我有几个RNA-seq数据集。使用去卷积方法，我对它们中的每一个进行了细胞类型富集分析，然后将结果组合成一个数据帧，产生了1000+个列样本和38个行样本。数据集来自不同的癌症文章。因此，自然地，在使用火山图或t-SNE可视化数据之前，我需要纠正癌症类型AND数据集的批量效应，这样源就不会影响结果。我使用了以下代码：
scores.batch = limma::removeBatchEffect(scores ,metadata$Cancer_Type, metadata$dataset)
然而，对于一些样本中的某些细胞类型，我得到了负分，这当然毫无意义。出了问题。
得分矩阵的dput：
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0, 0, 0.0868373941926339), .Dim = c(20L, 20L), .Dimnames = list(
c("Adipocytes", "B-cells", "Basophils", "CD4+ memory T-cells",
"CD4+ naive T-cells", "CD4+ T-cells", "CD4+ Tcm", "CD4+ Tem",
"CD8+ naive T-cells", "CD8+ T-cells", "CD8+ Tcm", "Class-switched memory B-cells",
"DC", "Endothelial cells", "Eosinophils", "Epithelial cells",
"Fibroblasts", "Hepatocytes", "ly Endothelial cells", "Macrophages"
), c("Pt1", "Pt10", "Pt103", "Pt106", "Pt11", "Pt17", "Pt2",
"Pt24", "Pt26", "Pt27", "Pt28", "Pt29", "Pt31", "Pt36", "Pt37",
"Pt38", "Pt39", "Pt4", "Pt46", "Pt47")))
我再次厌倦了只校正Cancer_Type，但结果并不好(t-SNE没有根据我的需要对数据进行聚类(。
这里可能有什么问题？
Limma假设这两种类型的批处理效果是相加的，因此是独立的。写limma::removeBatchEffect(x = scores, batch = cancer_type, batch2 = study意味着癌症类型与研究数据集之间没有关系。然而，一项研究很可能是关于一种癌症类型，另一项研究是关于另一种癌症类型。因此，limma模型被打破的假设很可能被打破了。
但是，您可以只创建一个批处理，并将其用作单个批处理参数：
metadata$merged_batch <- paste0(metadata$Cancer_Type, metadata$dataset)
scores.batch = limma::removeBatchEffect(scores, batch = metadata$merged_batch)