一、引言

生命科學(xué)中的數(shù)據(jù)分析和可視化是一個具有挑戰(zhàn)性的領(lǐng)域。隨著技術(shù)和理論的不斷發(fā)展，研究人員需要處理越來越復(fù)雜和龐大的數(shù)據(jù)集，以研究生物體在不同尺度上的結(jié)構(gòu)和功能，探索不同生物過程和疾病的機制。在這個領(lǐng)域，GO（Gene Ontology）富集分析已成為一種常見的技術(shù)，用于識別在給定的基因集合中與特定生物過程和機制有關(guān)的基因集合，是研究生物信息學(xué)、基因組學(xué)和轉(zhuǎn)錄組學(xué)的重要方法之一[1,2]。然而，GO富集分析的結(jié)果往往是一張龐大的表格，需要通過可視化才能更好地理解和分析。在這方面，R語言作為一個強大的數(shù)據(jù)分析工具，也是生命科學(xué)研究領(lǐng)域中廣泛使用的計算統(tǒng)計工具之一[3,4]。因此，本文將介紹GO富集分析技術(shù)并重點介紹R語言及其繪圖包，如ggplot2和clusterProfiler等，用于可視化GO富集分析結(jié)果[5,6,7]。本文還將提供一些使用R和GO富集可視化的基本方法和技巧，并以實例說明如何從生物大數(shù)據(jù)中捕捉關(guān)鍵信號。最后，我們將討論GO富集可視化在生物信息學(xué)中的未來發(fā)展和可能的研究方向。

二、數(shù)據(jù)集

2.1 數(shù)據(jù)集導(dǎo)入

該數(shù)據(jù)集是GOplot自帶的數(shù)據(jù)集，可以用來學(xué)習(xí)Go富集分析和可視化，接下來我們引入數(shù)據(jù)集

library(GOplot)
data(EC)

數(shù)據(jù)集展示：

>?#?查看數(shù)據(jù)
>?head(EC$eset)
????Gene_Symbol?????Brain_A?????Brain_B????Brain_C?????Heart_A?????Heart_B?????Heart_C
1?0610007P14Rik??0.58382130??0.81117820?-0.2480545?-0.67075443?-0.58700850?-0.65176487
2?0610008F07Rik??0.13262606??0.11230135??0.3122339??0.10193062?-0.06617737??0.09765196
3?0610009B22Rik?-0.09357643?-0.20074654??0.2505083?-1.02136140?-0.18762684?-0.69150734
4?0610009D07Rik??0.06708336??0.12616920?-0.1234570?-0.74400520?-0.42762280?-0.43724203
5?0610009O20Rik??0.02191877??0.13460398??0.2718530??0.27460957?-0.07751560??0.00000000
6?0610010F05Rik?-0.10588837?-0.05568028??0.0115509??0.06084633?-0.18752098??0.01155090

>?#?查看數(shù)據(jù)
>?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

>?#?查看數(shù)據(jù)
>?head(EC$david)
??Category?????????ID?????????????????????????????Term
1???????BP?GO:0007507????????????????heart?development
2???????BP?GO:0001944??????????vasculature?development
3???????BP?GO:0001568?????????blood?vessel?development
4???????BP?GO:0048729?????????????tissue?morphogenesis
5???????BP?GO:0048514???????blood?vessel?morphogenesis
6???????BP?GO: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

>?#?查看數(shù)據(jù)
>?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

>?#?查看數(shù)據(jù)
>?EC$process
[1]?"heart?development"????????"phosphorylation"??????????"vasculature?development"??"blood?vessel?development"?"tissue?morphogenesis"?????"cell?adhesion"???????????
[7]?"plasma?membrane"

2.2 數(shù)據(jù)預(yù)處理

使用cirlce_dat函數(shù)整合GO注釋結(jié)果數(shù)據(jù)和基因差異表達分析數(shù)據(jù)結(jié)合起來，形成作圖對象。

circ?<-?circle_dat(EC$david,?EC$genelist)
head(circ)

結(jié)果展示：

??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

「字段簡單介紹」：

1. category：GO term分為3大類，分別為BP（生物學(xué)過程），CC（細胞組分）和MF（分子功能）
2. ID和term來屬于數(shù)據(jù)庫的字段；
3. count為劃分到相應(yīng)term的基因數(shù)目；
4. logFC為差異表達基因的log標(biāo)準(zhǔn)化的倍數(shù)變化； adj_pval為校正過的p-value值，adj_pval < 0.05顯著富集；

三、數(shù)據(jù)可視化

3.1 柱形圖

• 所有種類

GOBar(circ,?display?=?'multiple',?zsc.col?=?c('blue',?'white',?'red'))?

