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topictcga

Notebooks for "A topic model analysis of TCGA transcriptomic data of breast and lung cancer"

https://github.com/fvalle1/topictcga

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: mdpi.com, zenodo.org
  • ○
    Academic email domains
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    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (10.4%) to scientific vocabulary

Keywords

cancer-machine-learning lifelines stochastic-block-model tcga-data topic-model-analysis topic-modeling
Last synced: 6 months ago · JSON representation

Repository

Notebooks for "A topic model analysis of TCGA transcriptomic data of breast and lung cancer"

Basic Info
  • Host: GitHub
  • Owner: fvalle1
  • License: gpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 171 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 1
  • Open Issues: 1
  • Releases: 2
Topics
cancer-machine-learning lifelines stochastic-block-model tcga-data topic-model-analysis topic-modeling
Created almost 6 years ago · Last pushed almost 4 years ago
Metadata Files
Readme License Citation

README.md

Docker Image CI DOI

A topic model analysis of TCGA

Notebooks and libraries for "A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data"

Analyse results

In order to analyse results and reproduce plots in the paper without rerunning hSBM use the following notebook hSBM_postprocess.ipynb

This repository, following the structure of the paper, is divided into three parts. See Readme.md in each folder for a detailed description of the specific pipeline.

breast

breast analyses, stochastic block modelling and predictor

lung

lung analyses, stochastic block modelling, survival analysis and predictor

unified lung

lung data from unified dataset as discussed in the paper

tree plotter

A submodule useful to plot hierarchies

Run

You can simply create a Docker container with all dependencies installed

bash docker run -v $PWD:/home/jovyan/work -p 8888:8888 --rm -it --name topic_tcga docker.pkg.github.com/fvalle1/topictcga/topic:latest

then point your browser to localhost

hSBM_Topicmodel

The run_graph.ipynb notebook can be used to run hierarchical Stochastic Block Modelling.

Data

The data processed in our analysis when not available trough git can be accessed via DataVersionControl bash dvc pull -r mydrive name_of_the_file_to_download.dvc

License

Please see LICENSE

Owner

  • Name: Filippo Valle
  • Login: fvalle1
  • Kind: user
  • Location: Turin, Italy
  • Company: @Elemento-Modular-Cloud

Chief Technology Officer of @Elemento-Modular-Cloud | Complex Systems researcher @BioPhys-Turin

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Dependencies

requirements.txt pypi
  • cairocffi *
  • gobject *
  • gseapy *
  • jupyter *
  • matplotlib *
  • numpy *
  • pandas *
  • scanpy *
  • scipy *
  • seaborn *
  • sklearn *
  • tensorflow *
  • topicpy *
  • watermark *