## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(dpi = 300)
knitr::opts_chunk$set(cache=FALSE)
## ----echo = FALSE,hide=TRUE, message=FALSE,warning=FALSE----------------------
devtools::load_all(".")
## ----message=FALSE, warning=FALSE, include=FALSE------------------------------
library(SummarizedExperiment)
library(dplyr)
library(DT)
## ----eval = FALSE-------------------------------------------------------------
# # You can define a list of samples to query and download providing relative TCGA barcodes.
# listSamples <- c(
# "TCGA-E9-A1NG-11A-52R-A14M-07","TCGA-BH-A1FC-11A-32R-A13Q-07",
# "TCGA-A7-A13G-11A-51R-A13Q-07","TCGA-BH-A0DK-11A-13R-A089-07",
# "TCGA-E9-A1RH-11A-34R-A169-07","TCGA-BH-A0AU-01A-11R-A12P-07",
# "TCGA-C8-A1HJ-01A-11R-A13Q-07","TCGA-A7-A13D-01A-13R-A12P-07",
# "TCGA-A2-A0CV-01A-31R-A115-07","TCGA-AQ-A0Y5-01A-11R-A14M-07"
# )
#
# # Query platform Illumina HiSeq with a list of barcode
# query <- GDCquery(
# project = "TCGA-BRCA",
# data.category = "Transcriptome Profiling",
# data.type = "Gene Expression Quantification",
# barcode = listSamples
# )
#
# # Download a list of barcodes with platform IlluminaHiSeq_RNASeqV2
# GDCdownload(query)
#
# # Prepare expression matrix with geneID in the rows and samples (barcode) in the columns
# # rsem.genes.results as values
# BRCA.Rnaseq.SE <- GDCprepare(query)
#
# BRCAMatrix <- assay(BRCA.Rnaseq.SE,"unstranded")
# # For gene expression if you need to see a boxplot correlation and AAIC plot to define outliers you can run
# BRCA.RNAseq_CorOutliers <- TCGAanalyze_Preprocessing(BRCA.Rnaseq.SE)
## ----eval = TRUE, echo = FALSE,size = 8---------------------------------------
library(TCGAbiolinks)
dataGE <- dataBRCA[sample(rownames(dataBRCA),10),sample(colnames(dataBRCA),7)]
knitr::kable(
dataGE[1:10,2:3], digits = 2,
caption = "Example of a matrix of gene expression (10 genes in rows and 2 samples in columns)",
row.names = TRUE
)
## ----fig.width=6, fig.height=4, echo=FALSE, fig.align="center"----------------
library(png)
library(grid)
img <- readPNG("PreprocessingOutput.png")
grid.raster(img)
## ----eval = FALSE-------------------------------------------------------------
# library(TCGAbiolinks)
#
# # normalization of genes
# dataNorm <- TCGAanalyze_Normalization(
# tabDF = BRCA.RNAseq_CorOutliers,
# geneInfo = geneInfoHT
# )
#
# # quantile filter of genes
# dataFilt <- TCGAanalyze_Filtering(
# tabDF = dataNorm,
# method = "quantile",
# qnt.cut = 0.25
# )
#
# # selection of normal samples "NT"
# samplesNT <- TCGAquery_SampleTypes(
# barcode = colnames(dataFilt),
# typesample = c("NT")
# )
#
# # selection of tumor samples "TP"
# samplesTP <- TCGAquery_SampleTypes(
# barcode = colnames(dataFilt),
# typesample = c("TP")
# )
#
# # Diff.expr.analysis (DEA)
# dataDEGs <- TCGAanalyze_DEA(
# mat1 = dataFilt[,samplesNT],
# mat2 = dataFilt[,samplesTP],
# Cond1type = "Normal",
# Cond2type = "Tumor",
# fdr.cut = 0.01 ,
# logFC.cut = 1,
# method = "glmLRT"
# )
#
# # DEGs table with expression values in normal and tumor samples
# dataDEGsFiltLevel <- TCGAanalyze_LevelTab(
# FC_FDR_table_mRNA = dataDEGs,
# typeCond1 = "Tumor",
# typeCond2 = "Normal",
# TableCond1 = dataFilt[,samplesTP],
# TableCond2 = dataFilt[,samplesNT]
# )
#
## ----eval = TRUE, echo = FALSE------------------------------------------------
library(TCGAbiolinks)
