Fig. 5: Nicheformer accurately predicts neighborhood compositions at multiple niche resolutions for the brain, liver and lung.

a, We define the neighborhood of a cell as its local neighborhood given a radius and an index cell. The neighborhood cell density is then defined by the number of cells in the neighborhood, and the neighborhood compositions are the proportions of neighboring cell types. b, Neighborhoods are computed at multiple resolutions resulting in different neighborhood size distributions. Each barplot shows the distribution of the number of neighbors across the brain, liver and lung datasets. We extract neighborhoods with the mean number of neighbors 10, 20, 50 and 100 for each dataset. Neighborh., neighborhood. c, The fine-tuned and linear-probing Nicheformer models outperform for brain and lung linear-probing models trained on Geneformer, scGPT, scVI and PCA embeddings in terms of mean absolute error across all neighborhood sizes. Still, it struggles to outperform all benchmarks in liver, where scVI models are very competitive. This is an issue related to the previous liver performance reported in the previous section (Extended Data Figs. 2a and 8f). d, Left, Fine-tuned Nicheformer performance on the MERFISH mouse brain data grouped by index cell type. Shown are the absolute error values between predicted and observed neighborhood composition vectors for held-out test cells. For each box in d, the centerline defines the median, the height of the box is given by the interquartile range (IQR), the whiskers are given by 1.5 times the IQR, and outliers are given as points beyond the minimum or maximum whisker. Center, Index cell-type abundances in the entire MERFISH mouse brain dataset. Right, UMAPs of MERFISH mouse brain Nicheformer embedding with the selected index cell type as color superimposed. e, UMAP of the Nicheformer embedding of all immune cells in the MERFISH mouse brain dataset with region label as color superimposed.

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