SEARCH · Search PubMed

Results for “spatial”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed1991

Spatial interactions in apparent contrast: inhibitory effects among grating patterns of different spatial frequencies, spatial positions and orientations.

Suppression of the apparent contrast of a small 4 cycle wide suprathreshold sine wave grating patch by a high contrast sine wave grating surround pattern was studied as a function of the spatial frequency, orientation and spatial extent of the surround. The data are consistent with the existence of a complex network of inhibitory interconnections among mechanisms that mediate contrast perception. These connections must extend over spatial distances equivalent to more than 12 cycles of the central grating patch.

Adaptation, Physiological↗

PubMed1985

Spatial and spatial-frequency primitives in spatial-interval discrimination.

Thresholds for spatial-interval discrimination were determined under conditions designed to introduce randomness in the spatial-frequency content of the stimuli from trial to trial. Neither a random scaling of the display nor the addition of flanking bars at varying distances affected the observer's ability to judge the relative interval between pairs of bars, provided that the flanking bars were clearly resolved from the targets. Performance deteriorated only if the flanking bars were too close (less than about 2 arc min) or if the display was optically blurred. We conclude that the results pose difficulties for spatial-frequency theories of interval discrimination.

Discrimination, Psychological↗

PubMed1979

Stimulus-response spatial contiguity vs. S-R spatial discontiguity in auditory spatial tasks. I. Acquisition by normal dogs.

Twelve dogs were trained in spatial tasks with auditory location cues. One group, tested on delayed response with stimuli and responses spatially contiguous, solved the task at once, whereas the other group, trained with actual stimuli and responses spatially discontiguous, attained criterion after errors. The differences in behavior of these groups suggest that two learning strategies may be involved. In the first group - approaching a specific (directly determined by auditory targeting reflex) feeder by an unspecific directional response. In the other group - approaching a non-specific feeder by a specific directional response, established in the differentiation learning.

Animals↗

PubMed2026

jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.

MOTIVATION: Spatial transcriptomics technologies record genome-wide measurements of gene expression with high spatial resolution. These technologies generate large and high-dimensional datasets requiring efficient automated methods for their analysis. We introduce joint spatial PCA (jsPCA), a novel, fast, scalable and interpretable method for the automatic identification of spatial domains and variable genes in multi-slice and multi-sample spatial transcriptomics data. RESULTS: jsPCA relies on a simple mathematical formulation of a spatial covariance defined as the product of the gene expression covariance with the spatial autocorrelation. The principal components of this spatial covariance yield a biologically meaningful low-dimensional representation. From this representation, spatial domains are derived by simple clustering and spatially variable genes are identified directly from the principal component coefficients. A joint representation of multiple slices and samples without spatial alignment is obtained by computing common principal components via joint diagonalization. By leveraging data sparsity and non-convex manifold optimization, jsPCA leads to computing time in the order of seconds to minutes, substantially outperforming state-of-the-art approaches. We benchmarked jsPCA against 10 state-of-the-art methods on two reference databases. Our approach demonstrated excellent performance, comparable or better than state-of-the-art methods, while being much faster, interpretable, and scalable to very large datasets.

Journal Article↗

PubMed2026

Dissecting spatial patterning and signaling with directional diffusion in spatial multi-omics.

Spatial multi-omics sequencing enables the simultaneous profiling of transcriptomics, proteomics, and epigenomics at a spatial resolution, offering insights into complex tissue organization and molecular regulation. However, the effective integration of multiple omics modalities in a spatial context remains a major challenge. Here, we present SpaDDM, a spatial multi-omics integration framework based on directional diffusion models (DDMs), which supports spatial pattern identification, cross-omics alignment, and inter-and intracellular signaling flow analysis. SpaDDM employs DDM-based graph networks to learn omics-specific representations by jointly incorporating spatial coordinates and molecular measurements within each modality, followed by an attention mechanism to align features across modalities. We benchmarked SpaDDM on diverse spatial multi-omics datasets, including transcriptomics-epigenomics and transcriptomics-proteomics combinations across multiple tissues and species. SpaDDM consistently outperformed existing methods by more accurately deciphering spatial tissue patterns and effectively reducing the boundary noise between spatial regions. Moreover, the learned low-dimensional coembedded representations of individual cells serve as integral mediators for inferring the signaling flows that underlie spatial patterning. Finally, we demonstrated that SpaDDM alignment of complementary information across multi-omics layers facilitates cross-omics translation and significantly improves the prediction of cell state alignments.

