MOVICS系列教程(二) COMP Module
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 month
surv.median.line = "h", # draw horizontal line at median survival
xyrs.est = c(5,10), # estimate 5 and 10-year survival
fig.name = "KAPLAN-MEIER CURVE OF CONSENSUSMOIC")
图片
# survival comparison
surv.brca <- compSurv(moic.res = cmoic.brca,
surv.info = surv.info,
convt.time = "m", # convert day unit to month
surv.median.line = "h", # draw horizontal line at median survival
xyrs.est = c(5,10), # estimate 5 and 10-year survival
fig.name = "KAPLAN-MEIER CURVE OF CONSENSUSMOIC")
图片
# mutational frequency comparison
mut.brca <- compMut(moic.res = cmoic.brca,
mut.matrix = brca.tcga$mut.status, # binary somatic mutation matrix
doWord = TRUE, # generate table in .docx format
doPlot = TRUE, # draw OncoPrint
freq.cutoff = 0.05, # keep those genes that mutated in at least 5% of samples
p.adj.cutoff = 0.05, # keep those genes with adjusted p value < 0.05 to draw OncoPrint
innerclust = TRUE, # perform clustering within each subtype
annCol = annCol, # same annotation for heatmap
annColors = annColors, # same annotation color for heatmap
width = 6,
height = 2,
fig.name = "ONCOPRINT FOR SIGNIFICANT MUTATIONS",
tab.name = "INDEPENDENT TEST BETWEEN SUBTYPE AND MUTATION")
图片
# compare TMB
tmb.brca <- compTMB(moic.res = cmoic.brca,
maf = maf,
rmDup = TRUE, # remove duplicated variants per sample
rmFLAGS = FALSE, # keep FLAGS mutations
exome.size = 38, # estimated exome size
test.method = "nonparametric", # statistical testing method
fig.name = "DISTRIBUTION OF TMB AND TITV")
图片
# compare FGA, FGG, and FGL
fga.brca <- compFGA(moic.res = cmoic.brca,
segment = segment,
iscopynumber = FALSE, # this is a segmented copy number file
cnathreshold = 0.2, # threshold to determine CNA gain or loss
test.method = "nonparametric", # statistical testing method
fig.name = "BARPLOT OF FGA")
图片
# drug sensitivity comparison
drug.brca <- compDrugsen(moic.res = cmoic.brca,
norm.expr = fpkm[,cmoic.brca$clust.res$samID], # double guarantee sample order
drugs = c("Cisplatin", "Paclitaxel"), # a vector of names of drug in GDSC
tissueType = "breast", # choose specific tissue type to construct ridge regression model
test.method = "nonparametric", # statistical testing method
prefix = "BOXVIOLIN OF ESTIMATED IC50")
图片
# customize the factor level for pstage
surv.info$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")
图片
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！
