29 26
30 
-More information about the toolkit can be found at the toolkit [homepage](https://camplab.net/sctk/).
31 
-
32 27 
 ## Report Issues
33 28
34 29 
 If you face any difficulty in installing or using the toolkit or have identified a bug in the toolkit, please feel free to open up a [GitHub issue](https://github.com/compbiomed/singleCellTK/issues).
Browse code

Update README.md

Irzam Sarfraz authored on 06/10/2021 07:51:01 • GitHub committed on 06/10/2021 07:51:01
Showing 1 changed files
README.md
History View file @ 2f25085bf
... ... 
@@ -8,17 +8,29 @@ The Single Cell ToolKit (SCTK) is an analysis platform that provides an **R inte
8 8
9 9 
 A comprehensive list of available functions is listed in the Reference section.  (add link here)
10 10
11 
+More information about the toolkit can be found at the toolkit [homepage](https://camplab.net/sctk/).
12 
+
11 13 
 ## Installation
12 14
13 
-Detailed intstructions on how to install singleCellTK are available in the link below:
15 
+Detailed intstructions on how to install singleCellTK are available at the link below:
14 16
15 17 
 (add link here)
16 18
17 19 
 ## Features
18 
-
20 
+The toolkit offers mulitple ways to analyze your single cell data both through the R console, commandline (QC) and graphical user interface with the ability to use a large number of algorithms from both R & Python integrated within the toolkit.
21 
+#### Console Analysis
22 
+Traditional analysis of single-cell RNA-seq data can be performed in the R console using wrapper functions for a multitude of tools and algorithms.
23 
+#### Interactive Analysis
24 
+The shiny app allows users without programming experience to easily analyze their single cell RNA-seq data with a graphical user interface.
25 
+#### Reports
26 
+Comprehensive html reports developed with rmarkdown allows users to document, explore, and share their analyses.
27 
+#### Interoperability
28 
+Tools from both R and Python can be seamlessly integrated within the same analysis workflow.
29 
+
30 
+More information about the toolkit can be found at the toolkit [homepage](https://camplab.net/sctk/).
19 31
20 32 
 ## Report Issues
21 33
22 
-(add link here to issues)
34 
+If you face any difficulty in installing or using the toolkit or have identified a bug in the toolkit, please feel free to open up a [GitHub issue](https://github.com/compbiomed/singleCellTK/issues).
23 35
24 36
Browse code

