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BFAST：用于零膨胀空间转录组学数据的贝叶斯因子分析的联合维度降低和空间聚类。

BFAST: joint dimension reduction and spatial clustering with Bayesian factor analysis for zero-inflated spatial transcriptomics data.

机构信息

BGI-Research, 313, Gaoteng Avenue, Jiulongpo, Chongqing 400039, China.

BGI-Research, 9, Yunhua Road, Yantian, Shenzhen 518083, China.

出版信息

Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae594.

DOI:10.1093/bib/bbae594
PMID:39552067
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11570543/
Abstract

The development of spatially resolved transcriptomics (ST) technologies has made it possible to measure gene expression profiles coupled with cellular spatial context and assist biologists in comprehensively characterizing cellular phenotype heterogeneity and tissue microenvironment. Spatial clustering is vital for biological downstream analysis. However, due to high noise and dropout events, clustering spatial transcriptomics data poses numerous challenges due to the lack of effective algorithms. Here we develop a novel method, jointly performing dimension reduction and spatial clustering with Bayesian Factor Analysis for zero-inflated Spatial Transcriptomics data (BFAST). BFAST has showcased exceptional performance on simulation data and real spatial transcriptomics datasets, as proven by benchmarking against currently available methods. It effectively extracts more biologically informative low-dimensional features compared to traditional dimensionality reduction approaches, thereby enhancing the accuracy and precision of clustering.

摘要

空间分辨转录组学（ST）技术的发展使得测量与细胞空间背景相关的基因表达谱成为可能，并帮助生物学家全面描述细胞表型异质性和组织微环境。空间聚类对于生物学的下游分析至关重要。然而，由于存在大量噪声和缺失事件，缺乏有效的算法使得聚类空间转录组学数据面临着诸多挑战。在这里，我们开发了一种新方法，即使用贝叶斯因子分析对零膨胀空间转录组学数据（BFAST）进行降维和空间聚类。BFAST 在模拟数据和真实空间转录组学数据集上的表现非常出色，与现有的方法相比，它通过基准测试证明了这一点。与传统的降维方法相比，它可以有效地提取更多具有生物学意义的低维特征，从而提高聚类的准确性和精度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/29e77617b00e/bbae594f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/14d6e2075ae6/bbae594f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/98371d5da82b/bbae594f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/6eb78710ee56/bbae594f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/54a0defd6435/bbae594f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/29e77617b00e/bbae594f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/14d6e2075ae6/bbae594f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/98371d5da82b/bbae594f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/6eb78710ee56/bbae594f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/54a0defd6435/bbae594f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/360b/11570543/29e77617b00e/bbae594f5.jpg

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本文引用的文献

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Nat Commun. 2023 Jul 10;14(1):4059. doi: 10.1038/s41467-023-39748-z.
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An invasive zone in human liver cancer identified by Stereo-seq promotes hepatocyte-tumor cell crosstalk, local immunosuppression and tumor progression.Stereo-seq 鉴定的人类肝癌侵袭区促进了肝细胞与肿瘤细胞的串扰、局部免疫抑制和肿瘤进展。
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Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST.
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Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.使用DNA纳米球图案化阵列构建的小鼠器官发生时空转录组图谱。
Cell. 2022 May 12;185(10):1777-1792.e21. doi: 10.1016/j.cell.2022.04.003. Epub 2022 May 4.
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Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder.利用自适应图注意自动编码器从空间分辨转录组学中破译空间域。
Nat Commun. 2022 Apr 1;13(1):1739. doi: 10.1038/s41467-022-29439-6.
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Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data.单细胞 RNA-seq 和空间转录组学数据的联合降维和聚类分析。
Nucleic Acids Res. 2022 Jul 8;50(12):e72. doi: 10.1093/nar/gkac219.
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SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes.SC-MEB：基于经验贝叶斯的隐马尔可夫随机场空间聚类。
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