前言
https://blog.csdn.net/weixin_41368414/article/details/123265071
目前基于RNA做分析的文章中幾乎都有 GSEA 的分析內(nèi)容，并且都是出現(xiàn)在正文，那么這個也是表達(dá)基因篩選的一種重要方式，下面我將整個流程梳理一下，供大家參考。
GSEA（Gene Set EnrichmentAnalysis），即基因集富集分析，它的基本思想是使用預(yù)定義的基因，將基因按照在兩類樣本中的差異表達(dá)程度排序，然后檢驗預(yù)先設(shè)定的基因集合是否在這個排序表的頂端或者底端富集。
GSEA和GO、KEGG pathway不同的地方在于，后兩者會提前設(shè)定一個閾值，只關(guān)注差異變化大的基因（相當(dāng)于重點班）。這樣子容易遺漏部分差異表達(dá)不顯著卻有重要生物學(xué)意義的基因（成績一般，但是很有天賦）。所以GSEA分析比較適用于，傳統(tǒng)分析方法篩選后樣本過少的數(shù)據(jù)集。
GSEA數(shù)據(jù)庫收集了很多分子標(biāo)記數(shù)據(jù)，有9大分類的基因，如下：
九大分類如下：
數(shù)據(jù)的讀取我們?nèi)匀皇褂玫氖?TCGA-COAD 的數(shù)據(jù)集，表達(dá)數(shù)據(jù)的讀取以及臨床信息分組的獲得我們上期已經(jīng)提過，我們使用的是edgeR 軟件包計算出來的差異表達(dá)結(jié)果，提取上調(diào)基因 2832 的 ENSEMBL 號，
###########基因列表
DEG=read.table("DEG-resdata.xls",sep="\t",check.names=F,header = T)
geneList<-DEG[DEG$sig=="Up",]$Row.names
table(DEG$sig)
##
## Down Up
## 1296 2832
直接用之前的轉(zhuǎn)錄組差異分析后的數(shù)據(jù)來演示，數(shù)據(jù)格式如下：
head(DEG[,1:7]) ## Row.names logFC logCPM LR PValue ## 1 ENSG00000142959 -5.924830 3.20085238 1457.1137 8.159939e-319 ## 2 ENSG00000163815 -4.223787 2.59792097 1145.7948 3.679815e-251 ## 3 ENSG00000107611 -5.131288 1.71477711 1122.0643 5.289173e-246 ## 4 ENSG00000162461 -4.101967 1.51480635 1085.9423 3.752089e-238 ## 5 ENSG00000163959 -4.295806 3.39558390 1080.7407 5.067873e-237 ## 6 ENSG00000144410 -6.284258 -0.02616284 916.3497 2.739233e-201 ## FDR sig ## 1 2.592984e-314 Down ## 2 5.846674e-247 Down ## 3 5.602468e-242 Down ## 4 2.980753e-234 Down ## 5 3.220836e-233 Down ## 6 1.450744e-197 Down
因為GSEA只需要SYMBOL（基因名）和foldchange （或logFC)兩列，所以我們提取這兩列，如下：
geneList<-DEG[,1:2]
head(geneList)
## Row.names logFC
## 1 ENSG00000142959 -5.924830
## 2 ENSG00000163815 -4.223787
## 3 ENSG00000107611 -5.131288
## 4 ENSG00000162461 -4.101967
## 5 ENSG00000163959 -4.295806
## 6 ENSG00000144410 -6.284258
首先我們同樣需要安裝軟件包并加載，這里面主程序就是 clusterProfiler 軟件包，如下：
######## if(!require(clusterProfiler)){ BiocManager::install("clusterProfiler") } if(!require(org.Hs.eg.db)){ BiocManager::install("org.Hs.eg.db") } if(!require(DOSE)){ BiocManager::install("DOSE") } if(!require(topGO)){ BiocManager::install("topGO") } if(!require(pathview)){ BiocManager::install("pathview") } if(!require(KEGG.db)){ BiocManager::install("KEGG.db") } if(!require(enrichplot)){ BiocManager::install("enrichplot") } library(org.Hs.eg.db) library(clusterProfiler) library(DOSE) library(topGO) library(pathview) library(KEGG.db) library(enrichplot)
如基因名是symbol，需要將基因ID轉(zhuǎn)換為Entrez ID格式。Entrez ID實際上是指的Entrez gene ID，是對應(yīng)于染色體上一個gene location的。每一個發(fā)現(xiàn)的基因都會被編制一個統(tǒng)一的編號，而Entrez ID是指的來自于NCBI旗下的Entrez gene數(shù)據(jù)庫所使用的編號。因為Entrez ID具有特異性，所以后續(xù)分析更適合用Entrez ID
