`TCGAanalyze`: Analyze data from TCGA.

You can easily analyze data using following functions:

`TCGAanalyze_Preprocessing`: Preprocessing of Gene Expression data (IlluminaHiSeq_RNASeqV2)

You can easily search TCGA samples, download and prepare a matrix of gene expression.

# 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 = "Gene expression",
                  data.type = "Gene expression quantification",
                  experimental.strategy = "RNA-Seq",
                  platform = "Illumina HiSeq",
                  file.type = "results",
                  barcode = listSamples, 
                  legacy = TRUE)

# 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
BRCARnaseqSE <- GDCprepare(query)

BRCAMatrix <- assay(BRCARnaseqSE,"raw_count") # or BRCAMatrix <- assay(BRCARnaseqSE,"raw_count")

# For gene expression if you need to see a boxplot correlation and AAIC plot to define outliers you can run
BRCARnaseq_CorOutliers <- TCGAanalyze_Preprocessing(BRCARnaseqSE)

The result is shown below:

Example of a matrix of gene expression (10 genes in rows and 2 samples in columns)
	TCGA-BH-A0AU-01A-11R-A12P-07	TCGA-A7-A13D-01A-13R-A12P-07
SRPK1\|6732	3007	4839
ASPHD1\|253982	86	933
ATG10\|83734	104	185
ZDHHC23\|254887	545	703
SNORA51\|677831	0	0
ADARB1\|104	778	1669
ISX\|91464	0	0
C12orf74\|338809	0	0
RAPGEFL1\|51195	2545	526
GRINA\|2907	35911	13475

The result from TCGAanalyze_Preprocessing is shown below:

`TCGAanalyze_DEA` `&` `TCGAanalyze_LevelTab`: Differential expression analysis (DEA)

Perform DEA (Differential expression analysis) to identify differentially expressed genes (DEGs) using the TCGAanalyze_DEA function.

TCGAanalyze_DEA performs DEA using following functions from R :

edgeR::DGEList converts the count matrix into an edgeR object.
edgeR::estimateCommonDisp each gene gets assigned the same dispersion estimate.
edgeR::exactTest performs pair-wise tests for differential expression between two groups.
edgeR::topTags takes the output from exactTest(), adjusts the raw p-values using the False Discovery Rate (FDR) correction, and returns the top differentially expressed genes.

This function receives as arguments:

mat1 The matrix of the first group (in the example, group 1 is the normal samples),
mat2 The matrix of the second group (in the example, group 2 is tumor samples)
Cond1type Label for group 1
Cond1type Label for group 2

Next, we filter the output of dataDEGs by abs(LogFC) >=1, and uses the TCGAanalyze_LevelTab function to create a table with DEGs (differentially expressed genes), 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).

# Downstream analysis using gene expression data  
# TCGA samples from IlluminaHiSeq_RNASeqV2 with type rsem.genes.results
# save(dataBRCA, geneInfo , file = "dataGeneExpression.rda")
library(TCGAbiolinks)

# normalization of genes
dataNorm <- TCGAanalyze_Normalization(tabDF = dataBRCA, geneInfo =  geneInfo)

# 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(dataDEGs,"Tumor","Normal",
                                          dataFilt[,samplesTP],dataFilt[,samplesNT])

The result is shown below:

Table of DEGs after DEA
mRNA	logFC	FDR	Tumor	Normal	Delta
FN1	2.88	1.296151e-19	347787.48	41234.12	1001017.3
COL1A1	1.77	1.680844e-08	358010.32	89293.72	633086.3
C4orf7	5.20	2.826474e-50	87821.36	2132.76	456425.4
COL1A2	1.40	9.480478e-06	273385.44	91241.32	383242.9
GAPDH	1.32	3.290678e-05	179057.44	63663.00	236255.5
CLEC3A	6.79	7.971002e-74	27257.16	259.60	185158.6
IGFBP5	1.24	1.060717e-04	128186.88	53323.12	158674.6
CPB1	4.27	3.044021e-37	37001.76	2637.72	157968.8
CARTPT	6.72	1.023371e-72	21700.96	215.16	145872.8
DCD	7.26	1.047988e-80	19941.20	84.80	144806.3

Other examples are in the next sections.

