TCGAquery: Searching TCGA open-access data for download

You can easily search TCGA samples using the TCGAquery function. Using a summary of filters as used in the TCGA portal, the function works with the following parameters:

• tumor Tumor or list of tumors. The list of tumor is shown in the examples.
• platform Platform or list of tumors. The list of platforms is shown in the examples.
• samples List of TCGA barcodes
• level Options: 1,2,3,“mage-tab”
• center
• version List of Platform/Tumor/Version to be changed

The next subsections will detail each of the search parameters. Below, we show some search examples:

query <- TCGAquery(tumor = c("LGG","GBM"), level = 3,
                   platform = c("HumanMethylation450","HumanMethylation27"),
                   samples = c("TCGA-19-4065","TCGA-E1-5322-01A-01D-1467-05"),
                   version = list(c("HumanMethylation450","LGG",1),
                                  c("HumanMethylation450","GBM",5)))

query <- TCGAquery(tumor = "gbm")

query <- TCGAquery(tumor = "gbm", level = 3)
query <- TCGAquery(tumor = "gbm", level = 2)
query <- TCGAquery(tumor = "gbm", level = 1)
query <- TCGAquery(tumor = "gbm", level = "mage-tab")

query <- TCGAquery(tumor = "gbm", platform = "IlluminaHiSeq_RNASeqV2")

query <- TCGAquery(tumor = "gbm", center = "mskcc.org")

# 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 all available platforms with a list of barcode
query <- TCGAquery(samples = listSamples)

# Query with a partial barcode
query <- TCGAquery(samples = "TCGA-61-1743-01A")

TCGAquery_Version: Retrieve versions information of the data in TCGA

In case the user want to have an overview of the size and number of samples and date of old versions, you can use the TCGAquery_Version function.

The parameters of this function are:

• tumor
• platform

For example, the code below queries the version history for the IlluminaHiSeq_RNASeqV2 platform .

library(TCGAbiolinks)

BRCA_RNASeqV2_version <- TCGAquery_Version(tumor = "brca",
                                           platform = "illuminahiseq_rnaseqv2")

The result is shown below:

query <- TCGAquery(tumor = c("LGG","GBM"), platform = c("HumanMethylation450"), level = 3,
                   version = list(c("HumanMethylation450","LGG",1)))

TCGAquery_clinic & TCGAquery_clinicFilt: Working with clinical data.

You can retrive clinical data using the clinic function. The parameters of this function are:

• cancer (“OV”,“BRCA”,“GBM”, etc)
• clinical_data_type (“clinical_patient”,“clinical_drug”, etc)

A full list of cancer and clinical data type can be found in the help of the function.

# Get clinical data
clinical_brca_data <- TCGAquery_clinic("brca","clinical_patient")
clinical_uvm_data_bio <- TCGAquery_clinic("uvm","biospecimen_normal_control")
clinical_brca_data_bio <- TCGAquery_clinic("brca","biospecimen_normal_control")
clinical_brca_data <- TCGAquery_clinic("brca","clinical_patient")

Also, some functions to work with clinical data are provided. For example the function TCGAquery_clinicFilt will filter your data, returning the list of barcodes that matches all the filter.

The parameters of TCGAquery_clinicFilt are:

• barcode List of barcodes
• clinical_patient_data clinical patient data obtained with clinic function Ex: clinical_patient_data <- TCGAquery_clinic(“LGG”,“clinical_patient”)
• HER her2 neu immunohistochemistry receptor status: “Positive” or “Negative”
• gender “MALE” or “FEMALE”
• PR Progesterone receptor status: “Positive” or “Negative”
• stage Pathologic Stage: “stage_IX”, “stage_I”, “stage_IA”, “stage_IB”, “stage_IIX”, “stage_IIA”, “stage_IIB”, “stage_IIIX”,“stage_IIIA”, “stage_IIIB”, “stage_IIIC”, “stage_IV” -
• ER Estrogen receptor status: “Positive” or “Negative”

An example of the function is below:

bar <- c("TCGA-G9-6378-02A-11R-1789-07", "TCGA-CH-5767-04A-11R-1789-07",  
        "TCGA-G9-6332-60A-11R-1789-07", "TCGA-G9-6336-01A-11R-1789-07",
        "TCGA-G9-6336-11A-11R-1789-07", "TCGA-G9-7336-11A-11R-1789-07",
        "TCGA-G9-7336-04A-11R-1789-07", "TCGA-G9-7336-14A-11R-1789-07",
        "TCGA-G9-7036-04A-11R-1789-07", "TCGA-G9-7036-02A-11R-1789-07",
        "TCGA-G9-7036-11A-11R-1789-07", "TCGA-G9-7036-03A-11R-1789-07",
        "TCGA-G9-7036-10A-11R-1789-07", "TCGA-BH-A1ES-10A-11R-1789-07",
        "TCGA-BH-A1F0-10A-11R-1789-07", "TCGA-BH-A0BZ-02A-11R-1789-07",
        "TCGA-B6-A0WY-04A-11R-1789-07", "TCGA-BH-A1FG-04A-11R-1789-08",
        "TCGA-D8-A1JS-04A-11R-2089-08", "TCGA-AN-A0FN-11A-11R-8789-08",
        "TCGA-AR-A2LQ-12A-11R-8799-08", "TCGA-AR-A2LH-03A-11R-1789-07",
        "TCGA-BH-A1F8-04A-11R-5789-07", "TCGA-AR-A24T-04A-55R-1789-07",
        "TCGA-AO-A0J5-05A-11R-1789-07", "TCGA-BH-A0B4-11A-12R-1789-07",
        "TCGA-B6-A1KN-60A-13R-1789-07", "TCGA-AO-A0J5-01A-11R-1789-07",
        "TCGA-AO-A0J5-01A-11R-1789-07", "TCGA-G9-6336-11A-11R-1789-07",
        "TCGA-G9-6380-11A-11R-1789-07", "TCGA-G9-6380-01A-11R-1789-07",
        "TCGA-G9-6340-01A-11R-1789-07","TCGA-G9-6340-11A-11R-1789-07")

