Overview

This repository contains code for reproducing the results of our eLife paper "Automated, high-dimensional evaluation of physiological aging and resilience in outbred mice." (DOI: https://doi.org/10.7554/eLife.72664110e72664)

Structure

data_processing contains the paper-specific code for pre-processing the data from its raw form off the metabolic cage into a per-run / per-trace feature matrix which we use for most of the results in the paper.

caspar contains the code for the Combined Age and Survival Prediction of Aging Rate (CASPAR) model - a regression model that combines both chronological age and mortality data as signals and takes advantage of longitudinal measurements. A standalone version of CASPAR containing only the general purpose model will be released shortly.

network contains the code to reproduce our results on applying Time Varying Graph Lasso (TVGL) and the nonparanormal SKEPTIC estimator to infer a sparse time-varying graphical model on our data and to run consensus (spectral) clustering on the inferred networks as described in our paper.

data contains per-run feature data in HDF format. Features are namespaced by keys. Important keys include per-mouse metadata (mouse metadata), per-run metadata (trace metadata) and all per-trace features (all features). You will require git-lfs to download the data.

To access specific subsets of features / keys, you can use the short snippet below:

def read_results_from_h5(filename, keys=None):
  if keys is None:
    f = h5py.File(filename, 'r')    
    keys = list(f.keys())
  result = {}
  for key in keys:
    result[key] = pd.read_hdf(filename, key=key)
  return result

Where filename points to the location of the trace_features.h5 file.