Multiomics↗

PubMed2026

DPAS-Graph: adaptive spatial-feature relation learning for spatial RNA-to-protein prediction and virtual protein profiling.

Paired spatial multi-omics provides a supervised basis for learning RNA-protein correspondence in situ, but predicting protein abundance from spatial transcriptomic data alone remains challenging across tissue contexts and protein panels. Here, we present DPAS-Graph, an adaptive relation-learning framework for spatial RNA-to-protein prediction. Rather than directly merging spatial proximity and transcriptomic similarity as fixed graph priors, DPAS-Graph represents them as two relation channels on a shared edge support and updates their contributions during representation learning for protein prediction. Its Niche-Coupled Field Encoder combines layer-wise edge-relation modeling, intra-branch relation refinement, and cross-branch residual correction to learn spot representations for protein abundance prediction. In a leave-one-dataset-out benchmark across seven paired spatial multi-omics datasets, DPAS-Graph achieved lower aggregate prediction errors and improved spot-level agreement of protein expression profiles, with gains mainly reflected in error-based metrics and PCC-Spot. Spatial autocorrelation and protein-derived domain agreement analyses were further used to characterize the spatial behavior of the predicted protein maps. When applied to external RNA-only spatial sections, DPAS-Graph generated qualitatively interpretable marker-level virtual protein maps, illustrating its use as a complementary tool for protein-level interpretation of transcriptomics-only spatial data.

RNA↗

PubMed1990

Spatial interval discrimination with blurred lines: black and white are separate but not equal at multiple spatial scales.

We used Gaussian blurred lines of same- and opposite-polarity to measure the effects of blur on 3-line spatial interval discrimination (bisection). The results of our experiments can be summarized as follows. Spatial interval discrimination (3-line bisection) thresholds are proportional to the separation of the lines (i.e. Weber's law). At the optimal separation, spatial interval discrimination thresholds for same-polarity lines represent a "hyperacuity" as small as 2 sec arc. For same-polarity Gaussian blurred lines, over a wide range of the blur standard deviations (sigma), the optimal threshold occurs when the separation is approx. 2 sigma, and the optimal threshold is about 0.02 sigma, or a Weber fraction (delta s/s) of 0.01. For opposite-polarity lines, under conditions where same-polarity stimuli yield the best thresholds (at a separation approximately 2 sigma), spatial interval thresholds are an order of magnitude worse than that for same-polarity lines, suggesting that the localization of stimili of opposite-polarity is much worse than that of same-polarity stimuli over a wide range of spatial scales. At large separations, greater than about 5 sigma, spatial interval discrimination thresholds are more or less independent of both contrast and polarity. While hyperacuity is generally thought of in terms of the tiny spatial thresholds which are obtained at small separations with stimuli comprised of thin lines, the present results, and those of others, suggest that for same-polarity stimuli, hyperacuity thresholds are a general property of the visual system, occurring at many spatial scales. The present results also suggest that the poor localization of opposite-polarity lines occurs at multiple spatial scales, when the line separation is less than about five times the stimulus spread. We consider several models which can account for particular features of our data.

Contrast Sensitivity↗

PubMed1992

Contrast sensitivity as a function of spatial frequency, viewing distance and eccentricity with and without spatial noise.

Using computer graphics and a two-alternative forced-choice method we measured threshold contrast as a function of viewing distance, spatial frequency, and eccentricity for gratings with and without added, white two-dimensional spatial noise. Our experiments showed that in spatial noise contrast sensitivity was independent of viewing distance as long as contrast sensitivity was lower with noise than without. With increasing spatial frequency (f) the grating area (A) was reduced in order to keep the relative grating size (Af2) constant. At all spatial frequencies the test gratings thus had the same amount of detail and contour. Noise spectral density was reduced in direct proportion to grating area in order to keep the physical signal-to-noise ratio constant. An increase in spatial frequency was thus accompanied with reductions in grating area and noise spectral density similar to those produced by a corresponding increase in viewing distance. In agreement, contrast detection in spatial noise was found to be independent of spatial frequency as long as contrast sensitivity was lower with noise than without. The effect of increasing eccentricity on visual performance can be compensated for by reducing the viewing distance (M-scaling). Hence, without M-scaling the effect of increasing eccentricity is similar to that of increasing viewing distance. In agreement, we found that contrast sensitivity in spatial noise was independent of eccentricity as long as contrast sensitivity was lower with noise than without.

