中国人肝癌全基因组项目部分图表重现

生信菜鸟团

发布于 2024-04-25 18:27:06

1140

发布于 2024-04-25 18:27:06

项目简介

前面推文介绍过文章 Deep whole-genome analysis of 494 hepatocellular carcinomas，详情见：中国人肝癌全基因组项目。

该项目包含494个肝癌病人的 WGS分析结果。作者在文章附件上传了部分数据，同时构建了网页数据库供读者使用。因对文章结果感兴趣，因此从文章附件和网页数据库：http://lifeome.net:8080/clca/#/下载了部分数据进行文章图表重现，数据包括：病人的临床信息、体细胞突变结果，突变特征、拷贝数变异、结构变异、ecDNA等。因为方法上的差异，所以重现结果无法做到和原文一致，如有差异，请以原文分析结果为准。

数据处理

数据下载

这次重现数据来自于文章附件和网页数据库，无需注册登录即可直接下载，很方便：

临床信息

从数据库下载到的临床信息，有 494 个患者，相关的信息有：Province、 Gender、 BCLC、 Age、 Hepatitis、 Cirrhosis/Fibrosis、 Edmondson、 Smoking、 Alcohol、 Multiple、 lesions、 Recurrence、 Death，前 20 位患者的临床信息如下表所示：

# 情况环境并载入R包
rm(list = ls())
library(maftools)
library(stringr)
library(ggpubr)
library(tidyr)
library(data.table)
library(pheatmap)
library(ggrepel)
library(ggsci)
library(ggplot2)
library(VennDiagram)
library(ggVennDiagram)

clinical = rio::import("Cases_20240315.xlsx")
head(clinical,n=20)

CaseID	Province	Gender	BCLC	Age	Hepatitis	Cirrhosis/Fibrosis	Edmondson	Smoking	Alcohol	Multiple lesions	Recurrence	Death
CLCA_0001	Fujian	Male	A	63	HBV	Cirrhosis	Level III	No	No	No	No	No
CLCA_0002	Henan	Female	A	76	HBV	Cirrhosis	Level III	No	No	No	No	No
CLCA_0003	Jiangsu	Male	C	61	HBV	Cirrhosis	Level III	Yes	Yes	No	Yes	Not Available
CLCA_0004	Zhejiang	Male	A	66	HBV	Cirrhosis	Level III	Yes	Yes	No	Not Available	Not Available
CLCA_0005	Jiangsu	Male	B	74	HBV	Fibrosis	Level III	No	No	No	No	No
CLCA_0006	Jiangxi	Male	B	65	HBV	Fibrosis	Level III	No	No	No	No	No
CLCA_0007	Zhejiang	Male	B	68	HBV	Cirrhosis	Level II	Yes	No	No	Not Available	Not Available
CLCA_0008	Jiangsu	Male	C	66	HBV	Cirrhosis	Level III	Yes	Yes	Yes	Yes	Yes
CLCA_0009	Jiangsu	Male	B	69	HBV	Cirrhosis	Level III	Yes	Yes	Yes	No	No
CLCA_0010	Zhejiang	Male	0	65	HBV	Fibrosis	Level III	No	No	No	Yes	No
CLCA_0011	Liaoning	Male	B	64	HBV	Fibrosis	Level III	Yes	No	No	Not Available	Not Available
CLCA_0012	Anhui	Male	B	74	HBV	Fibrosis	Level III	No	No	No	Yes	No
CLCA_0013	Fujian	Male	C	57	HBV	Fibrosis	Level III	Yes	Yes	Yes	Not Available	Not Available
CLCA_0014	Jiangsu	Male	C	70	HBV	Fibrosis	Level III	Yes	No	No	Yes	Yes
CLCA_0015	Anhui	Male	C	49	HBV	Cirrhosis	Level III	Yes	No	No	Not Available	Not Available
CLCA_0016	Jiangsu	Male	A	47	HBV	Fibrosis	Level III	No	No	No	No	No
CLCA_0017	Fujian	Male	C	61	HBV	Fibrosis	Level III	No	No	Yes	Not Available	Not Available
CLCA_0018	Jiangsu	Male	B	60	HBV	Cirrhosis	Level III	Yes	Yes	Yes	Not Available	Not Available
CLCA_0019	Jiangxi	Male	B	79	HBV	Cirrhosis	Level II	No	No	No	No	No
CLCA_0020	Zhejiang	Male	0	56	HBV	Cirrhosis	Level III	No	No	No	Yes	No

突变信息

虽然文章中提到鉴定出来的突变有 9287828个，但下载得到的突变信息 Excel表格（可以简单处理为maf格式），显示的也只有 283223个突变位点，这个比例约为3%。因为上传的是注释后的结果，作者WGS 得到的 9287828个突变位点，有很多是落在非编码区或者未知的区域的，只有 283223个约3%的突变位点可以被注释到。

somatic = rio::import("Mutations_20240314.xlsx")
head(somatic,n=20)

CaseID	Gene	Chr	Start	End	Classification	Type	Ref	Allele	RefReads	AlleleReads	c.HGVS	p.HGVS	transcript
CLCA_0001	RNF223	chr1	1006750	1006750	3'UTR	SNP	A	G	143	18	.	.	.
CLCA_0001	PRKCZ	chr1	2062535	2062535	promoter	SNP	T	C	129	45	.	.	.
CLCA_0001	PRKCZ	chr1	2103770	2103770	nonsynonymous SNV	SNP	A	T	123	56	c.1228A>T	p.T410S	NM_002744
CLCA_0001	LINC00982	chr1	2978977	2978977	lncRNA	SNP	A	T	194	19	.	.	.
CLCA_0001	PRDM16	chr1	3352980	3352980	3'UTR	SNP	G	A	161	79	.	.	.
CLCA_0001	LINC01134	chr1	3831499	3831499	lncRNA	SNP	A	T	129	56	.	.	.
CLCA_0001	AJAP1	chr1	4849691	4849691	3'UTR	SNP	A	T	178	60	.	.	.
CLCA_0001	CHD5	chr1	6163698	6163698	3'UTR	SNP	A	T	173	17	.	.	.
CLCA_0001	ICMT	chr1	6293565	6293565	nonsynonymous SNV	SNP	T	A	136	49	c.423A>T	p.L141F	NM_012405
CLCA_0001	HES2	chr1	6472873	6472873	3'UTR	SNP	T	A	167	8	.	.	.
CLCA_0001	HES2	chr1	6476021	6476021	3'UTR	SNP	A	T	149	52	.	.	.
CLCA_0001	ESPN	chr1	6520683	6520683	3'UTR	SNP	C	T	103	39	.	.	.
CLCA_0001	LOC102725193	chr1	7449404	7449404	lncRNA	SNP	C	T	195	82	.	.	.
CLCA_0001	RERE	chr1	8418104	8418104	promoter	SNP	C	A	202	10	.	.	.
CLCA_0001	G000447	chr1	9217104	9217104	lncRNA	SNP	T	A	199	15	.	.	.
CLCA_0001	H6PD	chr1	9323798	9323798	nonsynonymous SNV	SNP	A	T	136	59	c.1246A>T	p.R416W	NM_004285
CLCA_0001	H6PD	chr1	9324512	9324512	nonsynonymous SNV	SNP	A	T	126	57	c.1960A>T	p.M654L	NM_004285
CLCA_0001	NMNAT1	chr1	10042751	10042751	stopgain	SNP	A	T	148	57	c.832A>T	p.K278*	NM_022787
CLCA_0001	G000514	chr1	10686025	10686025	lncRNA	SNP	A	T	187	20	.	.	.
CLCA_0001	EXOSC10	chr1	11151098	11151098	nonsynonymous SNV	SNP	G	A	180	11	c.616C>T	p.P206S	NM_001001998

# 但是作者文章中方法部分没有提到注释用到的软件，查看其突变注释分类可以看到并非像 VEP 、ANNOVAR 软件注释的
table(somatic$Classification)

## 
##                      3'UTR                      5'UTR 
##                      73142                      20544 
##        frameshift deletion       frameshift insertion 
##                       1971                        698 
##                     lncRNA                lncrna.prom 
##                      48380                      10845 
##     nonframeshift deletion    nonframeshift insertion 
##                        435                         66 
## nonframeshift substitution          nonsynonymous SNV 
##                        409                      52418 
##                   promoter                   splicing 
##                      67674                       2349 
##                  startloss                   stopgain 
##                        158                       4001 
##                   stoploss 
##                        133

图表重现

突变图谱

文章中的 fig 1b是体细胞突变图谱，展示的是每个患者特定基因的突变情况，患者有添加上临床信息

# 简单将数据处理一下，以方便后续进行 maftools 处理及可视化
colnames(somatic) = c("Tumor_Sample_Barcode","Hugo_Symbol","Chromosome",
                      "Start_Position","End_Position","Strand","Variant_Classification",
                      "Variant_Type","Reference_Allele","Tumor_Seq_Allele2","RefReads","AlleleReads",  
                      "c.HGVS","p.HGVS","transcript")
colnames(clinical)[1] = "Tumor_Sample_Barcode"
# 将临床信息和突变信息读入到 maftools中
maf = read.maf(maf = somatic,vc_nonSyn=unique(somatic$Variant_Classification),clinicalData = clinical)

## -Validating
## --Non MAF specific values in Variant_Classification column:
##   promoter
##   nonsynonymous SNV
##   lncRNA
##   stopgain
##   splicing
##   lncrna.prom
##   nonframeshift substitution
##   frameshift deletion
##   stoploss
##   frameshift insertion
##   startloss
##   nonframeshift deletion
##   nonframeshift insertion
## -Summarizing
## --Possible FLAGS among top ten genes:
##   TTN
## -Processing clinical data
## -Finished in 7.440s elapsed (49.0s cpu)

# 可以从文章附件中提取到 oncogenes
onco_genes=read.table("onco_genes.txt",header = F)[,1]

# 突变图谱可视化，添加上临床信息
oncoplot(maf,
         genes = onco_genes,
         keepGeneOrder = T,
         annotationFontSize = 1.2,
         legendFontSize = 1.0,
         removeNonMutated = FALSE,
         anno_height = 2,
         clinicalFeatures = c("Gender",
                              "Hepatitis",
                              "BCLC",
                              "Cirrhosis/Fibrosis",
                              "Edmondson",
                              "Multiple_lesions",
                              "Smoking",
                              "Alcohol",
                              "Recurrence")
         )

