TCGAanalyze_LevelTab | R Documentation |
TCGAanalyze_LevelTab allows user to add information related to DEGs genes from Differentially expression analysis (DEA) such as mean values and in two conditions.
TCGAanalyze_LevelTab(
FC_FDR_table_mRNA,
typeCond1,
typeCond2,
TableCond1,
TableCond2,
typeOrder = TRUE
)
FC_FDR_table_mRNA |
Output of dataDEGs filter by abs(LogFC) >=1 |
typeCond1 |
a string containing the class label of the samples in TableCond1 (e.g., control group) |
typeCond2 |
a string containing the class label of the samples in TableCond2 (e.g., case group) |
TableCond1 |
numeric matrix, each row represents a gene, each column represents a sample with Cond1type |
TableCond2 |
numeric matrix, each row represents a gene, each column represents a sample with Cond2type |
typeOrder |
typeOrder |
table with DEGs, log Fold Change (FC), false discovery rate (FDR), the gene expression level for samples in Cond1type, and Cond2type, and Delta value (the difference of gene expression between the two conditions multiplied logFC)
dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)
dataFilt <- TCGAanalyze_Filtering(tabDF = dataBRCA, method = "quantile", qnt.cut = 0.25)
samplesNT <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))
samplesTP <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))
dataDEGs <- TCGAanalyze_DEA(
dataFilt[,samplesNT],
dataFilt[,samplesTP],
Cond1type = "Normal",
Cond2type = "Tumor"
)
dataDEGsFilt <- dataDEGs[abs(dataDEGs$logFC) >= 1,]
dataTP <- dataFilt[,samplesTP]
dataTN <- dataFilt[,samplesNT]
dataDEGsFiltLevel <- TCGAanalyze_LevelTab(
FC_FDR_table_mRNA = dataDEGsFilt,
typeCond1 = "Tumor",
typeCond2 = "Normal",
TableCond1 = dataTP,
TableCond2 = dataTN
)
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