Update README.md

Irzam Sarfraz authored on 06/10/2021 07:33:24 • GitHub committed on 06/10/2021 07:33:24
Showing 1 changed files
README.md
History View file @ f7128d7d5
... ... 
@@ -1,140 +1,24 @@
1 1 
 # Single Cell TK
2 2 
   <!-- badges: start -->
3 3 
 [![R-CMD-check](https://github.com/compbiomed/singleCellTK/workflows/R-CMD-check/badge.svg)](https://github.com/compbiomed/singleCellTK/actions)
4 
-[![BioC status](https://www.bioconductor.org/shields/build/release/bioc/singleCellTK.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/singleCellTK)
5 4 
 [![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/master/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
6 5 
 <!-- badges: end -->
7 6
8 7 
 The Single Cell ToolKit (SCTK) is an analysis platform that provides an **R interface to several popular scRNA-seq preprocessing, quality control, and visualization tools**. SCTK imports raw or filtered counts from various single cell sequencing technologies and upstream tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R as well as Python, SCTK performs extensive quality control measures including doublet detection and batch effect correction. Additionally, SCTK summarizes results and related visualizations in a comprehensive R markdown and/or HTML report. SCTK provides a standardized single cell analysis workflow by representing the counts data and the results using the [SingleCellExperiment](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) R object. Furthermore, SCTK enables seamless downstream analysis by exporting data and results in flat .txt and Python Anndata formats.
9 8
10 
-A comprehensive list of available functions is listed in the Reference section.
9 
+A comprehensive list of available functions is listed in the Reference section.  (add link here)
11 10
12 11 
 ## Installation
13 12
14 
-### System setup
13 
+Detailed intstructions on how to install singleCellTK are available in the link below:
15 14
16 
-If you are the first time to install R, please don't install 32 bit R. Make sure to uncheck the '32-bit Files' box when you see the following window:
15 
+(add link here)
17 16
18 
-![](exec/png/32bit-R.png)
17 
+## Features
19 18
20 
-#### Window's user
21 
-For window's users, please install [rtools](https://cran.r-project.org/bin/windows/Rtools/history.html) based on your R version. Make sure to click 'Edit the system PATH' box when you see this window:
22 19
23 
-![](exec/png/rtools.png)
20 
+## Report Issues
24 21
25 
-After installing rtools, install 'devtools' package with the following command. If it asks whether install the package that requires compilation, type 'y'.
26 
-```
27 
-install.packages('devtools')
28 
-```
22 
+(add link here to issues)
29 23
30 
-#### macOS user
31 
-For macbook's users, please install gfortran with brew. If you have not installed brew, please check [this link](https://brew.sh/) to set up brew on your machine.
32 
-```
33 
-brew install gcc
34 
-```
35 24
36 
-After that, install 'devtools' package with the following command.
37 
-```
38 
-install.packages('devtools')
39 
-```
40 
-
41 
-### Release Version
42 
-
43 
-You can download the release version of the Single Cell Toolkit in
44 
-[Bioconductor v3.10](https://bioconductor.org/packages/release/bioc/html/singleCellTK.html):
45 
-
46 
-```r
47 
-if (!requireNamespace("BiocManager", quietly=TRUE))
48 
-  install.packages("BiocManager")
49 
-BiocManager::install("singleCellTK")
50 
-```
51 
-
52 
-### Devel Version
53 
-
54 
-You can download the development version of the Single Cell Toolkit in
55 
-[Bioconductor v3.11](https://bioconductor.org/packages/devel/bioc/html/singleCellTK.html)
56 
-or from this repository:
57 
-
58 
-```r
59 
-# install.packages("devtools")
60 
-devtools::install_github("compbiomed/singleCellTK")
61 
-```
62 
-
63 
-### R 3.4 Version
64 
-
65 
-If you are still running an earlier version of R than 3.5, you can install
66 
-the following version from this repository:
67 
-
68 
-```r
69 
-# install.packages("devtools")
70 
-devtools::install_github("compbiomed/singleCellTK", ref="r_3_4")
71 
-```
72 
-
73 
-#### Troubleshooting Installation
74 
-
75 
-For the majority of users, the commands above will install the latest version
76 
-of the singleCellTK without any errors. Rarely, you may encounter an error due
77 
-to previously installed versions of some packages that are required for the
78 
-singleCellTK. If you encounter an error during installation, use the commands
79 
-below to check the version of Bioconductor that is installed:
80 
-
81 
-```r
82 
-if (!requireNamespace("BiocManager", quietly=TRUE))
83 
-    install.packages("BiocManager")
84 
-BiocManager::version()
85 
-```
86 
-
87 
-If the version number is not 3.6 or higher, you must upgrade Bioconductor to
88 
-install the toolkit:
89 
-
90 
-```r
91 
-BiocManager::install()
92 
-```
93 
-
94 
-After you install Bioconductor 3.6 or higher, you should be able to install the
95 
-toolkit using `devtools::install_github("compbiomed/singleCellTK")`. If you
96 
-still encounter an error, ensure your Bioconductor packages are up to date by
97 
-running the following command.
98 
-
99 
-```r
100 
-BiocManager::valid()
101 
-```
102 
-
103 
-If the command above does not return `TRUE`, run the following command to
104 
-update your R packages:
105 
-
106 
-```r
107 
-BiocManager::install()
108 
-```
109 
-
110 
-Then, try to install the toolkit again:
111 
-
112 
-```r
113 
-devtools::install_github("compbiomed/singleCellTK")
114 
-```
115 
-
116 
-If you still encounter an error, please [contact us](mailto:dfj@bu.edu) and
117 
-we'd be happy to help.
118 
-
119 
-## Develop singleCellTK
120 
-
121 
-To contribute to singleCellTK, follow these steps:
122 
-
123 
-__Note__: Development of the singleCellTK is done using the latest version of R.
124 
-
125 
-1. Fork the repo using the "Fork" button above.
126 
-2. Download a local copy of your forked repository "```git clone https://github.com/{username}/singleCellTK.git```"
127 
-3. Open Rstudio
128 
-4. Go to "File" -> "New Project" -> "Existing Directory" and select your git repository directory
129 
-
130 
-You can then make your changes and test your code using the Rstudio build tools.
131 
-There is a lot of information about building packages available here: http://r-pkgs.had.co.nz/.
132 
-
133 
-Information about building shiny packages is available here: http://shiny.rstudio.com/tutorial/.
134 
-
135 
-When you are ready to upload your changes, commit them locally, push them to your
136 
-forked repo, and make a pull request to the compbiomed repository.
137 
-
138 
-Report bugs and request features on our [GitHub issue tracker](https://github.com/compbiomed/singleCellTK/issues).
139 
-
140 
-Join us on [slack](https://compbiomed.slack.com/)!
Browse code

