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Analysis of GDSC Data

A brief video summary of this project can be found here on YouTube.

/assets

Contains relevant csv files

Raw data

  • GDSC2_fitted_dose_response_25Feb20.csv - contains the raw IC50 data for 135,242 drug / cell line combinations; data can be downloaded here
  • cell_list.csv - contains GDSC-generated information about the included cell lines, including tissue and TCGA classification; data can be downloaded here
  • drug_data.csv - contains GDSC-generated information about the included drugs, including drug pathways and targets; data can be found here

Processed data

  • cell.csv - results of the analysis from GDSC_Project.ipynb for the cell data, including contains two dimensions from PCA and t-SNE for plotting, the cluster identities from k-nearest neighbors using full, PCA-transformed, and low rank approximations of the data, and the mean lnIC50 per cell line
  • cell_lrm.csv - low-rank approximation of the cell matrix

![formula](https://render.githubusercontent.com/render/math?math=\color{white}\large\U \Sigma^{\frac{1}{2}})

  • drug.csv - results of the analysis from GDSC_Project.ipynb for the drug data, including contains two dimensions from PCA and t-SNE for plotting, the cluster identities from k-nearest neighbors using full, PCA-transformed, and low rank approximations of the data, and the mean lnIC50 per compound
  • drug_lrm.csv - low-rank approximation of the drug matrix

![formula](https://render.githubusercontent.com/render/math?math=\color{white}\large\Sigma^{\frac{1}{2}} V^T)

/src

Contains relevant scripts and notebooks

  • GDSC_Project.R - contains
  • GDSC_Project.ipynb - contains
  • kmeans.py
    • find_kmeans: find an optimal number clusters via the elbow method and fit k-means with this many clusters
    • plot_kmeans: plot the SSE vs. clusters and elbow point for cell and drug data
  • lowrank.py
    • fit_svd: fit SVD model iteratively for a given rank r
  • pca.py
    • find_pc: returns the eigenvalues and eigenvectors of the covariance matrix
    • project_pca: transforms the original matrix via projection using a specified number of principal components
    • plot_pca: plot the variance by principal component and cumulativev variance by principal component
  • utils.py
    • import_data: loads GDSC data and pre-process into a wide matrix
    • process_data: produces mean-centered data and masks

Summary

![formula](https://render.githubusercontent.com/render/math?math=\color{white}\large\D_{i, j} \approx \sum_{l=1}^ra_l[i]b_l[j] = U \Sigma V^T) ![formula](https://render.githubusercontent.com/render/math?math=\color{white}\large\U \Sigma^{\frac{1}{2}}) ![formula](https://render.githubusercontent.com/render/math?math=\color{white}\large\Sigma^{\frac{1}{2}} V^T)