Last updated: 2023-08-18
Checks: 2 0
Knit directory: gbcd-workflow/analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
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The results in this page were generated with repository version a788fb9. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
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file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
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Untracked files:
Untracked: .DS_Store
Untracked: hnscc/.DS_Store
Untracked: pdac/.DS_Store
Untracked: simulations/.DS_Store
Untracked: simulations/output/.DS_Store
Unstaged changes:
Modified: README.md
Modified: code/fit_ebsnmf.R
Modified: hnscc/hnscc_gbcd.R
Modified: hnscc/result_analysis.R
Modified: pdac/result_analysis.R
Modified: simulations/ebsnmf.R
Modified: simulations/gbcd.R
Modified: simulations/output/iter2_gb_snmf.rds
Modified: simulations/output/iter2_point_exponential_snmf.rds
Modified: simulations/point_exponential_cd.R
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/index.Rmd) and HTML
(docs/index.html) files. If you’ve configured a remote Git
repository (see ?wflow_git_remote), click on the hyperlinks
in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a788fb9 | YushaLiu | 2023-08-18 | Update index.Rmd |
| html | 3e5891c | Peter Carbonetto | 2023-08-09 | Small fix to the overview page. |
| Rmd | 8d6197d | Peter Carbonetto | 2023-08-09 | workflowr::wflow_publish("index.Rmd") |
| html | 0e2b9b8 | Peter Carbonetto | 2023-08-09 | Built the overview page. |
| Rmd | 167e536 | Peter Carbonetto | 2023-08-09 | Updated the workflowr config. |
| Rmd | db41a57 | Peter Carbonetto | 2023-08-09 | Ran wflow_start(). |
This repository contains code and data resources to accompany our research paper:
Yusha Liu, Peter Carbonetto, Jason Willwerscheid, Scott A. Oakes, Kay F. Macleod, and Matthew Stephens (2023). Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition studies with multiple conditions. bioRxiv doi:10.1101/2023.08.15.553436.
We provide the following resources:
A vignette that shows how to use GBCD to dissect tumor transcriptional heterogeneity through analysis of multi-tumor single-cell RNA-seq (scRNA-seq) data. We illustrate this using a head and neck squamous cell carcinoma (HNSCC) dataset analyzed in our research paper.
The scripts that reproduce the results and figures presented in the research paper.
If you find any material in this repository useful for your work, please cite our research paper.
All source code and software in this repository are made available under the terms of the MIT license.
See here for the source repository. This is what you will find in the repository:
├── analysis
├── code
├── docs
├── hnscc
├── pdac
└── simulations
The analysis contains R Markdown source files for the workflowr website, including a vignette illustrating how to apply GBCD to analyze your own scRNA-seq data from malignant cells collected from multiple patients and/or studies.
The code directory contains the R source code to implement GBCD, using the functions defined in the ebnm and flashier packages previously developed by our lab.
The docs directory contains webpages generated from the R Markdown files in the analysis directory.
The hnscc directory stores the preprocessed HNSCC dataset analyzed in our research paper, and the results and analysis scripts of the HNSCC data to reproduce the relevant figures in the research paper.
The pdac directory stores the results and analysis scripts of the pancreatic cancer adenocarcinoma data to reproduce the relevant figures in the research paper.
The simulations directory stores the results and analysis scripts related to the simulation study to reproduce the relevant figures in the research paper.
Please note that running these scripts may give you results that are slightly different from those presented in the paper (which were generated much earlier), particularly the GBCD results, due to version updates of the model fitting algorithm. However, the conclusions reported in the paper remain unaffected.