TED:消除肿瘤微环境卷积的完全贝叶斯方法

上传者: 42104906 | 上传时间: 2022-04-26 16:44:50 | 文件大小: 59.15MB | 文件类型: ZIP
TED(现称为BayesPrism) 使用统计边际化(BayesPrism)推断贝叶斯细胞比例重建:肿瘤微环境组成和基因表达的完全贝叶斯推断。 BayesPrism由反卷积模块和嵌入学习模块组成。去卷积模块利用来自scRNA-seq的细胞类型特异性表达谱,并实施完全贝叶斯推断,以根据肿瘤样品的大量RNA-seq表达共同估算细胞类型组成和细胞类型特异性基因表达的后验分布。嵌入学习模块使用期望最大化(EM)来使用肿瘤途径的线性组合来近似肿瘤表达,同时以反卷积模块估算的非肿瘤细胞的表达和分数为条件。 v1.1:添加了新功能,允许使用从scRNA-seq数据(例如,通过更精细的聚类)获得的细胞亚型/细胞状态信息,从而产生更细粒度的细胞类型,以更好地代表异质群体。它可以用来定义例如肿瘤微环境中的髓样或淋巴细胞群。 BayesPrism将计算这些子类型/状态的后验和。 v1.2:增加了功能cle

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