Removed lifecycle badge

Irzam Sarfraz authored on 15/09/2021 06:49:58
Showing 1 changed files
README.md
History View file @ e50dd52ae
... ... 
@@ -3,7 +3,6 @@
3 3 
 [![R-CMD-check](https://github.com/compbiomed/singleCellTK/workflows/R-CMD-check/badge.svg)](https://github.com/compbiomed/singleCellTK/actions)
4 4 
 [![BioC status](https://www.bioconductor.org/shields/build/release/bioc/singleCellTK.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/singleCellTK)
5 5 
 [![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/master/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
6 
-[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)
7 6 
 <!-- badges: end -->
8 7
9 8 
 The Single Cell ToolKit (SCTK) is an analysis platform that provides an **R interface to several popular scRNA-seq preprocessing, quality control, and visualization tools**. SCTK imports raw or filtered counts from various single cell sequencing technologies and upstream tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R as well as Python, SCTK performs extensive quality control measures including doublet detection and batch effect correction. Additionally, SCTK summarizes results and related visualizations in a comprehensive R markdown and/or HTML report. SCTK provides a standardized single cell analysis workflow by representing the counts data and the results using the [SingleCellExperiment](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) R object. Furthermore, SCTK enables seamless downstream analysis by exporting data and results in flat .txt and Python Anndata formats.
Browse code

Update README.md

Irzam Sarfraz authored on 07/09/2021 04:58:08 • GitHub committed on 07/09/2021 04:58:08
Showing 1 changed files
README.md
History View file @ f57bf1b1b
... ... 
@@ -1,12 +1,10 @@
1 1 
 # Single Cell TK
2 2 
   <!-- badges: start -->
3 3 
 [![R-CMD-check](https://github.com/compbiomed/singleCellTK/workflows/R-CMD-check/badge.svg)](https://github.com/compbiomed/singleCellTK/actions)
4 
-[![Travis build status](https://travis-ci.org/compbiomed/singleCellTK.svg?branch=master)](https://travis-ci.org/compbiomed/singleCellTK)
5 
-[![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/master/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
6 4 
 [![BioC status](https://www.bioconductor.org/shields/build/release/bioc/singleCellTK.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/singleCellTK)
5 
+[![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/master/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
7 6 
 [![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)
8 
-
9 
-  <!-- badges: end -->
7 
+<!-- badges: end -->
10 8
11 9 
 The Single Cell ToolKit (SCTK) is an analysis platform that provides an **R interface to several popular scRNA-seq preprocessing, quality control, and visualization tools**. SCTK imports raw or filtered counts from various single cell sequencing technologies and upstream tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R as well as Python, SCTK performs extensive quality control measures including doublet detection and batch effect correction. Additionally, SCTK summarizes results and related visualizations in a comprehensive R markdown and/or HTML report. SCTK provides a standardized single cell analysis workflow by representing the counts data and the results using the [SingleCellExperiment](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) R object. Furthermore, SCTK enables seamless downstream analysis by exporting data and results in flat .txt and Python Anndata formats.
12 10
Browse code

Added github actions

Irzam Sarfraz authored on 05/09/2021 14:16:06
Showing 1 changed files
README.md
History View file @ b1622b199
... ... 
@@ -1,10 +1,13 @@
1 1 
 # Single Cell TK
2 
-
2 
+  <!-- badges: start -->
3 
+[![R-CMD-check](https://github.com/compbiomed/singleCellTK/workflows/R-CMD-check/badge.svg)](https://github.com/compbiomed/singleCellTK/actions)
3 4 
 [![Travis build status](https://travis-ci.org/compbiomed/singleCellTK.svg?branch=master)](https://travis-ci.org/compbiomed/singleCellTK)
4 5 
 [![codecov](https://codecov.io/gh/compbiomed/singleCellTK/branch/master/graph/badge.svg)](https://codecov.io/gh/compbiomed/singleCellTK)
5 6 
 [![BioC status](https://www.bioconductor.org/shields/build/release/bioc/singleCellTK.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/singleCellTK)
6 7 
 [![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)
7 8
9 
+  <!-- badges: end -->
10 
+
8 11 
 The Single Cell ToolKit (SCTK) is an analysis platform that provides an **R interface to several popular scRNA-seq preprocessing, quality control, and visualization tools**. SCTK imports raw or filtered counts from various single cell sequencing technologies and upstream tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R as well as Python, SCTK performs extensive quality control measures including doublet detection and batch effect correction. Additionally, SCTK summarizes results and related visualizations in a comprehensive R markdown and/or HTML report. SCTK provides a standardized single cell analysis workflow by representing the counts data and the results using the [SingleCellExperiment](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) R object. Furthermore, SCTK enables seamless downstream analysis by exporting data and results in flat .txt and Python Anndata formats.
9 12
10 13 
 A comprehensive list of available functions is listed in the Reference section.
Browse code