S <- TCGAquery_SampleTypes(bar,"TP")
S2 <- TCGAquery_SampleTypes(bar,"NB")

# Retrieve multiple tissue types  NOT FROM THE SAME PATIENTS
SS <- TCGAquery_SampleTypes(bar,c("TP","NB"))

# Retrieve multiple tissue types  FROM THE SAME PATIENTS
SSS <- TCGAquery_MatchedCoupledSampleTypes(bar,c("NT","TP"))

# Get clinical data
clinical_brca_data <- TCGAquery_clinic("brca","clinical_patient")
female_erpos_herpos <- TCGAquery_clinicFilt(bar,clinical_brca_data,
                                            HER="Positive",
                                            gender="FEMALE",
                                            ER="Positive")

The result is shown below:

[1] "TCGA-AN-A0FN" "TCGA-BH-A1F8"

TCGAquery_subtype: Working with molecular subtypes data.

The Cancer Genome Atlas (TCGA) Research Network has reported integrated genome-wide studies of various diseases. We have added some of the subtypes defined by these report in our package. The BRCA (Cancer Genome Atlas Research Network and others 2012a), COAD (Cancer Genome Atlas Research Network and others 2012b), GBM (Ceccarelli, Michele and Barthel, Floris P and Malta, Tathiane M and Sabedot, Thais S and Salama, Sofie R and Murray, Bradley A and Morozova, Olena and Newton, Yulia and Radenbaugh, Amie and Pagnotta, Stefano M and others 2016), HNSC (Cancer Genome Atlas Research Network and others 2015a), KICH (Davis, Caleb F and Ricketts, Christopher J and Wang, Min and Yang, Lixing and Cherniack, Andrew D and Shen, Hui and Buhay, Christian and Kang, Hyojin and Kim, Sang Cheol and Fahey, Catherine C and others 2014), KIRC(Cancer Genome Atlas Research Network and others 2013a), KIRP (Linehan, W Marston and Spellman, Paul T and Ricketts, Christopher J and Creighton, Chad J and Fei, Suzanne S and Davis, Caleb and Wheeler, David A and Murray, Bradley A and Schmidt, Laura and Vocke, Cathy D and others 2016), LGG (Ceccarelli, Michele and Barthel, Floris P and Malta, Tathiane M and Sabedot, Thais S and Salama, Sofie R and Murray, Bradley A and Morozova, Olena and Newton, Yulia and Radenbaugh, Amie and Pagnotta, Stefano M and others 2016), LUAD (Cancer Genome Atlas Research Network and others 2014a), LUSC(Cancer Genome Atlas Research Network and others 2012c), PRAD(Cancer Genome Atlas Research Network and others 2015b), READ (Cancer Genome Atlas Research Network and others 2012b), SKCM (Cancer Genome Atlas Research Network and others 2015c), STAD (Cancer Genome Atlas Research Network and others 2014b), THCA (Cancer Genome Atlas Research Network and others 2014c), UCEC (Cancer Genome Atlas Research Network and others 2013b) tumors have data added. These subtypes will be automatically added in the summarizedExperiment object through TCGAprepare. But you can also use the TCGAquery_subtype function to retrive this information.

# Check with subtypes from TCGAprepare and update examples
GBM_path_subtypes <- TCGAquery_subtype(tumor = "gbm")

LGG_path_subtypes <- TCGAquery_subtype(tumor = "lgg")

LGG_clinic <- TCGAquery_clinic(tumor = "LGG",
                               clinical_data_type = "clinical_patient")

A subset of the lgg subytpe is shown below:

TCGAquery_integrate: Summary of the common numbers of patient samples in different platforms

Some times researches would like to use samples from different platforms from the same patient. In order to help the user to have an overview of the number of samples in commun we created the function TCGAquery_integrate that will receive the data frame returned from TCGAquery and produce a matrix n platforms x n platforms with the values of samples in commum.

Some search examples are shown below

query <- TCGAquery(tumor = "brca",level = 3)
matSamples <- TCGAquery_integrate(query)
matSamples[c(1,4,9),c(1,4,9)]

The result of the 3 platforms of TCGAquery_integrate result is shown below:

TCGAquery_investigate: Find most studied TFs in pubmed

Find most studied TFs in pubmed related to a specific cancer, disease, or tissue

# First perform DEGs with TCGAanalyze
# See previous section
library(TCGAbiolinks)

# Select only transcription factors (TFs) from DEGs
TFs <- EAGenes[EAGenes$Family =="transcription regulator",]
TFs_inDEGs <- intersect(TFs$Gene, dataDEGsFiltLevel$mRNA )
dataDEGsFiltLevelTFs <- dataDEGsFiltLevel[TFs_inDEGs,]

