热门图表展示：高效呈现大数据的热力图(Heatmap) - 高通量数据分析的经典视觉工具
最编程
2024-02-10 12:15:21
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高通量数据搞来搞去，都离不开那几图来呈现结果，因为那些图实在是太经典了，高度概括了测序数据的分析结果。
热图heatmap：
- 基因表达矩阵的直观可视化
- 数据之间的两两比较，相关性分析，相似性分析
可视化的对象也非常简单，就是一个表，也叫dataframe或matrix。
我最喜欢用pheatmap这个R包，非常好用，也基本涵盖了你想要的所有功能。
我的数据，就是两组样本的相关性矩阵，来看相似性。
corM <- cor(as.matrix(human.BP.expr), as.matrix(mouse.BP.expr), method = "spearman")
library(pheatmap) library(ggplot2)
改个label的名字
rownames(corM) <- plyr::mapvalues(rownames(corM),
from = c("IMR_ENCC","UE_ENCC","HSCR_5c3","HSCR_20c7","HSCR_10c2","HSCR_1c11","HSCR_17c8","HSCR_23c9","HSCR_6c5"),
to = c("IMR90","UE02302","HSCR#5","HSCR#20","HSCR#10","HSCR#1","HSCR#17","HSCR#23","HSCR#6"))
colnames(corM) <- plyr::mapvalues(colnames(corM),
from = c("E13.5_CNCC"),
to = c("E13.5 "))
设置顺序
corM <- corM[c("IMR90","UE02302","HSCR#5","HSCR#20","HSCR#10","HSCR#1","HSCR#17","HSCR#23","HSCR#6"),
c("E9.5","E10.5","E11.5","E12.5","E13.5","E16.5","P19","P21","E13.5 ")]
设置侧边栏的bar的分组和颜色
annotation_row = data.frame(Severity=c("Control","Control","S-HSCR","S-HSCR","S-HSCR","S-HSCR","L-HSCR","L-HSCR","TCA"),
row.names=c("IMR90","UE02302","HSCR#5","HSCR#20","HSCR#10","HSCR#1","HSCR#17","HSCR#23","HSCR#6"))
annotation_col = data.frame(row.names=c("E9.5","E10.5","E11.5","E12.5","E13.5","E16.5","P19","P21","E13.5 "),
Lineage=c("ENCC","ENCC","ENCC","ENCC","ENCC","ENCC","ENCC","ENCC","CNCC"))
annotation_colors = list(Severity=c(Control="#4DAF4A", `S-HSCR`="#E41A1C", `L-HSCR`="#984EA3", TCA="gray70"),
Lineage=c(ENCC="green", CNCC="#eb6566"))
核心画图函数
options(repr.plot.width=7.0, repr.plot.height=6)
p <- pheatmap(corM, cluster_rows = F, cluster_cols = F, display_numbers = T, border_color=NA,
number_color="white", fontsize_number = 11, angle_col=315,
annotation_row=annotation_row, annotation_colors=annotation_colors,
annotation_col = annotation_col, gaps_col =c(8),
color = colorRampPalette(rev(hcl.colors(100)))(100))
# brewer.pal(n = 7, name = "RdYlBu")
其中有几个参数要重点说明：
display_numbers：是否显示数值
border_color：是否有边框的线
angle_col：label的旋转，非常方便
gaps_col：自行分组，非常实用的功能，就是拆分，这里不用聚类，随意分
hcl.colors：非常好看的热图的配色
保存PDF
pdf("Figures/correlation.enteric.evidence.pdf",width = 7, height = 6)
p
dev.off()
参考：
pheatmap绘制“热图”，你需要的都在这
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