GOplot 包通过封装好的函数可视化基因功能分析结果
#1. 安装
> install.packages('GOplot')#2. GOplot 内置数据
##2.1 脑和心脏内皮细胞的转录组数据
Form paper: Nolan et al. 2013，GEO accession: GSE47067.
| Name | Description | Dimension |
|---|---|---|
| EC$eset | Data frame of normalized expression values of brain and heart endothelial cells (3 replicates) | 20644 x 7 |
| EC$genelist | Data frame of differentially expressed genes (adjusted p-value < 0.05) | 2039 x 7 |
| EC$david | Data frame of results from a functional analysis of the differentially expressed genes performed with DAVID | 174 x 5 |
| EC$genes | Data frame of selected genes with logFC | 37 x 2 |
| EC$process | Character vector of selected enriched biological processes | 7 |
##2.2 查看内置数据格式
导入数据
> library(GOplot) > data(EC)
基因富集结果查看
> head(EC$david,2) Category ID Term 1 BP GO:0007507 heart development 2 BP GO:0001944 vasculature development Genes 1 DLC1, NRP2, NRP1, EDN1, PDLIM3, GJA1, TTN, GJA5, ZIC3, TGFB2, CERKL, GATA6, COL4A3BP, GAB1, SEMA3C, MKL2, SLC22A5, MB, PTPRJ, RXRA, VANGL2, MYH6, TNNT2, HHEX, MURC, MIB1, FOXC2, FOXC1, ADAM19, MYL2, TCAP, EGLN1, SOX9, ITGB1, CHD7, HEXIM1, PKD2, NFATC4, PCSK5, ACTC1, TGFBR2, NF1, HSPG2, SMAD3, TBX1, TNNI3, CSRP3, FOXP1, KCNJ8, PLN, TSC2, ATP6V0A1, TGFBR3, HDAC9 2 GNA13, ACVRL1, NRP1, PGF, IL18, LEPR, EDN1, GJA1, FOXO1, GJA5, TGFB2, WARS, CERKL, APOE, CXCR4, ANG, SEMA3C, NOS2, MKL2, FGF2, RAPGEF1, PTPRJ, RECK, EFNB2, VASH1, PNPLA6, THY1, MIB1, NUS1, FOXC2, FOXC1, CAV1, CDH2, MEIS1, WT1, CDH5, PTK2, FBXW8, CHD7, PLCD1, PLXND1, FIGF, PPAP2B, MAP2K1, TBX4, TGFBR2, NF1, TBX1, TNNI3, LAMA4, MEOX2, ECSCR, HBEGF, AMOT, TGFBR3, HDAC7 adj_pval 1 2.17e-06 2 1.04e-05
查看选择的基因
> head(EC$genelist)## ID logFC AveExpr t P.Value adj.P.Val B ## 1 Slco1a4 6.645388 1.2168670 88.65515 1.32e-18 2.73e-14 29.02715 ## 2 Slc19a3 6.281525 1.1600468 69.95094 2.41e-17 2.49e-13 27.62917 ## 3 Ddc 4.483338 0.8365231 65.57836 5.31e-17 3.65e-13 27.18476 ## 4 Slco1c1 6.469384 1.3558865 59.87613 1.62e-16 8.34e-13 26.51242 ## 5 Sema3c 5.515630 2.3252117 58.53141 2.14e-16 8.81e-13 26.33626 ## 6 Slc38a3 4.761755 0.9218670 54.11559 5.58e-16 1.76e-12 25.70308
构建画图数据：circle_dat()
> circ <- circle_dat(EC$david, EC$genelist) > head(circ)
category ID term count genes logFC adj_pval zscore BP GO:0007507 heart development 54 DLC1 -0.9707875 2.17e-06 -0.8164966 BP GO:0007507 heart development 54 NRP2 -1.5153173 2.17e-06 -0.8164966 BP GO:0007507 heart development 54 NRP1 -1.1412315 2.17e-06 -0.8164966 BP GO:0007507 heart development 54 EDN1 1.3813006 2.17e-06 -0.8164966 BP GO:0007507 heart development 54 PDLIM3 -0.8876939 2.17e-06 -0.8164966 BP GO:0007507 heart development 54 GJA1 -0.8179480 2.17e-06 -0.8164966
zscore: 每个GO term下上调(logFC>0)基因数和下调基因数的差与注释到GO term基因数平方根的商。
