https://github.com/bhklab/signaturesets
Compendium of published molecular signatures
Science Score: 49.0%
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Repository
Compendium of published molecular signatures
Basic Info
- Host: GitHub
- Owner: bhklab
- License: mit
- Language: HTML
- Default Branch: main
- Size: 4.54 MB
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Metadata Files
README.md
SignatureSets: An R Package for RNA-Based Immuno-Oncology Signatures
📖 Introduction
The SignatureSets package provides access to a curated and extensive compendium of RNA-based Immuno-Oncology (IO) signatures. All signatures included in this package are:
- Published in peer-reviewed literature.
- Publicly available through trusted repositories and resources.
References to the original publications and resources for each signature are included in the package documentation, refer to the vignettes or accessible via the web application predictio.ca.
- Detailed signature information:
A full list of the included signatures, along with their source references and additional metadata, is available indata-raw/signature_information.csv.
🔧 Setup
The latest version of SignatureSets repository can be found on the SignatureSets GitHub repository. The package is not yet on CRAN or Bioconductor. To set up the repository, please download this folder locally:
``` bash
git clone https://github.com/bhklab/SignatureSets cd SignatureSets
```
Repository Structure
plaintext
SignatureSets/
├── 📁 data/ # Processed signature data (.rda files)
├── 📁 data-raw/ # Processed signature data and metadata (.csv files)
├── 📁 vignettes/ # Summaries and usage examples of signature data
└── 📄 README.md # Repository overview and documentation
🧬 Signature Association Types
In total, 55 Immuno-Oncology (IO) gene signatures have been curated in the SignatureSets package. These signatures were extracted from the literature and manually annotated using GENCODE version 40, with HUGO Gene Symbols as the primary identifiers linked to Entrez Gene IDs and Ensembl Gene IDs. The signatures are categorized based on their association with IO therapy outcomes:
65% (36 signatures): Associated with sensitivity to IO therapy, indicating potential positive responses such as immune activation or enhanced checkpoint inhibitor efficacy.
35% (19 signatures): Associated with resistance to IO therapy, highlighting mechanisms like immune evasion, suppressive tumor microenvironments, or other resistance pathways.
📊 Methods for Computing IO Signature Scores
Signature scores are computed using standardized methods tailored to the characteristics of each signature, as described in their original publications.
Unweighted Signatures: Scores are computed using Gene Set Variation Analysis (GSVA) or Single Sample Gene Set Enrichment Analysis(ssGSEA) to assess pathway enrichment. GSVA calculates enrichment scores for gene sets without weighting individual genes.
Weighted Signatures: Scores are computed as a weighted mean expression, where weights are assigned as follows: +1 for increased expression and -1 for decreased expression.
Specific Algorithm: Certain signature scores are computed based on their respective original publications, e.g., the PredictIO signature.
🔍 Visualization
More details about signature score computations and their applications can be found on the PredictioR GitHub repository
📝 Citation
If the data from the SignatureSets package are used in your publication, please cite the following paper(s):
- Bareche, Y., Kelly, D., Abbas-Aghababazadeh, F. et al., Annals of Oncology 2022.
Version Information
📌 This is version: v1.0, corresponding to the release used in the paper.
Owner
- Name: BHKLAB
- Login: bhklab
- Kind: organization
- Location: Toronto, Ontario, Canada
- Website: http://www.pmgenomics.ca/bhklab/
- Repositories: 168
- Profile: https://github.com/bhklab
The Haibe-Kains Laboratory @ Princess Margaret Cancer Centre
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