结果显示只有493名患者，少了一位，但这从下载到的数据就是这样，处理过程并没有改变患者数量，缺失的患者 ID 是 CLCA_0209，从数据库网页下载到的表格中就缺失这个患者的突变信息

sort(unique(somatic$Tumor_Sample_Barcode))

##   [1] "CLCA_0001" "CLCA_0002" "CLCA_0003" "CLCA_0004" "CLCA_0005" "CLCA_0006"
##   [7] "CLCA_0007" "CLCA_0008" "CLCA_0009" "CLCA_0010" "CLCA_0011" "CLCA_0012"
##  [13] "CLCA_0013" "CLCA_0014" "CLCA_0015" "CLCA_0016" "CLCA_0017" "CLCA_0018"
##  [19] "CLCA_0019" "CLCA_0020" "CLCA_0021" "CLCA_0022" "CLCA_0023" "CLCA_0024"
##  [25] "CLCA_0025" "CLCA_0026" "CLCA_0027" "CLCA_0028" "CLCA_0029" "CLCA_0030"
##  [31] "CLCA_0031" "CLCA_0032" "CLCA_0033" "CLCA_0034" "CLCA_0035" "CLCA_0036"
##  [37] "CLCA_0037" "CLCA_0038" "CLCA_0039" "CLCA_0040" "CLCA_0041" "CLCA_0042"
##  [43] "CLCA_0043" "CLCA_0044" "CLCA_0045" "CLCA_0046" "CLCA_0047" "CLCA_0048"
##  [49] "CLCA_0049" "CLCA_0050" "CLCA_0051" "CLCA_0052" "CLCA_0053" "CLCA_0054"
##  [55] "CLCA_0055" "CLCA_0056" "CLCA_0057" "CLCA_0058" "CLCA_0059" "CLCA_0060"
##  [61] "CLCA_0061" "CLCA_0062" "CLCA_0063" "CLCA_0064" "CLCA_0065" "CLCA_0066"
##  [67] "CLCA_0067" "CLCA_0068" "CLCA_0069" "CLCA_0070" "CLCA_0071" "CLCA_0072"
##  [73] "CLCA_0073" "CLCA_0074" "CLCA_0075" "CLCA_0076" "CLCA_0077" "CLCA_0078"
##  [79] "CLCA_0079" "CLCA_0080" "CLCA_0081" "CLCA_0082" "CLCA_0083" "CLCA_0084"
##  [85] "CLCA_0085" "CLCA_0086" "CLCA_0087" "CLCA_0088" "CLCA_0089" "CLCA_0090"
##  [91] "CLCA_0091" "CLCA_0092" "CLCA_0093" "CLCA_0094" "CLCA_0095" "CLCA_0096"
##  [97] "CLCA_0097" "CLCA_0098" "CLCA_0099" "CLCA_0100" "CLCA_0101" "CLCA_0102"
## [103] "CLCA_0103" "CLCA_0104" "CLCA_0105" "CLCA_0106" "CLCA_0107" "CLCA_0108"
## [109] "CLCA_0109" "CLCA_0110" "CLCA_0111" "CLCA_0112" "CLCA_0113" "CLCA_0114"
## [115] "CLCA_0115" "CLCA_0116" "CLCA_0117" "CLCA_0118" "CLCA_0119" "CLCA_0120"
## [121] "CLCA_0121" "CLCA_0122" "CLCA_0123" "CLCA_0124" "CLCA_0125" "CLCA_0126"
## [127] "CLCA_0127" "CLCA_0128" "CLCA_0129" "CLCA_0130" "CLCA_0131" "CLCA_0132"
## [133] "CLCA_0133" "CLCA_0134" "CLCA_0135" "CLCA_0136" "CLCA_0137" "CLCA_0138"
## [139] "CLCA_0139" "CLCA_0140" "CLCA_0141" "CLCA_0142" "CLCA_0143" "CLCA_0144"
## [145] "CLCA_0145" "CLCA_0146" "CLCA_0147" "CLCA_0148" "CLCA_0149" "CLCA_0150"
## [151] "CLCA_0151" "CLCA_0152" "CLCA_0153" "CLCA_0154" "CLCA_0155" "CLCA_0156"
## [157] "CLCA_0157" "CLCA_0158" "CLCA_0159" "CLCA_0160" "CLCA_0161" "CLCA_0162"
## [163] "CLCA_0163" "CLCA_0164" "CLCA_0165" "CLCA_0166" "CLCA_0167" "CLCA_0168"
## [169] "CLCA_0169" "CLCA_0170" "CLCA_0171" "CLCA_0172" "CLCA_0173" "CLCA_0174"
## [175] "CLCA_0175" "CLCA_0176" "CLCA_0177" "CLCA_0178" "CLCA_0179" "CLCA_0180"
## [181] "CLCA_0181" "CLCA_0182" "CLCA_0183" "CLCA_0184" "CLCA_0185" "CLCA_0186"
## [187] "CLCA_0187" "CLCA_0188" "CLCA_0189" "CLCA_0190" "CLCA_0191" "CLCA_0192"
## [193] "CLCA_0193" "CLCA_0194" "CLCA_0195" "CLCA_0196" "CLCA_0197" "CLCA_0198"
## [199] "CLCA_0199" "CLCA_0200" "CLCA_0201" "CLCA_0202" "CLCA_0203" "CLCA_0204"
## [205] "CLCA_0205" "CLCA_0206" "CLCA_0207" "CLCA_0208" "CLCA_0210" "CLCA_0211"
## [211] "CLCA_0212" "CLCA_0213" "CLCA_0214" "CLCA_0215" "CLCA_0216" "CLCA_0217"
## [217] "CLCA_0218" "CLCA_0219" "CLCA_0220" "CLCA_0221" "CLCA_0222" "CLCA_0223"
## [223] "CLCA_0224" "CLCA_0225" "CLCA_0226" "CLCA_0227" "CLCA_0228" "CLCA_0229"
## [229] "CLCA_0230" "CLCA_0231" "CLCA_0232" "CLCA_0233" "CLCA_0234" "CLCA_0235"
## [235] "CLCA_0236" "CLCA_0237" "CLCA_0238" "CLCA_0239" "CLCA_0240" "CLCA_0241"
## [241] "CLCA_0242" "CLCA_0243" "CLCA_0244" "CLCA_0245" "CLCA_0246" "CLCA_0247"
## [247] "CLCA_0248" "CLCA_0249" "CLCA_0250" "CLCA_0251" "CLCA_0252" "CLCA_0253"
## [253] "CLCA_0254" "CLCA_0255" "CLCA_0256" "CLCA_0257" "CLCA_0258" "CLCA_0259"
## [259] "CLCA_0260" "CLCA_0261" "CLCA_0262" "CLCA_0263" "CLCA_0264" "CLCA_0265"
## [265] "CLCA_0266" "CLCA_0267" "CLCA_0268" "CLCA_0269" "CLCA_0270" "CLCA_0271"
## [271] "CLCA_0272" "CLCA_0273" "CLCA_0274" "CLCA_0275" "CLCA_0276" "CLCA_0277"
## [277] "CLCA_0278" "CLCA_0279" "CLCA_0280" "CLCA_0281" "CLCA_0282" "CLCA_0283"
## [283] "CLCA_0284" "CLCA_0285" "CLCA_0286" "CLCA_0287" "CLCA_0288" "CLCA_0289"
## [289] "CLCA_0290" "CLCA_0291" "CLCA_0292" "CLCA_0293" "CLCA_0294" "CLCA_0295"
## [295] "CLCA_0296" "CLCA_0297" "CLCA_0298" "CLCA_0299" "CLCA_0300" "CLCA_0301"
## [301] "CLCA_0302" "CLCA_0303" "CLCA_0304" "CLCA_0305" "CLCA_0306" "CLCA_0307"
## [307] "CLCA_0308" "CLCA_0309" "CLCA_0310" "CLCA_0311" "CLCA_0312" "CLCA_0313"
## [313] "CLCA_0314" "CLCA_0315" "CLCA_0316" "CLCA_0317" "CLCA_0318" "CLCA_0319"
## [319] "CLCA_0320" "CLCA_0321" "CLCA_0322" "CLCA_0323" "CLCA_0324" "CLCA_0325"
## [325] "CLCA_0326" "CLCA_0327" "CLCA_0328" "CLCA_0329" "CLCA_0330" "CLCA_0331"
## [331] "CLCA_0332" "CLCA_0333" "CLCA_0334" "CLCA_0335" "CLCA_0336" "CLCA_0337"
## [337] "CLCA_0338" "CLCA_0339" "CLCA_0340" "CLCA_0341" "CLCA_0342" "CLCA_0343"
## [343] "CLCA_0344" "CLCA_0345" "CLCA_0346" "CLCA_0347" "CLCA_0348" "CLCA_0349"
## [349] "CLCA_0350" "CLCA_0351" "CLCA_0352" "CLCA_0353" "CLCA_0354" "CLCA_0355"
## [355] "CLCA_0356" "CLCA_0357" "CLCA_0358" "CLCA_0359" "CLCA_0360" "CLCA_0361"
## [361] "CLCA_0362" "CLCA_0363" "CLCA_0364" "CLCA_0365" "CLCA_0366" "CLCA_0367"
## [367] "CLCA_0368" "CLCA_0369" "CLCA_0370" "CLCA_0371" "CLCA_0372" "CLCA_0373"
## [373] "CLCA_0374" "CLCA_0375" "CLCA_0376" "CLCA_0377" "CLCA_0378" "CLCA_0379"
## [379] "CLCA_0380" "CLCA_0381" "CLCA_0382" "CLCA_0383" "CLCA_0384" "CLCA_0385"
## [385] "CLCA_0386" "CLCA_0387" "CLCA_0388" "CLCA_0389" "CLCA_0390" "CLCA_0391"
## [391] "CLCA_0392" "CLCA_0393" "CLCA_0394" "CLCA_0395" "CLCA_0396" "CLCA_0397"
## [397] "CLCA_0398" "CLCA_0399" "CLCA_0400" "CLCA_0401" "CLCA_0402" "CLCA_0403"
## [403] "CLCA_0404" "CLCA_0405" "CLCA_0406" "CLCA_0407" "CLCA_0408" "CLCA_0409"
## [409] "CLCA_0410" "CLCA_0411" "CLCA_0412" "CLCA_0413" "CLCA_0414" "CLCA_0415"
## [415] "CLCA_0416" "CLCA_0417" "CLCA_0418" "CLCA_0419" "CLCA_0420" "CLCA_0421"
## [421] "CLCA_0422" "CLCA_0423" "CLCA_0424" "CLCA_0425" "CLCA_0426" "CLCA_0427"
## [427] "CLCA_0428" "CLCA_0429" "CLCA_0430" "CLCA_0431" "CLCA_0432" "CLCA_0433"
## [433] "CLCA_0434" "CLCA_0435" "CLCA_0436" "CLCA_0437" "CLCA_0438" "CLCA_0439"
## [439] "CLCA_0440" "CLCA_0441" "CLCA_0442" "CLCA_0443" "CLCA_0444" "CLCA_0445"
## [445] "CLCA_0446" "CLCA_0447" "CLCA_0448" "CLCA_0449" "CLCA_0450" "CLCA_0451"
## [451] "CLCA_0452" "CLCA_0453" "CLCA_0454" "CLCA_0455" "CLCA_0456" "CLCA_0457"
## [457] "CLCA_0458" "CLCA_0459" "CLCA_0460" "CLCA_0461" "CLCA_0462" "CLCA_0463"
## [463] "CLCA_0464" "CLCA_0465" "CLCA_0466" "CLCA_0467" "CLCA_0468" "CLCA_0469"
## [469] "CLCA_0470" "CLCA_0471" "CLCA_0472" "CLCA_0473" "CLCA_0474" "CLCA_0475"
## [475] "CLCA_0476" "CLCA_0477" "CLCA_0478" "CLCA_0479" "CLCA_0480" "CLCA_0481"
## [481] "CLCA_0482" "CLCA_0483" "CLCA_0484" "CLCA_0485" "CLCA_0486" "CLCA_0487"
## [487] "CLCA_0488" "CLCA_0489" "CLCA_0490" "CLCA_0491" "CLCA_0492" "CLCA_0493"
## [493] "CLCA_0494"