Adult↗

PubMed2025

scBSP: a fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data.

MOTIVATION: Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in 2D and 3D space. However, the limited sequencing depth, increasing spatial resolution, and growing spatial spots in spatial omics technologies present significant computational challenges in identifying biologically meaningful molecules with variable spatial distributions across various omics modalities. RESULTS: We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in large-scale spatial omics data. scBSP demonstrates significantly enhanced computational efficiency, processing high-resolution spatial omics data within seconds, and exhibits robust cross-platform performance by consistently identifying spatially variable features with high reproducibility across various sequencing platforms. AVAILABILITY AND IMPLEMENTATION: scBSP is available for download from R CRAN at https://cran.r-project.org/web/packages/scBSP/index.html and PyPI at https://pypi.org/project/scbsp/.

Software↗

PubMed1988

Spatial vision of the achromat: spatial frequency and orientation-specific adaptation.

1. The psychophysical technique of selective adaptation to stationary sine-wave gratings of varying spatial frequency and orientation was used to investigate the central processing of spatial information in the visual system of the complete achromat. 2. For adapting spatial frequencies of 1 and 2 cycles/deg, the spatial frequency and orientation selectivity of contrast threshold elevation is similar for achromatic and trichromatic vision. 3. For adapting frequencies below 1 cycle/deg, the achromat shows threshold elevations of normal magnitude with symmetrical spatial frequency and orientation tuning for adapting frequencies as low as 0.09 cycles/deg with 'bandwidth' estimates similar to those found at high frequencies in the trichromat. Below 0.66 cycles/deg no after-effect could be obtained in the trichromat, and the frequency tuning at 0.66 cycles/deg was skewed towards higher frequencies. 4. The interocular transfer of low-frequency adaptation in the achromat was 50%, which is the same value obtained at higher frequencies. 5. The time course of the decay of low spatial frequency adaptation in the achromat was similar to that found at higher frequencies. 6. Control experiments show no low-frequency adaptation in peripheral vision or in central vision in the dark-adapted trichromat indicating that low spatial frequency adaptation cannot be elicited through the rod system of the trichromat. 7. It is proposed that the observed range shift of adaptable spatial frequency mechanisms in the achromat's visual cortex is the result of an arrest at an early stage of sensory development. The visual cortex of the achromat is comparable, with respect to spatial processing, to that of the young, visually normal human infant.

Adaptation, Ocular↗

PubMed2026

Spatial clustering and transmission networks of multidrug-resistant tuberculosis in Rwanda: a national retrospective genomic and spatial epidemiological study.

BACKGROUND: Approximately 96% of rifampicin resistance/multidrug-resistant tuberculosis (RR/MDR-TB) cases in Rwanda result from direct transmission rather than acquired resistance. However, the nationwide spatial distribution and transmission dynamics of RR/MDR-TB remain poorly characterised. This study aims to analyse spatial patterns of RR/MDR-TB in Rwanda and explore relationships between spatial proximity and RR/MDR-TB strains' genetic relatedness. METHODS: We conducted a retrospective analysis of 249 confirmed RR-TB cases across Rwanda from 2017 to 2024, using the known geolocations of patients' residences. Spatial and space-time clustering was assessed using Kulldorff's scan statistics. Demographic and socioeconomic determinants were evaluated using multivariable regression. For 201 cases with whole-genome sequencing data, we performed transmission analysis using a 5-SNP threshold to define recent transmission clusters and investigated spatial relationships within genetically related strains. RESULTS: Significant spatial clustering of RR/MDR-TB was identified in 21 sectors, mainly in Nyarugenge, southern Gasabo and western Kicukiro (relative risk: 10.06; p<0.001). Our multivariable analysis showed that population density is positively associated with case notification rates. Molecular analysis revealed 88.5% of cases belonged to genotype clusters defined using a 12-SNP threshold, with 73.6% forming clusters at a strict 5-SNP threshold. Spatial K-function analysis of the six major clusters revealed heterogeneous transmission patterns, characterised by both tightly clustered outbreaks and regional transmission networks that spanned administrative boundaries. Most clusters (5/6) extended beyond Kigali, indicating that transmission networks operate across administrative divides. CONCLUSION: RR/MDR-TB in Rwanda shows significant spatial clustering with transmission occurring through both localised and regional networks. Integrating genomic and spatial data reveals transmission patterns that extend beyond household contacts and administrative boundaries. These findings underscore the need to implement geographically targeted interventions that address community-level transmission to control RR/MDR-TB in Rwanda effectively.

