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  1. Home
  3. T2pdecoder Enables Protein-centric Analyses From Transcriptomic Data.
  1. Home
  3. T2pdecoder Enables Protein-centric Analyses From Transcriptomic Data.

Related Experiment Video

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
07:44

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

T2Pdecoder enables protein-centric analyses from transcriptomic data.

Hui Wang1, Jihong Tang1, Yimeng Qiao1

  • 1Division of Life Science, Department of Chemical and Biological Engineering, State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology, Hong Kong SAR, China.

Nature Communications
|June 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

T2Pdecoder, a deep learning model, predicts protein abundance from RNA data for IDH-mutant gliomas. This approach enhances understanding of biological processes and cancer subtypes, offering insights beyond RNA-only analyses.

More Related Videos

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Related Experiment Videos

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis
07:44

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Published on: June 8, 2020

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

  • Computational biology
  • Genomics
  • Proteomics

Background:

  • Protein quantification is less extensive than RNA quantification, particularly in IDH-mutant gliomas.
  • Predicting protein levels from RNA is challenging due to weak correlation but valuable for biological insights.
  • Existing methods predict limited protein subsets, restricting proteomic applications.

Purpose of the Study:

  • To develop an integrative multi-omics deep learning model, T2Pdecoder, for predicting broad protein abundance profiles from RNA data.
  • To leverage shared embedding spaces between protein and RNA for improved prediction accuracy.
  • To enable protein-centric analyses from transcriptomic data in cancer research.

Main Methods:

  • T2Pdecoder utilizes a deep learning approach integrating multi-omics data.
  • The model learns a shared embedding space between RNA and protein expression data.
  • Evaluation performed on diverse glioma datasets, comparing against RNA-only baselines.

    • Main Results:

    • T2Pdecoder showed modest but consistent improvements in predicting protein abundance compared to RNA-only methods.
    • The model more accurately recapitulated protein-level pathway enrichment patterns.
    • Applications revealed functional glioma subgroups with survival differences and identified cell markers from single-cell RNA data.

    Conclusions:

    • T2Pdecoder facilitates protein-centric analyses using transcriptomic data.
    • The model offers complementary biological insights beyond conventional RNA-only analyses in cancer research.
    • T2Pdecoder aids in reducing batch effects in single-cell RNA data and identifying cell markers.