HTSeq data: Downstream analysis BRCA

CancerProject <- "TCGA-BRCA"
DataDirectory <- paste0("../GDC/",gsub("-","_",CancerProject))
FileNameData <- paste0(DataDirectory, "_","HTSeq_Counts",".rda")

query <- GDCquery(project = CancerProject,
                  data.category = "Transcriptome Profiling",
                  data.type = "Gene Expression Quantification", 
                  workflow.type = "HTSeq - 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]

queryDown <- GDCquery(project = CancerProject, 
                      data.category = "Transcriptome Profiling",
                      data.type = "Gene Expression Quantification", 
                      workflow.type = "HTSeq - Counts", 
                      barcode = c(dataSmTP_short, dataSmNT_short))
                    
GDCdownload(query = queryDown,
            directory = DataDirectory)

dataPrep <- GDCprepare(query = queryDown, 
                       save = TRUE, 
                       directory =  DataDirectory,
                       save.filename = FileNameData)

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")

miRNA expression data: Downstream analysis BRCA

require(TCGAbiolinks)

CancerProject <- "TCGA-BRCA"
DataDirectory <- paste0("../GDC/",gsub("-","_",CancerProject))
FileNameData <- paste0(DataDirectory, "_","miRNA_gene_quantification",".rda")

query.miR <- GDCquery(project = CancerProject, 
                  data.category = "Gene expression",
                  data.type = "miRNA gene quantification",
                  file.type = "hg19.mirna",
                  legacy = TRUE)

samplesDown.miR <- getResults(query.miR,cols=c("cases"))

dataSmTP.miR <- TCGAquery_SampleTypes(barcode = samplesDown.miR,
                                  typesample = "TP")

dataSmNT.miR <- TCGAquery_SampleTypes(barcode = samplesDown.miR,
                                  typesample = "NT")
dataSmTP_short.miR <- dataSmTP.miR[1:10]
dataSmNT_short.miR <- dataSmNT.miR[1:10]

queryDown.miR <- GDCquery(project = CancerProject, 
                      data.category = "Gene expression",
                      data.type = "miRNA gene quantification",
                      file.type = "hg19.mirna",
                      legacy = TRUE,
                      barcode = c(dataSmTP_short.miR, dataSmNT_short.miR))

GDCdownload(query = queryDown.miR,
            directory = DataDirectory)

dataAssy.miR <- GDCprepare(query = queryDown.miR, 
                           save = TRUE, 
                           save.filename = FileNameData, 
                           summarizedExperiment = TRUE,
                           directory =DataDirectory )
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")

`TCGAanalyze_EAcomplete & TCGAvisualize_EAbarplot`: Enrichment Analysis

Researchers, in order to better understand the underlying biological processes, often want to retrieve a functional profile of a set of genes that might have an important role. This can be done by performing an enrichment analysis.

We will perform an enrichment analysis on gene sets using the TCGAanalyze_EAcomplete function. Given a set of genes that are up-regulated under certain conditions, an enrichment analysis will identify classes of genes or proteins that are over-represented using annotations for that gene set.

To view the results you can use the TCGAvisualize_EAbarplot function as shown below.

library(TCGAbiolinks)
# Enrichment Analysis EA
# Gene Ontology (GO) and Pathway enrichment by DEGs list
Genelist <- rownames(dataDEGsFiltLevel)

system.time(ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes Normal Vs Tumor",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)

The result is shown below:

The figure shows canonical pathways significantly overrepresented (enriched) by the DEGs (differentially expressed genes). The most statistically significant canonical pathways identified in DEGs list are listed according to their p value corrected FDR (-Log) (colored bars) and the ratio of list genes found in each pathway over the total number of genes in that pathway (Ratio, red line).