# Order table DEGs TFs according to Delta decrease
dataDEGsFiltLevelTFs <- dataDEGsFiltLevelTFs[order(dataDEGsFiltLevelTFs$Delta,decreasing = TRUE),]

# Find Pubmed of TF studied related to cancer
tabDEGsTFPubmed <- TCGAquery_investigate("breast", dataDEGsFiltLevelTFs, topgenes = 10)

The result is shown below:

TCGAquery_Social: Searching questions,answers and literature

The TCGAquery_Social function has two type of searches, one that searches for most downloaded packages in CRAN or BioConductor and one that searches the most related question in biostar.

library(TCGAbiolinks)

# Define a list of package to find number of downloads
listPackage <-c("limma","edgeR","survcomp")

tabPackage <- TCGAquery_Social(siteToFind ="bioconductor.org",listPackage)


# define a keyword to find in support.bioconductor.org returing a table with suggested packages
tabPackageKey <- TCGAquery_Social(siteToFind ="support.bioconductor.org" ,KeyInfo = "tcga")

library(TCGAbiolinks)

# Find most related question in biostar with TCGA
tabPackage1 <- TCGAquery_Social(siteToFind ="biostars.org",KeyInfo = "TCGA")

# Find most related question in biostar with package
tabPackage2 <- TCGAquery_Social(siteToFind ="biostars.org",KeyInfo = "package")

TCGAdownload: Example of use

# get all samples from the query and save them in the TCGA folder
# samples from IlluminaHiSeq_RNASeqV2 with type rsem.genes.results
# samples to normalize later
TCGAdownload(query, path = "data", type = "rsem.genes.results")

TCGAdownload(query, path = "data", type = "rsem.isoforms.normalized_results")

TCGAdownload(query, path = "dataBrca", type = "rsem.genes.results",
             samples = c("TCGA-E9-A1NG-11A-52R-A14M-07",
                         "TCGA-BH-A1FC-11A-32R-A13Q-07"))

Comment: The function will structure the folders to save the data as: Path given by the user/Experiment folder

TCGAdownload: Table of types available for downloading

• RNASeqV2: junction_quantification,rsem.genes.results, rsem.isoforms.results, rsem.genes.normalized_results, rsem.isoforms.normalized_results, bt.exon_quantification
• RNASeq: exon.quantification,spljxn.quantification, gene.quantification
• genome_wide_snp_6: hg18.seg,hg19.seg,nocnv_hg18.seg,nocnv_hg19.seg

Example of use

# get all samples from the query and save them in the TCGA folder
# samples from IlluminaHiSeq_RNASeqV2 with type rsem.genes.results
# samples to normalize later
data <- TCGAprepare(query, dir = "data", save = TRUE, filename = "myfile.rda")

As an example, for the platform IlluminaHiSeq_RNASeqV2 we prepared two samples (TCGA-DY-A1DE-01A-11R-A155-07 and TCGA-DY-A0XA-01A-11R-A155-07) for the rsem.genes.normalized_results type. In order to create the object mapped the gene_id to the hg19. The genes_id not found are then removed from the final matrix. The default output is a SummarizedExperiment is shown below.

library(TCGAbiolinks)
library(SummarizedExperiment)
head(assay(dataREAD,"normalized_count"))

             TCGA-DY-A1DE-01A-11R-A155-07 TCGA-DY-A0XA-01A-11R-A155-07
A1BG|1                            13.6732                      13.0232
A1CF|29974                        53.4379                     140.5455
A2M|2                           5030.4792                    1461.9358
A2ML1|144568                       0.0000                      18.2001
A4GALT|53947                     170.1189                      89.9895
A4GNT|51146                        0.9805                       0.0000

In order to create the SummarizedExperiment object we mapped the rows of the experiments into GRanges. In order to map miRNA we used the miRNA from the anotation database TxDb.Hsapiens.UCSC.hg19.knownGene, this will exclude the miRNA from viruses and bacteria. In order to map genes, genes alias, we used the biomart hg19 database (hsapiens_gene_ensembl from grch37.ensembl.org).

In case you prefere to have the raw data. You can get a data frame without any modification setting the summarizedExperiment to false.

library(TCGAbiolinks)
class(dataREAD_df)

[1] "data.frame"

dim(dataREAD_df)

[1] 20531     2

head(dataREAD_df)

            TCGA-DY-A1DE-01A-11R-A155-07 TCGA-DY-A0XA-01A-11R-A155-07
?|100130426                       0.0000                       0.0000
?|100133144                      11.5308                      32.9877
?|100134869                       4.1574                      12.5126
?|10357                         222.1498                     102.8308
?|10431                        1258.9778                     774.5168
?|136542                          0.0000                       0.0000

Example of use: Preparing the data with CNV data (Genome_Wide_SNP_6)

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

# Define a list of samples to query and download providing relative TCGA barcodes.
samplesList <- c("TCGA-02-0046-10A-01D-0182-01",
                "TCGA-02-0052-01A-01D-0182-01",
                "TCGA-02-0033-10A-01D-0182-01",
                "TCGA-02-0034-01A-01D-0182-01",
                "TCGA-02-0007-01A-01D-0182-01")

# Query platform Genome_Wide_SNP_6 with a list of barcode
query <- TCGAquery(tumor = "gbm", level = 3, platform = "Genome_Wide_SNP_6")

# Download a list of barcodes with platform Genome_Wide_SNP_6
TCGAdownload(query, path = "samples")

# Prepare matrix
GBM_CNV <- TCGAprepare(query, dir = "samples", type = ".hg19.seg.txt")

Table of types available for the TCGAprepare

• RNASeqV2: junction_quantification,rsem.genes.results, rsem.isoforms.results, rsem.genes.normalized_results, rsem.isoforms.normalized_results, bt.exon_quantification
• RNASeq: exon.quantification,spljxn.quantification, gene.quantification
• genome_wide_snp_6: hg18.seg,hg19.seg,nocnv_hg18.seg,nocnv_hg19.seg

Preparing the data for other packages

This section will show how to integrate TCGAbiolinks with other packages. Our intention is to provide as many integrations as possible.

