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Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide…

Genomics · Quantitative Biology 2024-08-02 Boyi Guo , Wodan Ling , Sang Ho Kwon , Pratibha Panwar , Shila Ghazanfar , Keri Martinowich , Stephanie C. Hicks

With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The…

Genomics · Quantitative Biology 2024-04-11 Sikta Das Adhikari , Jiaxin Yang , Jianrong Wang , Yuehua Cui

Spatial variable genes (SVGs) reveal critical information about tissue architecture, cellular interactions, and disease microenvironments. As spatial transcriptomics (ST) technologies proliferate, accurately identifying SVGs across diverse…

Applications · Statistics 2025-10-21 Jiawen Chen , Jinwei Zhang , Dongshen Peng , Yutong Song , Aitong Ruan , Yun Li , Didong Li

In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 33…

Quantitative Methods · Quantitative Biology 2024-10-04 Guanao Yan , Shuo Harper Hua , Jingyi Jessica Li

Spatially resolved transcriptomics (ST) measures gene expression along with the spatial coordinates of the measurements. The analysis of ST data involves significant computation complexity. In this work, we propose gene expression…

Genomics · Quantitative Biology 2022-05-24 Zhuoyan Xu , Kris Sankaran

The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context.…

Applications · Statistics 2025-02-20 Bencong Zhu , Alberto Cassese , Marina Vannucci , Michele Guindani , Qiwei Li

Spatial transcriptomics measures the expression of thousands of genes in a tissue sample while preserving its spatial structure. This class of technologies has enabled the investigation of the spatial variation of gene expressions and their…

Methodology · Statistics 2025-10-23 Andrea Sottosanti , Davide Risso , Francesco Denti

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of…

Quantitative Methods · Quantitative Biology 2022-02-08 Boxiang Liu , Yanjun Li , Liang Zhang

Spatial Transcriptomics is a novel technology that aligns histology images with spatially resolved gene expression profiles. Although groundbreaking, it struggles with gene capture yielding high corruption in acquired data. Given potential…

Computer Vision and Pattern Recognition · Computer Science 2024-09-30 Gabriel Mejia , Daniela Ruiz , Paula Cárdenas , Leonardo Manrique , Daniela Vega , Pablo Arbeláez

Spatial transcriptomics is a modern sequencing technology that allows the measurement of the activity of thousands of genes in a tissue sample and map where the activity is occurring. This technology has enabled the study of the so-called…

Methodology · Statistics 2022-09-15 Andrea Sottosanti , Davide Risso

Spatial transcriptomics provides an unprecedented perspective for deciphering tissue spatial heterogeneity. However, high-resolution spatial transcriptomic technology remains constrained by limited gene coverage, technical complexity, and…

Biomolecules · Quantitative Biology 2026-05-19 Xinlei Huang , Weihao Dai , Zijun Qin , Xin Yu , Di Wang , Yanran Liu , Lixin Cheng , Xubin Zheng

Advances in spatial transcriptomics (ST) technologies enable systematic molecular characterization of tumor microenvironment, tumor gradients and gene regulatory networks. Cancer progression is known to vary along pathological gradients,…

Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses…

Spatially Resolved Transcriptomics (SRT) is a cutting-edge technique that captures the spatial context of cells within tissues, enabling the study of complex biological networks. Recent graph-based methods leverage both gene expression and…

Machine Learning · Computer Science 2025-06-24 Yunhak Oh , Junseok Lee , Yeongmin Kim , Sangwoo Seo , Namkyeong Lee , Chanyoung Park

With the rapid advancement of Spatial Resolved Transcriptomics (SRT) technology, it is now possible to comprehensively measure gene transcription while preserving the spatial context of tissues. Spatial domain identification and gene…

Genomics · Quantitative Biology 2024-08-14 Donghai Fang , Fangfang Zhu , Dongting Xie , Wenwen Min

Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed…

Quantitative Methods · Quantitative Biology 2023-03-30 Alex J. Lee , Robert Cahill , Reza Abbasi-Asl

Spatial Transcriptomics (ST) profiles thousands of gene expression values at discrete spots with precise coordinates on tissue sections, preserving spatial context essential for clinical and pathological studies. With rising sequencing…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Yishun Zhu , Jiaxin Qi , Jian Wang , Yuhua Zheng , Jianqiang Huang

Spatial transcriptomics (ST) technologies enable transcriptome-wide gene expression profiling while preserving spatial resolution, offering unprecedented opportunities to uncover complex spatial structures. Due to the ultra-high…

Applications · Statistics 2026-03-11 Changhu Wang , Qiyun Huang , Zihao Chen , Jin Liu , Ruibin Xi

In order to understand the complexities of cellular biology, researchers are interested in two important metrics: the genetic expression information of cells and their spatial coordinates within a tissue sample. However, state-of-the art…

Machine Learning · Computer Science 2023-11-02 J. Ding , S. N. Zaman , P. Y. Chen , D. Wang

Background: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However,…

Genomics · Quantitative Biology 2025-04-18 Shuo Shuo Liu , Shikun Wang , Yuxuan Chen , Anil K. Rustgi , Ming Yuan , Jianhua Hu
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