eg <- bitr(geneList$Row.names, fromType="ENSEMBL", toType=c("ENTREZID","ENSEMBL",'SYMBOL'), OrgDb="org.Hs.eg.db") ## 'select()' returned 1:many mapping between keys and columns ## Warning in bitr(geneList$Row.names, fromType = "ENSEMBL", toType = ## c("ENTREZID", : 30.89% of input gene IDs are fail to map... head(eg) ## ENSEMBL ENTREZID SYMBOL ## 1 ENSG00000142959 266675 BEST4 ## 2 ENSG00000163815 7123 CLEC3B ## 3 ENSG00000107611 8029 CUBN ## 4 ENSG00000162461 284723 SLC25A34 ## 5 ENSG00000163959 200931 SLC51A ## 6 ENSG00000144410 130749 CPO
合并基因列表，就是我們需要輸入的文件了，如下：
mergedata<-merge(eg,geneList,by.x="ENSEMBL",by.y="Row.names") head(mergedata) ## ENSEMBL ENTREZID SYMBOL logFC ## 1 ENSG00000003249 79007 DBNDD1 2.474017 ## 2 ENSG00000004776 126393 HSPB6 -3.543461 ## 3 ENSG00000004799 5166 PDK4 -3.281233 ## 4 ENSG00000004846 340273 ABCB5 -3.084616 ## 5 ENSG00000005001 64063 PRSS22 4.095319 ## 6 ENSG00000005981 51666 ASB4 2.237660 mergedata_sort <- mergedata[order(mergedata$logFC, decreasing = T),]#先按照logFC降序排序 gene_fc = mergedata_sort$logFC #把foldchange按照從大到小提取出來 names(gene_fc) <- mergedata_sort$ENTREZID #給上面提取的foldchange加上對應(yīng)上ENTREZID head(gene_fc) ## 4102 26831 26832 5653 646960 4105 ## 10.010124 9.817905 9.807069 9.553685 9.448116 9.440482
需要gmt文件，http://www.gsea-msigdb.org/gsea/downloads.jsp 路徑下載，選擇合適的，我們這里是結(jié)直腸癌中癌和癌旁的差異比較分析，所以我們需要找到致癌基因，選擇 c5: Ontology gene sets 即 c5.all.v7.5.1.entrez.gmt 文件，然后進(jìn)行 GSEA 富集分析，如下：
library(stats) kegg_gmt <- read.gmt("c5.all.v7.5.1.entrez.gmt") #讀gmt文件 gsea <- GSEA(gene_fc, TERM2GENE = kegg_gmt) #GSEA分析 head(gsea) ## ID ## GOBP_ADAPTIVE_IMMUNE_RESPONSE GOBP_ADAPTIVE_IMMUNE_RESPONSE ## GOBP_CATION_TRANSPORT GOBP_CATION_TRANSPORT ## GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY ## GOBP_IMMUNOGLOBULIN_PRODUCTION GOBP_IMMUNOGLOBULIN_PRODUCTION ## GOBP_ION_TRANSPORT GOBP_ION_TRANSPORT ## GOBP_LIPID_METABOLIC_PROCESS GOBP_LIPID_METABOLIC_PROCESS ## Description ## GOBP_ADAPTIVE_IMMUNE_RESPONSE GOBP_ADAPTIVE_IMMUNE_RESPONSE ## GOBP_CATION_TRANSPORT GOBP_CATION_TRANSPORT ## GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY ## GOBP_IMMUNOGLOBULIN_PRODUCTION GOBP_IMMUNOGLOBULIN_PRODUCTION ## GOBP_ION_TRANSPORT GOBP_ION_TRANSPORT ## GOBP_LIPID_METABOLIC_PROCESS GOBP_LIPID_METABOLIC_PROCESS ## setSize enrichmentScore ## GOBP_ADAPTIVE_IMMUNE_RESPONSE 111 -0.4743354 ## GOBP_CATION_TRANSPORT 185 -0.2838816 ## GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY 52 -0.4982248 ## GOBP_IMMUNOGLOBULIN_PRODUCTION 51 -0.6152482 ## GOBP_ION_TRANSPORT 252 -0.2763134 ## GOBP_LIPID_METABOLIC_PROCESS 163 -0.2922133 ## NES pvalue ## GOBP_ADAPTIVE_IMMUNE_RESPONSE -4.455485 1e-10 ## GOBP_CATION_TRANSPORT -2.957025 1e-10 ## GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY -3.636516 1e-10 ## GOBP_IMMUNOGLOBULIN_PRODUCTION -4.430346 1e-10 ## GOBP_ION_TRANSPORT -2.791438 1e-10 ## GOBP_LIPID_METABOLIC_PROCESS -2.998976 1e-10 ## p.adjust ## GOBP_ADAPTIVE_IMMUNE_RESPONSE 1.548421e-08 ## GOBP_CATION_TRANSPORT 1.548421e-08 ## GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY 1.548421e-08 ## GOBP_IMMUNOGLOBULIN_PRODUCTION 1.548421e-08 ## GOBP_ION_TRANSPORT 1.548421e-08 ## GOBP_LIPID_METABOLIC_PROCESS 1.548421e-08 ## qvalues rank ## GOBP_ADAPTIVE_IMMUNE_RESPONSE 1.040997e-08 1137 ## GOBP_CATION_TRANSPORT 1.040997e-08 1082 ## GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY 1.040997e-08 1130 ## GOBP_IMMUNOGLOBULIN_PRODUCTION 1.040997e-08 1137 ## GOBP_ION_TRANSPORT 1.040997e-08 852 ## GOBP_LIPID_METABOLIC_PROCESS 1.040997e-08 916 ## leading_edge ## GOBP_ADAPTIVE_IMMUNE_RESPONSE tags=86%, list=40%, signal=54% ## GOBP_CATION_TRANSPORT tags=64%, list=38%, signal=42% ## GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY tags=88%, list=39%, signal=55% ## GOBP_IMMUNOGLOBULIN_PRODUCTION tags=100%, list=40%, signal=61% ## GOBP_ION_TRANSPORT tags=51%, list=30%, signal=40% ## GOBP_LIPID_METABOLIC_PROCESS tags=58%, list=32%, signal=42% ## core_enrichment ## GOBP_ADAPTIVE_IMMUNE_RESPONSE 28885/28432/28420/28943/28834/28908/6977/28803/28876/28785/28820/28903/28781/4063/28442/729230/28784/28815/974/28892/3514/28775/28409/28388/28774/11126/28900/28941/28518/6363/28823/28449/28448/28923/28412/660/5579/28804/28392/30835/28938/1191/28811/28912/28423/634/28935/3662/28786/28444/28464/28450/973/28821/28937/28875/28874/28915/1380/28921/10990/3493/28385/28401/10462/28906/57379/28408/3570/28893/28772/28817/28776/28780/2208/10332/28896/28930/28457/339390/28516/28883/83417/28410/28940/28881/28870/608/79908/23495/28877/730/3494/28919/3512/28452 ## GOBP_CATION_TRANSPORT 3773/79901/1756/9472/6328/493/2316/6549/729230/285195/6263/6330/2852/114780/3738/387849/2668/245972/170850/3782/9058/130497/6363/492/57113/5023/5579/482/170572/2824/143425/2903/534/4129/3359/6355/1179/5174/169026/931/6568/6616/84239/84502/844/1272/1393/53826/5649/6387/3741/146395/1261/148/9992/6446/6572/6543/3777/6564/57030/3778/57282/162514/287/57158/6860/9963/341359/140738/5348/3779/4842/2273/846/4638/285242/5350/1908/3736/2641/11280/6335/10050/3769/22871/1258/7432/1268/3745/6327/115584/22953/6865/6550/159963/60482/117/644139/2823/6338/140803/845/6340/8671/6332/286133/477/1804/201140/246213/5354/340024/6561/55532/347741/6555/92736 ## GOBP_IMMUNE_RESPONSE_REGULATING_SIGNALING_PATHWAY 28432/28420/2206/28834/7098/28442/974/3514/28409/81793/28388/11126/26228/28449/28448/28412/660/5579/28392/148823/948/23228/28423/634/933/640/3662/28444/28464/28450/973/931/1380/3493/28385/28401/28408/64922/10917/11148/28457/28410/79908/3494/9971/28452 ## GOBP_IMMUNOGLOBULIN_PRODUCTION 28943/51237/28908/28803/28876/28785/28820/28903/28781/28784/28815/28892/3514/28775/28774/28900/28941/28518/28823/28923/29802/28804/28938/28811/28912/933/28935/28786/28821/28937/28875/28874/28915/28921/28906/57379/28893/28772/28817/28776/28780/28896/28930/28516/28883/28940/28881/28870/28877/28919 ## GOBP_ION_TRANSPORT 57113/220963/5023/5579/2350/482/170572/2824/5322/143425/2903/948/534/634/4129/3359/6355/1179/5028/5174/169026/931/6568/6616/84239/84502/844/1272/1393/53826/5649/2893/6387/3741/146395/387700/1261/148/9992/64805/28965/6446/55065/2565/6572/2901/6543/3777/1188/6564/2566/57030/3778/57282/162514/391013/287/57158/6860/9963/203859/341359/140738/5348/3779/4842/2273/846/4638/285242/5350/1908/3736/2641/11280/6335/10050/3769/22871/553/5030/2899/1258/7432/353189/1268/3745/6327/115584/22953/6865/54831/6550/159963/60482/117/5593/644139/11136/2823/123264/1836/6338/140803/200931/845/6340/8671/1811/6332/760/286133/477/9429/1804/8647/201140/762/6514/246213/5354/22802/340024/6561/766/55532/266675/347741/6555/92736 ## GOBP_LIPID_METABOLIC_PROCESS 6817/5288/124/1562/6363/5959/283985/5408/5023/660/389396/5333/2494/5322/1191/3158/948/23228/79154/634/3291/345557/1565/129807/84803/10690/54988/54657/653/30815/10924/10317/64805/28965/23584/5105/159296/137872/2168/3248/1580/54511/80168/5742/1907/6822/54658/51046/13/391013/57016/339221/374569/84830/27306/2169/7366/8708/10170/54857/5166/53345/54979/9227/3294/133121/1268/185/54575/9971/6799/7148/80157/7367/126/124872/51059/6338/2938/54576/125/10351/1103/57733/1576/8647/8029/5354/3284/2538/338/335/345/337
ID ：信號通路
Description ：信號通路的描述
setSize ：富集到該信號通路的基因個數(shù)
enrichmentScore ：富集分?jǐn)?shù)，也就是ES
NES ：標(biāo)準(zhǔn)化以后的ES，全稱normalized enrichment score
pvalue：富集的P值
p.adjust ：校正后的P值
qvalues ：FDR （false discovery rate）錯誤發(fā)現(xiàn)率
rank ：排名
core_enrichment：富集到該通路的基因列表。
氣泡圖解讀需要說明一下，富集程度通過Gene ratio、Pvalue和富集到此基因集上的基因個數(shù)來衡量。
橫坐標(biāo)是Gene ratio，數(shù)值越大表示富集程度越大。Count 位于該基因集下的差異表達(dá)基因數(shù)
縱坐標(biāo)是富集程度較高的基因集（一般選取富集最顯著的20條進(jìn)行展示，不足20條則全部列出）。
Pvalue取值范圍[0, 1]，以顏色表示，越紅表示Pvalue越小，說明富集越明顯。
點的大小表示該基因集下差異基因的個數(shù)，點越大表示基因數(shù)越多**。**
氣泡圖繪制，如下：
dotplot(gsea)