dataDEGsFiltLevel$FDR <- format(dataDEGsFiltLevel$FDR, scientific = TRUE)
knitr::kable(
dataDEGsFiltLevel[1:10,], digits = 2,
caption = "Table of DEGs after DEA", row.names = FALSE
)
## ----eval=FALSE,echo=TRUE,message=FALSE,warning=FALSE-------------------------
#
# query <- GDCquery(
# project = "TCGA-BRCA",
# data.category = "Transcriptome Profiling",
# data.type = "Gene Expression Quantification",
# workflow.type = "STAR - Counts"
# )
#
# samplesDown <- getResults(query,cols=c("cases"))
#
# dataSmTP <- TCGAquery_SampleTypes(
# barcode = samplesDown,
# typesample = "TP"
# )
#
# dataSmNT <- TCGAquery_SampleTypes(
# barcode = samplesDown,
# typesample = "NT"
# )
# dataSmTP_short <- dataSmTP[1:10]
# dataSmNT_short <- dataSmNT[1:10]
#
# query.selected.samples <- GDCquery(
# project = "TCGA-BRCA",
# data.category = "Transcriptome Profiling",
# data.type = "Gene Expression Quantification",
# workflow.type = "STAR - Counts",
# barcode = c(dataSmTP_short, dataSmNT_short)
# )
#
# GDCdownload(
# query = query.selected.samples
# )
#
# dataPrep <- GDCprepare(
# query = query.selected.samples,
# save = TRUE
# )
#
# dataPrep <- TCGAanalyze_Preprocessing(
# object = dataPrep,
# cor.cut = 0.6,
# datatype = "HTSeq - Counts"
# )
#
# dataNorm <- TCGAanalyze_Normalization(
# tabDF = dataPrep,
# geneInfo = geneInfoHT,
# method = "gcContent"
# )
#
# boxplot(dataPrep, outline = FALSE)
#
# boxplot(dataNorm, outline = FALSE)
#
# dataFilt <- TCGAanalyze_Filtering(
# tabDF = dataNorm,
# method = "quantile",
# qnt.cut = 0.25
# )
#
# dataDEGs <- TCGAanalyze_DEA(
# mat1 = dataFilt[,dataSmTP_short],
# mat2 = dataFilt[,dataSmNT_short],
# Cond1type = "Normal",
# Cond2type = "Tumor",
# fdr.cut = 0.01 ,
# logFC.cut = 1,
# method = "glmLRT"
# )
#
## ----eval=FALSE,echo=TRUE,message=FALSE,warning=FALSE-------------------------
# require(TCGAbiolinks)
#
# query.miRNA <- GDCquery(
# project = "TCGA-BRCA",
# experimental.strategy = "miRNA-Seq",
# data.category = "Transcriptome Profiling",
# data.type = "miRNA Expression Quantification"
# )
#
# GDCdownload(query = query.miRNA)
#
# dataAssy.miR <- GDCprepare(
# query = query.miRNA
# )
# rownames(dataAssy.miR) <- dataAssy.miR$miRNA_ID
#
# # using read_count's data
# read_countData <- colnames(dataAssy.miR)[grep("count", colnames(dataAssy.miR))]
# dataAssy.miR <- dataAssy.miR[,read_countData]
# colnames(dataAssy.miR) <- gsub("read_count_","", colnames(dataAssy.miR))
#
# dataFilt <- TCGAanalyze_Filtering(
# tabDF = dataAssy.miR,
# method = "quantile",
# qnt.cut = 0.25
# )
#
# dataDEGs <- TCGAanalyze_DEA(
# mat1 = dataFilt[,dataSmNT_short.miR],
# mat2 = dataFilt[,dataSmTP_short.miR],
# Cond1type = "Normal",
# Cond2type = "Tumor",
# fdr.cut = 0.01 ,
# logFC.cut = 1,
# method = "glmLRT"
# )
#
## ----eval = FALSE-------------------------------------------------------------
# library(TCGAbiolinks)
# # Enrichment Analysis EA
# # Gene Ontology (GO) and Pathway enrichment by DEGs list
# Genelist <- rownames(dataDEGsFiltLevel)
#
# ansEA <- TCGAanalyze_EAcomplete(
# TFname = "DEA genes Normal Vs Tumor",
# RegulonList = Genelist
# )
#
# # Enrichment Analysis EA (TCGAVisualize)
# # Gene Ontology (GO) and Pathway enrichment barPlot
#
# TCGAvisualize_EAbarplot(
# tf = rownames(ansEA$ResBP),
# GOBPTab = ansEA$ResBP,
# GOCCTab = ansEA$ResCC,
# GOMFTab = ansEA$ResMF,
# PathTab = ansEA$ResPat,
# nRGTab = Genelist,
# nBar = 10
# )
#
## ----fig.width=6, fig.height=4, echo=FALSE, fig.align="center"----------------
library(png)
library(grid)
img <- readPNG("EAplot.png")
grid.raster(img)
## ----eval = FALSE-------------------------------------------------------------
# clin.gbm <- GDCquery_clinic("TCGA-GBM", "clinical")
# TCGAanalyze_survival(
# data = clin.gbm,
# clusterCol = "gender",
# main = "TCGA Set\n GBM",
# height = 10,
# width=10
# )
## ----fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE----
library(png)
library(grid)
img <- readPNG("case2_surv.png")
grid.raster(img)
## ----eval = FALSE-------------------------------------------------------------
# library(TCGAbiolinks)
# # Survival Analysis SA
#
# clinical_patient_Cancer <- GDCquery_clinic("TCGA-BRCA","clinical")
# dataBRCAcomplete <- log2(BRCA_rnaseqv2)
#
# tokenStop <- 1
#
# tabSurvKMcomplete <- NULL
#
# for( i in 1: round(nrow(dataBRCAcomplete)/100)){
# message( paste( i, "of ", round(nrow(dataBRCAcomplete)/100)))
# tokenStart <- tokenStop
# tokenStop <- 100 * i
# tabSurvKM <- TCGAanalyze_SurvivalKM(
# clinical_patient_Cancer,
# dataBRCAcomplete,
# Genelist = rownames(dataBRCAcomplete)[tokenStart:tokenStop],
# Survresult = F,
# ThreshTop = 0.67,
# ThreshDown = 0.33
# )
#
# tabSurvKMcomplete <- rbind(tabSurvKMcomplete,tabSurvKM)
# }
#
# tabSurvKMcomplete <- tabSurvKMcomplete[tabSurvKMcomplete$pvalue < 0.01,]
# tabSurvKMcomplete <- tabSurvKMcomplete[order(tabSurvKMcomplete$pvalue, decreasing=F),]
#
# tabSurvKMcompleteDEGs <- tabSurvKMcomplete[
# rownames(tabSurvKMcomplete) %in% dataDEGsFiltLevel$mRNA,
# ]
## ----fig.width=6, fig.height=4, echo=FALSE, fig.align="center"----------------
tabSurvKMcompleteDEGs$pvalue <- format(tabSurvKMcompleteDEGs$pvalue, scientific = TRUE)
knitr::kable(tabSurvKMcompleteDEGs[1:5,1:4],
digits = 2,
caption = "Table KM-survival genes after SA",
row.names = TRUE)
knitr::kable(tabSurvKMcompleteDEGs[1:5,5:7],
digits = 2,
row.names = TRUE)
## ----eval = FALSE-------------------------------------------------------------
# data <- TCGAanalyze_DMC(
# data = data,
# groupCol = "methylation_subtype",
# group1 = "CIMP.H",
# group2 = "CIMP.L",
# p.cut = 10^-5,
# diffmean.cut = 0.25,
# legend = "State",
# plot.filename = "coad_CIMPHvsCIMPL_metvolcano.png"
# )
## ----fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE----
library(png)