文章中的突变图谱还对患者进行了分组，Group1 是在oncogene 发生coding突变的患者,Group2则为仅发生 synonymous 突变的患者，Group3为在oncogene上未发生突变的患者（在其他基因有发生突变）。结果显示Group1为418人，Group2为39人，Group3为36人，另外前面提到过突变信息缺少一名患者CLCA_0209。

onco_genes_group1 = onco_genes[1:23]
onco_genes_group2 = onco_genes[24:54]
coding_mutations = c("nonsynonymous SNV",
                     "stopgain",
                     "splicing",
                     "nonframeshift substitution",
                     "frameshift deletion",
                     "stoploss",
                     "frameshift insertion",
                     "startloss",
                     "nonframeshift deletion",
                     "nonframeshift insertion"
                     )
noncoding_mutations = c("3'UTR","5'UTR","lncRNA","lncrna.prom","promoter")

group1.id = unique(somatic[(somatic$Hugo_Symbol %in% onco_genes_group1) & (somatic$Variant_Classification %in% coding_mutations), 1])

group2.id = setdiff(unique(somatic[(somatic$Hugo_Symbol %in% onco_genes_group2) , 1]),group1.id) 

group3.id = setdiff(unique(somatic$Tumor_Sample_Barcode), c(group1.id,group2.id))
group.df = data.frame(Tumor_Sample_Barcode = c(group1.id,
                                               group2.id,
                                               group3.id),
                      Group = c(rep("Group1",times=length(group1.id)),
                                rep("Group2",times=length(group2.id)),
                                rep("Group3",times=length(group3.id)))
                      )
table(group.df$Group)

## 
## Group1 Group2 Group3 
##    418     39     36

重新做突变图谱可视化加上 Group 分组信息：

clinical = merge(clinical,group.df,by="Tumor_Sample_Barcode")
maf = read.maf(maf = somatic,
               vc_nonSyn=unique(somatic$Variant_Classification),
               clinicalData = clinical)

## -Validating
## --Non MAF specific values in Variant_Classification column:
##   promoter
##   nonsynonymous SNV
##   lncRNA
##   stopgain
##   splicing
##   lncrna.prom
##   nonframeshift substitution
##   frameshift deletion
##   stoploss
##   frameshift insertion
##   startloss
##   nonframeshift deletion
##   nonframeshift insertion
## -Summarizing
## --Possible FLAGS among top ten genes:
##   TTN
## -Processing clinical data
## -Finished in 8.028s elapsed (52.9s cpu)

# 添加上临床信息
oncoplot(maf,
         genes = onco_genes,
         keepGeneOrder = T,
         sortByAnnotation = T,
         annotationFontSize = 1.2,
         legendFontSize = 1.0,
         removeNonMutated = FALSE,
         anno_height = 2,
         clinicalFeatures = c("Group",
                              "Gender",
                              "Hepatitis",
                              "BCLC",
                              "Cirrhosis/Fibrosis",
                              "Edmondson",
                              "Multiple_lesions",
                              "Smoking",
                              "Alcohol",
                              "Recurrence")
         )

突变特征

作者使用的是 mSigHdp 和 SigProfilerExtractor 包进行突变特征分析：

We used mSigHdp (v.1.1.2) and SigProfilerExtractor from SigProfiler bioinformatics tool suite (v.1.1.0)6 to extract SBS, DBS and ID signatures.For SigProfiler signature extraction, 1,000 iterations were performed (nmf_replicates = 1000). We report only signatures supported by both mSigHdp and SigProfiler.

得到的Signature 结果是：

We identified 17 single-base substitution (SBS), 3 doublet-base substitution (DBS) and 8 small insertion-and-deletion (ID) signatures.

除了正文的 fig2 之外，还有 Extended Data fig2

考虑到作者用的方法较为复杂，这里改用maftools 里的signature 分析流程和 sigminer 包的分析流程两种方法：

# 突变特征方法一：maftools ----
library(maftools)
library(NMF)
library(pheatmap)
library(barplot3d)
library(BSgenome.Hsapiens.UCSC.hg19)
# 先构建三连核苷酸矩阵
maf.tnm = trinucleotideMatrix(maf = maf, 
                              #prefix = 'chr', 
                              #add = TRUE, 
                              ref_genome = "BSgenome.Hsapiens.UCSC.hg19")

## -Extracting 5' and 3' adjacent bases
## -Extracting +/- 20bp around mutated bases for background C>T estimation
## -Estimating APOBEC enrichment scores
## --Performing one-way Fisher's test for APOBEC enrichment
## ---APOBEC related mutations are enriched in  0.408 % of samples (APOBEC enrichment score > 2 ;  2  of  490  samples)
## -Creating mutation matrix
## --matrix of dimension 493x96

# 运行 NMF非负矩阵分解，并拟合
# 如果突变较少，需要设置 pConstant = 0.1
maf.sign = estimateSignatures(mat = maf.tnm, nTry = 12)

## -Running NMF for 12 ranks
## Compute NMF rank= 2  ... + measures ... OK
## Compute NMF rank= 3  ... + measures ... OK
## Compute NMF rank= 4  ... + measures ... OK
## Compute NMF rank= 5  ... + measures ... OK
## Compute NMF rank= 6  ... + measures ... OK
## Compute NMF rank= 7  ... + measures ... OK
## Compute NMF rank= 8  ... + measures ... OK
## Compute NMF rank= 9  ... + measures ... OK
## Compute NMF rank= 10  ... + measures ... OK
## Compute NMF rank= 11  ... + measures ... OK
## Compute NMF rank= 12  ... + measures ... OK

## -Finished in 00:07:04 elapsed (00:01:26 cpu)

# 确定最佳突变特征数量
plotCophenetic(res = maf.sign)

# 使用非负矩阵分解将矩阵分解为n签名
maf.sig = extractSignatures(mat = maf.tnm, n = 5)
# 与 COSMIC 的突变特征比较，计算余弦相似度
maf.v3.cosm = compareSignatures(nmfRes = maf.sig, sig_db = "SBS")
# 热图展示余弦相似度
pheatmap::pheatmap(mat = maf.v3.cosm$cosine_similarities, cluster_rows = FALSE, main = "cosine similarity against validated signatures")

# 可视化突变特征
maftools::plotSignatures(nmfRes = maf.sig, title_size = 1.2, sig_db = "SBS")

从 maftools 的突变特征分析结果上看，得到的 5 个突变特征分别与 COSMIC 数据库的 SBS30、SBS24、SBS6、SBS5、SBS22 余弦相似度较高。这与原文的结果相差较大，且 maftools 的方法仅分析 SBS 模式的 signature，如果要分析 DBS 或者 INDEL 等 signature，可以使用 sigminer（虽然sigminer 也提供了 SigProfiler的方法，不过用法也相对复杂，这里暂时不考虑。） sigminer 分析的 SBS突变特征有 8个，DBS 有4个，INDEL 有 8个：

# 突变特征方法二：sigminer ----
library(sigminer)
## SBS ----
mt_tally <- sig_tally(
  maf,
  ref_genome = "BSgenome.Hsapiens.UCSC.hg19",
  useSyn = TRUE,
  mode = "SBS"
)
mt_sig2 <- sig_unify_extract(mt_tally$nmf_matrix, 
                             range = 10, 
                             nrun = 10)

