Last Updated: 09/24/2024
We developed an R method, SOCIAL (Single-cell transcriptOmics Cell-cell Interaction ALgorithm), to identify significant ligand-receptor interactions between two specific cell types, drawing upon insights from Kumar et al.'s, CellPhoneDB (v1), and our own LIRICS framework. Our decision to create our own code stemmed from four primary motivations: 1. Leveraging the strengths of previous methods: By combining aspects of the three approaches, we aimed to maximize the accuracy and robustness of our ligand-receptor interaction predictions. 2. Implementing an R-based solution: While the first method lacked publicly accessible code and the second was in Python, we sought to create an R-based solution for accessibility and ease of use. 3. Incorporating our comprehensive database: Our ligand-receptor interaction database (LIRICS) provided rich and informative annotations, enhancing the depth of our analysis. 4. Accommodating variations in ligand-receptor interaction activity observed across patients.
SOCIAL comprises three main steps: 1. Querying the LIRICS database: Initially, we queried the LIRICS database to identify plausible ligand-receptor interactions; 2. Computing interaction scores: Next, we computed the ligand-receptor interaction score by multiplying the average expression levels of the ligand and receptor complexes for each interaction pair and cell type. 3. Permutation testing: Following that, we performed permutation tests (utilizing 100 iterations in our study) by randomly shuffling cell type labels. This allowed us to derive empirical p-values by calculating the fraction of permutation tests resulting in a higher interaction score than the foreground score determined in step 2. A lower p-value suggests a higher likelihood of the interaction occurring. 4. Optionally, ligand-receptor interactions can be further denoted as significantly activated if the average expression level of both the ligand and receptor genes is greater than the median across all samples.
install.packages('devtools')
library(devtools)
devtools::install_github("KWangLab/SOCIAL")- To reproduce the Nat. Comms. results from Sahni et al. (for Jerby-Arnon et al. single-cell cohort), see: https://github.com/KWangLab/SOCIAL/blob/main/vignettes/SOCIAL_Jerby-Arnon.Rmd
- To implement SOCIAL in your own work, see: https://github.com/KWangLab/SOCIAL/blob/main/vignettes/SOCIAL.Rmd
Sample SOCIAL relevant data can be found at https://zenodo.org/records/13172848.
SOCIAL was developed on R (v4.4.1) using R packages: dplyr (v1.1.4), magrittr (v2.0.3), parallel (v4.4.1), pROC (v1.18.5), rBayesianOptimization (v1.2.1), tidyr (v1.3.1), abind (v1.4-5), Matrix (v1.7-0), urr (v1.0.2), reshape2 (1.4.4), rslurm (v0.6.2), and stats (v4.4.1). All analyses were done on R (v4.4.1).
If using SOCIAL, please cite:
Sahni, S., Wang, B., Wu, D. et al. A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. Nat Commun 15, 8867 (2024). https://doi.org/10.1038/s41467-024-52555-4
- Sahil Sahni
- Sushanth Patkar
- Kun Wang (kwang222@illinois.edu)^
- Eytan Ruppin (eytan.ruppin@nih.gov)^
^equally-contributing corresponding author(s)
SOCIAL figures was created with BioRender.com.