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两套数据的样本是不同的，聚类的名字也可以不同，然后submap会通过比较两套数据之间任意两个聚类是否存在共性，进而判断两套独立数据的聚类结果是否具有一致性，即证明了聚类结果的可复现性。 独立数据中分别进行聚类。 两套数据的样本是不同的,聚类的名字也可以不同,然 后submap 会通过比较两套数据之间任意两个聚类是否存在共性,进而判断两套独立数据的聚类结果是否具有一致性,即 submap 是一个用于分析基因表达数据的工具，本文为官方使用说明。 输入数据集要求具有共同的标识符。 自动提取的组织类型包括DLBCL、乳腺癌和DLBCL（带有生存数据）数据集，分别为5565、661、1213和3795个。
Maps subclasses between two data sets SubMap算法是一种用于分析肿瘤治疗反应的方法，它可以帮助我们理解肿瘤细胞对药物的敏感性。 下面是SubMap算法的详细步骤及代码示例： 步骤1：准备数据 首先，我们需要准备两个数据集：基线数据集和治疗后数据集。 Contribute to genepattern/submap development by creating an account on github.
Input data sets should have common identifiers
The intersection of these data sets is automatically extracted. 在使用submap算法预测单个样本或者某亚型对免疫治疗的响应可能性时，submap算法会将该样本或者该亚型的基因表达谱与已有的免疫治疗数据集进行比较，得到一组16个p值，分别对应16种不同的免疫治疗反应类型。
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