`TCGAanalyze_survival`: Survival Analysis

When analyzing survival, different problems come up than the ones discussed so far. One question is how do we deal with subjects dropping out of a study. For example, assuming that we test a new cancer drug. While some subjects die, others may believe that the new drug is not effective, and decide to drop out of the study before the study is finished. A similar problem would be faced when we investigate how long a machine lasts before it breaks down.

Using the clinical data, it is possible to create a survival plot with the function TCGAanalyze_survival as follows:

clin.gbm <- GDCquery_clinic("TCGA-GBM", "clinical")
TCGAanalyze_survival(clin.gbm,
                     "gender",
                     main = "TCGA Set\n GBM",height = 10, width=10)

The arguments of TCGAanalyze_survival are:

clinical_patient TCGA Clinical patient with the information days_to_death
clusterCol Column with groups to plot. This is a mandatory field, the caption will be based in this column
legend Legend title of the figure
xlim xlim x axis limits e.g. xlim = c(0, 1000). Present narrower X axis, but not affect survival estimates.
main main title of the plot
ylab y-axis text of the plot
xlab x-axis text of the plot
filename The name of the pdf file
color Define the colors of the lines.
pvalue Show pvalue in the plot.
risk.table Show or not the risk table
conf.int Show confidence intervals for point estimaes of survival curves.

The result is shown below:

`TCGAanalyze_SurvivalKM`: Correlating gene expression and Survival Analysis

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,
    ]

The result is shown below:

Table KM-survival genes after SA
	pvalue	Cancer Deaths	Cancer Deaths with Top	Cancer Deaths with Down
DCTPP1	6.204170e-08	66	46	20
APOO	9.390193e-06	65	49	16
LOC387646	1.039097e-05	69	48	21
PGK1	1.198577e-05	71	49	22
CCNE2	2.100348e-05	65	48	17

	Mean Tumor Top	Mean Tumor Down	Mean Normal
DCTPP1	13.31	12.01	11.74
APOO	11.40	10.17	10.01
LOC387646	7.92	4.64	5.90
PGK1	15.66	14.18	14.28
CCNE2	11.07	8.23	7.03

`TCGAanalyze_DMR`: Differentially methylated regions Analysis

We will search for differentially methylated CpG sites using the TCGAanalyze_DMR function. In order to find these regions we use the beta-values (methylation values ranging from 0.0 to 1.0) to compare two groups.

First, it calculates the difference between the mean DNA methylation of each group for each probe.

Second, it test for differential expression between two groups using the wilcoxon test adjusting by the Benjamini-Hochberg method. The default arguments was set to require a minimum absolute beta-values difference of 0.2 and an adjusted p-value of < 0.01.

After these tests, we save a volcano plot (x-axis:diff mean methylation, y-axis: statistical significance) that will help the user identify the differentially methylated CpG sites, then the results are saved in a csv file (DMR_results.groupCol.group1.group2.csv) and finally the object is returned with the calculus in the rowRanges.

The arguments of TCGAanalyze_DMR are:

Argument	Description
data	SummarizedExperiment obtained from the TCGAPrepare
groupCol	Columns with the groups inside the SummarizedExperiment object. (This will be obtained by the function colData(data))
group1	In case our object has more than 2 groups, you should set the name of the group
group2	In case our object has more than 2 groups, you should set the name of the group
calculate.pvalues.probes	In order to get the probes faster the user can select to calculate the pvalues only for the probes with a difference in DNA methylation. The default is to calculate to all probes. Possible values: “all”, “differential”. Default “all”
plot.filename	Filename. Default: volcano.pdf, volcano.svg, volcano.png. If set to FALSE, there will be no plot.
ylab	y axis text
xlab	x axis text
title	main title. If not specified it will be “Volcano plot (group1 vs group2)
legend	Legend title
color	vector of colors to be used in graph
label	vector of labels to be used in the figure. Example: c(“Not Significant”,“Hypermethylated in group1”, “Hypomethylated in group1”))
xlim	x limits to cut image
ylim	y limits to cut image
p.cut	p values threshold. Default: 0.01
probe.names	is probe.names
diffmean.cut	diffmean threshold. Default: 0.2
paired	Wilcoxon paired parameter. Default: FALSE
adj.method	Adjusted method for the p-value calculation
overwrite	Overwrite the pvalues and diffmean values if already in the object for both groups? Default: FALSE
cores	Number of cores to be used in the non-parametric test Default = groupCol.group1.group2.rda
save	Save object with results? Default: TRUE

data <- TCGAanalyze_DMR(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")