library(grid)
img <- readPNG("figure5met.png")
grid.raster(img)
## ----fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE----
library(jpeg)
library(grid)
img <- readJPEG("case2_Heatmap.jpg")
grid.raster(img)
## ----eval = FALSE-------------------------------------------------------------
# # normalization of genes
# dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)
#
# # quantile filter of genes
# dataFilt <- TCGAanalyze_Filtering(
# tabDF = dataNorm,
# method = "quantile",
# qnt.cut = 0.25
# )
#
# # selection of normal samples "NT"
# group1 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))
# # selection of normal samples "TP"
# group2 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))
#
# # Principal Component Analysis plot for ntop selected DEGs
# pca <- TCGAvisualize_PCA(
# dataFilt = dataFilt,
# dataDEGsFiltLevel = dataDEGsFiltLevel,
# ntopgenes = 200,
# group1 = group1,
# group2 = group2
# )
## ----fig.width=6, fig.height=4, echo=FALSE, fig.align="center"----------------
library(png)
library(grid)
img <- readPNG("PCAtop200DEGs.png")
grid.raster(img)
## ----include=FALSE,echo=FALSE, fig.height=5, message=FALSE, warning=FALSE,eval=FALSE----
# query <- GDCquery(
# project = "TCGA-GBM",
# data.category = "DNA Methylation",
# platform = "Illumina Human Methylation 27",
# barcode = c(
# "TCGA-02-0058-01A-01D-0186-05", "TCGA-12-1597-01B-01D-0915-05",
# "TCGA-12-0829-01A-01D-0392-05", "TCGA-06-0155-01B-01D-0521-05",
# "TCGA-02-0099-01A-01D-0199-05", "TCGA-19-4068-01A-01D-1228-05",
# "TCGA-19-1788-01A-01D-0595-05", "TCGA-16-0848-01A-01D-0392-05"
# )
# )
# GDCdownload(query, method = "api")
# data <- GDCprepare(query)
#
## ----eval=FALSE, echo=TRUE, fig.height=5, message=FALSE, warning=FALSE--------
# query <- GDCquery(
# project = "TCGA-GBM",
# data.category = "DNA methylation",
# platform = "Illumina Human Methylation 27",
# barcode = c(
# "TCGA-02-0058-01A-01D-0186-05", "TCGA-12-1597-01B-01D-0915-05",
# "TCGA-12-0829-01A-01D-0392-05", "TCGA-06-0155-01B-01D-0521-05",
# "TCGA-02-0099-01A-01D-0199-05", "TCGA-19-4068-01A-01D-1228-05",
# "TCGA-19-1788-01A-01D-0595-05", "TCGA-16-0848-01A-01D-0392-05"
# )
# )
# GDCdownload(query, method = "api")
# data <- GDCprepare(query)
## ----eval = FALSE-------------------------------------------------------------
# starburst <- TCGAvisualize_starburst(
# met = coad.SummarizeExperiment,
# exp = different.experssion.analysis.data,
# group1 = "CIMP.H",
# group2 = "CIMP.L",
# met.platform = "450K",
# genome = "hg19",
# met.p.cut = 10^-5,
# exp.p.cut = 10^-5,
# names = TRUE
# )
## ----fig.width=6, fig.height=4, echo = FALSE, fig.align="center",hide=TRUE, message=FALSE,warning=FALSE----
library(png)
library(grid)
img <- readPNG("figure5star.png")
grid.raster(img)
## ----sessionInfo--------------------------------------------------------------
sessionInfo()