arxiv: 2604.18742 · v1 · submitted 2026-04-20 · 📊 stat.AP · stat.ME

Recognition: unknown

JASPER: Joint Bayesian Analysis of Spatial Expression via Regression

Bani K. Mallick, Pritam Dey, Rajarshi Guhaniyogi, Yang Ni

Pith reviewed 2026-05-10 03:13 UTC · model grok-4.3

classification 📊 stat.AP stat.ME

keywords spatial transcriptomicsBayesian modelinggene expressionspatial analysisinter-gene correlationsbasis function regressionpathway enrichmentfalse positive reduction

0 comments

The pith

JASPER uses joint Bayesian basis function regression to model spatial gene expression across multiple genes simultaneously, improving detection accuracy over independent methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents JASPER as a Bayesian framework designed to analyze spatially resolved transcriptomics data by jointly modeling expression patterns for many genes at once. Existing approaches often analyze genes separately and depend on preset spatial covariance structures, which can lead to incorrect identifications of which genes vary by location. JASPER instead employs spatial basis functions within a regression setup and incorporates inter-gene dependencies through Bayesian modeling. A sympathetic reader would care because accurate detection of spatially varying genes is essential for understanding tissue organization in diseases like cancer. If successful, this could lead to more reliable insights from spatial transcriptomic experiments.

Core claim

JASPER is a Bayesian framework that jointly models spatial expression patterns across multiple genes using a spatial basis function regression approach. It addresses limitations in existing methods by accounting for inter-gene correlations without relying on predefined spatial covariance kernels. Demonstrations on real-world spatial transcriptomic datasets, including a human breast cancer dataset, and simulation experiments show superior performance. The method identifies genes with stronger spatial correlation and greater biological relevance, as confirmed through overlap comparisons, enrichment analysis, and pathway analysis with independent biological databases.

What carries the argument

The joint Bayesian spatial basis function regression that couples multiple gene expressions through shared spatial basis expansions and hierarchical priors to capture both spatial variation and inter-gene correlations.

If this is right

Identified genes exhibit higher spatial correlation strengths than those from competing methods.
Greater overlap with independently validated spatially relevant genes.
Improved enrichment in relevant biological pathways and processes.
Lower false positive and false negative rates in both simulated and real data scenarios.
Enhanced interpretability of spatial patterns in complex tissues.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar joint modeling could apply to other spatially resolved omics technologies beyond transcriptomics.
The approach might integrate with non-spatial single-cell data for hybrid analyses.
Adaptive selection of basis functions could further reduce sensitivity to model choices.
Results suggest potential for better biomarker discovery in spatial contexts.

Load-bearing premise

That jointly modeling inter-gene correlations through spatial basis function regression will reduce false positives and negatives without adding biases from the specific basis functions or Bayesian prior choices.

What would settle it

Running JASPER and existing methods on a new independent spatial transcriptomics dataset and checking if JASPER's gene list has significantly lower overlap with known non-spatial genes or fails to show stronger pathway enrichments.

Figures

Figures reproduced from arXiv: 2604.18742 by Bani K. Mallick, Pritam Dey, Rajarshi Guhaniyogi, Yang Ni.

**Figure 2.** Figure 2: Directed acyclic graph representation of the proposed JASPER model for two [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** Figure 3: Simulation-based comparison of JASPER, SPARK, and SpatialDE in terms [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

**Figure 4.** Figure 4: Normalized spatial expression of genes COX6C (a-b) and EFNA1 (c - d) in two [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗

**Figure 5.** Figure 5: (a)-(c) Expression patterns of three genes from the STARmap dataset. (a) [PITH_FULL_IMAGE:figures/full_fig_p023_5.png] view at source ↗

**Figure 6.** Figure 6: (a) - (d) Some expression patterns of different genes from the Mouse olfactory [PITH_FULL_IMAGE:figures/full_fig_p026_6.png] view at source ↗

read the original abstract

Spatially resolved transcriptomics is a fast-developing set of technologies that enables the measurement of localized gene expression across spatial locations in a sample. Detecting spatially varying genes is critical for analyzing such data, yet existing methods often fail to account for inter-gene correlations, leading to inflated false positive and false negative rates. Additionally, most prominent methods rely on predefined spatial covariance kernels, making them sensitive to the complexity of spatial expression patterns. Motivated by a human breast cancer dataset, we address these limitations in existing literature through JASPER (Joint Bayesian Analysis of SPatial Expression via Regression), a Bayesian framework that jointly models spatial expression patterns across multiple genes using a spatial basis function regression approach. We demonstrate the superior performance of JASPER compared to existing methods in several real-world spatial transcriptomic datasets and supporting simulation experiments. JASPER identifies genes with stronger spatial correlation and greater biological relevance, as validated by overlap comparison, enrichment analysis, and pathway analysis using independent biological databases. Our results highlight the ability of JASPER to improve the statistical and biological interpretability of spatial transcriptomics data, making it a powerful tool for uncovering spatial gene expression patterns in complex biological systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

JASPER offers a joint Bayesian basis-function regression for multi-gene spatial transcriptomics that targets inter-gene correlations and kernel limitations, but the performance gains need concrete numbers to judge their size.

