SERUM-PLASMA-BRIDGING-2025

Overview

This repository contains the code used to generate figures for the manuscript "Bridging the Gap: A Systematic Approach to Integrating Serum and Plasma Proteomic Datasets for Biomarker Studies" (currently under review). This study presents a computational approach for bridging between serum and plasma proteomic datasets derived from the aptamer-based SomaScan assay, enabling cross-specimen data use in the context of the PROphet model designed to predict immunotherapy outcomes.

Citation

If you use this code or data in your research, please cite: Lahav, C., Dahan, N., Harel, M., Elon, Y., Sela, I., Geyer, P.E., Schneider, M.A., Muley, T., Bacchiocchi, A., Marte, J., Floudas, C.S., Halaban, R., Sznol, M., Christopoulos, P., Gulley, J.L. Bridging the Gap: A Systematic Approach to Integrating Serum and Plasma Proteomic Datasets for Biomarker Studies. 2025. (Under review)

Data

The code in this repository requires supplementary tables that will be available upon publication of the manuscript. Place the files in a subfolder named data.

Code

This repository consists of a single Python script generate_manuscript_figures.py that generates figures presented in the manuscript as listed below.

Figures

• Figure 3C: Association between median plasma-to-serum ratio and plasma-serum protein correlation.
• Figure 5: Inter-cohort agreement of scaling parameters.
• Figure S3B: Correlations between plasma and serum proteomes.
• Figure S4: Relationship between Pearson-Spearman correlation differences and protein measurability.
• Figure S5B: Serum-plasma Spearman correlation distributions of protein measurements in different protein sets.
• Figure S7B: Concordance between internal and external plasma datasets.
• Figure S8B: Concordance between internal and external serum datasets.
• Figure S9 matched samples: Comparison of linear scaling parameters derived from different cohorts.
• Figure S10: Inter-cohort agreement of serum-plasma protein correlations. ## Reproducibility To reproduce the figures from our manuscript:
• Clone this repository
• Install the required dependencies (see Requirements section)
• Download the supplementary tables (will be available upon publication) and place them in the script directory
• Run the Python script with the appropriate parameters ## Requirements This code was developed and tested using Python 3.12.5. The following package dependencies are required:
  • numpy==2.0.2
  • pandas==2.2.3
  • scipy==1.14.1
  • matplotlib==3.9.4
  • seaborn==0.13.2 ## License This project is licensed under the GNU General Public License - see the LICENSE file for details. ## Contact For questions regarding the code or manuscript, please open an issue in this repository or contact the corresponding author(s) (contact information will be updated upon publication).