Update Import UI doc

Yichen Wang authored on 18/02/2021 16:20:23
Showing 1 changed files
README.md
History View file @ be3ea822d
... ... 
@@ -5,18 +5,22 @@
5 5 
 [![BioC status](https://www.bioconductor.org/shields/build/release/bioc/singleCellTK.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/singleCellTK)
6 6 
 [![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)
7 7
8 
+The Single Cell ToolKit (SCTK) is an analysis platform that provides an **R interface to several popular scRNA-seq preprocessing, quality control, and visualization tools**. SCTK imports raw or filtered counts from various single cell sequencing technologies and upstream tools such as 10x CellRanger, BUStools, Optimus, STARSolo, and more. By integrating several publicly available tools written in R as well as Python, SCTK performs extensive quality control measures including doublet detection and batch effect correction. Additionally, SCTK summarizes results and related visualizations in a comprehensive R markdown and/or HTML report. SCTK provides a standardized single cell analysis workflow by representing the counts data and the results using the [SingleCellExperiment](https://www.bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) R object. Furthermore, SCTK enables seamless downstream analysis by exporting data and results in flat .txt and Python Anndata formats.
9 
+
10 
+A comprehensive list of available functions is listed in the Reference section.
11 
+
8 12 
 ## Installation
9 13
10 14 
 ### System setup
11 15
12 16 
 If you are the first time to install R, please don't install 32 bit R. Make sure to uncheck the '32-bit Files' box when you see the following window:
13 17
14 
-![](/exec/png/32bit-R.png)
18 
+![](exec/png/32bit-R.png)
15 19
16 20 
 #### Window's user
17 21 
 For window's users, please install [rtools](https://cran.r-project.org/bin/windows/Rtools/history.html) based on your R version. Make sure to click 'Edit the system PATH' box when you see this window:
18 22
19 
-![](/exec/png/rtools.png)
23 
+![](exec/png/rtools.png)
20 24
21 25 
 After installing rtools, install 'devtools' package with the following command. If it asks whether install the package that requires compilation, type 'y'.
22 26 
 ```
Browse code

Move png to exec/png and ignore it when build R package

rz2333 authored on 29/09/2020 21:27:43
Showing 1 changed files
README.md
History View file @ abe383a64
... ... 
@@ -11,12 +11,12 @@
11 11
12 12 
 If you are the first time to install R, please don't install 32 bit R. Make sure to uncheck the '32-bit Files' box when you see the following window:
13 13
14 
-![](/exec/32bit-R.png)
14 
+![](/exec/png/32bit-R.png)
15 15
16 16 
 #### Window's user
17 17 
 For window's users, please install [rtools](https://cran.r-project.org/bin/windows/Rtools/history.html) based on your R version. Make sure to click 'Edit the system PATH' box when you see this window:
18 18
19 
-![](/exec/rtools.png)
19 
+![](/exec/png/rtools.png)
20 20
21 21 
 After installing rtools, install 'devtools' package with the following command. If it asks whether install the package that requires compilation, type 'y'.
22 22 
 ```
Browse code

Update sctk qc pipeline to allow only droplet or cell input

rz2333 authored on 22/06/2020 17:48:28
Showing 1 changed files
README.md
History View file @ 46357f0d7
... ... 
@@ -7,6 +7,33 @@
7 7
8 8 
 ## Installation
9 9
10 
+### System setup
11 
+
12 
+If you are the first time to install R, please don't install 32 bit R. Make sure to uncheck the '32-bit Files' box when you see the following window:
13 
+
14 
+![](/exec/32bit-R.png)
15 
+
16 
+#### Window's user
17 
+For window's users, please install [rtools](https://cran.r-project.org/bin/windows/Rtools/history.html) based on your R version. Make sure to click 'Edit the system PATH' box when you see this window:
18 
+
19 
+![](/exec/rtools.png)
20 
+
21 
+After installing rtools, install 'devtools' package with the following command. If it asks whether install the package that requires compilation, type 'y'.
22 
+```
23 
+install.packages('devtools')
24 
+```
25 
+
26 
+#### macOS user
27 
+For macbook's users, please install gfortran with brew. If you have not installed brew, please check [this link](https://brew.sh/) to set up brew on your machine.
28 
+```
29 
+brew install gcc
30 
+```
31 
+
32 
+After that, install 'devtools' package with the following command.
33 
+```
34 
+install.packages('devtools')
35 
+```
36 
+
10 37 
 ### Release Version
11 38
12 39 
 You can download the release version of the Single Cell Toolkit in
... ... 
@@ -39,10 +66,6 @@ the following version from this repository:
39 66 
 devtools::install_github("compbiomed/singleCellTK", ref="r_3_4")
40 67 
 ```
41 68
42 
-If you are the first time to install R, please don't install 32 bit R. Make sure to uncheck the '32-bit Files' box when you see the following window:
43 
-
44 
-![](/exec/32bit-R.png)
45 
-
46 69 
 #### Troubleshooting Installation
47 70
48 71 
 For the majority of users, the commands above will install the latest version
Browse code

update example code and README.md

rz2333 authored on 12/06/2020 22:01:44
Showing 1 changed files
README.md
History View file @ 08c1ce844
... ... 
@@ -39,6 +39,10 @@ the following version from this repository:
39 39 
 devtools::install_github("compbiomed/singleCellTK", ref="r_3_4")
40 40 
 ```
41 41
42 
+If you are the first time to install R, please don't install 32 bit R. Make sure to uncheck the '32-bit Files' box when you see the following window:
43 
+
44 
+![](/exec/32bit-R.png)
45 
+
42 46 
 #### Troubleshooting Installation
43 47
44 48 
 For the majority of users, the commands above will install the latest version
Browse code