The output will be a plot such as the figure below. The green dots are the probes that are hypomethylated in group 2 compared to group 1, while the red dots are the hypermethylated probes in group 2 compared to group 1

Also, the TCGAanalyze_DMR function will save the plot as pdf and return the same SummarizedExperiment that was given as input with the values of p-value, p-value adjusted, diffmean and the group it belongs in the graph (non significant, hypomethylated, hypermethylated) in the rowRanges. The collumns will be (where group1 and group2 are the names of the groups):

diffmean.group1.group2 (mean.group2 - mean.group1)
diffmean.group2.group1 (mean.group1 - mean.group2)
p.value.group1.group2
p.value.adj.group1.group2
status.group1.group2 (Status of probes in group2 in relation to group1)
status.group2.group1 (Status of probes in group1 in relation to group2)

This values can be view/acessed using the rowRanges acessesor (rowRanges(data)).

Observation: Calling the same function again, with the same arguments will only plot the results, as it was already calculated. If you want to have them recalculated, please set overwrite to TRUE or remove the calculated collumns.

`TCGAvisualize`: Visualize results from analysis functions with TCGA’s data.

You can easily visualize results from some following functions:

`TCGAvisualize_Heatmap`: Create heatmaps with cluster bars

In order to have a better view of clusters, we normally use heatmaps. TCGAvisualize_Heatmap will plot a heatmap and add to each sample bars representing different features. This function is a wrapper to the package ComplexHeatmap package,

The arguments of this function are:

data The object with the heatmap data (expression, methylation)
col.metadata Metadata for the columns (patients). It should have the column bcr_patient_barcode or patient or ID with the patients barcodes.
row.metadata Metadata for the rows genes (expression) or probes (methylation)
col.colors A list of names colors
row.colors A list of named colors
show_column_names Show column names names? Dafault: FALSE
show_row_names Show row names? Dafault: FALSE
cluster_rows Cluster rows ? Dafault: FALSE
cluster_columns Cluster columns ? Dafault: FALSE
sortCol Name of the column to be used to sort the columns
title Title of the plot
type Select the colors of the heatmap values. Possible values are “expression” (default), “methylation”
scale Use z-score to make the heamat? If we want to show differences between genes, it is good to make Z-score by samples (force each sample to have zero mean and standard deviation=1). If we want to show differences between samples, it is good to make Z-score by genes (force each gene to have zero mean and standard deviation=1). Possibilities: “row”, “col. Default”none"

For more information please take a look on case study #2.

The result is shown below:

`TCGAvisualize_Volcano`: Create volcano plot

Creates a volcano plot for DNA methylation or expression

The arguments of this function are:

Argument	Description
x	x-axis data
y	y-axis data
filename	Filename. Default: volcano.pdf, volcano.svg, volcano.png
ylab	y axis text
xlab	x axis text
title	main title. If not specified it will be “Volcano plot (group1 vs group2)
legend	Legend title
label	vector of labels to be used in the figure. Example: c(“Not Significant”,“Hypermethylated in group1”, “Hypomethylated in group1”))#’
xlim	x limits to cut image
ylim	y limits to cut image
color	vector of colors to be used in graph
names	Names to be ploted if significant. Should be the same size of x and y
names.fill	Names should be filled in a color box? Default: TRUE
show.names	What names will be showd? Possibilities: “both”, “significant”, “highlighted”
x.cut	x-axis threshold. Default: 0.0 If you give only one number (e.g. 0.2) the cut-offs will be -0.2 and 0.2. Or you can give diffenrent cutt-ofs as a vector (e.g. c(-0.3,0.4))
y.cut	p-values threshold.
height	Figure height
width	Figure width
highlight	List of genes/probes to be highlighted. It should be in the names argument.
highlight.color	Color of the points highlighted
names.size	Size of the names text
dpi	Figure dpi

For more information please take a look on case study #3.