## 10000 24224.97 25193.74 315481.7 2.800485e-07 8 8 
## 10000 24616.58 24754.58 303697.1 3.845836e-06 9 9 
## 20000 24616.44 24750.54 303665.8 5.317353e-07 9 9 
## 10000 24616.41 24748.37 303630.9 5.924343e-06 9 9 
## 20000 24614.28 24739.68 303965.6 2.87272e-05 9 9 
## 30000 24612.43 24743.56 304385.7 2.572798e-06 9 9 
## 10000 24314.31 25043.34 315276.9 0.0001300736 8 8 
## 20000 24294.42 25089.31 316380.3 5.047734e-06 8 8 
## 30000 24292.8 25085.5 315789.9 2.433658e-06 8 8 
## 40000 24287.52 25093.22 315099.2 1.110607e-05 8 8 
## 50000 24284.55 25087.06 314354.7 8.901478e-05 8 8 
## 10000 24605.98 24685.06 304196 1.692444e-05 9 9 
## 10000 23889.64 25687.55 328564.6 4.943007e-07 7 7 
## 10000 24226.84 25185.32 316387.9 1.451832e-07 8 8 
## 10000 24604.67 24688.01 303900.7 2.310264e-06 9 9

sim <- get_sig_similarity(mt_sig2, sig_db = "SBS")
pheatmap::pheatmap(sim$similarity)

show_sig_profile(mt_sig2, mode = "SBS", style = "cosmic", x_label_angle = 90)

## DBS ----
mt_tally_DBS <- sig_tally(
  maf,
  ref_genome = "BSgenome.Hsapiens.UCSC.hg19",
  useSyn = TRUE,
  mode = "DBS"
)
mt_sig2_DBS <- sig_unify_extract(mt_tally_DBS$nmf_matrix, 
                             range = 10, 
                             nrun = 10)

sim_DBS <- get_sig_similarity(mt_sig2_DBS, sig_db = "DBS")
pheatmap::pheatmap(sim_DBS$similarity)

show_sig_profile(mt_sig2_DBS, mode = "DBS", style = "cosmic", x_label_angle = 90)

## INDEL ----
mt_tally_ID <- sig_tally(
  maf,
  ref_genome = "BSgenome.Hsapiens.UCSC.hg19",
  useSyn = TRUE,
  mode = "ID"
)
mt_sig2_ID <- sig_unify_extract(mt_tally_ID$nmf_matrix, 
                             range = 10, 
                             nrun = 10)

sim_ID <- get_sig_similarity(mt_sig2_ID, sig_db = "ID")
pheatmap::pheatmap(sim_ID$similarity)

show_sig_profile(mt_sig2_ID, mode = "ID", style = "cosmic", x_label_angle = 90)

ecDNA 分析

文章的 fig 3 a是ecDNA分析，以饼图形式展示，类型有 BFB、Circular(ecDNA)、Heavily rearranged、Linear 和 No fSCNA 其中 fig3a 原文注释信息是：

The proportion of different amplicons across the CLCA cohort. Circular, breakage–fusion–bridge (BFB), heavily rearranged and linear, and no focal somatic copy-number amplification detected (fSCNA) amplicon categories are shown.

且正文中也提到了：

ecDNA was detected in 27.3% of CLCA tumours

如果这个对应饼图的 Circular(ecDNA) 部分，那就是说在 27.3% 的肿瘤患者中检测到了 ecDNA 事件。

我们可以在文章附件可以找到该图的数据，且数据显示，每一个患者可能发生4种 amp 事件的任意组合。（注：文章上传的附件Supplementary Table 4：41586_2024_7054_MOESM6_ESM.xlsx 中Table 4g 第一行第三列是 Heavily rearranged rearranged，我的理解应该改为 Heavily rearranged ，以下读入的数据仅手动修改了这一项，其余的没做修改）

amp = readxl::read_xlsx("41586_2024_7054_MOESM6_ESM.xlsx",sheet = 7,skip = 2)
amp = as.data.frame(amp)
# 第一列是患者ID，
# 第二列是amp的类型，
# 第三列是发生某一 amp 类型的 interval counts 数
head(amp,n=100)

sample_name	class	NIntervals	Intervals	OncogenesAmplified	TotalIntervalSize	AmplifiedIntervalSize	AverageAmplifiedCopyCount	Chromosomes	SeqenceEdges	BreakpointEdges	CoverageShifts	MeanshiftSegmentsCopyCount>5	Foldbacks	CoverageShiftsWithBreakpointEdges
CLCA_0001	Heavily rearranged rearranged	9	chr1:179385001-203717000,chr3:129798365-129808957,chr4:9699978-9720571,chr7:5927483-5948075,chr7:6851001-39963000,chr7:62463058-62473650,chr8:18014530-18025122,chr15:40853596-40864189,chr21:33796774-33807367	ETV1,CDC73,HOXA13,JAZF1,HOXA11,PTPRC,HNRNPA2B1,HOXA9,TPR,	57538154	52083461	2.765754199	7	145	59	0	0	0	0
CLCA_0001	Heavily rearranged	4	chr7:149730451-149741044,chr7:152588001-159138663,chr10:98451408-98662000,chr18:19772106-19792698	,	6792443	1210371	2.635659582	3	86	34	1	0	0	1
CLCA_0001	Circular	10	chr1:112603401-112813993,chr3:56765044-56775637,chr6:119458107-119568700,chr7:105224001-148714000,chr9:6484724-6695316,chr9:33786290-33806883,chr10:20048538-20059130,chr10:35290516-35311108,chr12:74004366-74014958,chr16:26592312-26612904	CREB3L2,KIAA1549,POT1,SMO,MET,EZH2,BRAF,	44115340	33806618	2.657480486	8	189	77	10	0	2	9
CLCA_0001	Heavily rearranged	10	chr1:26455415-26466008,chr5:94594394-94604987,chr7:64739205-64749797,chr11:77108686-77119279,chr12:34386996-34413582,chr12:34419001-34560000,chr12:38392853-38603445,chr13:27923699-28134291,chr19:23248743-23259335,chr19:28301468-28342061	,	682335	177647	4.184546951	7	113	25	0	0	0	0
CLCA_0001	Heavily rearranged	5	chr1:17217822-17238415,chr1:144593226-144603819,chr1:146382400-147865001,chr1:148549086-148559679,chr1:149205805-149246397	BCL9,	1564977	1074755	2.731614502	1	36	13	3	0	0	3
CLCA_0006	Circular	6	chr4:8438956-8449575,chr6:16220142-16230760,chr7:140356252-140376871,chr10:44079274-44099893,chr11:68389001-69076000,chr19:50449724-50460342	,	760098	700923	6.917366833	6	35	10	1	0	1	1
CLCA_0008	Linear	1	chr12:1-5343000	CCND2,KDM5A,	5343000	58827	3.867129349	1	18	6	3	0	2	3
CLCA_0008	Heavily rearranged	4	chr2:179287992-179308657,chr17:46222000-78477201,chr19:28933400-33881601,chrX:48988160-49017173	CEBPA,CCNE1,CANT1,SRSF2,MSI2,COL1A1,RNF43,DDX5,PRKAR1A,CLTC,BRIP1,HLF,CD79B,H3F3B,	37253084	32087537	2.673472586	4	101	37	5	0	0	5
CLCA_0008	Heavily rearranged	5	chr1:16865110-16885775,chr1:16987734-17018400,chr1:144593611-144614277,chr1:146382400-147845001,chr1:148539013-148559679	BCL9,	1555269	1143064	3.077854662	1	20	5	1	0	0	1
CLCA_0010	Heavily rearranged	10	chr4:435628-456203,chr5:34438809-34459385,chr5:94594411-94604987,chr6:119458120-119568696,chr7:64873330-64896483,chr8:47908000-146364022,chr10:50452244-50462819,chr12:132926005-132946581,chr18:29064978-29085553,chr19:28319060-28339636	NCOA2,RECQL4,CHCHD7,EXT1,RAD21,TCEA1,UBR5,NDRG1,MYC,PLAG1,COX6C,HEY1,	98713790	98276040	4.541944931	9	281	120	8	1	3	8
CLCA_0011	Heavily rearranged	5	chr1:17217881-17238420,chr1:144589853-144610392,chr1:146382400-147865001,chr1:148549140-148559679,chr1:149191007-149241546	BCL9,	1584762	1013046	2.739061578	1	46	15	2	0	0	2
CLCA_0011	Heavily rearranged	9	chr1:112612943-112823482,chr1:204034001-220189000,chr7:35964539-35985077,chr7:62462925-62473464,chr7:116223104-116243643,chr9:6494254-6704793,chr10:20048538-20059076,chr17:39246276-39266814,chr17:39296183-39316721	MDM4,SLC45A3,ELK4,	16679316	11301097	2.574830765	5	65	21	6	0	0	6
CLCA_0011	Heavily rearranged	14	chr1:4647909-4658448,chr1:156349001-171291000,chr1:174158001-202291000,chr1:226540001-227068000,chr2:117477265-117487804,chr4:437786-455812,chr5:94585136-94605674,chr18:29065444-29085983,chr19:19915204-19965742,chr19:20608823-20639361,chr19:20944620-20955158,chr19:24032644-24043183,chrX:4682464-4693002,chrY:19514391-19524930	CDC73,PRCC,FCGR2B,NTRK1,PTPRC,TPR,SDHC,ABL2,PBX1,	43806422	12521489	2.555923692	8	147	54	2	0	0	2
CLCA_0011	Heavily rearranged	3	chr1:235674001-242041000,chr5:692512-727516,chr5:766156-796694	FH,	6432544	60563	3.810204351	2	25	12	0	0	0	0
CLCA_0011	Heavily rearranged	2	chr1:227774001-233912001,chr7:157264846-157275384	,	6148540	4185741	2.542445875	2	13	5	0	0	0	0
CLCA_0440	BFB	1	chr11:68690001-69655000	CCND1,	965000	807634	11.64473457	1	15	4	5	1	1	4
CLCA_0440	Linear	1	chr7:77263001-78044000	,	781000	761473	6.945048095	1	5	1	1	0	1	1
CLCA_0443	Circular	7	chr6:922938-943526,chr6:15337944-15348533,chr8:64227297-64237886,chr8:102644618-102665206,chr11:78306545-78317133,chr12:9841925-9862513,chr13:74163001-115169878	ERCC5,	41100414	40427161	11.58781582	5	214	79	52	7	18	48
CLCA_0446	Heavily rearranged	1	chr2:195584001-196962000	,	1378000	1333450	3.102743007	1	19	8	1	0	0	1
CLCA_0446	Linear	1	chr2:81784001-83676000	,	1892000	1891198	3.586781312	1	35	17	0	0	0	0
CLCA_0446	BFB	1	chr16:48881001-51378000	CYLD,	2497000	2238057	4.281281454	1	47	22	3	0	0	2
CLCA_0446	Linear	1	chr20:21647001-23960000	,	2313000	2312969	3.797447309	1	31	13	1	0	0	1
CLCA_0446	Linear	1	chr6:1895001-3964000	,	2069000	2065337	3.474956733	1	27	12	0	0	0	0
CLCA_0446	Circular	2	chr20:12306001-15355000,chr20:19155447-19165995	,	3059549	3058791	4.059239442	1	52	22	2	0	0	1
CLCA_0446	Heavily rearranged	2	chr1:170813001-179368000,chr1:222176476-222187024	ABL2,	8565549	8538860	3.531468993	1	87	42	0	0	0	0
CLCA_0446	Heavily rearranged	5	chr15:20470882-20491430,chr17:50675340-50695889,chr19:28933400-33881601,chr21:28757235-28767784,chrX:48996665-49017214	CEBPA,CCNE1,	5020401	4958439	3.348515983	5	59	24	0	0	0	0
CLCA_0446	Heavily rearranged	6	chr1:16865098-16885646,chr1:17214199-17244747,chr1:144591196-144603819,chr1:146382400-147866149,chr1:148549130-148569679,chr1:149203321-149233869	BCL9,	1598571	1412987	3.654959179	1	59	21	3	0	0	3
CLCA_0446	Heavily rearranged	9	chr4:438101-448649,chr5:94594438-94614987,chr7:64739206-64749754,chr7:64873322-64896456,chr12:38490794-38511342,chr17:42089204-42109752,chr18:28342001-29429000,chr19:23258743-23269291,chr19:28293719-28339630	,	1249342	1215391	3.195655085	7	85	25	0	0	0	0
CLCA_0447	Circular	9	chr1:112703401-112713953,chr2:544014-554566,chr3:8487494-8498046,chr3:137211704-137222256,chr5:37606001-40604000,chr6:32439501-32566760,chr9:1-12739000,chr10:20048538-20059090,chr15:54214001-63803000	CD274,JAK2,LIFR,TCF12,	25506025	25387775	9.946383591	8	266	120	22	5	15	16