Resolve conflict from upstream devel

rz2333 authored on 17/04/2020 18:43:57
Showing 1 changed files
README.md
History View file @ 06ab60301
... ... 
@@ -85,53 +85,6 @@ devtools::install_github("compbiomed/singleCellTK")
85 85 
 If you still encounter an error, please [contact us](mailto:dfj@bu.edu) and
86 86 
 we'd be happy to help.
87 87
88 
-## QC Outputs
89 
-There are several available QC algorithms that are implemented within singleCellTK as wrapper functions, which will be stored as `colData` within the output `singleCellExperiment` object. These are the currently available QC outputs:
90 
-
91 
-### General statistics
92 
-
93 
-| Output name | Description | Package |
94 
-| --- | --- | --- |
95 
-| sum | Total transcript counts in cell | scater |
96 
-| detected | Total genes detected in cell | scater |
97 
-| percent_top | Numeric value, the percentage of counts assigned to the percent_topage of most highly expressed genes. Each column of the matrix corresponds to an entry of the sorted percent_top, in increasing order | scater |
98 
-| subsets_mito_sum | Number of total mitochonrial transcript counts per cell | scater |
99 
-| subsets_mito_detected | Number of mitochondrial genes detected per cell | scater |
100 
-| subsets_mito_percent | Percentage of mitochondial transcript counts out of total gene counts | scater |
101 
-
102 
-
103 
-### Droplet-based statistics
104 
-
105 
-| Output name | Description | Package |
106 
-| --- | --- | --- |
107 
-| dropletUtils_emptyDrops_total | Integer, spicifies the total UMI count for each barcode | dropletUtils |
108 
-| dropletUtils_emptyDrops_pvalue | Numeric, the Monte Carlo p-value under the null model | dropletUtils |
109 
-| dropletUtils_emptyDrops_logprob | Numeric, the barcode's count log-probability of a vector under the null model | dropletUtils |
110 
-| dropletUtils_emptyDrops_fdr | Numeric, false discovery rate. Suggested fdr cut-off is 1% | dropletUtils |
111 
-| dropletUtils_emptyDrops_limited | Logical, indicates if a lower p-value could be obtained by increasing niters, a number of iterations for Monte Carlo p-value calculations | dropletUtils |
112 
-| dropletUtils_BarcodeRank_Knee | Numeric, specifies total count at the knee point | dropletUtils |
113 
-| dropletUtils_BarcodeRank_Inflection | Numeric,  specifies total count at the inflection point | dropletUtils |
114 
-
115 
-### Doublet detection
116 
-
117 
-| Output name | Description | Package |
118 
-| --- | --- | --- |
119 
-| doubletFinder_doublet_score | Numeric value that determines how likely a cell in the counts matrix is a doublet using artificially generated doublets | doubletFinder |
120 
-| doubletFinder_doublet_label | Whether the cell is deemed a doublet or not by the algorithm. Will be "Singlet" or "Doublet" | doubletFinder |
121 
-| scds_cxds_score | Numeric value that determines how likely a cell is a doublet, based on co-expression of gene pairs | scds |
122 
-| scds_bcds_score | Numeric value that determines how likely a cell is a doublet, using artificially generated doublets | scds |
123 
-| scds_hybrid_score | Numeric value that determines how likely a cell is a doublet, uses both cxds and bcds algorithm | scds |
124 
-| scran_doubletCells_Score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scran |
125 
-| scrublet_score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scrublet |
126 
-| scrublet_call | Whether the cell is deemed a doublet or not by the algorithm. Will be  | scrublet |
127 
-
128 
-### Ambient RNA detection
129 
-
130 
-| Output name | Description | Package |
131 
-| --- | --- | --- |
132 
-| decontX_Contamination | Probability of contamination determined by decontX | celda |
133 
-| decontX_Clusters | Clusters determined by Celda, a clustering algorithm that runs in the background of decontX | celda |
134 
-
135 88 
 ## Develop singleCellTK
136 89
137 90 
 To contribute to singleCellTK, follow these steps:
Browse code

