前言
Immugent在之前的推文：整合多组学数据进行分型之MOVICS中已经介绍了MOVICS的基本功能，从本篇推文开始，小编将会以一系列推文的形式对这个R包进行实操演示。
为了方便有兴趣的小伙伴进行复现，此系列推文都将以MOVICS包内置数据进行演示，大家可以直接将代码复制黏贴进Rstudio中，点点点即可！
主要函数
GET Module是MOVICS的第一个模块，主要功能是结合多组学数据对样本进行分亚型。下面是这个模块主要用到的函数，Immugent考虑到自己英文水平有限，怕译本走了味就没有对其进行翻译。
1.getElites(): get elites which are those features that pass the filtering procedure and are used for analyses
2.getClustNum(): get optimal cluster number by calculating clustering prediction index (CPI) and Gap-statistics get%algorithm_name%(): get results from one specific multi-omics integrative clustering algorithm with detailed parameters
3.getMOIC(): get a list of results from multiple multi-omics integrative clustering algorithm with parameters by default getConsensusMOIC(): get a consensus matrix that indicates the clustering robustness across different clustering algorithms and generate a consensus heatmap
4.getSilhouette(): get quantification of sample similarity using silhoutte score approach
5。getStdiz(): get a standardized data for generating comprehensive multi-omics heatmap
6.getMoHeatmap(): get a comprehensive multi-omics heatmap based on clustering results
其中的每一个函数都自带绘图功能，而且可以通过调整多项参数达到个性化分析的目的，最后一个是专门对亚型分子特征进行展示的热图，配色高达上可直接放进文章中。
主要流程
下面开始这个模块的代码展示：
#安装包
if (!requireNamespace("BiocManager", quietly = TRUE))install.packages("BiocManager")
if (!require("devtools")) install.packages("devtools")
devtools::install_github("xlucpu/MOVICS")#加载数据
library("MOVICS")
# load example data of breast cancer
load(system.file("extdata", "brca.tcga.RData", package = "MOVICS", mustWork = TRUE))
load(system.file("extdata", "brca.yau.RData", package = "MOVICS", mustWork = TRUE))# print name of example data
names(brca.tcga)
#> [1] "mRNA.expr" "lncRNA.expr" "meth.beta" "mut.status" "count"
#> [6] "fpkm" "maf" "segment" "clin.info"
names(brca.yau)
#> [1] "mRNA.expr" "clin.info"# extract multi-omics data
mo.data <- brca.tcga[1:4]# extract raw count data for downstream analyses
count <- brca.tcga$count# extract fpkm data for downstream analyses
fpkm <- brca.tcga$fpkm# extract maf for downstream analysis
maf <- brca.tcga$maf# extract segmented copy number for downstream analyses
segment <- brca.tcga$segment# extract survival information
surv.info <- brca.tcga$clin.info
# identify optimal clustering number (may take a while)
optk.brca <- getClustNum(data = mo.data,is.binary = c(F,F,F,T), # note: the 4th data is somatic mutation which is a binary matrixtry.N.clust = 2:8, # try cluster number from 2 to 8fig.name = "CLUSTER NUMBER OF TCGA-BRCA")
# perform iClusterBayes (may take a while)
iClusterBayes.res <- getiClusterBayes(data = mo.data,N.clust = 5,type = c("gaussian","gaussian","gaussian","binomial"),n.burnin = 1800,n.draw = 1200,prior.gamma = c(0.5, 0.5, 0.5, 0.5),sdev = 0.05,thin = 3)
为了和PAM50保持一致，和从图中观察所知，取5个亚群较为合适。
iClusterBayes.res <- getMOIC(data = mo.data,N.clust = 5,methodslist = "iClusterBayes", # specify only ONE algorithm heretype = c("gaussian","gaussian","gaussian","binomial"), # data type corresponding to the listn.burnin = 1800,n.draw = 1200,prior.gamma = c(0.5, 0.5, 0.5, 0.5),sdev = 0.05,thin = 3)
cmoic.brca <- getConsensusMOIC(moic.res.list = moic.res.list,fig.name = "CONSENSUS HEATMAP",distance = "euclidean",linkage = "average")
getSilhouette(sil = cmoic.brca$sil, # a sil object returned by getConsensusMOIC()fig.path = getwd(),fig.name = "SILHOUETTE",height = 5.5,width = 5)
还可以画个热图。
# convert beta value to M value for stronger signal
indata <- mo.data
indata$meth.beta <- log2(indata$meth.beta / (1 - indata$meth.beta))# data normalization for heatmap
plotdata <- getStdiz(data = indata,halfwidth = c(2,2,2,NA), # no truncation for mutationcenterFlag = c(T,T,T,F), # no center for mutationscaleFlag = c(T,T,T,F)) # no scale for mutationfeat <- iClusterBayes.res$feat.res
feat1 <- feat[which(feat$dataset == "mRNA.expr"),][1:10,"feature"]
feat2 <- feat[which(feat$dataset == "lncRNA.expr"),][1:10,"feature"]
feat3 <- feat[which(feat$dataset == "meth.beta"),][1:10,"feature"]
feat4 <- feat[which(feat$dataset == "mut.status"),][1:10,"feature"]
annRow <- list(feat1, feat2, feat3, feat4)# set color for each omics data
# if no color list specified all subheatmaps will be unified to green and red color pattern
mRNA.col <- c("#00FF00", "#008000", "#000000", "#800000", "#FF0000")
lncRNA.col <- c("#6699CC", "white" , "#FF3C38")
meth.col <- c("#0074FE", "#96EBF9", "#FEE900", "#F00003")
mut.col <- c("grey90" , "black")
col.list <- list(mRNA.col, lncRNA.col, meth.col, mut.col)# comprehensive heatmap (may take a while)
getMoHeatmap(data = plotdata,row.title = c("mRNA","lncRNA","Methylation","Mutation"),is.binary = c(F,F,F,T), # the 4th data is mutation which is binarylegend.name = c("mRNA.FPKM","lncRNA.FPKM","M value","Mutated"),clust.res = iClusterBayes.res$clust.res, # cluster resultsclust.dend = NULL, # no dendrogramshow.rownames = c(F,F,F,F), # specify for each omics datashow.colnames = FALSE, # show no sample namesannRow = annRow, # mark selected featurescolor = col.list,annCol = NULL, # no annotation for samplesannColors = NULL, # no annotation colorwidth = 10, # width of each subheatmapheight = 5, # height of each subheatmapfig.name = "COMPREHENSIVE HEATMAP OF ICLUSTERBAYES")
这排版，这配色，可以直接放在文章中使用。
总结
MOVICS第一个模块就是对多组学数据进行整合，通过联合多种统计算法揭示它们之间的关联，并总结出各组学的特征对样本进行分亚型。
在这个模块中，你必须给MOVICS提供至少两个组学的数据，而且组学之间是独立的，要具体根据研究的科学问题来输入正确的数据。分完亚型后，我们就需要揭示各亚群之间的分子特征差异，Immugent将会在下一个推文中进行讲解。