The example below shows how to use TCGAbiolinks with ELMER package (expression/methylation analysis) (Yao, L., Shen, H., Laird, P. W., Farnham, P. J., & Berman, B. P. 2015). The TCGAprepare_elmer for the DNA methylation data will Removing probes with NA values in more than 20% samples and remove the anottation data, fot the expression data it will take the log2(expression + 1) of the expression matrix in order to To linearize the relation between DNA methylation and expressionm also it will prepare the rownames as the specified by the package.

############## Get tumor samples with TCGAbiolinks
library(TCGAbiolinks)
path <- "kirc"
query <- TCGAquery(tumor = "KIRC", level = 3, platform = "HumanMethylation450")
TCGAdownload(query, path =path)

kirc.met <- TCGAprepare(query,dir = path,
                        save = TRUE,
                        filename = "metKirc.rda",
                        summarizedExperiment = FALSE)

kirc.met <- TCGAprepare_elmer(kirc.met,
                              platform = "HumanMethylation450",
                              save = TRUE,
                              met.na.cut = 0.2)

# Step 1.2 download expression data
query.rna <- TCGAquery(tumor="KIRC",level=3, platform="IlluminaHiSeq_RNASeqV2")
TCGAdownload(query.rna,path=path,type = "rsem.genes.normalized_results")

kirc.exp <- TCGAprepare(query.rna, dir=path, save = TRUE,
                        type = "rsem.genes.normalized_results",
                        filename = "expKirc.rda", summarizedExperiment = FALSE)

kirc.exp <- TCGAprepare_elmer(kirc.exp,
                              save = TRUE,
                              platform = "IlluminaHiSeq_RNASeqV2")

# Step 2 prepare mee object
library(ELMER)
library(parallel)

geneAnnot <- txs()
geneAnnot$GENEID <- paste0("ID",geneAnnot$GENEID)
geneInfo <- promoters(geneAnnot,upstream = 0, downstream = 0)
probe <- get.feature.probe()
mee <- fetch.mee(meth = kirc.met, exp = kirc.exp, TCGA = TRUE,
                 probeInfo = probe, geneInfo = geneInfo)
save(mee,file="case4mee.rda")

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 IlluminaHiSeq_RNASeqV2 with a list of barcode
query <- TCGAquery(tumor = "brca", samples = listSamples,
  platform = "IlluminaHiSeq_RNASeqV2", level = "3")

# Download a list of barcodes with platform IlluminaHiSeq_RNASeqV2
TCGAdownload(query, path = "../dataBrca", type = "rsem.genes.results",samples = listSamples)

# Prepare expression matrix with gene id in rows and samples (barcode) in columns
# rsem.genes.results as values
BRCARnaseq_assay <- TCGAprepare(query,"../dataBrca",type = "rsem.genes.results")

BRCAMatrix <- assay(BRCARnaseq_assay,"raw_counts")

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

The result is shown below:

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 :

1. edgeR::DGEList converts the count matrix into an edgeR object.
2. edgeR::estimateCommonDisp each gene gets assigned the same dispersion estimate.
3. edgeR::exactTest performs pair-wise tests for differential expression between two groups.
4. 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 parameters:

• 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

After, 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)

[1] "I Need about  2.5 seconds for this Complete Normalization Upper Quantile  [Processing 80k elements /s]  "
[1] "Step 1 of 4: newSeqExpressionSet ..."
[1] "Step 2 of 4: withinLaneNormalization ..."
[1] "Step 3 of 4: betweenLaneNormalization ..."
[1] "Step 4 of 4: exprs ..."

# 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")

[1] "there are Cond1 type Normal in  5 samples"
[1] "there are Cond2 type Tumor in  5 samples"
[1] "there are  15236 features as miRNA or genes "
[1] "I Need about  5.1 seconds for this DEA. [Processing 30k elements /s]  "

# 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:

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 find 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:

TCGAanalyze_survival Survival Analysis: Cox Regression and dnet package

When analyzing survival times, different problems come up than the ones dis- cussed so far. One question is how do we deal with subjects dropping out of a study. For example, assume 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 <- TCGAquery_clinic("gbm", "clinical_patient")
clin.lgg <- TCGAquery_clinic("lgg", "clinical_patient")

TCGAanalyze_survival(plyr::rbind.fill(clin.lgg,clin.gbm),
                     "radiation_therapy",
                     main = "TCGA Set\nLGG and 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
• cutoff xlim This parameter will be a limit in the x-axis. That means, that patients with days_to_deth > cutoff will be set to Alive.
• 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.