Debug doubletFinder, modify README

Yusuke Koga authored on 22/02/2020 17:07:09
Showing 1 changed files
README.md
History View file @ 82457c5cd
... ... 
@@ -88,7 +88,19 @@ we'd be happy to help.
88 88 
 ## QC Outputs
89 89 
 There are several available QC algorithms that are implemented within singleCellTK as wrapper functions, which will be stored as `colData` within the output `singleCellExperiment` object. These are the currently available QC outputs:
90 90
91 
-### Droplet-based
91 
+### General statistics
92 
+
93 
+| Output name | Description | Package |
94 
+| --- | --- | --- |
95 
+| sum | Total transcript counts in cell | scater |
96 
+| detected | Total genes detected in cell | scater |
97 
+| percent_top | Numeric value, the percentage of counts assigned to the percent_topage of most highly expressed genes. Each column of the matrix corresponds to an entry of the sorted percent_top, in increasing order | scater |
98 
+| subsets_mito_sum | Number of total mitochonrial transcript counts per cell | scater |
99 
+| subsets_mito_detected | Number of mitochondrial genes detected per cell | scater |
100 
+| subsets_mito_percent | Percentage of mitochondial transcript counts out of total gene counts | scater |
101 
+
102 
+
103 
+### Droplet-based statistics
92 104
93 105 
 | Output name | Description | Package |
94 106 
 | --- | --- | --- |
... ... 
@@ -99,32 +111,26 @@ There are several available QC algorithms that are implemented within singleCell
99 111 
 | dropletUtils_emptyDrops_limited | Logical, indicates if a lower p-value could be obtained by increasing niters, a number of iterations for Monte Carlo p-value calculations | dropletUtils |
100 112 
 | dropletUtils_BarcodeRank_Knee | Numeric, specifies total count at the knee point | dropletUtils |
101 113 
 | dropletUtils_BarcodeRank_Inflection | Numeric,  specifies total count at the inflection point | dropletUtils |
102 
-| sum | Total transcript counts in cell | scater |
103 
-| detected | Total genes detected in cell | scater |
104 
-| percent_top | Numeric value, the percentage of counts assigned to the percent_topage of most highly expressed genes. Each column of the matrix corresponds to an entry of the sorted percent_top, in increasing order | scater |
105 
-| subsets_mito_sum | Number of total mitochonrial transcript counts per cell | scater |
106 
-| subsets_mito_detected | Number of mitochondrial genes detected per cell | scater |
107 
-| subsets_mito_percent | Percentage of mitochondial transcript counts out of total gene counts | scater |
108 114
109 115 
 ### Doublet detection
110 116
111 117 
 | Output name | Description | Package |
112 118 
 | --- | --- | --- |
113 119 
 | doubletFinder_doublet_score | Numeric value that determines how likely a cell in the counts matrix is a doublet using artificially generated doublets | doubletFinder |
114 
-| doubletFinder_doublet_label | Whether the cell is deemed a doublet or not by the algorithm | doubletFinder |
120 
+| doubletFinder_doublet_label | Whether the cell is deemed a doublet or not by the algorithm. Will be "Singlet" or "Doublet" | doubletFinder |
115 121 
 | scds_cxds_score | Numeric value that determines how likely a cell is a doublet, based on co-expression of gene pairs | scds |
116 122 
 | scds_bcds_score | Numeric value that determines how likely a cell is a doublet, using artificially generated doublets | scds |
117 
-| scds_hybrid_score | Numeric value that determines how likely a cell is a doublet, uses both cxds and bcds approach | scds |
123 
+| scds_hybrid_score | Numeric value that determines how likely a cell is a doublet, uses both cxds and bcds algorithm | scds |
118 124 
 | scran_doubletCells_Score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scran |
119 125 
 | scrublet_score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scrublet |
120 
-| scrublet_call | Whether the cell is deemed a doublet or not by the algorithm | scrublet |
126 
+| scrublet_call | Whether the cell is deemed a doublet or not by the algorithm. Will be  | scrublet |
121 127
122 128 
 ### Ambient RNA detection
123 129
124 130 
 | Output name | Description | Package |
125 131 
 | --- | --- | --- |
126 
-| decontX_Contamination | Probability of contamination? | celda |
127 
-| decontX_Clusters | Clusters identified by decontX? | celda |
132 
+| decontX_Contamination | Probability of contamination determined by decontX | celda |
133 
+| decontX_Clusters | Clusters determined by Celda, a clustering algorithm that runs in the background of decontX | celda |
128 134
129 135 
 ## Develop singleCellTK
130 136
Browse code