# 每一列的大致信息
str(amp)

## 'data.frame':    2081 obs. of  15 variables:
##  $ sample_name                      : chr  "CLCA_0001" "CLCA_0001" "CLCA_0001" "CLCA_0001" ...
##  $ class                            : chr  "Heavily rearranged" "Heavily rearranged" "Circular" "Heavily rearranged" ...
##  $ NIntervals                       : num  9 4 10 10 5 6 1 4 5 10 ...
##  $ Intervals                        : chr  "chr1:179385001-203717000,chr3:129798365-129808957,chr4:9699978-9720571,chr7:5927483-5948075,chr7:6851001-399630"| __truncated__ "chr7:149730451-149741044,chr7:152588001-159138663,chr10:98451408-98662000,chr18:19772106-19792698" "chr1:112603401-112813993,chr3:56765044-56775637,chr6:119458107-119568700,chr7:105224001-148714000,chr9:6484724-"| __truncated__ "chr1:26455415-26466008,chr5:94594394-94604987,chr7:64739205-64749797,chr11:77108686-77119279,chr12:34386996-344"| __truncated__ ...
##  $ OncogenesAmplified               : chr  "ETV1,CDC73,HOXA13,JAZF1,HOXA11,PTPRC,HNRNPA2B1,HOXA9,TPR," "," "CREB3L2,KIAA1549,POT1,SMO,MET,EZH2,BRAF," "," ...
##  $ TotalIntervalSize                : num  57538154 6792443 44115340 682335 1564977 ...
##  $ AmplifiedIntervalSize            : num  52083461 1210371 33806618 177647 1074755 ...
##  $ AverageAmplifiedCopyCount        : num  2.77 2.64 2.66 4.18 2.73 ...
##  $ Chromosomes                      : num  7 3 8 7 1 6 1 4 1 9 ...
##  $ SeqenceEdges                     : num  145 86 189 113 36 35 18 101 20 281 ...
##  $ BreakpointEdges                  : num  59 34 77 25 13 10 6 37 5 120 ...
##  $ CoverageShifts                   : num  0 1 10 0 3 1 3 5 1 8 ...
##  $ MeanshiftSegmentsCopyCount>5     : num  0 0 0 0 0 0 0 0 0 1 ...
##  $ Foldbacks                        : num  0 0 2 0 0 1 2 0 0 3 ...
##  $ CoverageShiftsWithBreakpointEdges: num  0 1 9 0 3 1 3 5 1 8 ...

# 总共有 2081 列，amp 信息中，一位患者可以有多行记录，class 类型即为上面提到的类型。
nrow(amp)

## [1] 2081

# 如果直接对表格的第三列 class 进行可视化，会发现结果缺失了 No fSCNA 类型，且比例也不对
library(ggstatsplot)
ggpiestats(
  data = amp,
  x = class,
  palette = "Set1",
  #title = "Amplicon",
  results.subtitle = F
)

# 这是因为附件的 amp 数据，只包含发生拷贝数变异 Amplicon 的信息，如果患者没有发生，即 No fSCNA 类型，则没有记录在表格中。
table(amp$sample_name)

## 
## CLCA_0001 CLCA_0006 CLCA_0008 CLCA_0010 CLCA_0011 CLCA_0013 CLCA_0015 CLCA_0016 
##         5         1         3         1         5         7         1         1 
## CLCA_0017 CLCA_0018 CLCA_0021 CLCA_0022 CLCA_0023 CLCA_0025 CLCA_0026 CLCA_0029 
##         3         6         2         3         2         2        29         3 
## CLCA_0031 CLCA_0034 CLCA_0038 CLCA_0039 CLCA_0040 CLCA_0044 CLCA_0045 CLCA_0046 
##         6         2         4         4         2         7         3         2 
## CLCA_0052 CLCA_0056 CLCA_0059 CLCA_0060 CLCA_0061 CLCA_0062 CLCA_0065 CLCA_0066 
##         6        24         5         1         1         5         1         4 
## CLCA_0067 CLCA_0068 CLCA_0069 CLCA_0070 CLCA_0071 CLCA_0072 CLCA_0073 CLCA_0074 
##         3         3         1         4         4         3         4         7 
## CLCA_0078 CLCA_0079 CLCA_0080 CLCA_0089 CLCA_0090 CLCA_0091 CLCA_0092 CLCA_0093 
##         2         4         7         5         4         3         3         4 
## CLCA_0095 CLCA_0096 CLCA_0097 CLCA_0098 CLCA_0099 CLCA_0100 CLCA_0101 CLCA_0102 
##         4        13         8         2         1         4         2         1 
## CLCA_0103 CLCA_0104 CLCA_0105 CLCA_0106 CLCA_0107 CLCA_0108 CLCA_0109 CLCA_0110 
##         3         2         4         3         2         2         1         1 
## CLCA_0111 CLCA_0112 CLCA_0113 CLCA_0114 CLCA_0115 CLCA_0116 CLCA_0118 CLCA_0119 
##         1        13         2         9         3         4       149        13 
## CLCA_0120 CLCA_0121 CLCA_0122 CLCA_0123 CLCA_0125 CLCA_0126 CLCA_0128 CLCA_0129 
##       235        20        31       217        39         1       201         2 
## CLCA_0130 CLCA_0132 CLCA_0133 CLCA_0135 CLCA_0137 CLCA_0139 CLCA_0140 CLCA_0141 
##         4         5         2         2         1         1         3         5 
## CLCA_0143 CLCA_0144 CLCA_0145 CLCA_0146 CLCA_0147 CLCA_0148 CLCA_0150 CLCA_0153 
##         7         2         9         2         1         3         8        45 
## CLCA_0154 CLCA_0156 CLCA_0157 CLCA_0158 CLCA_0159 CLCA_0160 CLCA_0165 CLCA_0166 
##         7         4         2         5         8         1         4         3 
## CLCA_0167 CLCA_0168 CLCA_0171 CLCA_0173 CLCA_0174 CLCA_0176 CLCA_0177 CLCA_0178 
##         6         5         3         1         1         7         1         3 
## CLCA_0182 CLCA_0187 CLCA_0188 CLCA_0189 CLCA_0190 CLCA_0191 CLCA_0192 CLCA_0194 
##         5         1         1         1         3         8         3         1 
## CLCA_0197 CLCA_0198 CLCA_0201 CLCA_0202 CLCA_0203 CLCA_0204 CLCA_0205 CLCA_0206 
##         2         2         4         5         5         2         3         1 
## CLCA_0207 CLCA_0208 CLCA_0210 CLCA_0212 CLCA_0215 CLCA_0216 CLCA_0217 CLCA_0218 
##         4         1         3         2         7         2         2         5 
## CLCA_0219 CLCA_0221 CLCA_0222 CLCA_0223 CLCA_0224 CLCA_0227 CLCA_0229 CLCA_0231 
##         4        10         6         8         5         1         2         1 
## CLCA_0232 CLCA_0233 CLCA_0235 CLCA_0236 CLCA_0237 CLCA_0239 CLCA_0243 CLCA_0245 
##         2         1         2         1         1         6         9         1 
## CLCA_0246 CLCA_0248 CLCA_0249 CLCA_0251 CLCA_0254 CLCA_0255 CLCA_0256 CLCA_0257 
##         4         1         1         1         1         1         1         1 
## CLCA_0258 CLCA_0259 CLCA_0261 CLCA_0263 CLCA_0265 CLCA_0268 CLCA_0270 CLCA_0271 
##         2         4         4         3         3         2         2         5 
## CLCA_0277 CLCA_0278 CLCA_0281 CLCA_0282 CLCA_0283 CLCA_0284 CLCA_0285 CLCA_0289 
##         1         2         1         2         3         2         5         4 
## CLCA_0291 CLCA_0293 CLCA_0294 CLCA_0295 CLCA_0296 CLCA_0301 CLCA_0303 CLCA_0305 
##         1         1         3         4         1         3         2         1 
## CLCA_0309 CLCA_0310 CLCA_0311 CLCA_0314 CLCA_0315 CLCA_0316 CLCA_0317 CLCA_0321 
##         1         1         2         2         3         1         3         1 
## CLCA_0323 CLCA_0324 CLCA_0325 CLCA_0327 CLCA_0330 CLCA_0331 CLCA_0332 CLCA_0334 
##         2         4        11         1         4         4         3         2 
## CLCA_0336 CLCA_0337 CLCA_0338 CLCA_0341 CLCA_0342 CLCA_0343 CLCA_0344 CLCA_0345 
##         5         5         3         1         2         3         3         4 
## CLCA_0346 CLCA_0347 CLCA_0348 CLCA_0349 CLCA_0351 CLCA_0352 CLCA_0354 CLCA_0356 
##         2         4         2         3         4         6         1         5 
## CLCA_0357 CLCA_0359 CLCA_0365 CLCA_0366 CLCA_0367 CLCA_0369 CLCA_0372 CLCA_0373 
##        14         2         3        10         3        11         1         5 
## CLCA_0375 CLCA_0376 CLCA_0377 CLCA_0378 CLCA_0379 CLCA_0382 CLCA_0384 CLCA_0385 
##         3         1        12         1         2         6        13         4 
## CLCA_0387 CLCA_0388 CLCA_0389 CLCA_0390 CLCA_0391 CLCA_0392 CLCA_0393 CLCA_0394 
##         1         1         4        20         1        12         4         1 
## CLCA_0395 CLCA_0398 CLCA_0399 CLCA_0400 CLCA_0401 CLCA_0402 CLCA_0403 CLCA_0404 
##         3         9         1         1         4         4         2         4 
## CLCA_0406 CLCA_0407 CLCA_0408 CLCA_0409 CLCA_0410 CLCA_0411 CLCA_0412 CLCA_0413 
##        11         5         7         2         2         2         2         1 
## CLCA_0414 CLCA_0416 CLCA_0418 CLCA_0419 CLCA_0420 CLCA_0421 CLCA_0424 CLCA_0425 
##         1         4         2        10         4         1         4         1 
## CLCA_0426 CLCA_0428 CLCA_0429 CLCA_0433 CLCA_0435 CLCA_0439 CLCA_0440 CLCA_0443 
##         2         1        14         3         7         2         2         1 
## CLCA_0446 CLCA_0447 CLCA_0448 CLCA_0450 CLCA_0451 CLCA_0458 CLCA_0461 CLCA_0462 
##        10        18         3         1         7         2         3         1 
## CLCA_0465 CLCA_0467 CLCA_0470 CLCA_0472 CLCA_0474 CLCA_0475 CLCA_0477 CLCA_0478 
##         5         1         1         3         2         1        16         3 
## CLCA_0479 CLCA_0480 CLCA_0481 CLCA_0482 CLCA_0484 CLCA_0485 CLCA_0486 CLCA_0487 
##         2        20         7        23         2         9         7         4 
## CLCA_0488 CLCA_0492 CLCA_0493 CLCA_0494 
##         1         1         3         2