Rwanda↗

PubMed1987

Summation of very close spatial frequencies: the importance of spatial probability summation.

In accounting for pattern thresholds it is necessary to consider probability summation (or equivalent nonlinear pooling) not only across detectors selective for different spatial frequencies but also across detectors in different spatial positions. Interestingly, calculation on this basis shows that the amount of summation between components of closely similar spatial frequency in a large grating is primarily determined by the variation in sensitivity of detectors at different spatial locations and is little affected by the spatial-frequency bandwidths of the detectors. To test this conclusion, we have measured the amount of summation between two components with spatial frequencies very close to 6 c/deg in two regions of the visual field: in the fovea (a region where sensitivity is very non-uniform) and in the periphery (where sensitivity is nearly uniform). As predicted, there was less summation between components of very closely similar frequencies in the nearly-uniform peripheral region than in the non-uniform foveal region. Measurements in the fovea of the summation of two components with spatial frequencies very near to either 1.5, 6 or 24 c/deg showed, as expected, that the amount of summation depends upon the ratio of the frequencies rather than their absolute difference, indicating that probability summation takes place over an area related to spatial frequency rather than over a fixed area.

Female↗

PubMed1988

Patterns of visual-spatial performance and 'spatial ability': dissociation of ethnic and sex differences.

Is there a common basis for the ethnic and sex differences that are characteristically obtained on psychometric tests of spatial ability? Three experiments approached this question by observing subject differences in the recognition and reconstruction of visual-spatial displays. The pattern of performance on these experimental tasks was compared with that on a traditional spatial ability test. In the first experiment, two samples of 40 students, balanced for sex, from Zimbabwe and Scotland respectively, attempted a forced-choice recognition task for meaningful scenes. Both ethnic groups and both sexes showed equivalent performance. The same subjects then undertook a task involving the reproduction of an arrangement of blocks into two-dimensional plan and elevation views. On this task, involving spatial reorientation, the Zimbabweans made over three times as many errors as the Scots. In a third experiment the requirement for spatial reorientation was added to the original recognition task and this was performed by a further 40 subjects. A significant difference between ethnic groups now emerged and this effect covaried with spatial ability. Again, however, no sex difference was observed. The overall pattern of results points to spatial reorientation as a major factor in the cross-ethnic differences. The absence of a sex difference on the experimental tasks contrasts with its appearance in both samples on the spatial ability test and represents a puzzling obstacle to our current understanding. This dissociation of sex and ethnic differences provides evidence against the hypothesis that they stem from the same source.

Adult↗

PubMed1991

Object spatial frequencies, retinal spatial frequencies, noise, and the efficiency of letter discrimination.

To determine which spatial frequencies are most effective for letter identification, and whether this is because letters are objectively more discriminable in these frequency bands or because can utilize the information more efficiently, we studied the 26 upper-case letters of English. Six two-octave wide filters were used to produce spatially filtered letters with 2D-mean frequencies ranging from 0.4 to 20 cycles per letter height. Subjects attempted to identify filtered letters in the presence of identically filtered, added Gaussian noise. The percent of correct letter identifications vs s/n (the root-mean-square ratio of signal to noise power) was determined for each band at four viewing distances ranging over 32:1. Object spatial frequency band and s/n determine presence of information in the stimulus; viewing distance determines retinal spatial frequency, and affects only ability to utilize. Viewing distance had no effect upon letter discriminability: object spatial frequency, not retinal spatial frequency, determined discriminability. To determine discrimination efficiency, we compared human discrimination to an ideal discriminator. For our two-octave wide bands, s/n performance of humans and of the ideal detector improved with frequency mainly because linear bandwidth increased as a function of frequency. Relative to the ideal detector, human efficiency was 0 in the lowest frequency bands, reached a maximum of 0.42 at 1.5 cycles per object and dropped to about 0.104 in the highest band. Thus, our subjects best extract upper-case letter information from spatial frequencies of 1.5 cycles per object height, and they can extract it with equal efficiency over a 32:1 range of retinal frequencies, from 0.074 to more than 2.3 cycles per degree of visual angle.