# Run GO and KEGG enrichment analysis #GO富集 GO <- gseGO( gene_fc, #gene_fc ont = "BP",# "BP"、"MF"和"CC"或"ALL" OrgDb = org.Hs.eg.db,#人類注釋基因 keyType = "ENTREZID", pvalueCutoff = 0.05, pAdjustMethod = "BH",#p值校正方法 ) head(GO[,1:4]) ## ID Description ## GO:0002250 GO:0002250 adaptive immune response ## GO:0002253 GO:0002253 activation of immune response ## GO:0002377 GO:0002377 immunoglobulin production ## GO:0002764 GO:0002764 immune response-regulating signaling pathway ## GO:0006139 GO:0006139 nucleobase-containing compound metabolic process ## GO:0006396 GO:0006396 RNA processing ## setSize enrichmentScore ## GO:0002250 110 -0.4726934 ## GO:0002253 50 -0.5141143 ## GO:0002377 51 -0.6152482 ## GO:0002764 52 -0.4982248 ## GO:0006139 443 0.3195185 ## GO:0006396 105 0.4952210
可以進(jìn)行一些調(diào)整以接近文獻(xiàn)
1）修改GSEA線條顏色
2）添加P值的table
3）展示指定的GO或pathway
4）展示多個GO或pathway
5）只展示上兩部
展示指定的通路，繪制GSEA 圖形，這里選擇我們注釋的一個結(jié)果 “GO:0002250”，如下：
gseaplot2(GO, "GO:0002250", color = "firebrick", rel_heights=c(1, .2, .6))
展示多個GO， 展示多個GSEA - GO 富集結(jié)果，添加P值的table，如下：
go<-GO$ID[1:4]
gseaplot2(GO,
go,
pvalue_table = TRUE,
color = colorspace::rainbow_hcl(4),
base_size = 10)
不添加P值的table，如下：
gseaplot2(GO,
go,
pvalue_table = FALSE,
color = colorspace::rainbow_hcl(4),
base_size = 10)
只展示上兩部，如下：
gseaplot2(GO,go,
color = colorspace::rainbow_hcl(4),
subplots=c(1,2),
pvalue_table = FALSE)
#KEGG富集 KEGG<-gseKEGG( gene_fc, organism = "hsa", keyType = "kegg", exponent = 1, minGSSize = 10, maxGSSize = 500, eps = 1e-10, pvalueCutoff = 0.05, pAdjustMethod = "BH", verbose = TRUE, use_internal_data = FALSE, seed = FALSE, by = "fgsea" ) sortKEGG<-KEGG[order(KEGG$enrichmentScore, decreasing = T),]#按照enrichment score從高到低排序 head(sortKEGG) ## ID Description setSize ## hsa04613 hsa04613 Neutrophil extracellular trap formation 36 ## hsa05322 hsa05322 Systemic lupus erythematosus 35 ## hsa05203 hsa05203 Viral carcinogenesis 25 ## hsa05034 hsa05034 Alcoholism 48 ## hsa04080 hsa04080 Neuroactive ligand-receptor interaction 81 ## hsa01100 hsa01100 Metabolic pathways 143 ## enrichmentScore NES pvalue p.adjust qvalues ## hsa04613 0.5819289 2.796643 6.536810e-08 3.819955e-06 2.637779e-06 ## hsa05322 0.5712915 2.717399 3.160108e-07 9.547559e-06 6.592841e-06 ## hsa05203 0.5117522 2.204931 3.377795e-04 3.247880e-03 2.242746e-03 ## hsa05034 0.4554528 2.402982 1.454689e-05 2.272951e-04 1.569533e-04 ## hsa04080 -0.2327878 -1.935807 1.679454e-03 1.208257e-02 8.343336e-03 ## hsa01100 -0.3054455 -3.049852 3.470165e-10 4.337706e-08 2.995300e-08 ## rank leading_edge ## hsa04613 731 tags=72%, list=25%, signal=55% ## hsa05322 731 tags=71%, list=25%, signal=54% ## hsa05203 520 tags=52%, list=18%, signal=43% ## hsa05034 865 tags=62%, list=30%, signal=44% ## hsa04080 792 tags=48%, list=28%, signal=36% ## hsa01100 933 