Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA,
Pac Symp Biocomput. 2022;27:175-186.
Spatially resolved characterization of the transcriptome and proteome promises to provide further clarity on cancer pathogenesis and etiology, which may inform future clinical practice through classifier development for clinical outcomes. However, batch effects may potentially obscure the ability of machine learning methods to derive complex associations within spatial omics data. Profiling thirty-five stage three colon cancer patients using the GeoMX Digital Spatial Profiler, we found that mixed-effects machine learning (MEML) methods† may provide utility for overcoming significant batch effects to communicate key and complex disease associations from spatial information. These results point to further exploration and application of MEML methods within the spatial omics algorithm development life cycle for clinical deployment.
空间分辨的转录组和蛋白质组学特征有望进一步阐明癌症的发病机制和病因，通过开发用于临床结果的分类器，为未来的临床实践提供信息。然而，批次效应可能会削弱机器学习方法从空间组学数据中得出复杂关联的能力。通过使用 GeoMX 数字空间分析器对 35 名 III 期结肠癌患者进行分析，我们发现混合效应机器学习 (MEML) 方法† 可能有助于克服显著的批次效应，从而从空间信息中传递关键和复杂的疾病关联。这些结果表明，在空间组学算法开发的生命周期中，进一步探索和应用 MEML 方法对于临床应用具有重要意义。
