14/ To put a cherry on top, AMICI discovered a spatial population of CD8 T cells influencing Invasive tumor cells towards ER-dependence (beyond known intrinsic cell cycles ‼️), explaining worse prognosis for immune-infiltrated ER+ tumors.

11/ When we brought even more complexity with a Xenium breast cancer sample, AMICI presented a complex story 📖of CD4 T cell-driven activation of CD8 T cells as well as M1 → M2 macrophage polarization driven by Invasive tumor cells 💥.

10/ Leveling up, AMICI recovered known interactions and downstream genes modulating these interactions between astrocytes and oligodendrocytes in MERFISH mouse cortex 🐁 🧠 data.

9/ Not only did AMICI capture the true interactions and their corresponding length scales, but it surpassed other methods on 3 different tasks: predicting mediating genes ♺, predicting the interacting senders 🔊, and predicting the interacting receivers 📞!

8/ We constructed semi-synthetic spatial data from PBMC cells with defined ground-truth interactions, where receiver subpopulations express distinct gene programs 🧪only when within specific ranges of their sender cell types 👯.

6/ How does AMICI work? It redefines attention for spatial transcriptomics where receiver cells attend to neighboring senders, and their aggregated weights determine a receiver’s phenotype 🧬. AMICI masks the receiver’s expression and learns to reconstruct it 🏗️from its spatial neighbors.

5/ With this we present AMICI, an attention-based framework that adaptively learns cell interactions across spatial scales, resolves context-dependent subpopulations, and uses sparsity regularization to pinpoint 🎯 specific neighbors driving transcriptional changes.

1/ I’m excited to share recent work on inferring cell-cell interactions using attention by @justjhong.bsky.social and me, supervised by @elhamazizi.bsky.social . Open the thread 🧵 for a brief overview of our method. bioRxiv link: www.biorxiv.org/content/10.1....

14/ To put a cherry on top, AMICI discovered a spatial population of CD8 T cells influencing Invasive tumor cells towards ER-dependence (beyond known intrinsic cell cycles ‼️), explaining worse prognosis for immune-infiltrated ER+ tumors.

11/ When we brought even more complexity with a Xenium breast cancer sample, AMICI presented a complex story 📖of CD4 T cell-driven activation of CD8 T cells as well as M1 → M2 macrophage polarization driven by Invasive tumor cells 💥.

10/ Leveling up, AMICI recovered known interactions and downstream genes modulating these interactions between astrocytes and oligodendrocytes in MERFISH mouse cortex 🐁🧠data.

9/ Not only did AMICI capture the true interactions and their corresponding length scales, but it surpassed other methods on 3 different tasks: predicting mediating genes ♺, predicting the interacting senders 🔊, and predicting the interacting receivers 📞!

8/ We constructed semi-synthetic spatial data from PBMC cells with defined ground-truth interactions, where receiver subpopulations express distinct gene programs 🧪only when within specific ranges of their sender cell types 👯.

6/ How does AMICI work? It redefines attention for spatial transcriptomics where receiver cells attend to neighboring senders, and their aggregated weights determine a receiver’s phenotype 🧬. AMICI masks the receiver’s expression and learns to reconstruct it 🏗️ from its spatial neighbors.

5/ With this we present AMICI, an attention-based framework that adaptively learns cell interactions across spatial scales, resolves context-dependent subpopulations, and uses sparsity regularization to pinpoint 🎯 specific neighbors driving transcriptional changes.