In Tong-Chen/YSX: For Yishengxin Training

exprTable <- read.table("exprTable.txt", sep="\t", row.names=1, header=T)
exprTable

绘制一个聚类热图很简单

library(pheatmap)
pheatmap(exprTable)

如何自定义分支顺序呢

自己做个hclust传进去，顺序跟pheatmap默认是一样的

exprTable_t <- as.data.frame(t(exprTable))

col_dist = dist(exprTable_t)

hclust_1 <- hclust(col_dist)

pheatmap(exprTable, cluster_cols = hclust_1)

人为指定顺序排序样品

按发育时间排序样品

manual_order = c("Zygote", "X2_cell", "X4_cell", "X8_cell", "Morula",  "ICM")

dend = reorder(as.dendrogram(hclust_1), wts=order(match(manual_order, rownames(exprTable_t))))

# dend = reorder(as.dendrogram(hclust_1), wts=order(match(manual_order, rownames(exprTable_t))), agglo.FUN = max)

col_cluster <- as.hclust(dend)

pheatmap(exprTable, cluster_cols = col_cluster)

按某个基因的表达由小到大排序

可以按任意指标排序，基因表达是一个例子。

dend = reorder(as.dendrogram(hclust_1), wts=exprTable_t$Tet3)

col_cluster <- as.hclust(dend)

pheatmap(exprTable, cluster_cols = col_cluster)

按某个基因的表达由大到小排序

dend = reorder(as.dendrogram(hclust_1), wts=exprTable_t$Tet3*(-1))

col_cluster <- as.hclust(dend)

pheatmap(exprTable, cluster_cols = col_cluster)

按分支名字（样品名字）的字母顺序排序

col_cluster <- hclust_1 %>% as.dendrogram %>% sort %>% as.hclust
pheatmap(exprTable, cluster_cols = col_cluster)

梯子形排序：最小的分支在右侧

col_cluster <- hclust_1 %>% as.dendrogram %>% ladderize(TRUE) %>% as.hclust
pheatmap(exprTable, cluster_cols = col_cluster)

梯子形排序：最小的分支在左侧

col_cluster <- hclust_1 %>% as.dendrogram %>% ladderize(FALSE) %>% as.hclust
pheatmap(exprTable, cluster_cols = col_cluster)

按特征值排序

样本量多时的自动较忧排序

sv = svd(exprTable)$v[,1]
dend = reorder(as.dendrogram(hclust_1), wts=sv)
col_cluster <- as.hclust(dend)

pheatmap(exprTable, cluster_cols = col_cluster)

exprTable_cor <- cor(exprTable)
exprTable_cor

pheatmap(exprTable_cor, cluster_rows = T, cluster_cols = T)

cor_cluster = hclust(as.dist(1-exprTable_cor))
pheatmap(exprTable_cor, cluster_rows = cor_cluster, cluster_cols = cor_cluster)

cor_sum <- rowSums(exprTable_cor)
dend = reorder(as.dendrogram(cor_cluster), wts=cor_sum)

col_cluster <- as.hclust(dend)

pheatmap(exprTable_cor, cluster_rows = col_cluster, cluster_cols = col_cluster)

manual_order = c("Zygote", "X2_cell", "X4_cell", "X8_cell", "Morula",  "ICM")

dend = reorder(as.dendrogram(cor_cluster), wts=order(match(manual_order, rownames(exprTable_cor))),agglo.FUN = max)
col_cluster <- as.hclust(dend)

pheatmap(exprTable_cor, cluster_rows = col_cluster, cluster_cols = col_cluster)

Refercens

1. https://stackoverflow.com/questions/52446477/r-hclust-common-order-for-multiple-trees
2. https://www.biostars.org/p/237067/

height = height[height>0]

height

hang_dend = hang.dendrogram(dend)

plot(hang_dend)

height = get_nodes_attr(hang_dend, "height")

height

Tong-Chen/YSX documentation built on Jan. 25, 2021, 2:49 a.m.