A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

Liu, Teng; Fang, Zhao-Yu; Zhang, Zongbo; Yu, Yongxiang; Li, Min; Yin, Ming-Zhu

doi:10.1016/j.csbj.2023.11.055

Cited by 9 publications

(2 citation statements)

References 73 publications

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“…To quantify the reliability of simulated cell groups in scRNA-seq data or spatial domains in SRT data, we adopted six metrics that are commonly used to evaluate different aspects of clustering performance [ 59 ], including average silhouette width (ASW) [ 60 ], Dunn index, Connectivity [ 61 ], Davies-Bouldin index (DB index), clustering deviation index (CDI) [ 62 ], and ROUGE [ 63 ]. The detailed information of the metrics is described in Additional file 1 : Supplementary Note Sect.…”

Section: Methodsmentioning

confidence: 99%

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

Duo,

Li,

Lan

et al. 2024

Genome Biol

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Background Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines. Results We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe (https://github.com/duohongrui/simpipe; https://doi.org/10.5281/zenodo.11178409), and an online tool Simsite (https://www.ciblab.net/software/simshiny/) for data simulation. Conclusions No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.

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Section: Methodsmentioning

confidence: 99%

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

Duo,

Li,

Lan

et al. 2024

Genome Biol

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“…2019 ; Liu et al . 2024 ). Unlike other common methods which failed to utilize the spatial coordinates and histology image information, GNNs enable learning from a bucket of gene expression data, spot spatial coordinates, i .…”

Section: Graph Neural Network On Spatial Transcriptomicsmentioning

confidence: 99%

Foundation models in molecular biology

Si,

Zou,

Gao

et al. 2024

swwlxb

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Determining correlations between molecules at various levels is an important topic in molecular biology. Large language models have demonstrated a remarkable ability to capture correlations from large amounts of data in the field of natural language processing as well as image generation, and correlations captured from data using large language models can also be applicable to solving a wide range of specific tasks, hence large language models are also referred to as foundation models. The massive amount of data that exists in the field of molecular biology provides an excellent basis for the development of foundation models, and the recent emergence of foundation models in the field of molecular biology has really pushed the entire field forward. We summarize the foundation models developed based on RNA sequence data, DNA sequence data, protein sequence data, single-cell transcriptome data, and spatial transcriptome data respectively, and further discuss the research directions for the development of foundation models in molecular biology.

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Graph pooling in graph neural networks: methods and their applications in omics studies

Wang,

Hou,

Sheng

et al. 2024

Artif Intell Rev

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A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics

Cited by 9 publications

References 73 publications

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

Foundation models in molecular biology

Graph pooling in graph neural networks: methods and their applications in omics studies

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