Published August 29, 2023 | Version v1
Dataset Open

Harnessing genotype-phenotype nonlinearity to accelerate biological prediction

Description

Description:

We are sharing phenotypic data sets used in the pub “Harnessing genotype-phenotype nonlinearity to accelerate biological prediction”. Contained within are a set of publicly available empirical phenotype data sets, synthetic phenotypes we generated, and the output of an autoencoder model trained on them.

Walkthroughs for all analyses using these data are available on GitHub.

Files: 

  • “ail_cleaned_phenos.RDS”: .RDS file of mouse phenotypes from Bogue et al. 2015
  • “all_autoencoder_phenotype_predictions.csv“: .csv file containing accuracy statistics for all phenotype-phenotype autoencoder models analyzed in the pub
  • “arapheno_cleaned_phenos.RDS“: .RDS file of Arabidopsis phenotypes from Exposito-Alonso et al. 2019
  • “autoencoder_phenos.zip“: directory of synthetic phenotype sets used to train the autoencoder models
  • “dgrp_cleaned_phenos.RDS”: .RDS file of DGRP fruit fly phenotypes compiled from multiple sources, originally reported in Mackay et al. 2012
  • “jax_cleaned_phenos.RDS”: .RDS file of mouse phenotypes from Gonzales et al. 2018
  • “nematode_cleaned_phenos.RDS”: .RDS file of nematode phenotypes from Snoek et al. 2019
  • “phen_pleio_int_01_0_1.pk”: pickle of all synthetic phenotypes analyzed in the pub
  • “yeast_cleaned_phenos.RDS”: .RDS file of yeast phenotypes from Bloom et al. 2019

A full description of how the synthetic phenotypes and autoencoder predictions were generated is available in the associated pub.

Files

all_autoencoder_phenotype_predictions.csv

Files (919.7 MB)

Name Size Download all
md5:e25574590a2ea451336a269602dfc5ac
342.3 kB Download
md5:69805723e24a58d140059e1b23c9eaeb
124.5 kB Preview Download
md5:5182d8a621faaf446ea6aea4a1e95813
290.9 kB Download
md5:518fc4230e9d103b53f4cc01076203e4
92.2 MB Preview Download
md5:c8b9aeb3f188d3847b3fe5bfeb77bd8a
71.5 kB Download
md5:25076c10aa3d6a8b9c0efb1493ee562e
51.3 kB Download
md5:79e8147b4c7dcbdd091ebf92147cce11
7.9 kB Download
md5:f8d8b33cdc8b565b0e4f156970b293f2
823.2 MB Download
md5:892d4dbd60b729b0f25639ce0e832495
3.4 MB Download