#3. 画图
##3.1 条形图(GOBar())
画BP下的GO term
> GOBar(subset(circ, category == 'BP')
分面同时展示BP, CC, MF的GO term
> GOBar(circ, display = 'multiple')
#3.2 气泡图(GOBubble())
> GOBubble(circ, labels = 3)
上图中：X轴是z-score； Y轴是多重矫正后p值的负对数；圈大小展示GO Term下基因数。
分面同时展示BP, CC, MF的气泡图
> GOBubble(circ, title = 'Bubble plot', colour = c('orange', 'darkred', 'gold'), display = 'multiple', labels = 3)
#2.3 圈图展示基因富集分析结果(GOCircle())
> GOCircle(circ)
默认展示circ 数据前10个GO Term，通过参数nsub调整需要展示的GO Term
根据GO Term选择要展示的GO Term
> GOCircle(circ, nsub = c('GO:0007507', 'GO:0001568', 'GO:0001944', 'GO:0048729', 'GO:0048514', 'GO:0005886', 'GO:0008092', 'GO:0008047'))选择要展示的GO Term数量
GOCircle(circ, nsub = 10)
#2.4 展示基因与GO Terms关系的圈图 (GOChord())
chord_dat ()将作图数据构建成GOChord() 要求的输入格式；一个二进制的关系矩阵，1表示基因属于该GO Term，0与之相反。
选择感兴趣的基因
> head(EC$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
选择感兴趣的GO Term
> EC$process ## [1] "heart development" "phosphorylation" ## [3] "vasculature development" "blood vessel development" ## [5] "tissue morphogenesis" "cell adhesion" ## [7] "plasma membrane"
构建画图数据
#chord_dat(data, genes, process) #genes、process其中任何一个参数不指定，默认使用对应的全部数据 > chord <- chord_dat(circ, EC$genes, EC$process) > head(chord) ## heart development phosphorylation vasculature development ## PTK2 0 1 1 ## GNA13 0 0 1 ## LEPR 0 0 1 ## APOE 0 0 1 ## CXCR4 0 0 1 ## RECK 0 0 1 ## blood vessel development tissue morphogenesis cell adhesion ## PTK2 1 0 0 ## GNA13 1 0 0 ## LEPR 1 0 0 ## APOE 1 0 0 ## CXCR4 1 0 0 ## RECK 1 0 0 ## plasma membrane logFC ## PTK2 1 -0.6527904 ## GNA13 1 0.3711599 ## LEPR 1 2.6539788 ## APOE 1 0.8698346 ## CXCR4 1 -2.5647537 ## RECK 1 3.6926860
画图
chord <- chord_dat(data = circ, genes = EC$genes, process = EC$process)
GOChord(chord, space = 0.02, gene.order = 'logFC', gene.space = 0.25, gene.size = 5)
GOChord() 参数
GOChord(data, title, space, gene.order, gene.size, gene.space, nlfc = 1, lfc.col, lfc.min, lfc.max, ribbon.col, border.size, process.label, limit) #data: 二进制矩阵 #title：标题 #space：基因对应方块之间的距离 #gene.order：基因排列顺序 #gene.size：基因标签大小 #nlfc：logFC 列的数目 #lfc.col：LFC颜色，定义模式：c(color for low values, color for the mid point, color for the high values) #lfc.min：LFC最小值 #lfc.max：LFC最大值 #ribbon.col：向量定义基因与GO Term间条带颜色 #border.size:基因与GO Term间条带边框粗细 #process.label:GO Term 图例文字大小 #limit:c(3, 2)，两个数字；第一个参数筛选基因(保留至少存在于3个GO Term的基因),第二个参数筛选GO Term(保留至少包含2个基因的GO Term )
#3.5 基因与GO Term的热图(GOHeat)
nlfc = 1：颜色对应logFC nlfc = 0：颜色对应每个基因注释了到了几个GO Term
> GOHeat(chord, nlfc = 1, fill.col = c('red', 'yellow', 'green'))
聚类(GOCluster)
> GOCluster(circ, EC$process, clust.by = 'logFC', term.width = 2)
GOCluster()调用R内置函数hclust 对基因表水平达或根据功能分内进行层次聚类。
> GOCluster(circ, EC$process, clust.by = 'logFC', term.width = 2)