`TCGAvisualize_PCA`: Principal Component Analysis plot for differentially expressed genes

In order to better understand our genes, we can perform a PCA to reduce the number of dimensions of our gene set. The function TCGAvisualize_PCA will plot the PCA for different groups.

The arguments of this function are:

dataFilt The expression matrix after normalization and quantile filter
dataDEGsFiltLevel The TCGAanalyze_LevelTab output
ntopgenes number of DEGs genes to plot in PCA
** group1 a string containing the barcode list of the samples in control group
** group2 a string containing the barcode list of the samples in disease group

# 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,dataDEGsFiltLevel, ntopgenes = 200, group1, group2)

The result is shown below:

`TCGAvisualize_meanMethylation`: Mean DNA Methylation Analysis

Using the data and calculating the mean DNA methylation per group, it is possible to create a mean DNA methylation boxplot with the function TCGAvisualize_meanMethylation as follows:

query <- GDCquery(project = "TCGA-GBM",
                  data.category = "DNA methylation",
                  platform = "Illumina Human Methylation 27",
                  legacy = TRUE, 
                  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)

# "shortLetterCode" is a column in the SummarizedExperiment::colData(data) matrix
TCGAvisualize_meanMethylation(data, groupCol = "shortLetterCode",filename = NULL)

##   groups      Mean    Median       Max       Min
## 1     NT 0.5101750 0.5058660 0.5301624 0.4948791
## 2     TP 0.4827425 0.4894990 0.5058586 0.4400024
## 3     TR 0.4946644 0.4955979 0.5166819 0.4579414
##            NT         TP        TR
## NT         NA 0.03201344 0.1190572
## TP 0.03201344         NA 0.3363125
## TR 0.11905718 0.33631252        NA

The arguments of TCGAvisualize_meanMethylation are:

Argument	Description
data	SummarizedExperiment object obtained from GDCPrepare
groupCol	Columns in colData(data) that defines the groups. If no columns defined a columns called “Patients” will be used
subgroupCol	Columns in colData(data) that defines the subgroups.
shapes	Shape vector of the subgroups. It must have the size of the levels of the subgroups. Example: shapes = c(21,23) if for two levels
print.pvalue	Print p-value for two groups
plot.jitter	Plot jitter? Default TRUE
jitter.size	Plot jitter size? Default 3
filename	The name of the pdf that will be saved
ylab	y axis text in the plot
xlab	x axis text in the plot
title	main title in the plot
labels	Labels of the groups
group.legend	Name of the group legend. DEFAULT: groupCol
subgroup.legend	Name of the subgroup legend. DEFAULT: subgroupCol
color	vector of colors to be used in graph
y.limits	Change lower/upper y-axis limit
sort	Sort boxplot by mean or median. Possible values: mean.asc, mean.desc, median.asc, meadian.desc
order	Order of the boxplots
legend.position	Legend position (“top”, “right”,“left”,“bottom”)
legend.title.position	Legend title position (“top”, “right”,“left”,“bottom”)
legend.ncols	Number of columns of the legend
add.axis.x.text	Add text to x-axis? Default: FALSE
width	Plot width default:10
height	Plot height default:10
dpi	Pdf dpi default:600
axis.text.x.angle	Angle of text in the x axis

Other examples using the parameters:

# setting y limits: lower 0, upper 1
TCGAvisualize_meanMethylation(data,groupCol = "shortLetterCode", 
                              filename = NULL, y.limits = c(0,1))

