我在R中工作，有一个命名的字符向量列表.每个载体描述了生物途径中存在的基因.
请参见以下示例:
gene_sets = list(pathwayX= c("Gene3"),pathwayY= c("Gene2", "Gene3"),pathwayz= c("Gene1", "Gene2","Gene3"))
> gene_sets
$pathwayX
"Gene3"
$pathwayY
"Gene2" "Gene3"
$pathwayZ
"Gene1" "Gene2" "Gene3"
我想做的是将列表转换成一个二进制矩阵，将基因作为列，路径作为行.0表示途径中不存在基因，1表示存在基因.
| Gene1 | Gene2 | Gene3 | |
|---|---|---|---|
| pathwayX | 0 | 0 | 1 |
| pathwayY | 0 | 1 | 1 |
| pathwayZ | 1 | 1 | 1 |
我们可以使用
t(+sapply(gene_sets, "%in%", x = c("Gene1", "Gene2", "Gene3")))
如果你想动态获得c("Gene1", "Gene2", "Gene3")，我们可以
GeneID <- sort(unique(unlist(gene_sets)))
mat <- t(+sapply(gene_sets, "%in%", x = GeneID)) ## matrix output
colnames(mat) <- GeneID
# Gene1 Gene2 Gene3
#pathwayX 0 0 1
#pathwayY 0 1 1
#pathwayz 1 1 1
data.frame(mat) ## data.frame output
我的印象是，基因问题通常是大而稀疏的.如果你在现实中有几十万个基因和通路，那么下面的稀疏矩阵解决方案是最好的 Select .
pathwayID <- names(gene_sets)
n1 <- lengths(gene_sets, use.names = FALSE) ## number of genes in each pathway
genesVec <- unlist(gene_sets, use.names = FALSE)
GeneID <- sort(unique(genesVec))
i <- rep(1:length(n1), n1)
j <- match(genesVec, GeneID)
Matrix::sparseMatrix(i = i, j = j, x = rep.int(1, length(i)),
dimnames = list(pathwayID, GeneID))
#3 x 3 sparse Matrix of class "dgCMatrix"
# Gene1 Gene2 Gene3
#pathwayX . . 1
#pathwayY . 1 1
#pathwayz 1 1 1