The result is shown below:

library(TCGAbiolinks)
# Survival Analysis SA

clinical_patient_Cancer <- TCGAquery_clinic("brca","clinical_patient")
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$pvalue < 0.01,]
tabSurvKMcomplete <- tabSurvKMcomplete[!duplicated(tabSurvKMcomplete$mRNA),]
rownames(tabSurvKMcomplete) <-tabSurvKMcomplete$mRNA
tabSurvKMcomplete <- tabSurvKMcomplete[,-1]
tabSurvKMcomplete <- tabSurvKMcomplete[order(tabSurvKMcomplete$pvalue, decreasing=F),]

tabSurvKMcompleteDEGs <- tabSurvKMcomplete[
    rownames(tabSurvKMcomplete) %in% dataDEGsFiltLevel$mRNA,
    ]

The result is shown below:

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.

Firstly, it calculates the difference between the mean DNA methylation of each group for each probes.

Secondly, it calculates the p-value using the wilcoxon test adjusting by the Benjamini-Hochberg method. The default parameters was set to require a minimum absolute beta-values difference of 0.2 and a p-value adjusted of < 0.01.

After these analysis, we save a volcano plot (x-axis:diff mean methylation, y-axis: significance) that will help the user identify the differentially methylated CpG sites and return the object with the calculus in the rowRanges.

The arguments of volcanoPlot are:

• 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
• filename pdf filename. Default: volcano.pdf
• legend Legend title
• color vector of colors to be used in graph
• title main title. If not specified it will be “Volcano plot (group1 vs group2)
• ylab y axis text
• xlab x axis text
• xlim x limits to cut image
• ylim y limits to cut image
• label vector of labels to be used in the figure. Example: c(“Not Significant”, “Hypermethylated in group1”, “Hypomethylated in group1”))
• p.cut p values threshold. Default: 0.01
• diffmean.cut diffmean threshold. Default: 0.2
• adj.method Adjusted method for the p-value calculation
• paired Wilcoxon paired parameter. Default: FALSE
• overwrite Overwrite the pvalues and diffmean values if already in the object for both groups? Default: FALSE
• save save the object with the results?
• cores use multiple cores for non-parametric test

data <- TCGAanalyze_DMR(data, groupCol = "cluster.meth",subgroupCol = "disease",
                              group.legend  = "Groups", subgroup.legend = "Tumor",
                              print.pvalue = TRUE)

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. With you want to have them recalculated, please set overwrite to TRUE or remove the calculated collumns.

TCGAvisualize_PCA: Principal Component Analysis plot for differentially expressed genes

In order to understand better 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 parameters 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

# normalization of genes
dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)

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

# Principal Component Analysis plot for ntop selected DEGs
TCGAvisualize_PCA(dataFilt,dataDEGsFiltLevel, ntopgenes = 200)

The result is shown below:

TCGAvisualize_SurvivalCoxNET Survival Analysis: Cox Regression and dnet package

TCGAvisualize_SurvivalCoxNET can help an user to identify a group of survival genes that are significant from univariate Kaplan Meier Analysis and also for Cox Regression. It shows in the end a network build with community of genes with similar range of pvalues from Cox regression (same color) and that interaction among those genes is already validated in literatures using the STRING database (version 9.1).

library(TCGAbiolinks)
# Survival Analysis SA

clinical_patient_Cancer <- TCGAquery_clinic("brca","clinical_patient")
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$pvalue < 0.01,]
tabSurvKMcomplete <- tabSurvKMcomplete[!duplicated(tabSurvKMcomplete$mRNA),]
rownames(tabSurvKMcomplete) <-tabSurvKMcomplete$mRNA
tabSurvKMcomplete <- tabSurvKMcomplete[,-1]
tabSurvKMcomplete <- tabSurvKMcomplete[order(tabSurvKMcomplete$pvalue, decreasing=F),]

tabSurvKMcompleteDEGs <- tabSurvKMcomplete[rownames(tabSurvKMcomplete) %in% dataDEGsFiltLevel$mRNA,]

tflist <- EAGenes[EAGenes$Family == "transcription regulator","Gene"]
tabSurvKMcomplete_onlyTF <- tabSurvKMcomplete[rownames(tabSurvKMcomplete) %in% tflist,]

TabCoxNet <- TCGAvisualize_SurvivalCoxNET(clinical_patient_Cancer,dataBRCAcomplete,
                            Genelist = rownames(tabSurvKMcompleteDEGs),
                            scoreConfidence = 700,titlePlot = "TCGAvisualize_SurvivalCoxNET Example")

In particular the survival analysis with kaplan meier and cox regression allow user to reduce the feature / number of genes significant for survival. And using ‘dnet’ pipeline with ‘TCGAvisualize_SurvivalCoxNET’ function the user can further filter those genes according some already validated interaction according STRING database. This is important because the user can have an idea about the biology inside the survival discrimination and further investigate in a sub-group of genes that are working in as synergistic effect influencing the risk of survival. In the following picture the user can see some community of genes with same color and survival pvalues.

The result is shown below:

TCGAvisualize_meanMethylation: Sample 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:

TCGAvisualize_meanMethylation(data,"group")

The arguments of TCGAvisualize_meanMethylation are:

• data SummarizedExperiment object obtained from TCGAPrepare
• 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
• filename The name of the pdf that will be saved
• subgroup.legend Name of the subgroup legend. DEFAULT: subgroupCol
• group.legend Name of the group legend. DEFAULT: groupCol
• color vector of colors to be used in graph
• title main title in the plot
• ylab y axis text in the plot
• print.pvalue Print p-value for two groups in the plot
• xlab x axis text in the plot
• labels Labels of the groups

The result is shown below:

TCGAvisualize_starburst: Analyzing expression and methylation together

The starburst plot is proposed to combine information from two volcano plots, and is applied for a study of DNA methylation and gene expression. 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) is plotted for beta value for DNA methylation (x axis) and gene expression (y axis) for each gene. The black dashed line shows the FDR-adjusted P value of 0.01.