##   groups      Mean    Median       Max       Min
## 1     NT 0.5101750 0.5058660 0.5301624 0.4948791
## 2     TP 0.4827425 0.4894990 0.5058586 0.4400024
## 3     TR 0.4946644 0.4955979 0.5166819 0.4579414
##            NT         TP        TR
## NT         NA 0.03201344 0.1190572
## TP 0.03201344         NA 0.3363125
## TR 0.11905718 0.33631252        NA

# setting y limits: lower 0
TCGAvisualize_meanMethylation(data,groupCol = "shortLetterCode", 
                              filename = NULL, y.limits = 0)

##   groups      Mean    Median       Max       Min
## 1     NT 0.5101750 0.5058660 0.5301624 0.4948791
## 2     TP 0.4827425 0.4894990 0.5058586 0.4400024
## 3     TR 0.4946644 0.4955979 0.5166819 0.4579414
##            NT         TP        TR
## NT         NA 0.03201344 0.1190572
## TP 0.03201344         NA 0.3363125
## TR 0.11905718 0.33631252        NA

# Changing shapes of jitters to show subgroups
TCGAvisualize_meanMethylation(data,
                              groupCol = "subtype_Pan.Glioma.DNA.Methylation.Cluster", 
                              subgroupCol ="vital_status", filename = NULL)

##   groups      Mean    Median       Max       Min
## 1 group1 0.4928827 0.4948791 0.4968953 0.4857857
## 2 group2 0.5073724 0.5058660 0.5250553 0.4977332
## 3 group3 0.4873269 0.4878837 0.5301624 0.4400024
##             group1      group2    group3
## group1          NA 0.002984774 0.7007833
## group2 0.002984774          NA 0.1994449
## group3 0.700783301 0.199444924        NA

# Sorting bars by descending mean methylation
TCGAvisualize_meanMethylation(data,
                              groupCol = "subtype_Pan.Glioma.DNA.Methylation.Cluster",
                              sort="mean.desc",
                              filename=NULL)

##   groups      Mean    Median       Max       Min
## 1 group1 0.4928827 0.4948791 0.4968953 0.4857857
## 2 group2 0.5073724 0.5058660 0.5250553 0.4977332
## 3 group3 0.4873269 0.4878837 0.5301624 0.4400024
##             group1      group2    group3
## group1          NA 0.002984774 0.7007833
## group2 0.002984774          NA 0.1994449
## group3 0.700783301 0.199444924        NA

# Sorting bars by asc mean methylation
TCGAvisualize_meanMethylation(data,
                              groupCol = "subtype_Pan.Glioma.DNA.Methylation.Cluster",
                              sort = "mean.asc",
                              filename=NULL)

##   groups      Mean    Median       Max       Min
## 1 group1 0.4928827 0.4948791 0.4968953 0.4857857
## 2 group2 0.5073724 0.5058660 0.5250553 0.4977332
## 3 group3 0.4873269 0.4878837 0.5301624 0.4400024
##             group1      group2    group3
## group1          NA 0.002984774 0.7007833
## group2 0.002984774          NA 0.1994449
## group3 0.700783301 0.199444924        NA

TCGAvisualize_meanMethylation(data,
                              groupCol = "vital_status",
                              sort = "mean.asc",
                              filename=NULL)

##   groups      Mean    Median       Max       Min
## 1  ALIVE 0.4827425 0.4894990 0.5058586 0.4400024
## 2   DEAD 0.5024197 0.5013806 0.5301624 0.4579414
##            ALIVE       DEAD
## ALIVE         NA 0.09544749
## DEAD  0.09544749         NA

`TCGAvisualize_starburst`: Integration of gene expression and DNA methylation data

The starburst plot is proposed to combine information from two volcano plots, and is applied for a study of DNA methylation and gene expression. It first introduced in 2010 (Noushmehr et al. 2010). In order to reproduce this plot, we will use the TCGAvisualize_starburst function.

