ColormaNN manuscript figures - PanUKB - Variance explained by factors

Author

Saikat Banerjee

Published

July 24, 2025

Abstract

High quality plots used for PanUKB results.

Code

import os
import numpy as np
import pandas as pd
import pickle
import re

import matplotlib
import matplotlib.pyplot as plt
from pymir import mpl_stylesheet
from pymir import mpl_utils

mpl_stylesheet.banskt_presentation(splinecolor = 'black', dpi = 300)

Code

# NYGC Color Palette
nygc_colors = {
    'brown': '#7F0814',
    'darkred': '#d42e12',
    'orange': '#F37239',
    'darkyellow': '#F79320',
    'yellow': '#FFE438',
    'darkblue': '#003059',
    'blue': '#266DB6',
    'lightblue': '#A3D5ED',
    'darkgreen': '#006838',
    'green': '#0A8A42',
    'lightgreen': '#74B74A',
    'yellowgreen': '#BAD75F',
    'darkgray': '#1A1A1A',
    'gray': '#666666',
    'lightgray': '#CCCCCC',
    'khaki': '#ADA194',
    'darkkhaki': '#5E514D',
}

# Style sheet for manuscript
mpl_stylesheet.banskt_presentation(dpi = 300, fontsize = 28, 
    splinecolor = nygc_colors['darkgray'], black = nygc_colors['darkgray'])

Code

data_dir = "/gpfs/commons/home/sbanerjee/work/npd/PanUKB/data"
result_dir = "/gpfs/commons/home/sbanerjee/npddata/panukb/results/colormann-svd"

zscore_df = pd.read_pickle(os.path.join(data_dir, f"modselect/zscore_noRx.pkl"))
trait_df  = pd.read_pickle(os.path.join(data_dir, f"modselect/traits_all_with_desc.pkl"))
trait_df  = trait_df.query('trait_type != "prescriptions"')

Code

methods = ['nnm-sparse', 'nnm', 'rpca']
    
method_labels = {
    "rpca" : "Clorinn (RPCA)",
    "nnm" : "Clorinn (NNM)",
    "nnm-sparse" : "Clorinn (NNM-Sparse)",
    "truncated_svd": "DeGAs",
    "factorgo": "FactorGO",
}


fig = plt.figure(figsize = (36,10))
axs = [fig.add_subplot(131), fig.add_subplot(132), fig.add_subplot(133)]

for i, method in enumerate(methods):
    method_resdir = os.path.join(result_dir, method, "noRx")
    with (open(f"{method_resdir}/pca_comps.pkl", "rb")) as fh:
        U, S, V = pickle.load(fh)
    S_squared = np.square(S)
    explained_variance = np.cumsum(S_squared / np.sum(S_squared))
    axs[i].plot(np.arange(500), explained_variance[:500])
    axs[i].axvline(x = 200, linestyle='dashed', color = 'grey')
    axs[i].axhline(y = explained_variance[199], linestyle='dashed', color = 'grey')
    axs[i].set_xlabel("Number of factors")
    if i == 0:
        axs[i].set_ylabel("Cumulative explained variance")
    axs[i].set_title(method_labels[method], pad = 15)
    
plt.savefig('../plots/colormann-manuscript/panukb_explained_variance.pdf', bbox_inches='tight')
plt.show()