Jupyter Notebooks

This is a collection of all Jupyter notebooks for this project. It may be easier to navigate through the subcategories of the navigation.

Date Title
24-04-03 Manifold embedding of NNM loadings
24-04-03 UMAP on low rank matrix and loadings
24-04-03 Choose parameters for UMAP plot
24-04-02 Semantic clustering of PanUKB phenotypes
24-03-27 Interactive UMAP plot from PanUKB data
24-03-27 Rename descriptions for PanUKB phenotypes
24-03-18 Understanding the biology of diseases
24-03-18 Understanding the biology of diseases
24-03-07 Characterization of PanUKB hidden factors
24-02-28 Missing data imputation for UK Biobank
24-01-30 Prefiltering data from PanUKB
23-12-29 Comparison of different methods using numerical experiments
23-12-14 Comparing two different simulation strategies - Direct Model vs Genetics Model
23-12-12 Pan-UKB Hidden Factors v01
23-12-11 Pan-UKB Pleitropy of Diseases v01
23-12-08 Pan-UKB Principal Components v01
23-11-27 LD filtering of UKBB data
23-10-30 Preprocessing UKBB data
23-10-30 First look at NPD phenotypes in the UKBB data
23-10-23 Structure plot from GWAS phenotypes
23-09-25 Application of denoising methods on GWAS phenotypes
23-08-09 Which noise model is the best to capture the distinct GWAS phenotypes?
23-08-04 Demonstration of the Frank-Wolfe methods
23-08-02 Comparison of different noise models
23-07-29 Python implementation of Frank-Wolfe algorithm for NNM with sparse penalty
23-07-28 Choosing step size for Inexact ALM algorithm
23-07-24 Python Class for cross-validation of NNM-FW
23-07-19 Python Class for NNM using Frank-Wolfe algorithm
23-07-05 Simulation setup for benchmarking matrix factorization methods
23-07-01 How to simulate ground truth for multi-phenotype z-scores?
23-06-23 Metrices for evaluating clusters given true labels
23-06-05 Can the low rank approximation capture the distinct GWAS phenotypes?
23-05-23 Nuclear norm regularization using Frank-Wolfe algorithm
23-05-16 PCA of NPD summary statistics
23-05-16 Robust PCA implementation
23-05-12 Preprocess NPD summary statistics
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