The function creates Starburst plot for comparison of DNA methylation and gene expression. The log10 (FDR-corrected P value) for DNA methylation is plotted in the x axis, and for gene expression in the y axis, for each gene. The black dashed line shows the FDR-adjusted P value of 0.01.

starburst <- TCGAvisualize_starburst(coad.SummarizeExperiment, 
                                     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)

As a result, the function will a plot the figure below and return a matrix with the Gene_symbol and it status in relation to expression (up regulated/down regulated) and to methylation (Hyper/Hypo methylated). The case study 3, shows the complete pipeline for creating this figure.

Session Information

sessionInfo()

## R version 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.6-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] png_0.1-7                  DT_0.2                    
##  [3] dplyr_0.7.4                SummarizedExperiment_1.8.1
##  [5] DelayedArray_0.4.1         matrixStats_0.52.2        
##  [7] Biobase_2.38.0             GenomicRanges_1.30.1      
##  [9] GenomeInfoDb_1.14.0        IRanges_2.12.0            
## [11] S4Vectors_0.16.0           BiocGenerics_0.24.0       
## [13] TCGAbiolinks_2.6.10       
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.1.2             circlize_0.4.3             
##   [3] aroma.light_3.8.0           plyr_1.8.4                 
##   [5] selectr_0.3-1               ConsensusClusterPlus_1.42.0
##   [7] lazyeval_0.2.1              splines_3.4.3              
##   [9] BiocParallel_1.12.0         ggplot2_2.2.1              
##  [11] sva_3.26.0                  digest_0.6.13              
##  [13] foreach_1.4.4               htmltools_0.3.6            
##  [15] magrittr_1.5                memoise_1.1.0              
##  [17] cluster_2.0.6               doParallel_1.0.11          
##  [19] limma_3.34.5                ComplexHeatmap_1.17.1      
##  [21] Biostrings_2.46.0           readr_1.1.1                
##  [23] annotate_1.56.1             R.utils_2.6.0              
##  [25] prettyunits_1.0.2           colorspace_1.3-2           
##  [27] blob_1.1.0                  rvest_0.3.2                
##  [29] ggrepel_0.7.0               RCurl_1.95-4.10            
##  [31] jsonlite_1.5                roxygen2_6.0.1             
##  [33] genefilter_1.60.0           bindr_0.1                  
##  [35] survival_2.41-3             zoo_1.8-1                  
##  [37] iterators_1.0.9             glue_1.2.0                 
##  [39] survminer_0.4.1             gtable_0.2.0               
##  [41] zlibbioc_1.24.0             XVector_0.18.0             
##  [43] GetoptLong_0.1.6            shape_1.4.3                
##  [45] scales_0.5.0                DESeq_1.30.0               
##  [47] DBI_0.7                     edgeR_3.20.6               
##  [49] ggthemes_3.4.0              Rcpp_0.12.14               
##  [51] xtable_1.8-2                progress_1.1.2             
##  [53] cmprsk_2.2-7                foreign_0.8-69             
##  [55] bit_1.1-12                  matlab_1.0.2               
##  [57] km.ci_0.5-2                 htmlwidgets_0.9            
##  [59] httr_1.3.1                  RColorBrewer_1.1-2         
##  [61] pkgconfig_2.0.1             XML_3.98-1.9               
##  [63] R.methodsS3_1.7.1           locfit_1.5-9.1             
##  [65] labeling_0.3                rlang_0.1.6                
##  [67] reshape2_1.4.3              AnnotationDbi_1.40.0       
##  [69] munsell_0.4.3               tools_3.4.3                
##  [71] downloader_0.4              RSQLite_2.0                
##  [73] devtools_1.13.4             broom_0.4.3                
##  [75] evaluate_0.10.1             stringr_1.2.0              
##  [77] yaml_2.1.16                 knitr_1.18                 
##  [79] bit64_0.9-7                 survMisc_0.5.4             
##  [81] purrr_0.2.4                 bindrcpp_0.2               
##  [83] EDASeq_2.12.0               nlme_3.1-131               
##  [85] R.oo_1.21.0                 xml2_1.1.1                 
##  [87] biomaRt_2.34.1              compiler_3.4.3             
##  [89] curl_3.1                    testthat_2.0.0             
##  [91] tibble_1.4.1                geneplotter_1.56.0         
##  [93] stringi_1.1.6               highr_0.6                  
##  [95] GenomicFeatures_1.30.0      lattice_0.20-35            
##  [97] Matrix_1.2-12               commonmark_1.4             
##  [99] psych_1.7.8                 KMsurv_0.1-5               
## [101] pillar_1.0.1                GlobalOptions_0.0.12       
## [103] data.table_1.10.4-3         bitops_1.0-6               
## [105] rtracklayer_1.38.2          R6_2.2.2                   
## [107] latticeExtra_0.6-28         hwriter_1.3.2              
## [109] RMySQL_0.10.13              ShortRead_1.36.0           
## [111] gridExtra_2.3               codetools_0.2-15           
## [113] assertthat_0.2.0            rprojroot_1.3-2            
## [115] rjson_0.2.15                withr_2.1.1                
## [117] GenomicAlignments_1.14.1    Rsamtools_1.30.0           
## [119] mnormt_1.5-5                GenomeInfoDbData_1.0.0     
## [121] mgcv_1.8-22                 hms_0.4.0                  
## [123] tidyr_0.7.2                 rmarkdown_1.8              
## [125] ggpubr_0.1.6