Adult↗

PubMed2026

Spatial isoform sequencing at single-cell resolution reveals cell-type-specific spatial isoform variability in multiple brain cell types.

Spatial long-read technologies are increasingly common but usually lack single-cell resolution. This leaves unanswered whether spatially variable isoforms reflect variability within one cell type or differences in region-specific cell-type composition. Here, we developed Spl-ISO-Seq2 (500-nm resolution) and accompanying software, Spl-IsoQuant-2 and Spl-IsoFind, enabling long-read sequencing of >450 million barcodes versus 80,000 previously. Applying this to the adult mouse brain, we compared differential isoform abundance between known regions and spatial isoform patterns independent of predefined regions. Both identified overlapping hits, for example, Rps24 in oligodendrocytes. For known Snap25 spatial isoform variation, we show that it occurs in excitatory neurons. The region-agnostic approach also uncovered patterns missed by region-based comparisons, for example, for Ighm. Notably, many spatial isoform signals are not driven by cell-type composition alone. Finally, our software is applicable to many spatial and single-cell protocols, demonstrating reproducibility between platforms (for example, Visium HD/Stereo-seq). Overall, our experimental/analytical methods enable a submicron-resolution-isoform view and open avenues for spatial isoform disease research.

Animals↗

PubMed2025

Spatial mutual nearest neighbors for spatial transcriptomics data.

MOTIVATION: Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. RESULTS: Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. AVAILABILITY AND IMPLEMENTATION: Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).

Algorithms↗

PubMed2026

SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.

MOTIVATION: Understanding cellular states and regulatory programs requires integrative analysis of multiple omics layers. Although recent spatial sequencing technologies allow molecular profiling of cells within their tissue context, paired spatial multi-omics assays are still limited by technical complexity and cost. This creates a pressing need for diagonal integration methods that enable joint analysis of unpaired spatial omics datasets. RESULTS: We propose SIVA, a deep generative framework based on Spatially-Informed Variational Autoencoders with Anchor Guidance, for diagonal integration of spatial multi-modal data. SIVA employs modality-specific variational autoencoders (VAEs) with a hybrid latent embedding that integrates Gaussian process and standard Gaussian priors, enabling joint modeling of spatially structured variation and dominant underlying data distributions across modalities. To facilitate cross-modal alignment in the absence of one-to-one cell correspondence, SIVA adopts a dual integration strategy combining global distribution alignment via Maximum Mean Discrepancy and local correspondence guidance using mutual nearest neighbor anchors. Extensive experiments across multiple cross-slice integration scenarios demonstrate that SIVA achieves robust and accurate integration of unpaired spatial omics datasets, consistently outperforming existing methods. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/PelenJiang/SIVA.

Autoencoder↗

PubMed1990

The effects of temporal modulation and spatial location on the perceived spatial frequency of visual patterns.

The perceived spatial frequency of a visual pattern can increase when a pattern drifts or is presented at a peripheral visual field location, as compared with a foveally viewed, stationary pattern. We confirmed previously reported effects of motion on foveally viewed patterns and of location on stationary patterns and extended this analysis to the effect of motion on peripherally viewed patterns and the effect of location on drifting patterns. Most central to our investigation was the combined effect of temporal modulation and spatial location on perceived spatial frequency. The group data, as well as the individual sets of data for most observers, are consistent with the mathematical concept of separability for the effects of temporal modulation and spatial location on perceived spatial frequency. Two qualitative psychophysical models suggest explanations for the effects. Both models assume that the receptive-field sizes of a set of underlying psychophysical mechanisms monotonically change as a function of temporal modulation or visual field location, whereas the perceptual labels attached to a set of channels remain invariant. These models predict that drifting or peripheral viewing of a pattern will cause a shift in the perceived spatial frequency of the pattern to a higher apparent spatial frequency.

Adult↗