Using Deep Learning to Extrapolate Protein Expression Measurements

This repository contains the code for reproducing key results of the following paper: Using Deep Learning to Extrapolate Protein Expression Measurements by Mitra Parissa Barzine, Kārlis Freivalds, James C. Wright, Mārtiņš Opmanis, Darta Rituma, Fatemeh Zamanzad Ghavidel, Andrew F. Jarnuczak, Edgars Celms, Kārlis Čerāns, Inge Jonassen, Lelde Lace, Juan Antonio Vizcaíno, Jyoti Choudhary, Alvis Brazma, and Juris Viksna

Published in [Proteomics and Systems Biology]

Abstract: Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average R2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be “transferred” across experiments and species. For instance, the model derived from human tissues gave a R2 = 0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label-free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.

Summary of the approach

We use deep learning methods to predict the values for unobserved proteins in MS-proteomics experiments. Measured protein expression values in combination with context information such as functional annotations and RNAseq measurements are used to train a neural network which can predict protein expression for all proteins, including those not experimentally measured in any sample.

Running the code

See this guide on running the code.

Contact information

For help or issues with this code, please submit a GitHub issue.

For personal communication related to the paper please contact Juris Viksna (juris.viksna@lumii.lv).