Differential Correlation Analysis and Biological Function Inference on Single Cell Proteomics

Author(s): Enes Sefa Ayar,Laurent Gatto

Affiliation(s): Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium



Proteins are the key molecules in executing biological functions within cells. They operate in cooperation with other proteins to carry out these functions as part of protein complexes, or biological pathways. Thus, the correlation among these proteins implies a functional interdependence, offering insights into both biological functions and mechanisms. Differential correlation analysis promises to infer these biological functions and even underlying mechanisms by identifying similar or different correlation patterns in groups of proteins across conditions (ex. cell types, treatments). However, current approaches, particularly those developed for bulk measurements, may not be suitable for single-cell proteomics (SCP) datasets as they may overlook false positives and false negatives emerging due to batch effects or missing values. We aim to investigate the most suitable approach for uncovering functional correlation in SCP datasets. We compared two approaches used in SCP [1, 2] and two other network-based methods [3, 4], commonly used in RNAseq studies. This benchmark involves comparing these methods across various SCP datasets from scpdata package, each with different properties including sample size, protein coverage, and missing values. Thus far, our observations indicate the importance of addressing batch effect-driven correlations. Our benchmark assesses the methods based on biological relevance, statistical significance, and data simulations. References [1] Hu, M., Zhang, Y., Yuan, Y., Ma, W., Zheng, Y., Gu, Q., & Xie, X. S. (2023). Correlated protein modules revealing functional coordination of interacting proteins are detected by single-cell proteomics. The Journal of Physical Chemistry B, 127(27), 6006–6014. [2] Khan, S., Conover, R., Asthagiri, A. R., & Slavov, N. (2023). Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition. [3] Langfelder, P., & Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9(1). [4] Song, W.-M., & Zhang, B. (2015). Multiscale embedded gene co-expression network analysis. PLOS Computational Biology, 11(11).