.png){kind=icon}
Return
A multiperspective evaluation framework of spatial transcriptomics clustering methods
.png){kind=icon}
OA
AI
DOI：10.1093/nargab/lqag075.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Spatial transcriptomics (ST) allows the exploration of gene expression within tissue microenvironments, driving the development of multiple computational approaches for spatial domain identification. Evaluating these methods typically relies on label-dependent metrics, such as contingency matrices and information-theoretic measures, which require ground-truth annotations, and label-independent metrics, which assess transcriptomic similarity or spatial organization. However, annotations are often incomplete or unavailable, while label-independent metrics fail to jointly evaluate the integration of transcriptomic and spatial information, a core feature of ST clustering methods. To address these limitations, we introduce MultimetricST, a Python-based framework that provides a unified, flexible evaluation strategy integrating both cutting-edge and state-of-the-art label-dependent and label-independent metrics. We applied MultimetricST on two generated synthetic datasets and thirteen datasets derived from seven ST technologies to systematically evaluate the spatial domains identified by eleven state-of-the-art deep learning methods. Our framework highlights the strengths and limitations of each assessment strategy, providing an accessible and reproducible tool for comparative and robust evaluation, and method selection.
Journal
N
IF:
2.8
Papers:
259
Citations:
0
Organization
Cited Papers
ShareSaveSpatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.png)
Cell
OAAI
CellIF0
OAAIShareSaveData Filtering and Its Prioritization in Pipelines for Spatial Segmentation of Mass Spectrometry ImagingANALYTICAL CHEMISTRY
ANALYTICAL CHEMISTRYIF6.7
PREAI
ShareSaveShareSaveSRTsim: spatial pattern preserving simulations for spatially resolved transcriptomicsGENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAIShareSaveSpatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphSTNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveconST: an interpretable multi-modal contrastive learning framework for spatial transcriptomicsOAAI
IF0
OAAIShareSaveGeneralizing RNA velocity to transient cell states through dynamical modelingNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
OAAIShareSave
View more