This article first appeared in the public number "life letter depot", https://mp.weixin.qq.com/s/WG4JHs9RSm5IEJiiGEzDkg
Before the introduction of the use of maftools | scratch draw level published oncoplot (waterfall) oncoplot R-maftools package drawn genomics mutations result (MAF) or called "waterfall", as well as some of the details of the changes and comments.
The paper presents the maftools for other applications MAF file is easier to understand and reproduce, use this TCGA download LIHC data.
A data portion
setwd ( "C: \\ the Users \\ Maojie \\ Desktop \\ maftools-V2 \\ ")
Library ( maftools)
laml.maf = read.csv to ( "TCGA.LIHC.mutect.maf.csv", header = TRUE ) # the drawings show only some statistical maf, read-only data into a set of learning, without the addition of clinical data laml = read.maf ( maf = laml.maf) bASIC # view data laml An Object of class the MAF ID Summary Mean Median 1 : NCBI_Build
1 NA NA
2: Center 1 NA NA
3: Samples 364 NA NA
4: nGenes 12704 NA NA
5: Frame_Shift_Del 1413 3.893 3
6: Frame_Shift_Ins 551 1.518 1
7: In_Frame_Del 277 0.763 0
8: In_Frame_Ins 112 0.309 0
9: Missense_Mutation 28304 77.972 63
10: Nonsense_Mutation 1883 5.187 4
11: Nonstop_Mutation 45 0.124 0
12: Splice_Site 1051 2.895 2
13: Translation_Start_Site 65 0.179 0
14: total 33701 92.840 75
Information can be summary of gene MAF files, sample output file to laml prefix summary
write.mafSummary(maf = laml, basename = 'laml')
laml_geneSummary.txt
laml_sampleSummary.txt
Two drawing part
1, first draw the overall results of Fig MAF file
plotmafSummary(maf = laml, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE, titvRaw = FALSE)
2, oncoplot FIG.
#oncoplot for top ten mutated genes.
oncoplot(maf = laml, top = 20)
Add SCNA information, add P value information, add comments clinical information, can change the color and other reference links. .
3 Oncostrip
You can use oncostripthe function to show mutation of a specific gene in a sample, here for three liver genes of interest in more 'TP53', 'CTNNB1', 'ARID1A', as follows:
oncostrip(maf = laml, genes = c('TP53','CTNNB1', 'ARID1A'))
4 Transition ， Transversions
titv函数将SNP分类为Transitions_vs_Transversions，并以各种方式返回汇总表的列表。汇总数据也可以显示为一个箱线图，显示六种不同转换的总体分布，并作为堆积条形图显示每个样本中的转换比例。
laml.titv = titv(maf = laml, plot = FALSE, useSyn = TRUE)
#plot titv summary
plotTiTv(res = laml.titv)
5 Rainfall plots
使用rainfallPlot参数绘制rainfall plots，展示超突变的基因组区域。detectChangePoints设置为TRUE，rainfall plots可以突出显示潜在变化的区域.
brca <- system.file("extdata", "brca.maf.gz", package = "maftools")
brca = read.maf(maf = brca, verbose = FALSE)
rainfallPlot(maf = laml, detectChangePoints = TRUE, pointSize = 0.6)
6 Compare mutation load against TCGA cohorts
通过tcgaComapre函数实现laml（自有群体）与TCGA中已有的33个癌种队列的突变负载情况的比较。
#cohortName 给输入的队列命名
laml.mutload = tcgaCompare(maf = laml, cohortName = 'LIHC-2')
7 Genecloud
使用 geneCloud参数绘制基因云，每个基因的大小与它突变的样本总数成正比。
geneCloud(input = laml, minMut = 15)
8 Somatic Interactions
癌症中的许多引起疾病的基因共同发生或在其突变模式中显示出强烈的排他性。可以使用somaticInteractions函数使用配对Fisher 's精确检验来分析突变基因之间的的co-occurring 或者exclusiveness。
#exclusive/co-occurance event analysis on top 10 mutated genes.
Interact <- somaticInteractions(maf = laml, top = 25, pvalue = c(0.05, 0.1))
#提取P值结果
Interact$gene_sets
gene_set pvalue
1: CTNNB1, AXIN1, TP53 0.0001486912
2: CTNNB1, TP53, ARID1A 0.0018338597
3: AXIN1, TP53, APOB 0.0087076043
4: CSMD3, AXIN1, ALB 0.0130219628
5: AXIN1, TP53, ALB 0.0173199619
6: CTNNB1, AXIN1, ARID1A 0.0363739468
9 Comparing two cohorts (MAFs)
由于癌症的突变模式各不相同，因此可是 mafComapre参数比较两个不同队列的差异突变基因，检验方式为fisher检验。
#输入另一个 MAF 文件
Our_maf <- read.csv("Our_maf.csv",header=TRUE)
our_maf = read.maf(maf = Our_maf)
#Considering only genes which are mutated in at-least in 5 samples in one of the cohort to avoid bias due to genes mutated in single sample.
pt.vs.rt <- mafCompare(m1 = laml, m2 = our_maf, m1Name = 'LIHC', m2Name = 'OUR', minMut = 5)
print(pt.vs.rt)
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result部分会有每个基因分别在两个队列中的个数以及P值和置信区间等信息。
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SampleSummary 会有两个队列的样本数。
1) Forest plots
比较结果绘制森林图
forestPlot(mafCompareRes = pt.vs.rt, pVal = 0.01, color = c('royalblue', 'maroon'), geneFontSize = 0.8)
10 Oncogenic 信号通路
`OncogenicPathways 功能查看显著富集通路
OncogenicPathways(maf = laml)
#会输出统计结果
Pathway alteration fractions
Pathway N n_affected_genes fraction_affected
1: RTK-RAS 85 68 0.8000000
2: WNT 68 55 0.8088235
3: NOTCH 71 52 0.7323944
4: Hippo 38 30 0.7894737
5: PI3K 29 24 0.8275862
Enriched above can be selected to complete passage of a mutation of interest show:
PlotOncogenicPathways(maf = laml, pathways = "PI3K")
Well, that is the use of maftools package summary of omics data format of the MAF, analysis, visualization.
Background reply "maf file" to get files and code examples of maf
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