read the letter

JASPER is a joint Bayesian model that fits spatial expression across genes together using basis functions rather than analyzing genes separately or locking in a covariance kernel. The authors start from the practical problem in breast cancer data where single-gene methods miss correlated spatial signals and produce too many errors. The joint setup and the switch to basis functions are the actual moves that distinguish it from prior work in the area. They run simulations and apply the method to several real spatial transcriptomics datasets, then check the output genes against independent databases for enrichment and pathway overlap. Those biological checks are a reasonable way to add credibility beyond pure statistical metrics. The framework is clearly laid out and the motivation is tied to observable shortcomings in existing tools. The soft spots sit in the validation. The abstract asserts lower false positive and false negative rates plus stronger biological relevance, yet the summary gives no effect sizes, no table of error rates, and no direct head-to-head numbers against the main competitors. Without those, it is hard to know whether the joint modeling delivers a meaningful lift or whether the basis choice and priors are carrying part of the result. A reader would also want to see how sensitive the findings are to the number and placement of basis functions. This paper is for computational biologists and statisticians who work with spatial transcriptomics and want a method that respects gene correlations. Anyone running analyses on tissue samples with spatial resolution could test it on their own data. The thinking is coherent and the literature engagement is honest, so the work deserves a serious referee to examine the implementation, the simulation design, and the quantitative comparisons. I would send it to peer review with the expectation that the authors supply the missing performance details and robustness checks.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces JASPER, a Bayesian framework for jointly modeling spatial gene expression patterns across multiple genes via spatial basis function regression. It claims this approach accounts for inter-gene correlations and avoids sensitivity to predefined covariance kernels, yielding superior performance over existing methods on real spatial transcriptomic datasets (including a motivating human breast cancer example) and supporting simulations, with validation via overlap comparisons, enrichment analysis, and pathway analysis against independent biological databases.

Significance. If the quantitative claims hold, JASPER would represent a meaningful advance in spatial transcriptomics analysis by reducing false positive and false negative rates through explicit joint modeling of correlations, while offering greater flexibility than kernel-based alternatives. This could improve the reliability of identifying biologically relevant spatially patterned genes in complex tissues.

major comments (3)

Abstract: the central claim of 'superior performance' and 'stronger spatial correlation' is asserted without any quantitative metrics (e.g., precision-recall, AUC, or FDR values from simulations or real-data comparisons), making it impossible to evaluate the magnitude or statistical significance of the reported improvements.
Methods (basis construction and prior specification): the paper must demonstrate, via sensitivity checks or explicit equations, that the chosen spatial basis functions and joint priors do not offset the intended reduction in FP/FN rates; without this, the weakest assumption flagged in the review remains untested and load-bearing for the main claim.
Results (simulation and real-data sections): tables or figures reporting direct head-to-head error rates, overlap statistics, and enrichment p-values against the cited competing methods are required to substantiate the performance and biological-relevance assertions.

minor comments (1)

Abstract: consider adding one sentence summarizing the number of datasets, simulation settings, and key performance deltas to give readers an immediate sense of scale.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We are grateful to the referee for their insightful comments, which have prompted us to clarify and strengthen several aspects of the manuscript. Below, we provide a point-by-point response to the major comments.

read point-by-point responses

Referee: Abstract: the central claim of 'superior performance' and 'stronger spatial correlation' is asserted without any quantitative metrics (e.g., precision-recall, AUC, or FDR values from simulations or real-data comparisons), making it impossible to evaluate the magnitude or statistical significance of the reported improvements.

Authors: We concur that the abstract would benefit from quantitative support for the claims of superior performance. Accordingly, in the revised manuscript, we will revise the abstract to include key quantitative metrics, such as the AUC from simulation experiments and the percentage overlap with known spatially varying genes in real data, along with comparisons to baseline methods. revision: yes
Referee: Methods (basis construction and prior specification): the paper must demonstrate, via sensitivity checks or explicit equations, that the chosen spatial basis functions and joint priors do not offset the intended reduction in FP/FN rates; without this, the weakest assumption flagged in the review remains untested and load-bearing for the main claim.

Authors: In the Methods section, we have detailed the construction of the spatial basis functions and the specification of the joint priors. To address the concern regarding their potential impact on false positive and false negative rates, we will conduct and report sensitivity analyses in the revised version. These will include varying the number of basis functions and the hyperparameters of the priors, with results demonstrating that the performance advantages persist across these choices. revision: yes
Referee: Results (simulation and real-data sections): tables or figures reporting direct head-to-head error rates, overlap statistics, and enrichment p-values against the cited competing methods are required to substantiate the performance and biological-relevance assertions.

Authors: The Results section presents validation through overlap comparisons, enrichment analysis, and pathway analysis. To provide more direct substantiation, we will include additional tables and figures in the revised manuscript that report head-to-head error rates (such as precision-recall and FDR) from the simulations and quantitative overlap statistics with associated p-values for enrichment analyses against the competing methods. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces JASPER as a Bayesian spatial basis-function regression model that jointly accounts for inter-gene correlations, with performance evaluated on independent real datasets, simulations, and external biological databases for validation. No equations, basis definitions, or claims in the abstract reduce by construction to fitted inputs or self-citations; the central modeling choice and superiority claims rest on standard Bayesian priors and regression rather than tautological reparameterization. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based only on the abstract, specific free parameters, axioms, or invented entities cannot be identified. The approach relies on Bayesian priors for the regression model and spatial basis functions, which are typically standard but details are not provided.

pith-pipeline@v0.9.0 · 5515 in / 1134 out tokens · 48807 ms · 2026-05-10T03:13:13.066484+00:00 · methodology

discussion (0)

Reference graph

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