The parameters of this function are:

• met SummarizedExperiment with methylation data obtained from the TCGAprepare and processed by TCGAanalyze_DMR function. Expected colData columns: diffmean and p.value.adj
• exp Matrix with expression data obtained from the TCGAanalyze_DEA function. Expected colData columns: logFC, FDR
• filename pdf filename
• legend legend title
• color vector of colors to be used in graph
• label vector of labels to be used in graph
• title main title
• ylab y axis text
• xlab x axis text
• xlim x limits to cut image
• ylim y limits to cut image
• p.cut p value cut-off
• group1 The name of the group 1 Obs: Column p.value.adj.group1.group2 should exist
• group2 The name of the group 2. Obs: Column p.value.adj.group1.group2 should exist
• exp.p.cut expression p value cut-off
• met.p.cut methylation p value cut-off
• diffmean.cut If set, the probes with diffmean higher than methylation cut-off will be highlighted in the plot. And the data frame return will be subseted.
• logFC.cut If set, the probes with expression fold change higher than methylation cut-off will be highlighted in the plot. And the data frame return will be subseted.

starburst <- TCGAvisualize_starburst(coad.SummarizeExperiment,
                                     different.experssion.analysis.data,
                                     group1 = "CIMP.H",
                                     group2 = "CIMP.L",
                                     met.p.cut = 10^-5,
                                     exp.p.cut=10^-5,
                                     names = TRUE)

As 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 methylation (Hyper/Hypo methylated). The case study 3, shows the complete pipeline for creating this figure.

Introduction

This vignette shows a complete workflow of the TCGAbiolinks package. The code is divided in 4 case study:

• 1. Expression pipeline (BRCA)
• 2. Expression pipeline (GBM)
• 3. Methylation and methylation pipeline (COAD)
• 4. Elmer pipeline (KIRC)

Parameters definition

PlatformCancer <- "IlluminaHiSeq_RNASeqV2"
dataType <- "rsem.genes.results"
pathGBM<- "../dataGBM"
pathLGG <- "../dataLGG"

library(BiocInstaller)
useDevel()  # we need Devel for SummarizedExperiment package
library(SummarizedExperiment)
library(TCGAbiolinks)

Case study n. 1: Pan Cancer downstream analysis BRCA

In this case study, we downloaded 114 normal and 1097 breast cancer (BRCA) samples using TCGAquery, TCGAdownload and TCGAprepare.

library(TCGAbiolinks)

# defining common parameters
cancer <- "BRCA"
PlatformCancer <- "IlluminaHiSeq_RNASeqV2"
dataType <- "rsem.genes.results"
pathCancer <- paste0("../data",cancer)

datQuery <- TCGAquery(tumor = cancer, platform = PlatformCancer, level = "3")  
lsSample <- TCGAquery_samplesfilter(query = datQuery)

# get subtype information
dataSubt <- TCGAquery_subtype(tumor = cancer)

# Which samples are Primary Solid Tumor
dataSmTP <- TCGAquery_SampleTypes(barcode = lsSample$IlluminaHiSeq_RNASeqV2,
                                  typesample = "TP")
# Which samples are Solid Tissue Normal
dataSmTN <- TCGAquery_SampleTypes(barcode = lsSample$IlluminaHiSeq_RNASeqV2,
                                  typesample ="NT")
# get clinical data
dataClin <- TCGAquery_clinic(tumor = cancer,
                             clinical_data_type = "clinical_patient")

TCGAdownload(data = datQuery,
             path = pathCancer,
             type = dataType,
             samples =c(dataSmTP,dataSmTN))

dataAssy <- TCGAprepare(query = datQuery,
                        dir = pathCancer,
                        type = dataType,
                        save = TRUE,
                        summarizedExperiment = TRUE,
                        samples = c(dataSmTP,dataSmTN),
                        filename = paste0(cancer,"_",PlatformCancer,".rda"))

Using TCGAnalyze_DEA, we identified 3,390 differentially expression genes (DEG) (log fold change >=1 and FDR < 1%) between 114 normal and 1097 BRCA samples. In order to understand the underlying biological process from DEGs we performed an enrichment analysis using TCGAnalyze_EA_complete function.

dataPrep <- TCGAanalyze_Preprocessing(object = dataAssy,
                                      cor.cut = 0.6)                      

dataNorm <- TCGAanalyze_Normalization(tabDF = dataPrep,
                                      geneInfo = geneInfo,
                                      method = "gcContent")                

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

dataDEGs <- TCGAanalyze_DEA(mat1 = dataFilt[,dataSmTN],
                            mat2 = dataFilt[,dataSmTP],
                            Cond1type = "Normal",
                            Cond2type = "Tumor",
                            fdr.cut = 0.01 ,
                            logFC.cut = 1,
                            method = "glmLRT")  

TCGAbiolinks outputs bar chart with the number of genes for the main categories of three ontologies (GO:biological process, GO:cellular component, and GO:molecular function, respectively).

ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes Normal Vs Tumor",
                                RegulonList = rownames(dataDEGs))  

TCGAvisualize_EAbarplot(tf = rownames(ansEA$ResBP),
                        GOBPTab = ansEA$ResBP,
                        GOCCTab = ansEA$ResCC,
                        GOMFTab = ansEA$ResMF,
                        PathTab = ansEA$ResPat,
                        nRGTab = rownames(dataDEGs),
                        nBar = 20)

The figure resulted from the code above is shown below.

The Kaplan-Meier analysis was used to compute survival univariate curves, and
log-Ratio test was computed to assess the statistical significance by using TCGAanalyze_SurvivalKM function; starting with 3,390 DEGs genes we found 555 significantly genes with p.value <0.05.

dataSurv <- TCGAanalyze_SurvivalKM(clinical_patient = dataClin,
                                   dataGE = dataFilt,
                                   Genelist = rownames(dataDEGs),
                                   Survresult = FALSE,
                                   ThreshTop = 0.67,
                                   ThreshDown = 0.33,
                                   p.cut = 0.05)

Cox-regression analysis was used to compute survival multivariate curves, and cox p-value was computed to assess the statistical significance by using TCGAnalyze_SurvivalCoxNET function. Survival multivariate analysis found 160 significantly genes according to the cox p-value FDR 5.00e-02. From DEGs that we found to correlate significantly with survival by both univariate and multivariate analyses we analyzed the following network.

The interactome network graph was generated using STRING.,org.Hs.string version 10 (Human functional protein association network). The network graph was resized by dnet package considering only multivariate survival genes, with strong interaction (threshold = 700) we obtained a subgraphsub graph of 24 nodes and 31 edges.

require(dnet)  # to change
org.Hs.string <- dRDataLoader(RData = "org.Hs.string")

TabCoxNet <- TCGAvisualize_SurvivalCoxNET(dataClin,
                                          dataFilt,
                                          Genelist = rownames(dataSurv),
                                          scoreConfidence = 700,
                                          org.Hs.string = org.Hs.string,
                                          titlePlot = "Case Study n.1 dnet")

The figure resulted from the code above is shown below. ## Case study n. 2: Pan Cancer downstream analysis LGG

library(TCGAbiolinks)
cancer <- "LGG"
PlatformCancer <- "IlluminaHiSeq_RNASeqV2"
dataType <- "rsem.genes.results"
pathCancer <- paste0("../data",cancer)

# Result....Function.....parameters...p1...pn..................................time execution

datQuery <- TCGAquery(tumor = cancer, platform = PlatformCancer, level = "3")  # time = 0.093s
lsSample <- TCGAquery_samplesfilter(query = datQuery)
dataSubt <- TCGAquery_subtype(tumor = cancer)
dataSmTP <- TCGAquery_SampleTypes(barcode = lsSample$IlluminaHiSeq_RNASeqV2,
                                  typesample = "TP")
dataClin <- TCGAquery_clinic(tumor = cancer,
                             clinical_data_type = "clinical_patient")

            TCGAdownload(data = datQuery, path = pathCancer, type = dataType,
                         samples = dataSmTP )

dataAssy <- TCGAprepare(query = datQuery,
                        dir = pathCancer,
                        type = dataType,
                        save = TRUE,
                        summarizedExperiment = TRUE,
                        samples = dataSmTP,
                        filename = paste0(cancer,"_",PlatformCancer,".rda"))

dataPrep <- TCGAanalyze_Preprocessing(object = dataAssy,cor.cut = 0.6)        # time = 13.028s
dataNorm <- TCGAanalyze_Normalization(tabDF = dataPrep,
                                      geneInfo = geneInfo,
                                      method = "gcContent")                  # time = 165.577s

datFilt1 <- TCGAanalyze_Filtering(tabDF = dataNorm,method = "varFilter")
datFilt2 <- TCGAanalyze_Filtering(tabDF = datFilt1,method = "filter1")
datFilt <- TCGAanalyze_Filtering(tabDF = datFilt2,method = "filter2")

data_Hc1 <- TCGAanalyze_Clustering(tabDF = datFilt,method = "hclust", methodHC = "ward.D2")
data_Hc2 <- TCGAanalyze_Clustering(tabDF = datFilt,
                                   method = "consensus",
                                   methodHC = "ward.D2")        # time = 207.389
# deciding number of tree to cuts
cut.tree <-4
paste0(c("EC"),(1:cut.tree))

## consensusClusters contains barcodes for 4 groups
ans <- hclust(ddist <- dist(datFilt), method = "ward.D2")
hhc <- data_Hc2[[cut.tree]]$consensusTree
consensusClusters<-data_Hc2[[cut.tree]]$consensusClass
sampleOrder <- consensusClusters[hhc$order]

consensusClusters <- as.factor(data_Hc2[[cut.tree]]$clrs[[1]])
names(consensusClusters) <- attr(ddist, "Labels")
names(consensusClusters) <- substr(names(consensusClusters),1,12)

# adding information about gropus from consensus Cluster in clinical data
dataClin <- cbind(dataClin, groupsHC = matrix(0,nrow(dataClin),1))
rownames(dataClin) <- dataClin$bcr_patient_barcode

for( i in 1: nrow(dataClin)){
    currSmp <- dataClin$bcr_patient_barcode[i]
    dataClin[currSmp,"groupsHC"] <- as.character(consensusClusters[currSmp])
}