table(amp$class)

## 
##                BFB           Circular Heavily rearranged             Linear 
##                830                231                704                316

# 总共是494名患者，其中amp 表格记录的患者有 300 名
unique(amp$sample_name) %>% length()

## [1] 300

# 那么没有 amp 记录的患者就是 194 名，比例为 39% 和原图符合
194/494

## [1] 0.3927126

# 先简单粗暴地获取每一种amp类型的患者ID
BFB.id = unique(amp[amp$class == "BFB",1])
Circular.id = unique(amp[amp$class == "Circular",1])
Heavily_rearranged.id = unique(amp[amp$class == "Heavily rearranged",1])
Linear.id = unique(amp[amp$class == "Linear",1])
No_fSCNA.id = setdiff(clinical$Tumor_Sample_Barcode, unique(amp$sample_name))
length(BFB.id);length(Circular.id);length(Heavily_rearranged.id);length(Linear.id);length(No_fSCNA.id)

## [1] 81

## [1] 135

## [1] 233

## [1] 137

## [1] 193

韦恩图进行可视化可以发现，这样获取到的患者ID是有交集的，前面就提到过了，每一个患者可能发生4种 amp 事件的任意组合。所以有交集才是正常的。但这样的话，原文的饼图就无法解释了。

# 韦恩图进行可视化
amp.list = list(BFB.id,Circular.id,Heavily_rearranged.id,Linear.id,No_fSCNA.id)
names(amp.list) = c('BFB','Circular','Heavily_rearranged','Linear','No_fSCNA')
venn.plot1 <- venn.diagram(
  x = amp.list,
  col = "transparent",
  euler.d = TRUE,
  fill = c("#E64B35B2", "#4DBBD5B2", "#00A087B2", "#3C5488B2", "#F39B7FB2"),
  alpha = rep(0.6,time = 5),
  cex = 1.2,
  cat.cex = 1.0,
  # main = patients[i],
  main.cex = 1.0,
  print.mode = c("raw", "percent"),
  category.names = names(amp.list),
  filename = NULL
  
)
p = as_ggplot(venn.plot1)
print(p)

尝试探索一下数据以获取和原文中的比例接近的结果。从数据上看，发生 Circular(ecDNA) 患者是 135名， 135/494=27.3% 符合原文饼图比例。但其他amp事件Heavily rearranged、Linear、BFB 就不符合比例了,不满足。除非取差集，也就是对 amp 事件划分优先级，发生 Circular(ecDNA) 事件的患者不再记录其他事件，即 Circular(ecDNA) > BFB > Heavily rearranged >Linear，这样比例符合了，但无法理解这样做的意义何在？

# Circular(ecDNA) 
length(Circular.id)/494

## [1] 0.2732794

# BFB
setdiff(BFB.id,Circular.id) %>% length() /494

## [1] 0.09311741

# Heavily rearranged 
setdiff(Heavily_rearranged.id,c(BFB.id,Circular.id)) %>% length() /494

## [1] 0.2226721

# Linear 
setdiff(Linear.id,c(BFB.id,Circular.id,Heavily_rearranged.id)) %>% length() /494

## [1] 0.01821862

# 虽然这样结果和作者的结果吻合，但是这样做的意义何在呢？
amp2 = data.frame(sample_name = c(Circular.id,
                                  setdiff(BFB.id,Circular.id),
                                  setdiff(Heavily_rearranged.id,c(BFB.id,Circular.id)),
                                  setdiff(Linear.id,c(BFB.id,Circular.id,Heavily_rearranged.id)),
                                  No_fSCNA.id
                                  ),
                  class = c(rep("Circular",times = length(Circular.id)),
                            rep("BFB",times = length(setdiff(BFB.id,Circular.id))),
                            rep("Heavily_rearranged",times = length(setdiff(Heavily_rearranged.id,
                                                                            c(BFB.id,Circular.id)))),
                            rep("Linear",times = length(setdiff(Linear.id,
                                                                   c(BFB.id,
                                                                     Circular.id,
                                                                     Heavily_rearranged.id)))),
                            rep("No_fSCNA",times = length(No_fSCNA.id)))
                  )

# 饼图

ggpiestats(
  data = amp2,
  x = class,
  palette = "Set1",
  #title = "Amplicon",
  results.subtitle = F
)

fig3b 是 ecDNA 上的基因列表，进行柱状图可视化。但是根据作者上传的附件重现出来的结果和文章的 fig.3b 并不止一致，如文章原图中的 EXT1 MYC RAD21 NDRG1柱子高度相接近，但上面可视化出来的结果显示MYC 较高，其他的较低。

