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library(GDCRNATools)
library(DT)

load RNA counts data

data(rnaCounts)

image.png

load miRNAs counts data

data(mirCounts)

####### Normalization of RNAseq data #######
rnaExpr <- gdcVoomNormalization(counts = rnaCounts, filter = FALSE)

####### Normalization of miRNAs data #######
mirExpr <- gdcVoomNormalization(counts = mirCounts, filter = FALSE)

3.1.2 Parse metadata
####### Parse and filter RNAseq metadata #######
metaMatrix.RNA <- gdcParseMetadata(project.id = 'TCGA-CHOL',
data.type = 'RNAseq',
write.meta = FALSE)

metaMatrix.RNA <- gdcFilterDuplicate(metaMatrix.RNA)
metaMatrix.RNA <- gdcFilterSampleType(metaMatrix.RNA)
metaMatrix.RNA[1:5,]

注意comparision写法顺序，谁在前谁是分子，后者是分母

比较对相的来自 metaMatrix.RNA$sample_type函数的字符串

DEGAll <- gdcDEAnalysis(counts = rnaCounts,
group = metaMatrix.RNA$sample_type,
comparison = 'PrimaryTumor-SolidTissueNormal',
method = 'limma')

DEGAll[1:5,]

All DEGs

deALL <- gdcDEReport(deg = DEGAll, gene.type = 'all')

DE long-noncoding

deLNC <- gdcDEReport(deg = DEGAll, gene.type = 'long_non_coding')

DE protein coding genes

dePC <- gdcDEReport(deg = DEGAll, gene.type = 'protein_coding')

3.2.2 ceRNAs network analysis of DEGs
ceOutput <- gdcCEAnalysis(lnc = rownames(deLNC),
pc = rownames(dePC),
lnc.targets = 'starBase',
pc.targets = 'starBase',
rna.expr = rnaExpr,
mir.expr = mirExpr)
ceOutput[1:5,]

3.2.3 Export ceRNAs network to Cytoscape

ceOutput2 <- ceOutput[ceOutput $hyperPValue<0.01 & ceOutput$ corPValue<0.01 & ceOutput$regSim != 0,]

edges <- gdcExportNetwork(ceNetwork = ceOutput2, net = 'edges')
nodes <- gdcExportNetwork(ceNetwork = ceOutput2, net = 'nodes')

write.table(edges, file='edges.txt', sep='\t', quote=F)
write.table(nodes, file='nodes.txt', sep='\t', quote=F)

3.3.6 Correlation plot on a local webpage
shinyCorPlot() , a interactive plot function based on shiny package, can be easily operated by just clicking the genes in each drop down box (in the GUI window). By running shinyCorPlot() function, a local webpage would pop up and correlation plot between a lncRNA and mRNA would be automatically shown.

shinyCorPlot(gene1 = rownames(deLNC),
gene2 = rownames(dePC),
rna.expr = rnaExpr,
metadata = metaMatrix.RNA)