Characterization of spatial homogeneous regions in tissues with concordex
Kayla C. JacksoniD, A. Sina BooeshaghiiD, Ángel Gálvez-MerchániD, Lambda MosesiD, Tara ChariiD, Alexandra Kim, Orhan Hosten-MittermaieriD, Lior S. PachteriD
Abstract
The rapid advancement of spatially resolved transcriptomics (SRT) technologies has facilitated exploration of how gene expression varies across tissues. However, identifying spatially variable genes remains challenging due to confounding variation introduced by the spatial distribution of cell types. We introduce a new approach to identifying spatial domains that are homogeneous with respect to cell-type composition that facilitates the decomposition of gene expression patterns by cell-type and spatial variation. Our method, called concordex, is efficient and effective across technological platforms and tissue types, and using several biological datasets we show that it can be used to identify genes with subtle variation patterns that are missed when considering only cell-type variation, or spatial variation, alone. The con-cordex tool is freely available at https://github.com/pachterlab/concordexR.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 8
- FWCI
- 4.18
- Field percentile
- top 5%
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- DESpace: spatially variable gene detection via differential expression testing of spatial clusters
Peiying Cai, Mark D. Robinson, Simone Tiberi·BioinformaticsPeer-reviewed·Jan 15, 2024
- SMART: reference-free deconvolution for spatial transcriptomics using marker-gene-assisted topic models
Chen Xi Yang, Don D. Sin, RT Ng·bioRxiv·Jun 20, 2023
- DSTG: Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence
Qianqian Song, Jing Su·Briefings in BioinformaticsPeer-reviewed·Dec 18, 2020
- Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
Hongyu Zheng, Hirak Sarkar, Benjamin J. Raphael·Genome ResearchPeer-reviewed·Dec 1, 2025
- Robust decomposition of cell type mixtures in spatial transcriptomics
Dylan M. Cable, Evan M. Murray, … Rafael A. Irizarry·Nature BiotechnologyPeer-reviewed·Feb 18, 2021
- STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing
Dongqing Sun, Zhaoyang Liu, … Chenfei Wang·Nucleic Acids ResearchPeer-reviewed·Feb 19, 2022