DEGs for legacy and harmonized dataset
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Gisele • 0
@df6db70c
Last seen 9 weeks ago
Brazil

Hi,

I found different DEGs for legacy and harmonized dataset. I would like to know if my scripts are correct:

#######  harmonized dataset

queryDown <- GDCquery(project = CancerProject,data.category = "Transcriptome Profiling",
                                         data.type = "Gene Expression Quantification",workflow.type = "HTSeq - Counts",
                                         barcode = c(samplesTN$Sample.ID,samplesTP$V1))
GDCdownload(query = queryDown)
dataPrep <- GDCprepare(query = queryDown)

dataProcessing <- TCGAanalyze_Preprocessing(object = dataPrep, cor.cut = 0.6, datatype = "HTSeq - Counts")  

dataNorm <- TCGAanalyze_Normalization(tabDF = dataProcessing,geneInfo = geneInfoHT,method = "geneLength") 

dataFilt <- TCGAanalyze_Filtering(tabDF = dataNorm, method = "quantile", qnt.cut = 0.25)

dataPrep_raw <- UseRaw_afterFilter(dataPrep, dataFilt)

datasmTP <-  dataPrep$barcode[grep("TP",dataPrep_raw$shortLetterCode)]

datasmTN <- dataPrep$barcode[grep("NT",dataPrep_raw$shortLetterCode)]

datadownDEGs <- TCGAanalyze_DEA(mat1 = dataFilt[,datasmTN], mat2 = dataFilt[,datasmTP],Cond1type = "Normal", Cond2type = "Tumor", fdr.cut = 0.01,logFC.cut = 1, method = 'glmLRT')


####legacy dataset

query_modelo_1 <- GDCquery(project = "TCGA-BRCA", data.category = "Gene expression",
                  data.type = "Gene expression quantification", 
                  experimental.strategy = "RNA-Seq",
                  #sample.type = c("Primary Tumor", "Solid Tissue Normal"),
                  platform = "Illumina HiSeq",
                  file.type = "results",barcode= c(listSamples_tumor_modelo_1$Sample.ID,listSamples_normal_modelo_1$V1),
                  legacy = TRUE)


GDCdownload(query_modelo_1)


BRCARnaseqSE_modelo_1 <- GDCprepare(query_modelo_1)


BRCARnaseq_CorOutliers_modelo_1 <- TCGAanalyze_Preprocessing(BRCARnaseqSE_modelo_1,cor.cut = 0.6)

dataNorm_modelo_1 <- TCGAanalyze_Normalization(tabDF = BRCARnaseq_CorOutliers_modelo_1,method = "geneLength", geneInfo =  geneInfo)

dataFilt_modelo_1 <- TCGAanalyze_Filtering(tabDF = dataNorm_modelo_1,method = "quantile",qnt.cut =  0.25)

samplesNT_modelo_1 <-BRCARnaseqSE_modelo_1$barcode[grep("NT",BRCARnaseqSE_modelo_1$shortLetterCode)]
samplesTP_modelo_1 <-BRCARnaseqSE_modelo_1$barcode[grep("TP",BRCARnaseqSE_modelo_1$shortLetterCode)]


# Diff.expr.analysis (DEA)
dataDEGs_modelo_1 <- TCGAanalyze_DEA(mat1 = dataFilt_modelo_1[,samplesNT_modelo_1],mat2 = dataFilt_modelo_1[,samplesTP_modelo_1],
                                      Cond1type = "Normal",Cond2type = "Tumor",fdr.cut = 0.01,logFC.cut = 1,method = "glmLRT")


dataDEGsFiltLevel <- TCGAanalyze_LevelTab(dataDEGs_modelo_1, "Tumor", "Normal",dataFilt_modelo_1[,samplesNT_modelo_1], dataFilt_modelo_1[,samplesTP_modelo_1])




sessionInfo( )

Thanks

#DEGs #legacydataset #harmonizeddataset TCGAbiolinks • 71 views
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