tags=59%, list=32%, signal=42% ## core_enrichment ## hsa04613 8368/8346/8364/3018/8366/8360/8335/85235/8354/317772/8356/8331/8332/8367/8348/8342/8359/8344/8968/8340/8343/5582/2244/8336/8350/440689 ## hsa05322 8368/8346/8364/3018/8366/8360/8335/85235/8354/317772/8356/8331/8332/8367/8348/8342/8359/8344/8968/8340/8343/2904/8336/8350/440689 ## hsa05203 8368/8346/8364/3018/8366/8360/8367/8348/8342/8359/8344/8340/8343 ## hsa05034 8368/8346/8364/3018/8366/8360/8335/85235/8354/317772/8356/8331/8332/8367/8348/2906/8342/8359/8344/8968/8340/8343/2904/8336/8350/440689/7054/2792/1813/3013 ## hsa04080 3363/2903/6752/7068/155/5028/66004/1816/2893/9934/148/84539/2565/2901/3360/1907/2566/5179/1511/1908/2641/2587/553/5030/1129/2899/4852/7432/1268/185/22953/6865/9340/117/5539/4887/6750/5697/10022 ## hsa01100 51301/79153/5288/124/1562/771/5959/5408/5333/2878/377841/5322/7498/3158/534/4129/3291/129807/84803/10690/54988/11181/54657/5152/957/30815/5138/10317/8639/23382/57452/83539/5105/54511/80168/5742/25834/2949/111/54658/2875/197257/391013/57016/339221/27306/7366/4842/64902/79799/6476/60495/8708/10170/9227/3294/133121/5145/7166/229/54575/2651/290/6898/443/5837/270/7367/126/2938/54576/125/248/150572/760/57733/1576/762/8972/2939/1610/3284/766/2538/759
展示指定通路，如下：
gseaplot2(KEGG, "hsa04613", color = "firebrick", rel_heights=c(1, .2, .6))
展示指定多個通路，如下：
paths <- c("hsa04613", "hsa05322", "hsa05203", "hsa05034")#選取你需要展示的通路ID
gseaplot2(KEGG,
paths,
pvalue_table = TRUE,
color = colorspace::rainbow_hcl(4),
base_size = 20)
不添加Pvalue表格
gseaplot2(KEGG,paths,
color = colorspace::rainbow_hcl(4),
pvalue_table = FALSE)
只展示上面兩部分圖形，如下：
gseaplot2(KEGG,paths,
color = colorspace::rainbow_hcl(4),
subplots=c(1,2),
pvalue_table = TRUE)
可以選擇點圖 ES_geom = “dot”，如下：
gseaplot2(KEGG,paths,
color = colorspace::rainbow_hcl(4),
pvalue_table = FALSE)
1、圖最上面部分展示的是富集分?jǐn)?shù)（ES,enrichment score）值計算過程，從左至右每到一個基因，計算出一個ES值，連成線。在最左側(cè)或最右側(cè)有一個特別明顯的峰值就是基因集表型上的ES值。圖中間部分每一條線代表基因集中的一個基因，及其在基因列表中的排序位置。
2、最下面部分展示的是基因與表型關(guān)聯(lián)的矩陣，紅色為與第一個表型(class A)正相關(guān)，在class A中表達(dá)高，藍(lán)色與第二個表型(class B)正相關(guān)，在class B中表達(dá)高。
3、Leading-edge subset 對富集得分貢獻(xiàn)最大的基因成員。若富集得分為正值，則是峰左側(cè)的基因；若富集得分為負(fù)值，則是峰右側(cè)的基因。
4、FDR GSEA默認(rèn)提供所有的分析結(jié)果，并且設(shè)定FDR<0.25為可信的富集，最可能獲得有功能研究價值的結(jié)果。但如果樣品數(shù)目少，而且選擇了gene_set作為Permumation type則需要使用更為嚴(yán)格的標(biāo)準(zhǔn)，比如FDR<0.05。
個人感覺總結(jié)的已經(jīng)很全面了，不足的地方請大家指正。
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生物信息分析，SCI文章撰寫及生物信息基礎(chǔ)知識學(xué)習(xí)：R語言學(xué)習(xí)，perl基礎(chǔ)編程，linux系統(tǒng)命令，Python遇見更好的你
47篇原創(chuàng)內(nèi)容
公眾號
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