利用迁移学习提升空间转录组细胞异质性解析精度
TransST: Transfer Learning Embedded Spatial Factor Modeling of Spatial Transcriptomics Data
- 引入外部标注细胞数据，通过迁移学习增强目标数据的细胞层级解析
- 在乳腺癌数据中识别出5个生物学有意义的细胞簇，包括原位与侵袭性癌亚型
- 能有效区分脂肪组织与结缔组织，优于现有方法，适合生物标志物挖掘
空间转录组技术可同时捕捉器官中完整的RNA表达谱及其空间位置信息，但受限于较低分辨率和较浅测序深度，难以可靠提取真实生物信号。为此，我们提出一种新型迁移学习框架TransST，通过自适应融合外部来源的细胞标注信息，推断目标空间转录组数据的细胞异质性。在多个真实研究和模拟场景中的应用表明，该方法显著优于现有技术。例如，在乳腺癌研究中，TransST成功识别出五个具有生物学意义的细胞簇，包括原位癌和侵袭性癌两个亚型；此外，只有TransST能准确区分脂肪组织与结缔组织。综上，TransST在识别细胞亚群及检测相关驱动生物标志物方面兼具有效性与鲁棒性。
原文摘要 · Abstract (English)
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, limitations of the technology such as the relatively low resolution and comparatively insufficient sequencing depth make it difficult to reliably extract real biological signals from these data. To alleviate this challenge, we propose a novel transfer learning framework, referred to as TransST, to adaptively leverage the cell-labeled information from external sources in inferring cell-level heterogeneity of a target spatial transcriptomics data. Results: Applications in several real studies as well as a number of simulation settings show that our approach significantly improves existing techniques. For example, in the breast cancer study, TransST successfully identifies five biologically meaningful cell clusters, including the two subgroups of cancer in situ and invasive cancer; in addition, only TransST is able to separate the adipose tissues from the connective issues among all the studied methods. Conclusions: In summary, the proposed method TransST is both effective and robust in identifying cell subclusters and detecting corresponding driving biomarkers in spatial transcriptomics data.
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