GOplot | 更美观的富集分析可视化
数据准备
# 下载 install.packages('GOplot') library(GOplot) # 载入示例数据 data(EC) # 富集分析结果 head(EC$david) # 差异分析结果 head(EC$genelist) # 生成画图数据 circ <- circle_dat(EC$david, EC$genelist)
> head(circ) category ID term count genes logFC adj_pval zscore 1 BP GO:0007507 heart development 54 DLC1 -0.9707875 2.17e-06 -0.8164966 2 BP GO:0007507 heart development 54 NRP2 -1.5153173 2.17e-06 -0.8164966 3 BP GO:0007507 heart development 54 NRP1 -1.1412315 2.17e-06 -0.8164966 4 BP GO:0007507 heart development 54 EDN1 1.3813006 2.17e-06 -0.8164966 5 BP GO:0007507 heart development 54 PDLIM3 -0.8876939 2.17e-06 -0.8164966 6 BP GO:0007507 heart development 54 GJA1 -0.8179480 2.17e-06 -0.8164966
GOplot使用了zscore概念，但其并不是指Z-score标准化，而是指每个GO term下上调(logFC>0)基因数和下调基因数的差与注释到GO term基因数平方根的商。用于表示每个GO Term的上下调情况，公式：
可视化
条图
GOBar(subset(circ, category == 'BP'))
zscore用于表示每个Term的上下调情况 # 以terms的分类进行分面 GOBar(circ, display = 'multiple')
# 以terms的分类进行分面 切改变色阶颜色 GOBar(circ, display = 'multiple', title = 'Z-score coloured barplot', zsc.col = c('yellow', 'black', 'cyan'))
气泡图
z-score作为横坐标，校正p值的负对数作为纵坐标(y轴越高越显著)。所显示圆圈的面积与富集到term的基因数量成比例，颜色对应于类别。
# 生成y大于3的term的标签 GOBubble(circ, labels = 3)
# 添加标题、分面、修改颜色 GOBubble(circ, title = 'Bubble plot', colour = c('orange', 'darkred', 'gold'), display = 'multiple', labels = 3)
# 根据分类添加背景色 GOBubble(circ, title = 'Bubble plot with background colour', display = 'multiple', bg.col = T, labels = 3)
reduce_overlap减少冗余terms数目。该功能删除所有基因重叠大于或等于设定阈值的terms。保留每个组的一个terms作为代表，而不考虑GO层次结构。
# 删除所有基因重叠大于或等于 0.75的 terms reduced_circ <- reduce_overlap(circ, overlap = 0.75) GOBubble(reduced_circ, labels = 2.8)
圈图
GOCircle(circ)
# 可视化感兴趣的 terms IDs <- c('GO:0007507', 'GO:0001568', 'GO:0001944', 'GO:0048729', 'GO:0048514', 'GO:0005886', 'GO:0008092', 'GO:0008047') GOCircle(circ, nsub = IDs)
# 可视化前10个terms GOCircle(circ, nsub = 10)
弦图
显示了所选基因和术语列表之间的关系，以及这些基因的logFC。
数据准备
head(EC$genes) ## ID logFC ## 1 PTK2 -0.6527904 ## 2 GNA13 0.3711599 ## 3 LEPR 2.6539788 ## 4 APOE 0.8698346 ## 5 CXCR4 -2.5647537 ## 6 RECK 3.6926860 EC$process ## [1] "heart development" "phosphorylation" ## [3] "vasculature development" "blood vessel development" ## [5] "tissue morphogenesis" "cell adhesion" ## [7] "plasma membrane"
chord <- chord_dat(circ, EC$genes, EC$process) head(chord) ## heart development phosphorylation vasculature development ## PTK2 0 1 1 ## GNA13 0 0 1 ## LEPR 0 0 1 ## APOE 0 0 1 ## CXCR4 0 0 1 ## RECK 0 0 1 ## blood vessel development tissue morphogenesis cell adhesion ## PTK2 1 0 0 ## GNA13 1 0 0 ## LEPR 1 0 0 ## APOE 1 0 0 ## CXCR4 1 0 0 ## RECK 1 0 0 ## plasma membrane logFC ## PTK2 1 -0.6527904 ## GNA13 1 0.3711599 ## LEPR 1 2.6539788 ## APOE 1 0.8698346 ## CXCR4 1 -2.5647537 ## RECK 1 3.6926860
绘制
chord <- chord_dat(data = circ, genes = EC$genes, process = EC$process) GOChord(chord, space = 0.02, gene.order = 'logFC', gene.space = 0.25, gene.size = 5)
#只显示富集到至少3个terms的基因 GOChord(chord, limit = c(3, 0), gene.order = 'logFC')
热图
GOHeat(chord[,-8], nlfc = 0) #nlfc = 0,则以count为色阶
GOHeat(chord, nlfc = 1, fill.col = c('red', 'yellow', 'green')) #nlfc = 0,则以logFC 为色阶
GOCluster
GOCluster(circ, EC$process, clust.by = 'logFC', term.width = 2)
GOCluster(circ, EC$process, clust.by = 'term', lfc.col = c('darkgoldenrod1', 'black', 'cyan1'))
Venn diagram
l1 <- subset(circ, term == 'heart development', c(genes,logFC)) l2 <- subset(circ, term == 'plasma membrane', c(genes,logFC)) l3 <- subset(circ, term == 'tissue morphogenesis', c(genes,logFC)) GOVenn(l1,l2,l3, label = c('heart development', 'plasma membrane', 'tissue morphogenesis'))
参考
GOplot (wencke.github.io)