Merge from upstream

Yusuke Koga authored on 20/02/2020 20:33:02
Showing 1 changed files
README.md
History View file @ 37c3f93de
... ... 
@@ -95,8 +95,8 @@ There are several available QC algorithms that are implemented within singleCell
95 95 
 | dropletUtils_emptyDrops_total | Integer, spicifies the total UMI count for each barcode | dropletUtils |
96 96 
 | dropletUtils_emptyDrops_pvalue | Numeric, the Monte Carlo p-value under the null model | dropletUtils |
97 97 
 | dropletUtils_emptyDrops_logprob | Numeric, the barcode's count log-probability of a vector under the null model | dropletUtils |
98 
-| dropletUtils_emptyDrops_fdr | Numeric, the barcode's count log-probability of a vector under the null model | dropletUtils |
99 
-| dropletUtils_emptyDrops_limited | Numeric, the barcode's count log-probability of a vector under the null model | dropletUtils |
98 
+| dropletUtils_emptyDrops_fdr | Numeric, false discovery rate. Suggested fdr cut-off is 1% | dropletUtils |
99 
+| dropletUtils_emptyDrops_limited | Logical, indicates if a lower p-value could be obtained by increasing niters, a number of iterations for Monte Carlo p-value calculations | dropletUtils |
100 100 
 | dropletUtils_BarcodeRank_Knee | Numeric, specifies total count at the knee point | dropletUtils |
101 101 
 | dropletUtils_BarcodeRank_Inflection | Numeric,  specifies total count at the inflection point | dropletUtils |
102 102 
 | sum | Total transcript counts in cell | scater |
Browse code

Reorder fxn names in table

Yusuke Koga authored on 20/02/2020 20:29:00
Showing 1 changed files
README.md
History View file @ 58b2849b4
... ... 
@@ -110,14 +110,14 @@ There are several available QC algorithms that are implemented within singleCell
110 110
111 111 
 | Output name | Description | Package |
112 112 
 | --- | --- | --- |
113 
-| scran_doubletCells_Score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scran |
114 
-| scrublet_score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scrublet |
115 
-| scrublet_call | Whether the cell is deemed a doublet or not by the algorithm | scrublet |
116 
-| doubletFinderAnnScore | Numeric value that determines how likely a cell in the counts matrix is a doublet using artificially generated doublets | doubletFinder |
117 
-| doubletFinderLabel | Whether the cell is deemed a doublet or not by the algorithm | doubletFinder |
113 
+| doubletFinder_doublet_score | Numeric value that determines how likely a cell in the counts matrix is a doublet using artificially generated doublets | doubletFinder |
114 
+| doubletFinder_doublet_label | Whether the cell is deemed a doublet or not by the algorithm | doubletFinder |
118 115 
 | scds_cxds_score | Numeric value that determines how likely a cell is a doublet, based on co-expression of gene pairs | scds |
119 116 
 | scds_bcds_score | Numeric value that determines how likely a cell is a doublet, using artificially generated doublets | scds |
120 117 
 | scds_hybrid_score | Numeric value that determines how likely a cell is a doublet, uses both cxds and bcds approach | scds |
118 
+| scran_doubletCells_Score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scran |
119 
+| scrublet_score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scrublet |
120 
+| scrublet_call | Whether the cell is deemed a doublet or not by the algorithm | scrublet |
121 121
122 122 
 ### Ambient RNA detection
123 123
Browse code