Noushmehr, Houtan, Daniel J Weisenberger, Kristin Diefes, Heidi S Phillips, Kanan Pujara, Benjamin P Berman, Fei Pan, et al. 2010. “Identification of a Cpg Island Methylator Phenotype That Defines a Distinct Subgroup of Glioma.” Cancer Cell 17 (5). Elsevier: 510–22.

Analyzing and visualizing TCGA data

TCGAanalyze: Analyze data from TCGA.

TCGAanalyze_Preprocessing: Preprocessing of Gene Expression data (IlluminaHiSeq_RNASeqV2)

TCGAanalyze_DEA & TCGAanalyze_LevelTab: Differential expression analysis (DEA)

HTSeq data: Downstream analysis BRCA

miRNA expression data: Downstream analysis BRCA

TCGAanalyze_EAcomplete & TCGAvisualize_EAbarplot: Enrichment Analysis

TCGAanalyze_survival: Survival Analysis

TCGAanalyze_SurvivalKM: Correlating gene expression and Survival Analysis

TCGAanalyze_DMR: Differentially methylated regions Analysis

TCGAvisualize: Visualize results from analysis functions with TCGA’s data.

TCGAvisualize_Heatmap: Create heatmaps with cluster bars

TCGAvisualize_Volcano: Create volcano plot

TCGAvisualize_PCA: Principal Component Analysis plot for differentially expressed genes

TCGAvisualize_meanMethylation: Mean DNA Methylation Analysis

TCGAvisualize_starburst: Integration of gene expression and DNA methylation data

Session Information

`TCGAanalyze`: Analyze data from TCGA.

`TCGAanalyze_Preprocessing`: Preprocessing of Gene Expression data (IlluminaHiSeq_RNASeqV2)

`TCGAanalyze_DEA` `&` `TCGAanalyze_LevelTab`: Differential expression analysis (DEA)

`TCGAanalyze_EAcomplete & TCGAvisualize_EAbarplot`: Enrichment Analysis

`TCGAanalyze_survival`: Survival Analysis

`TCGAanalyze_SurvivalKM`: Correlating gene expression and Survival Analysis

`TCGAanalyze_DMR`: Differentially methylated regions Analysis

`TCGAvisualize`: Visualize results from analysis functions with TCGA’s data.

`TCGAvisualize_Heatmap`: Create heatmaps with cluster bars

`TCGAvisualize_Volcano`: Create volcano plot

`TCGAvisualize_PCA`: Principal Component Analysis plot for differentially expressed genes

`TCGAvisualize_meanMethylation`: Mean DNA Methylation Analysis

`TCGAvisualize_starburst`: Integration of gene expression and DNA methylation data