是不是覺得有寫不清晰，看不出x坐標(biāo)的標(biāo)注。沒關(guān)系，我們可以選擇性展示其中感興趣的部分。

• 展示特定種類

GOBar(subset(circ,?category?==?'MF'))

這樣就很清晰了！

3.2 氣泡圖

GOBubble(circ,?labels?=?2.8)

labels是一個很重要的參數(shù)，表示標(biāo)記adj_pval負(fù)對數(shù)大于等于設(shè)置值的GO term，氣泡的大小表明富集到該GO term的基因數(shù)目。我設(shè)置成了2.8，可讀性還是可以的,但是交叉的氣泡很多，我們可以通過reduce_overlap函數(shù)過濾一部分?jǐn)?shù)據(jù)，減少泡泡的數(shù)目，如下：

reduced_circ?<-?reduce_overlap(circ,?overlap?=?0.70)
GOBubble(reduced_circ,?labels?=?2.8)

視覺效果是不是比之前是要好一些！如果還是比較擁擠，我們可以分組繪圖，如下：

GOBubble(circ,?title?=?'Bubble?plot',?display?=?'multiple',?labels?=?3)??

3.3 環(huán)形圖

GOCircle(circ)

3.4 弦圖

數(shù)據(jù)整理：

chord?<-?chord_dat(circ,?EC$genes,?EC$process)
head(chord)

結(jié)果展示：

>?head(chord)
??????heart?development?phosphorylation?vasculature?development?blood?vessel?development?tissue?morphogenesis?cell?adhesion?plasma?membrane??????logFC
PTK2??????????????????0???????????????1???????????????????????1????????????????????????1????????????????????0?????????????0???????????????1?-0.6527904
GNA13?????????????????0???????????????0???????????????????????1????????????????????????1????????????????????0?????????????0???????????????1??0.3711599
LEPR??????????????????0???????????????0???????????????????????1????????????????????????1????????????????????0?????????????0???????????????1??2.6539788
APOE??????????????????0???????????????0???????????????????????1????????????????????????1????????????????????0?????????????0???????????????1??0.8698346
CXCR4?????????????????0???????????????0???????????????????????1????????????????????????1????????????????????0?????????????0???????????????1?-2.5647537
RECK??????????????????0???????????????0???????????????????????1????????????????????????1????????????????????0?????????????0???????????????1??3.6926860

畫圖：

GOChord(chord,?space?=?0.02,?gene.order?=?'logFC',?gene.space?=?0.25,?gene.size?=?5)

弦圖展示了感興趣的基因和某些GO term的關(guān)系，以基因差異表達倍數(shù)的大小排序。

3.5 熱圖

GOHeat(chord,?nlfc?=?1,?fill.col?=?c('red',?'blue',?'green'))

3.6 聚類圖

GOCluster(circ,?EC$process,?clust.by?=?'logFC',?term.width?=?2)

3.7 韋恩圖

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'))

四、總結(jié)

本文介紹了如何使用R和GO富集可視化技術(shù)，分析生物信息學(xué)中數(shù)據(jù)集的復(fù)雜性問題。通過R語言和相應(yīng)的包，如ggplot2，g:Profiler，moduleColor等，可以對生成的結(jié)果進行高效而準(zhǔn)確的可視化，以幫助研究人員更好地理解和分析復(fù)雜數(shù)據(jù)集。文章還提供了一些使用R和GO富集可視化技術(shù)的基本方法和技巧，并以實例說明如何從生物大數(shù)據(jù)中捕捉關(guān)鍵信號。最后，我們探討了GO富集可視化的未來趨勢和展望，旨在為生命科學(xué)研究者提供更好的技術(shù)支持，以研究更有意義、有價值的數(shù)據(jù)。R語言在數(shù)據(jù)分析和可視化領(lǐng)域的應(yīng)用將有望為大規(guī)模數(shù)據(jù)集的處理和分析，提供更有效、更快捷、更精確的解決方法。

參考文獻:

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[2] Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research, 2000, 28(1): 27-30.

[3] Gentleman R C, Carey V J, Bates D M, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome biology, 2004, 5(10): R80.

[4] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2017). Available online at https://www.R-project.org/.

[5] Yu G, Wang L G, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology, 2012, 16(5): 284-287.

[6] Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer, 2016.

[7] Kolde R, Laur S, Adler P, et al. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics, 2012, 28(4): 573-580.文章來源地址http://www.zghlxwxcb.cn/news/detail-497235.html