Add outputs table to README

Yusuke Koga authored on 20/02/2020 20:07:41
Showing 1 changed files
README.md
History View file @ 422d4ca97
... ... 
@@ -85,6 +85,47 @@ devtools::install_github("compbiomed/singleCellTK")
85 85 
 If you still encounter an error, please [contact us](mailto:dfj@bu.edu) and
86 86 
 we'd be happy to help.
87 87
88 
+## QC Outputs
89 
+There are several available QC algorithms that are implemented within singleCellTK as wrapper functions, which will be stored as `colData` within the output `singleCellExperiment` object. These are the currently available QC outputs:
90 
+
91 
+### Droplet-based
92 
+
93 
+| Output name | Description | Package |
94 
+| --- | --- | --- |
95 
+| dropletUtils_emptyDrops_total | Integer, spicifies the total UMI count for each barcode | dropletUtils |
96 
+| dropletUtils_emptyDrops_pvalue | Numeric, the Monte Carlo p-value under the null model | dropletUtils |
97 
+| dropletUtils_emptyDrops_logprob | Numeric, the barcode's count log-probability of a vector under the null model | dropletUtils |
98 
+| dropletUtils_emptyDrops_fdr | Numeric, the barcode's count log-probability of a vector under the null model | dropletUtils |
99 
+| dropletUtils_emptyDrops_limited | Numeric, the barcode's count log-probability of a vector under the null model | dropletUtils |
100 
+| dropletUtils_BarcodeRank_Knee | Numeric, specifies total count at the knee point | dropletUtils |
101 
+| dropletUtils_BarcodeRank_Inflection | Numeric,  specifies total count at the inflection point | dropletUtils |
102 
+| sum | Total transcript counts in cell | scater |
103 
+| detected | Total genes detected in cell | scater |
104 
+| percent_top | Numeric value, the percentage of counts assigned to the percent_topage of most highly expressed genes. Each column of the matrix corresponds to an entry of the sorted percent_top, in increasing order | scater |
105 
+| subsets_mito_sum | Number of total mitochonrial transcript counts per cell | scater |
106 
+| subsets_mito_detected | Number of mitochondrial genes detected per cell | scater |
107 
+| subsets_mito_percent | Percentage of mitochondial transcript counts out of total gene counts | scater |
108 
+
109 
+### Doublet detection
110 
+
111 
+| Output name | Description | Package |
112 
+| --- | --- | --- |
113 
+| scran_doubletCells_Score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scran |
114 
+| scrublet_score | Numeric value that determines how likely a cell in the counts matrix is a doublet | scrublet |
115 
+| scrublet_call | Whether the cell is deemed a doublet or not by the algorithm | scrublet |
116 
+| doubletFinderAnnScore | Numeric value that determines how likely a cell in the counts matrix is a doublet using artificially generated doublets | doubletFinder |
117 
+| doubletFinderLabel | Whether the cell is deemed a doublet or not by the algorithm | doubletFinder |
118 
+| scds_cxds_score | Numeric value that determines how likely a cell is a doublet, based on co-expression of gene pairs | scds |
119 
+| scds_bcds_score | Numeric value that determines how likely a cell is a doublet, using artificially generated doublets | scds |
120 
+| scds_hybrid_score | Numeric value that determines how likely a cell is a doublet, uses both cxds and bcds approach | scds |
121 
+
122 
+### Ambient RNA detection
123 
+
124 
+| Output name | Description | Package |
125 
+| --- | --- | --- |
126 
+| decontX_Contamination | Probability of contamination? | celda |
127 
+| decontX_Clusters | Clusters identified by decontX? | celda |
128 
+
88 129 
 ## Develop singleCellTK
89 130
90 131 
 To contribute to singleCellTK, follow these steps:
Browse code

Update README.md

Update Bioconductor version

Zhe Wang authored on 18/12/2019 15:51:30 • GitHub committed on 18/12/2019 15:51:30
Showing 1 changed files
README.md
History View file @ d67ac5dc3
... ... 
@@ -10,7 +10,7 @@
10 10 
 ### Release Version
11 11
12 12 
 You can download the release version of the Single Cell Toolkit in
13 
-[Bioconductor v3.9](https://bioconductor.org/packages/release/bioc/html/singleCellTK.html):
13 
+[Bioconductor v3.10](https://bioconductor.org/packages/release/bioc/html/singleCellTK.html):
14 14
15 15 
 ```r
16 16 
 if (!requireNamespace("BiocManager", quietly=TRUE))
... ... 
@@ -21,7 +21,7 @@ BiocManager::install("singleCellTK")
21 21 
 ### Devel Version
22 22
23 23 
 You can download the development version of the Single Cell Toolkit in
24 
-[Bioconductor v3.10](https://bioconductor.org/packages/devel/bioc/html/singleCellTK.html)
24 
+[Bioconductor v3.11](https://bioconductor.org/packages/devel/bioc/html/singleCellTK.html)
25 25 
 or from this repository:
26 26
27 27 
 ```r
Browse code

Update the readme with new bioc versions

David Jenkins authored on 04/05/2019 21:08:23
Showing 1 changed files
README.md
History View file @ 134b8afaf
... ... 
@@ -10,7 +10,7 @@
10 10 
 ### Release Version
11 11
12 12 
 You can download the release version of the Single Cell Toolkit in
13 
-[Bioconductor v3.7](https://bioconductor.org/packages/release/bioc/html/singleCellTK.html):
13 
+[Bioconductor v3.9](https://bioconductor.org/packages/release/bioc/html/singleCellTK.html):
14 14
15 15 
 ```r
16 16 
 if (!requireNamespace("BiocManager", quietly=TRUE))
... ... 
@@ -21,7 +21,7 @@ BiocManager::install("singleCellTK")
21 21 
 ### Devel Version
22 22
23 23 
 You can download the development version of the Single Cell Toolkit in
24 
-[Bioconductor v3.8](https://bioconductor.org/packages/devel/bioc/html/singleCellTK.html)
24 
+[Bioconductor v3.10](https://bioconductor.org/packages/devel/bioc/html/singleCellTK.html)
25 25 
 or from this repository:
26 26
27 27 
 ```r
...