Biology subjects

Gingerich, I. K.

Publications and source records attributed to Gingerich, I. K..

4 recordsLinked to original sources

bioRxiv2026

Parsimonious cell co-localization scoring for spatial transcriptomics

Spatial transcriptomics (ST) preserves tissue architecture while profiling gene expression, motivating methods that quantify whether annotated labels (such as cell types) preferentially co-occur in local neighborhoods. We introduce the Neighborhood Product Co-localization (NPC) score, a simple per-cell metric computed on a pruned spatial neighbor graph: for a set of m [≥] 2 labels, NPC is the product of their neighborhood proportions, optionally normalized by expected co-occurrence under independence and paired with permutation-based significance testing. NPC is interpretable (maximized under balanced neighborhoods), efficient to compute, and extends naturally from pairwise to multivariate microenvironment definitions. Using a mouse ovary MERFISH dataset, we show that NPC complements established Squidpy co-occurrence and neighborhood enrichment analyses by localizing co-localization hotspots in tissue space, recapitulating prominent global associations, and highlighting spatially restricted niches such as follicle boundaries; we further demonstrate multivariate NPC scoring by identifying coordinated endothelial-stroma-theca co-localization. Overall, NPC provides a practical framework for interpretable, single-cell resolution co-localization analysis in ST cohorts.

bioinformatics↗

bioRxiv2025

Neuronal architecture of the mouse insular cortex underlying its diverse functions

The insular cortex integrates interoceptive and exteroceptive information to mediate bodily homeostasis, emotion, learning, and potentially consciousness.1-4 However, the cellular and circuit substrates governing the insula and other associative cortices are poorly understood compared to primary cortices. Here, we quantify the dendritic morphology together with electrical properties, local inputs, and/or projections of 1,093 insular pyramidal neurons. These neurons are mapped onto a quantitative anatomical model of the insula based on a Nissl-staining coordinate framework. Using improved algorithms, we define 21 morphological, 12 electrical, and 9 input neuronal types, and identify several morphological and input types that are unique to the insula. Further, we find that morphological properties constrain and often predict inputs, electrical properties, or projection targets. Several morphological types are differentially distributed between the functionally distinct anterior and posterior insula, providing the substrates for a quantitative demarcation between the anterior and posterior insular subregions. Surprisingly, certain neuronal types receive intra-insular inputs originating far beyond canonical cortical columns. Functionally, these connections bridge a long-range thalamus-to-amygdalar circuit that potentially links sensory information to valence. Our work establishes a structure-and-function foundation for investigating the insular cortex.

neuroscience↗

bioRxiv2025

Benchmarking sketching methods on spatial transcriptomics data

High-throughput spatial transcriptomics (ST) now profiles hundreds of thousands of cells or locations per section, creating computational bottlenecks for routine analysis. Sketching, or intelligent sub-sampling, addresses scale by selecting small, representative subsets. While effective for scRNA-seq data, existing sketching methods, which optimize coverage in expression space but ignore physical location, can introduce spatial bias when applied to ST data. To explore the impact of sketching on ST analysis, we systematically benchmarked uniform sampling, leverage-score sampling, Geosketch (minimax/Hausdorff), and scSampler (maximin) across multiple real ST datasets (mouse ovary, MERFISH brain, human breast cancer, lung) and simulations, using three input representations: PCA embeddings, spatial coordinates, and spatially smoothed embeddings. We show that expression-only designs capture global transcriptomic heterogeneity but distort tissue architecture by over-sampling high-variability regions and under-sampling homogeneous areas. Coordinate-only sampling restores tissue coverage but misses transcriptional extremes. A simple spatially aware extension, computing leverage scores from a randomized SVD basis smoothed by a spatial weights matrix, strikes a favorable balance, recovering rare cell states while maintaining uniform tissue coverage and avoiding edge effects. Across robust Hausdorff distances, clustering stability (ARI), PCA loading drift, and local cell-type MSE, spatially smoothed leverage scores match or outperform alternatives. These results motivate joint spatial-transcriptomic sketching objectives to enable fast, unbiased analyses of increasingly large ST datasets.

bioinformatics↗

bioRxiv2024

Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data

Spatial transcriptomics (ST) provides critical insights into the spatial organization of gene expression, enabling researchers to unravel the intricate relationship between cellular environments and biological function. Identifying spatial domains within tissues is key to understanding tissue architecture and mechanisms underlying development and disease progression. Here, we present Randomized Spatial PCA (RASP), a novel spatially-aware dimensionality reduction method for ST data. RASP is designed to be orders-of-magnitude faster than existing techniques, scale to datasets with 100, 000+ locations, support flexible integration of non-transcriptomic covariates, and reconstruct de-noised, spatially-smoothed gene expression values. It employs a randomized two-stage PCA framework and configurable spatial smoothing. RASP was compared to BASS, GraphST, SEDR, SpatialPCA, STAGATE, and CellCharter using diverse ST datasets (10x Visium, Stereo-Seq, MERFISH, 10x Xenium) on human and mouse tissues. In these benchmarks, RASP delivers comparable or superior accuracy in tissue-domain detection while achieving substantial improvements in computational speed. Its efficiency not only reduces runtime and resource requirements but also makes it practical to explore a broad range of spatial-smoothing parameters in a high-throughput fashion. By enabling rapid re-analysis under different parameter settings, RASP empowers users to fine-tune the balance between resolution and noise suppression on large, high-resolution subcellular datasets--a critical capability when investigating complex tissue architecture.

bioinformatics↗