Code for Quantifying the Impact of Uninformative Features on the Performance of Supervised Classification and Dimensionality Reduction Algorithms

This is the repository associated with the paper Quantifying the Impact of Uninformative Features on the Performance of Supervised Classification and Dimensionality Reduction Algorithms.

Repository structure:

• Figs_CELL/ dimensionality reduction related figures for the cell data
• Figs_DR/ dimensionality reduction related figures for the synthetic data
• data/ --
  • pbmc_final.csv -- the source data for CITE-seq
• experiments/ -- experiments for synthetic data
  • ex[].py -- Python script to run experiments (this script is for Northwestern University cluster only)
  • [].sh.o[] -- output from experiments
• experiments_cell/ -- experiments for cell data
  • ex[].py -- Python script to run experiments (this script is for Northwestern University cluster only)
  • [].sh.o[] -- output from experiments
• notebooks/ -- notebooks for visualizations
• src/ -- other supporting codes for analysis and visualization