# oncoPredict包进行药物敏感性预测

## 安装包

- conda create -n oncoPredict -c conda-forge r-base=4.1.3
- conda activate oncoPredict
- conda install -c conda-forge r-tidyverse -y
- conda install -c conda-forge r-irkernel -y
- Rscript -e “IRkernel::installspec(name=’oncoPredict’, displayname=’r-oncoPredict’)”
- conda install -c conda-forge r-nloptr -y
- conda install -c conda-forge r-lme4 -y
- conda install -c conda-forge r-pbkrtest -y
- conda install -c conda-forge r-car -y
- conda install -c conda-forge r-biocmanager -y
- conda install -c conda-forge r-ggpubr -y
- conda install -c bioconda bioconductor-maftools -y
- BiocManager::install(‘oncoPredict’)
- 从oncoPredict作者提供的地址下载整理好的CTRP和GDSC，生信技能树的介绍。镜像
- unzip DataFiles.zip

## 读入训练数据

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    library(oncoPredict)
    CTRP2 <- readRDS('../../../oncoPredict/DataFiles/DataFiles//Training Data/CTRP2_Expr (TPM, not log transformed).rds')
    CTRP2 <- log10(CTRP2+1)
    GDSC2_Res <- readRDS('../../../oncoPredict/DataFiles/DataFiles//Training Data/CTRP2_Res.rds')
    GDSC2_Res <- exp(GDSC2_Res)

## 读入预测数据

f_dedup_IQR

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    load('../../../DEG/TCGA/PRAD_tp.rda')
    tpm <- data@assays@data$tpm_unstrand
    colnames(tpm) <- data@colData$patient
    tpm <- tpm[,f_rm_duplicated(colnames(tpm))]
    geneInfo <- as.data.frame(data@rowRanges)[c('gene_id','gene_type','gene_name')]
    tpm <- f_dedup_IQR(as.data.frame(tpm), geneInfo$gene_name)
    comm <- intersect(rownames(CTRP2), rownames(tpm))
    CTRP2 <- CTRP2[comm,]
    tpm <- tpm[comm,]
    tpm <- log10(tpm+1)

## 进行预测

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    library(oncoPredict)
    load('oncoPredict_calcPhenotype.rdata')
    keep <- rowSums(CTRP2) > 0.8*ncol(CTRP2)
    calcPhenotype(trainingExprData = CTRP2[keep,],
                  trainingPtype = GDSC2_Res,
                  testExprData = as.matrix(tpm),
                  batchCorrect = 'eb',  #   "eb" for ComBat
                  powerTransformPhenotype = TRUE,
                  removeLowVaryingGenes = 0.2,
                  minNumSamples = 10,
                  printOutput = TRUE,
                  removeLowVaringGenesFrom = 'rawData')

- save(CTRP2, GDSC2_Res, tpm, file = ‘oncoPredict_calcPhenotype.rdata’)
- nano oncoPredict_calcPhenotype.R
- conda activate oncoPredict
- Rscript ./oncoPredict_calcPhenotype.R –max-ppsize=500000

## 预测结果可视化

### 读入预测结果

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    testPtype <- read.csv('./calcPhenotype_Output/DrugPredictions.csv', row.names = 1)
    testPtype <- log(testPtype)
    testPtype

### 贴上分组

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    group <- readRDS('../fig5/tcga.predict.rds')
    df <- cbind(testPtype[rownames(group), c('CIL55', 'BRD4132')],group$group)
    colnames(df)[[ncol(df)]]  <- 'Risk Group'
    df

### 绘图

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    library(ggpubr)
    options(repr.plot.width=4, repr.plot.height=4)
    my_comparisons <- list(c("Low Risk", "High Risk"))
    ggviolin(df, x="Risk Group", y="CIL55", fill = "Risk Group",
    palette = c("#00AFBB", "#E7B800"),
    add = "boxplot", add.params = list(fill="white"))+
    stat_compare_means(comparisons = my_comparisons, label = "p.signif")#label这里表示选择显著性标记（星号）

数据库
oncoPredict包进行药物敏感性预测
https://occdn.limour.top/2298.html
Author
Limour
Posted on
September 4, 2022
Licensed under
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