psb_ma

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1. Flexible GLMM for Testing Cell Colocalization in Spatial Immunofluorescence Data PSB Workshop Siyuan Ma 2023/01/03
2. Multiplexed immunofluorescence (MxIF) data workflow 2 * Amitay et al., bioRxiv, 2022
3. • Do B-cells interact (i.e., colocalize) with CD8- T-cells, more than expected by chance? • Do B-cells interact with CD8- T-cells differently in patients that survived vs. didn’t survive at five years? * VectraPolarisData, Wrobel, Ghosh, Bioconductor, 2022 * Steinhart et al., Mol Cancer Res, 2021 3
4. Significance of colocalization depends on the neighborhood topology of the image 4 Cell A Cell B Others
5. Approach 1: permutation 5 * histoCAT, Schapiro et al., Nat. Methods, 2017 • Issues: • Null distribution difficult to generalize across images • How to compare between e.g. disease conditions?
6. 6 B-cell/CD8- T-cell interactions are enriched but also heterogeneous across images
7. Approach 2: per-cell modeling 7 • 7 out of 16 neighbors are CD8- T-cells. • In the entire image, ~1.31% are CD8- T-cells. • The difference quantifies the enrichment of B- cell/CD8- T-cell interactions. • Number of CD8- T-cell neighbors can be modelled via binomial distribution.
8. Generalized linear mixed effects model for immune interactions 8 For sample 𝑖, B-cell 𝑗: log 𝜋!" 1 − 𝜋!" = log * 𝜋! 1 − * 𝜋! + 𝛽 + 𝑒!" • 𝜋!": proportion of CD8- T-cells interacting with the 𝑗-th B-cell in sample 𝑖 • * 𝜋!: overall proportion of CD8- T-cells in sample 𝑖 (included as offset) • 𝛽: difference between the two, i.e., enrichment of B-cell/CD8- T-cell interactions compared random chance. • 𝑒!": spatial random effect. Cells close together maybe more closely correlated! * Kondo, Ma, et al., Gastro, 2021
9. Model flexibly incorporates covariates 9 log 𝜋𝑖𝑗 1 − 𝜋𝑖𝑗 = log * 𝜋𝑖 1 − * 𝜋𝑖 + 𝛽 + 𝛽%&'()%*%+, ∗ vysurvival + 𝑒𝑖𝑗
10. Summary 10 • Rigorous parameter and test statistics (effect sizes, standard errors, p- values) for immune interaction enrichment. • Cell-specific modelling generalizes across images. Random effects address spatial correlations. • Flexibly incorporate covariates. • Analytical p-values (no permutations). • Available with the R package spaMM.