# 获取 ecDNA 
ecDNA_amp = amp[amp$class=="Circular",]
head(ecDNA_amp,n=20)

sample_name	class	NIntervals	Intervals	OncogenesAmplified	TotalIntervalSize	AmplifiedIntervalSize	AverageAmplifiedCopyCount	Chromosomes	SeqenceEdges	BreakpointEdges	CoverageShifts	MeanshiftSegmentsCopyCount>5	Foldbacks	CoverageShiftsWithBreakpointEdges
CLCA_0001	Circular	10	chr1:112603401-112813993,chr3:56765044-56775637,chr6:119458107-119568700,chr7:105224001-148714000,chr9:6484724-6695316,chr9:33786290-33806883,chr10:20048538-20059130,chr10:35290516-35311108,chr12:74004366-74014958,chr16:26592312-26612904	CREB3L2,KIAA1549,POT1,SMO,MET,EZH2,BRAF,	44115340	33806618	2.65748	8	189	77	10	0	2	9
CLCA_0006	Circular	6	chr4:8438956-8449575,chr6:16220142-16230760,chr7:140356252-140376871,chr10:44079274-44099893,chr11:68389001-69076000,chr19:50449724-50460342	,	760098	700923	6.917367	6	35	10	1	0	1	1
CLCA_0443	Circular	7	chr6:922938-943526,chr6:15337944-15348533,chr8:64227297-64237886,chr8:102644618-102665206,chr11:78306545-78317133,chr12:9841925-9862513,chr13:74163001-115169878	ERCC5,	41100414	40427161	11.58782	5	214	79	52	7	18	48
CLCA_0446	Circular	2	chr20:12306001-15355000,chr20:19155447-19165995	,	3059549	3058791	4.059239	1	52	22	2	0	0	1
CLCA_0447	Circular	9	chr1:112703401-112713953,chr2:544014-554566,chr3:8487494-8498046,chr3:137211704-137222256,chr5:37606001-40604000,chr6:32439501-32566760,chr9:1-12739000,chr10:20048538-20059090,chr15:54214001-63803000	CD274,JAK2,LIFR,TCF12,	25506025	25387775	9.946384	8	266	120	22	5	15	16
CLCA_0447	Circular	1	chr3:171543001-172009000	,	466000	446113	4.344856	1	6	1	2	0	1	2
CLCA_0447	Circular	1	chr22:35836001-36188000	,	352000	351993	3.970767	1	7	3	0	0	0	0
CLCA_0447	Circular	1	chr15:65816001-66021000	,	205000	204997	4.891946	1	3	1	0	0	0	0
CLCA_0447	Circular	1	chr6:43482001-44354000	,	872000	852707	3.857693	1	9	2	1	0	0	1
CLCA_0447	Circular	1	chr15:67144001-67445000	,	301000	300105	5.693422	1	6	2	0	0	0	0
CLCA_0451	Circular	7	chr2:70504051-70524611,chr5:112940001-118170000,chr6:32432533-32579209,chr7:40448001-43603000,chr8:109545001-120080000,chr11:113948001-116387000,chr11:130256001-135006516	EXT1,RAD21,	26276754	41610	3.206914	6	209	92	1	0	0	1
CLCA_0461	Circular	16	chr1:4649294-4659888,chr1:150319900-223199001,chr1:225205000-233912001,chr4:437018-457611,chr5:40686831-40697425,chr5:94594161-94604755,chr7:64865863-64896457,chr12:38490794-38511387,chr17:42089772-42100365,chr18:29073967-29084561,chr19:20599211-20629805,chr19:20944362-20955213,chr19:23476178-23486772,chr19:28280720-28349688,chrX:4689289-4699883,chrY:19505241-19515835	H3F3A,ARNT,PRCC,FCGR2B,MUC1,CDC73,TPM3,NTRK1,SLC45A3,PTPRC,TPR,ELK4,SDHC,ABL2,MDM4,PBX1,	81853062	81341590	4.435998	10	366	127	10	0	0	9
CLCA_0465	Circular	4	chr11:2189298-2209845,chr11:59494001-59723000,chr11:60340001-61377000,chr11:68706001-70495000	CCND1,	3075548	2898925	4.283449	1	37	10	9	0	2	7
CLCA_0465	Circular	8	chr5:34438837-34459385,chr6:32432537-32579209,chr6:119458160-119658708,chr8:69214001-146364022,chr10:50452244-50462791,chr12:131860272-131880819,chr12:132926033-132936581,chr20:1380675-1391222	NCOA2,RECQL4,EXT1,RAD21,COX6C,NDRG1,MYC,UBR5,HEY1,	77569986	76966860	3.3338	6	376	156	9	1	2	8
CLCA_0470	Circular	1	chr1:154834001-155367000	MUC1,	533000	532996	5.465167	1	4	2	0	0	0	0
CLCA_0472	Circular	5	chr16:46518719-46529341,chr16:46552064-46649972,chr16:46715036-46735657,chr16:46767016-46787638,chr16:46825001-49898000	,	3222777	3200819	7.860472	1	100	43	8	0	0	7
CLCA_0480	Circular	3	chr6:32434297-32464893,chr6:32478990-32571656,chr20:832001-2745000	,	2036264	32725	3.50202	2	94	45	0	0	0	0
CLCA_0481	Circular	10	chr1:112603401-112814054,chr3:197842684-197853337,chr4:190896293-190916947,chr5:34438730-34459384,chr6:119458046-119668700,chr7:116222989-116233643,chr9:6494140-6604794,chr10:18594001-37659000,chr16:26592312-26612965,chr18:14772319-14782973	KIF5B,ABI1,MLLT10,	19690892	16577851	2.984219	10	151	56	6	0	0	6
CLCA_0482	Circular	1	chr1:196437001-197013000	,	576000	471329	3.063173	1	19	7	4	0	0	4
CLCA_0484	Circular	4	chr10:1746001-4002667,chr11:66975001-67656269,chr11:71568859-71579471,chr14:35431001-38257865	FOXA1,NKX2-1,	5775414	4977134	7.391996	3	41	14	10	3	3	7

    # 获取ecDNA 的 top20 基因
    genes = paste(ecDNA_amp$OncogenesAmplified[1:nrow(ecDNA_amp)],collapse = ",") %>% str_split(pattern = ",")
    genes = genes[[1]]
    top20 = rev(head(tail(sort(table(genes)),n=21),n=20))
    top20_gene = names(top20)
    # top20 gene 对应的 amp 类型
    amp_top20 = data.frame()
    for (i in top20_gene) {
      amp_gene = amp[grep(pattern = i,ignore.case = F,x = amp$OncogenesAmplified),]
      amp_gene$gene = i
      amp_top20 = rbind(amp_top20,amp_gene)
    }
    
    # 柱状图可视化
    amp_top20$gene = factor(amp_top20$gene,levels = top20_gene)
    amp_top20$class = factor(amp_top20$class,levels = c("Linear",
                                                        "Heavily rearranged",
                                                        "BFB",
                                                        "Circular"))
    
    p = ggplot(data = amp_top20) + 
      geom_bar( aes(x = gene, fill = class),
                #width = 0.5,
                #position =position_dodge2(padding = 0.5, preserve = "single"),
                stat = "count") + 
      # facet_grid(. ~ Patient, scales = 'free_x', space = 'free') + 
      theme_classic() + 
      theme(
            panel.border = element_blank()) +
      xlab(label = "top20 gene")+
      ylab(label = "Frequency") +
      scale_fill_manual(values = c("#377EB8", "#4DAF4A", "#FF7F00", "#984EA3"))
    p

还有就是，统计出来的 top20 基因列表和文章的不一致：

    top20_paper = c("CCND1","EXT1","MYC","RAD21","NDRG1",
                    "UBR5","COX6C","RECQL4","MUC1","TPM3",
                    "NCOA2","NTRK1","PBX1","PRCC","ARNT",
                    "FCGR2B","HEY1","SDHC","CHCHD7","MET")
    amp.list = list(top20_gene=top20_gene,top20_paper=top20_paper)


    library(ggvenn)
    ggvenn(amp.list, 
           show_elements = F, 
           show_percentage = T,
           label_sep = "\n", 
           fill_color = c("#E64B35B2", "#4DBBD5B2"),
           auto_scale = T
           )

    ggvenn(amp.list, 
           show_elements = T, 
           show_percentage = F,
           label_sep = "\n", 
           fill_color = c("#E64B35B2", "#4DBBD5B2"),
           auto_scale = T
           )

基因组重排的 circle plot

文章中的 fig.4b 是基因组重排的 circle plot，以 CLCA_0119 患者为例，circle plot 纳入了拷贝数变异信息 CN 和结构变异SV信息。

这部分信息可以从该文章报导的数据库上http://lifeome.net:8080/clca 获取到

    # 读入 CN 数据
    CN_data = readxl::read_xlsx("Copy_Number_Alteration_20240315.xlsx")
    CN_data = as.data.frame(CN_data)
    CN_data$Start = as.numeric(CN_data$Start)
    CN_data$End = as.numeric(CN_data$End)
    CN_data$CopyNumber = as.numeric(CN_data$CopyNumber)

    # 读入 SV 数据
    SV_data = readxl::read_xlsx("Structure_Variation_20240315.xlsx")
    SV_data = as.data.frame(SV_data)
    SV_data$PosA = as.integer(SV_data$PosA)
    SV_data$PosB = as.integer(SV_data$PosB)
    # 这里仔细查看发现 SV 数据的RelatedGeneB(s) 和 GeneB(s).Func 两列的信息应该颠倒过来了
    head(SV_data,n=20)

CaseID	ChrA	PosA	RelatedGeneA(s)	GeneA(s).Func	A.Strand	ChrB	PosB	RelatedGeneB(s)	GeneB(s).Func	B.Strand	Chromoplexy	Chromothripsis
CLCA_0023	chr1	8875290	RERE	intronic	+	chr1	8877433	UTR5	RERE	-	.	.
CLCA_0023	chr2	13604672	LOC100506474;LINC00276	intergenic	-	chr2	14858959	intergenic	FAM84A;NBAS	+	.	.
CLCA_0023	chr3	68999992	FAM19A4;EOGT	intergenic	+	chr3	69000212	intergenic	FAM19A4;EOGT	-	.	.
CLCA_0023	chr3	170362147	LOC101928583	ncRNA_intronic	+	chr3	170362183	ncRNA_intronic	LOC101928583	-	.	.
CLCA_0023	chr4	2495362	RNF4	intronic	+	chr4	2495727	intronic	RNF4	-	.	.
CLCA_0023	chr4	17266283	LINC02493;SNORA75B	intergenic	+	chr4	17266334	intergenic	LINC02493;SNORA75B	-	.	.
CLCA_0023	chr7	78027496	MAGI2	intronic	+	chr7	78028666	intronic	MAGI2	-	.	.
CLCA_0023	chr8	64638461	LOC102724612;LINC01289	intergenic	-	chr8	64667679	intergenic	LOC102724612;LINC01289	+	.	.
CLCA_0023	chr9	103941193	PLPPR1	intronic	+	chr9	103941257	intronic	PLPPR1	-	.	.
CLCA_0023	chr10	94663880	EXOC6	intronic	+	chr10	94663934	intronic	EXOC6	-	.	.
CLCA_0023	chr12	110239299	TRPV4	intronic	-	chr12	110243757	intronic	TRPV4	+	.	.
CLCA_0023	chr13	77667696	MYCBP2	intronic	+	chr13	77683238	intronic	MYCBP2	-	.	.
CLCA_0023	chr17	79257653	SLC38A10	intronic	+	chr17	79257943	intronic	SLC38A10	-	.	.
CLCA_0023	chr20	57261314	STX16-NPEPL1	ncRNA_intronic	+	chr20	57263239	ncRNA_intronic	STX16-NPEPL1	-	.	.
CLCA_0023	chr21	45289099	AGPAT3	intronic	+	chr21	45290317	intronic	AGPAT3	-	.	.
CLCA_0023	chr22	24834833	ADORA2A-AS1;SPECC1L-ADORA2A	ncRNA_intronic	+	chr22	24911204	exonic	UPB1	-	.	.
CLCA_0023	chrX	125812403	DCAF12L1;PRR32	intergenic	+	chrX	127402085	intergenic	ACTRT1;SMARCA1	-	.	.
CLCA_0023	chr6	46665473	TDRD6	intronic	-	chr17	56730435	intronic	TEX14	-	.	.
CLCA_0023	chr2	14856430	FAM84A;NBAS	intergenic	-	chr2	14917781	intergenic	FAM84A;NBAS	-	.	.
CLCA_0023	chr2	14917766	FAM84A;NBAS	intergenic	+	chr2	23985393	intronic	ATAD2B	+	.	.

