GOplot 可视化基因富集分析结果—科研工具箱-叨客学习资料网

请关注公众号【叨客学习资料】在使用网站的过程中有疑问，请来公众号进行反馈哦

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

来自文章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)
##   Category         ID                             Term
## 1       BPGO:0007507                heartdevelopment
## 2       BPGO:0001944          vasculaturedevelopment
## 3       BPGO:0001568         blood vesseldevelopment
## 4       BPGO:0048729             tissuemorphogenesis
## 5       BPGO:0048514       blood vesselmorphogenesis
## 6       BPGO:0051336 regulation of hydrolase activity
##                                                                                                                                                                                                                                                                                                                                                                             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
## 3        GNA13,ACVRL1, NRP1, PGF, IL18, LEPR, EDN1, GJA1, FOXO1, GJA5, TGFB2, WARS, CERKL,APOE, CXCR4, ANG, SEMA3C, NOS2, MKL2, FGF2, RAPGEF1, PTPRJ, RECK, 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
## 4                                   DLC1, ENAH,NRP1, PGF, ZIC2, TGFB2, CD44, ILK, SEMA3C, RET, AR, RXRA, VANGL2, LEF1, TNNT2,HHEX, MIB1, NCOA3, FOXC2, FOXC1, TGFB1I1, WNT5A, COBL, BBS4, FGFR3, TNC, BMPR2,CTNND1, EGLN1, NR3C1, SOX9, TCF7L1, IGF1R, FOXQ1, MACF1, HOXA5, BCL2, PLXND1,CAR2, ACTC1, TBX4, SMAD3, FZD3, SHANK3, FZD6, HOXB4, FREM2, TSC2, ZIC5, TGFBR3,APAF1
## 5                                                                                           GNA13, CAV1, ACVRL1, NRP1, PGF,IL18, LEPR, EDN1, GJA1, CDH2, MEIS1, WT1, TGFB2, WARS, PTK2, CERKL, APOE,CXCR4, ANG, SEMA3C, PLCD1, NOS2, MKL2, PLXND1, FIGF, FGF2, PTPRJ, TGFBR2, TBX4,NF1, TBX1, TNNI3, PNPLA6, VASH1, THY1, NUS1, MEOX2, ECSCR, AMOT, HBEGF, FOXC2,FOXC1, HDAC7
## 6                                                                              CAV1, XIAP, AGFG1, ADORA2A, TNNC1, TBC1D9, LEPR, ABHD5, EDN1, ASAP2,ASAP3, SMAP1, TBC1D12, ANG, TBC1D14, MTCH1, TBC1D13, TBC1D4, TBC1D30, DHCR24, HIP1,VAV3, NOS1, NF1, MYH6, RICTOR, TBC1D22A, THY1, PLCE1, RNF7, NDEL1, CHML, IFT57,ACAP2, TSC2, ERN1, APAF1, ARAP3, ARAP2, ARAP1, HTR2A, F2R
##      adj_pval
## 1 0.000002170
## 2 0.000010400
## 3 0.000007620
## 4 0.000119000
## 5 0.000720000
## 6 0.001171166

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 4.4833380.8365231 65.57836 5.31e-17  3.65e-1327.18476
## 4 Slco1c1 6.469384 1.3558865 59.87613 1.62e-16  8.34e-13 26.51242
## 5  Sema3c5.515630 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    54PDLIM3 -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。

zscore #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\')

GOBar #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)
##       heartdevelopment 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
##       bloodvessel 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
##       plasmamembrane      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 highvalues)
#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)

版权声明 1、本网站名称：叨客学习资料网
2、本站永久网址：https://www.leobba.cn
3、本网站的文章部分内容可能来源于网络，仅供大家学习与参考，如有侵权，请联系站长进行删除处理。
4、本站一切资源不代表本站立场，并不代表本站赞同其观点和对其真实性负责。
5、本站一律禁止以任何方式发布或转载任何违法的相关信息，访客发现请向站长举报
6、本站资源大多存储在云盘，如发现链接失效，请联系我们我们会第一时间更新。

THE END