前言

今天我们来演示MOVICS包的第二个模块，在上一篇推文中：MOVICS系列教程(一) GET Module分析后，我们得到了乳腺癌的5个亚型，那么此模块就是为了对这5种亚型间的分子特征进行展示。

因为需要上一部分的输出结果，大家必须跑完上一篇推文的代码才可以进行本篇推文的演示代码。

主要函数

同样的，我们先来看一下这个模块用到的函数：

compSurv(): compare survival outcome and generate a Kalan-Meier curve with pairwise comparison if possible

compClinvar(): compare and summarize clinical features among different identified subtypes

compMut(): compare mutational frequency and generate an OncoPrint with significant mutations

compTMB(): compare total mutation burden among subtypes and generate distribution of Transitions and Transversions

compFGA(): compare fraction genome altered among subtypes and generate a barplot for distribution comparison

compDrugsen(): compare estimated half maximal inhibitory concentration (IC50) for drug sensitivity and generate a boxviolin for distribution comparison

compAgree(): compare agreement of current subtypes with other pre-existed classifications and generate an alluvial diagram and an agreement barplot

主要是通过比较各亚型间肿瘤的主要特征(生存分析，临床特征，突变状态，TMB，药敏和一致性)，来揭示各亚型间不同的分子特征。

代码演示

# survival comparison
surv.brca <- compSurv(moic.res         = cmoic.brca,surv.info        = surv.info,convt.time       = "m", # convert day unit to monthsurv.median.line = "h", # draw horizontal line at median survivalxyrs.est         = c(5,10), # estimate 5 and 10-year survivalfig.name         = "KAPLAN-MEIER CURVE OF CONSENSUSMOIC")

clin.brca <- compClinvar(moic.res      = cmoic.brca,var2comp      = surv.info, # data.frame needs to summarize (must has row names of samples)strata        = "Subtype", # stratifying variable (e.g., Subtype in this example)factorVars    = c("PAM50","pstage","fustat"), # features that are considered categorical variablesnonnormalVars = "futime", # feature(s) that are considered using nonparametric testexactVars     = "pstage", # feature(s) that are considered using exact testdoWord        = TRUE, # generate .docx file in local pathtab.name      = "SUMMARIZATION OF CLINICAL FEATURES")

# mutational frequency comparison
mut.brca <- compMut(moic.res     = cmoic.brca,mut.matrix   = brca.tcga$mut.status, # binary somatic mutation matrixdoWord       = TRUE, # generate table in .docx formatdoPlot       = TRUE, # draw OncoPrintfreq.cutoff  = 0.05, # keep those genes that mutated in at least 5% of samplesp.adj.cutoff = 0.05, # keep those genes with adjusted p value < 0.05 to draw OncoPrintinnerclust   = TRUE, # perform clustering within each subtypeannCol       = annCol, # same annotation for heatmapannColors    = annColors, # same annotation color for heatmapwidth        = 6, height       = 2,fig.name     = "ONCOPRINT FOR SIGNIFICANT MUTATIONS",tab.name     = "INDEPENDENT TEST BETWEEN SUBTYPE AND MUTATION")

# compare TMB
tmb.brca <- compTMB(moic.res     = cmoic.brca,maf          = maf,rmDup        = TRUE, # remove duplicated variants per samplermFLAGS      = FALSE, # keep FLAGS mutationsexome.size   = 38, # estimated exome sizetest.method  = "nonparametric", # statistical testing methodfig.name     = "DISTRIBUTION OF TMB AND TITV")

# compare FGA, FGG, and FGL
fga.brca <- compFGA(moic.res     = cmoic.brca,segment      = segment,iscopynumber = FALSE, # this is a segmented copy number filecnathreshold = 0.2, # threshold to determine CNA gain or losstest.method  = "nonparametric", # statistical testing methodfig.name     = "BARPLOT OF FGA")

# drug sensitivity comparison
drug.brca <- compDrugsen(moic.res    = cmoic.brca,norm.expr   = fpkm[,cmoic.brca$clust.res$samID], # double guarantee sample orderdrugs       = c("Cisplatin", "Paclitaxel"), # a vector of names of drug in GDSCtissueType  = "breast", # choose specific tissue type to construct ridge regression modeltest.method = "nonparametric", # statistical testing methodprefix      = "BOXVIOLIN OF ESTIMATED IC50")

# customize the factor level for pstage
surv.info$pstage <- factor(surv.info$pstage, levels = c("TX","T1","T2","T3","T4"))# agreement comparison (support up to 6 classifications include current subtype)
agree.brca <- compAgree(moic.res  = cmoic.brca,subt2comp = surv.info[,c("PAM50","pstage")],doPlot    = TRUE,box.width = 0.2,fig.name  = "AGREEMENT OF CONSENSUSMOIC WITH PAM50 AND PSTAGE")

总结

相信你已经被上述各种炫酷的图片吸引住了，但是截止到目前为止，我们仍然只是从表型上找出乳腺癌的各亚型间分子功能的不同。而如果想更深入的挖掘其背后机制，就需要找出各亚型间这些差异表达的基因是哪些，这就是MOVICS第三个模块的作用了，Immugent将会在下一次推文中进行介绍，敬请期待！

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MOVICS系列教程(二) COMP Module

前言

主要函数

代码演示

总结

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