    ## 获取 CLCA_0119 患者的数据
    CLCA_0119_CN = CN_data[CN_data$CaseID == "CLCA_0119",2:9]
    CLCA_0119_SV = SV_data[SV_data$CaseID == "CLCA_0119",2:13]
    
    # RCircos plot
    library(RCircos)
    data(UCSC.HG19.Human.CytoBandIdeogram)
    RCircos.Set.Core.Components(cyto.info = UCSC.HG19.Human.CytoBandIdeogram,
                                chr.exclude=NULL,
                                tracks.inside =3, 
                                tracks.outside = 0)  
    RCircos.List.Plot.Parameters()
    RCircos.Set.Plot.Area()    
    RCircos.Chromosome.Ideogram.Plot()
    
    # 添加拷贝数变异信息，散点图
    RCircos.Scatter.Plot(scatter.data = CLCA_0119_CN, 
                         data.col=4,
                         track.num=1, 
                         side="in", 
                         by.fold=2);
    # 添加结构变异曲线
    
    ## 添加End，这里只是为了方便可视化，所以 End 是在 start 上加1，没有实际意义的
    CLCA_0119_SV$EndA = CLCA_0119_SV$PosA+1
    CLCA_0119_SV$EndB = CLCA_0119_SV$PosB+1
    
    ## 添加 Patterns 进行分类，原数据没有，但是文章的RCircos plot 有
    CLCA_0119_SV$Patterns = 
      ifelse(CLCA_0119_SV$A.Strand == "+" & CLCA_0119_SV$B.Strand == "+",
             yes = "Head to head(+/+)",
             ifelse(CLCA_0119_SV$A.Strand == "-" & CLCA_0119_SV$B.Strand == "-",
                    yes = "Tail to tail(-/-)",
                    ifelse(CLCA_0119_SV$A.Strand == "+" & CLCA_0119_SV$B.Strand == "-",
                           yes = "Deletion like(+/-)",
                           no = "Duplication like(-/+)")))
    ## 添加 PlotColor 设置颜色
    CLCA_0119_SV$PlotColor = 
      ifelse(CLCA_0119_SV$A.Strand == "+" & CLCA_0119_SV$B.Strand == "+",
             yes = "black",
             ifelse(CLCA_0119_SV$A.Strand == "-" & CLCA_0119_SV$B.Strand == "-",
                    yes = "#26853A",
                    ifelse(CLCA_0119_SV$A.Strand == "+" & CLCA_0119_SV$B.Strand == "-",
                           yes = "#EE7B1C",
                           no = "#15499D")))
    ## 重新进行列排序
    CLCA_0119_SV_link = CLCA_0119_SV[,c("ChrA","PosA","EndA","ChrB","PosB","EndB","PlotColor",
                                        "RelatedGeneA(s)", "GeneA(s).Func","A.Strand",
                                        "RelatedGeneB(s)", "GeneB(s).Func","B.Strand",
                                        "Chromoplexy","Chromothripsis","Patterns" )]
                                        
    CLCA_0119_SV_link$ChrA = factor(CLCA_0119_SV_link$ChrA,levels = c(paste0("chr",c(1:22,"X","Y"))))
    CLCA_0119_SV_link$ChrB = factor(CLCA_0119_SV_link$ChrB,levels = c(paste0("chr",c(1:22,"X","Y"))))
    
    RCircos.Link.Plot(
      link.data = CLCA_0119_SV_link,
      track.num = 2,
      # by.chromosome = T,
      #start.pos = 0.8,
      genomic.columns = 3,
      is.sorted = T
      
    )
    legend("bottomright", 
           #inset=.05, 
           title="Patterns of SVs", 
           legend = c(unique(CLCA_0119_SV_link$Patterns)),
           #lty=1, 
           pch=15, bty = "n",
           col=c("black", "#26853A","#EE7B1C","#15499D"))

    sessionInfo()    
    ## R version 4.3.2 (2023-10-31)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 20.04.6 LTS
    ## 
    ## Matrix products: default
    ## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so;  LAPACK version 3.9.0
    ## 
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
    ## 
    ## time zone: Asia/Shanghai
    ## tzcode source: system (glibc)
    ## 
    ## attached base packages:
    ##  [1] parallel  stats4    grid      stats     graphics  grDevices utils    
    ##  [8] datasets  methods   base     
    ## 
    ## other attached packages:
    ##  [1] RCircos_1.2.2                     ggvenn_0.1.10                    
    ##  [3] dplyr_1.1.4                       ggstatsplot_0.12.1               
    ##  [5] purrr_1.0.2                       sigminer_2.3.0                   
    ##  [7] doParallel_1.0.17                 iterators_1.0.14                 
    ##  [9] foreach_1.5.2                     BSgenome.Hsapiens.UCSC.hg19_1.4.3
    ## [11] BSgenome_1.70.2                   rtracklayer_1.62.0               
    ## [13] BiocIO_1.12.0                     Biostrings_2.70.3                
    ## [15] XVector_0.42.0                    GenomicRanges_1.54.1             
    ## [17] GenomeInfoDb_1.38.8               IRanges_2.36.0                   
    ## [19] S4Vectors_0.40.2                  barplot3d_1.0.1                  
    ## [21] NMF_0.26                          synchronicity_1.3.10             
    ## [23] bigmemory_4.6.1                   Biobase_2.62.0                   
    ## [25] BiocGenerics_0.48.1               cluster_2.1.6                    
    ## [27] rngtools_1.5.2                    registry_0.5-1                   
    ## [29] ggVennDiagram_1.4.9               VennDiagram_1.7.3                
    ## [31] futile.logger_1.4.3               ggsci_3.0.0                      
    ## [33] ggrepel_0.9.4                     pheatmap_1.0.12                  
    ## [35] data.table_1.15.4                 tidyr_1.3.0                      
    ## [37] ggpubr_0.6.0                      ggplot2_3.5.0                    
    ## [39] stringr_1.5.1                     maftools_2.18.0                  
    ## 
    ## loaded via a namespace (and not attached):
    ##   [1] splines_4.3.2               prismatic_1.1.1            
    ##   [3] bitops_1.0-7                ggplotify_0.1.2            
    ##   [5] tibble_3.2.1                R.oo_1.25.0                
    ##   [7] cellranger_1.1.0            datawizard_0.9.1           
    ##   [9] XML_3.99-0.16.1             lifecycle_1.0.4            
    ##  [11] rstatix_0.7.2               globals_0.16.2             
    ##  [13] lattice_0.22-5              MASS_7.3-60.0.1            
    ##  [15] insight_0.19.7              backports_1.4.1            
    ##  [17] magrittr_2.0.3              rmarkdown_2.25             
    ##  [19] yaml_2.3.8                  cowplot_1.1.2              
    ##  [21] RColorBrewer_1.1-3          multcomp_1.4-25            
    ##  [23] abind_1.4-5                 zlibbioc_1.48.2            
    ##  [25] R.utils_2.12.3              RCurl_1.98-1.14            
    ##  [27] yulab.utils_0.1.4           TH.data_1.1-2              
    ##  [29] sandwich_3.1-0              GenomeInfoDbData_1.2.11    
    ##  [31] correlation_0.8.4           listenv_0.9.0              
    ##  [33] parallelly_1.36.0           codetools_0.2-19           
    ##  [35] DelayedArray_0.28.0         DNAcopy_1.76.0             
    ##  [37] tidyselect_1.2.1            farver_2.1.1               
    ##  [39] matrixStats_1.2.0           GenomicAlignments_1.38.2   
    ##  [41] jsonlite_1.8.8              survival_3.5-7             
    ##  [43] emmeans_1.9.0               tools_4.3.2                
    ##  [45] rio_1.0.1                   Rcpp_1.0.12                
    ##  [47] glue_1.7.0                  SparseArray_1.2.4          
    ##  [49] xfun_0.42                   MatrixGenerics_1.14.0      
    ##  [51] withr_3.0.0                 formatR_1.14               
    ##  [53] BiocManager_1.30.22         fastmap_1.1.1              
    ##  [55] fansi_1.0.6                 digest_0.6.34              
    ##  [57] R6_2.5.1                    gridGraphics_0.5-1         
    ##  [59] estimability_1.4.1          colorspace_2.1-0           
    ##  [61] R.methodsS3_1.8.2           utf8_1.2.4                 
    ##  [63] generics_0.1.3              S4Arrays_1.2.1             
    ##  [65] parameters_0.21.3           pkgconfig_2.0.3            
    ##  [67] gtable_0.3.4                statsExpressions_1.5.2     
    ##  [69] furrr_0.3.1                 htmltools_0.5.7            
    ##  [71] carData_3.0-5               scales_1.3.0               
    ##  [73] bigmemory.sri_0.1.6         knitr_1.45                 
    ##  [75] lambda.r_1.2.4              rstudioapi_0.15.0          
    ##  [77] reshape2_1.4.4              rjson_0.2.21               
    ##  [79] uuid_1.1-1                  coda_0.19-4                
    ##  [81] cachem_1.0.8                zoo_1.8-12                 
    ##  [83] restfulr_0.0.15             pillar_1.9.0               
    ##  [85] vctrs_0.6.5                 car_3.1-2                  
    ##  [87] xtable_1.8-4                paletteer_1.5.0            
    ##  [89] evaluate_0.23               zeallot_0.1.0              
    ##  [91] mvtnorm_1.2-4               cli_3.6.2                  
    ##  [93] compiler_4.3.2              futile.options_1.0.1       
    ##  [95] Rsamtools_2.18.0            rlang_1.1.3                
    ##  [97] crayon_1.5.2                ggsignif_0.6.4             
    ##  [99] labeling_0.4.3              rematch2_2.1.2             
    ## [101] plyr_1.8.9                  forcats_1.0.0              
    ## [103] fs_1.6.3                    stringi_1.8.3              
    ## [105] gridBase_0.4-7              BiocParallel_1.36.0        
    ## [107] munsell_0.5.0               bayestestR_0.13.1          
    ## [109] Matrix_1.6-5                patchwork_1.1.3            
    ## [111] future_1.33.1               SummarizedExperiment_1.32.0
    ## [113] highr_0.10                  broom_1.0.5                
    ## [115] memoise_2.0.1               readxl_1.4.3

本文参与?腾讯云自媒体分享计划，分享自微信公众号。

原始发表：2024-04-21，如有侵权请联系?cloudcommunity@tencent.com 删除

数据库