https://github.com/greenelab/multiple-myeloma-classifier
Multi-class KRAS/NRAS Classifier for Multiple Myeloma
https://github.com/greenelab/multiple-myeloma-classifier
analysis cancer dataset gene-expression machine-learning methodology
Last synced: 3 months ago
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Multi-class KRAS/NRAS Classifier for Multiple Myeloma
- Host: GitHub
- URL: https://github.com/greenelab/multiple-myeloma-classifier
- Owner: greenelab
- License: bsd-3-clause
- Created: 2018-04-29T15:41:19.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-03-07T14:50:51.000Z (over 6 years ago)
- Last Synced: 2025-05-23T00:26:52.513Z (5 months ago)
- Topics: analysis, cancer, dataset, gene-expression, machine-learning, methodology
- Language: HTML
- Homepage:
- Size: 1.52 MB
- Stars: 4
- Watchers: 5
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Training and Evaluating a Multiclass Classifier for Multiple Myeloma
**Gregory Way and Casey Greene 2018**
[](https://doi.org/10.5281/zenodo.2566059)
In the following analysis, we train a multiclass classifier on RNAseq data of patients with Multiple Myeloma.
The classifier is trained to distinguish _KRAS_ and _NRAS_ mutations from wild-type tumors.
The analysis is presented in the following preprint:
> Yu-Hsiu Tony Lin, Gregory P. Way, Benjamin G. Barwick, Margarette C. Mariano, Makeba Marcoulis, Ian D. Ferguson, Christoph Driessen, Lawrence H. Boise, Casey S. Greene, Arun P. Wiita.
> _Integrated Phosphoproteomics and Transcriptional Classifiers Reveal Hidden RAS Signaling Dynamics in Multiple Myeloma_.
> February 29th, 2019. _biorXiv_ https://doi.org/10.1101/563312.
## Data
The data was provided by Arun Wiita and Tony Lin (UCSF) as part of the [MMRF CoMMpass Study](https://www.themmrf.org/research-partners/mmrf-data-bank/the-mmrf-commpass-study/).
The data was accessed in a Box link provided by Arun and Tony. Before performing the analysis, the appropriate data must be deposited into the appropriate folders.
The data are not included in this repository.
The analysis expects the following data in the `data/raw/` folder:
1. CoMMpass_train_set.csv
2. CoMMpass_train_set_labels.csv
3. CoMMpass_test_set.csv
4. CoMMpass_test_set_labels.csv
5. MMCL_RNAseq.csv
6. MMCL_RNAseq_labels.csv
7. gprofiler_results_1002952837509.xlsx
If the data are not deposited in the `data/raw` folder, the analysis will not run.
We will provide a reproducible solution by depositing data in an archived and versioned database at a later date.
## Computational Environment
We use conda (`version 4.5.2`) to manage our computational environment.
All scripts must be run in this environment.
After [installing conda](https://conda.io/docs/user-guide/install/index.html), run the following:
```bash
# Initialize the environment
conda env create --force --file environment.yml
# Activate the environment
conda activate multiple-myeloma-classifier
```
## Analysis Pipeline
The analysis consists of a series of notebooks that are designed to be run in order.
See `run_analysis.sh` for more details.
Notebooks are converted into python `.py` files and stored in the `scipts/` folder with:
```bash
jupyter nbconvert --to=script --FilesWriter.build_directory=scripts *.ipynb
```