# plotting survival for groups EC1, EC2, EC3, EC4

TCGAanalyze_survival(data = dataClin,
                     clusterCol = "groupsHC",
                     main = "TCGA kaplan meier survival plot from consensus cluster",
                     height = 10,
                     width=10,
                     legend = "RNA Group",
                     labels=paste0(c("EC"),(1:cut.tree)),
                     color = as.character(levels(consensusClusters)),
                     filename = "case2_surv.png")
dev.off()
TCGAvisualize_BarPlot(DFfilt = datFilt,
                      DFclin = dataClin,
                      DFsubt = dataSubt,
                      data_Hc2 = data_Hc2,
                      Subtype = "IDH.1p19q.Subtype",
                      cbPalette = c("cyan","tomato","gold"),
                      filename = "case2_Idh.png",
                      height = 10,
                      width=10,
                      dpi =300)

TCGAvisualize_BarPlot(DFfilt = datFilt,
                      DFclin = dataClin,
                      DFsubt = dataSubt,
                      data_Hc2 = data_Hc2,
                      Subtype = "MethylationCluster",
                      cbPalette = c("black","orchid3","palegreen4","sienna3", "steelblue4"),
                      filename = "case2_Met.png",
                      height = 10,
                      width=10,
                      dpi =300)

TCGAvisualize_BarPlot(DFfilt = datFilt,
                      DFclin = dataClin,
                      DFsubt = dataSubt,
                      data_Hc2 = data_Hc2,
                      Subtype = "AGE",
                      cbPalette = c("yellow2","yellow3","yellow4"),
                      filename = "case2_Age.png",
                      height = 10,
                      width=10,
                      dpi =300)

dev.off()
pdf(file="case2_Heatmap2.pdf")
TCGAvisualize_Heatmap(DFfilt = datFilt,
                      DFclin = dataClin,
                      DFsubt = dataSubt,
                      data_Hc2= data_Hc2)
dev.off()


# Convert images from pdf to png.
library(animation)
ani.options(outdir = getwd())

im.convert("case2_Heatmap2.pdf",
           output = "case2_Heatmap2.png",
           extra.opts="-density 150")

The figures resulted from the code above are shown below.

Case study n. 3: Integration of methylation and expression for COAD

In recent years, it was discovered that there is a relationship between DNA methylation and gene expression and the study of this relationship is often difficult to accomplish.

This case study will show the steps to conduct a study of the relationship between the two types of data.

First we downloaded COAD methylation data for HumanMethylation27k and HumanMethylation450k platforms, and COAD expression data for IlluminaGA_RNASeqV2.

TCGAbiolinks adds by default the classifications already published by researchers. We will use this classification to do our examples. We selected the groups CIMP.L and CIMP.H to do a expression and DNA methylation comparison.

Firstly, we do a DMR (different methylated region) analysis, which will give the difference of DNA methylation for the probes of the groups and their significance value. The output can be seen by a volcano plot.

Secondly, we do a DEA (differential expression analysis) which will give the fold change of gene expression and their significance value.

Finally, using both previous analysis we do a starburst plot to select the genes that are Candidate Biologically Significant.

Observation: over the time, the number of samples has increased and the clinical data updated. We used only the samples that had a classification in the examples.

#-----------------------------------
# STEP 1: Search, download, prepare |
#-----------------------------------
# 1.1 - Methylation
# ----------------------------------
query.met <- TCGAquery(tumor = c("coad"),
                       platform = c("HumanMethylation27",
                                    "HumanMethylation450"),
                       level = 3)

TCGAdownload(query.met, path = "/dados/ibm/comparing/biolinks/coad/")

coad.met <- TCGAprepare(query = query.met,
                        dir = "/dados/ibm/comparing/biolinks/coad/",
                        save = TRUE,
                        add.subtype= TRUE,
                        filename = "metcoad.rda",
                        reannotate = TRUE)

#-----------------------------------
# 1.2 - Expression
# ----------------------------------

coad.query.exp <- TCGAquery(tumor = "coad",
                       platform = "IlluminaGA_RNASeqV2",
                       level = 3)

TCGAdownload(coad.query.exp,
             path = "/dados/ibm/comparing/biolinks/RNA/",
             type = "rsem.genes.results")

coad.exp <- TCGAprepare(query = coad.query.exp,
                        dir = "/dados/ibm/comparing/biolinks/RNA/",
                        type = "rsem.genes.results",
                        add.subtype= TRUE,
                        save = T, filename = "coadexp.rda")


# removing the samples without classification
coad.met <- subset(coad.met,select = !(colData(coad.met)$methylation_subtype %in% c(NA)))

#--------------------------------------------
# STEP 2: Analysis
#--------------------------------------------
# 2.1 - Mean methylation of samples
# -------------------------------------------
TCGAvisualize_meanMethylation(coad.met,
                              groupCol = "methylation_subtype",
                              subgroupCol = "hypermutated",
                              group.legend  = "Groups",
                              subgroup.legend = "hypermutated",
                              filename = "coad_mean.png")

#-------------------------------------------------
# 2.2 - DMR - Methylation analysis - volcano plot
# ------------------------------------------------
coad.aux <- subset(coad.met,
                   select = colData(coad.met)$methylation_subtype %in% c("CIMP.L","CIMP.H"))


# na.omit
coad.aux <- subset(coad.aux,subset = (rowSums(is.na(assay(coad.aux))) == 0))

# Volcano plot
coad.aux <- TCGAanalyze_DMR(coad.aux, 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")

save(coad.aux,file = "coad_pvalue.rda")

#-------------------------------------------------
# 2.3 - DEA - Expression analysis - volcano plot
# ------------------------------------------------

coad.exp.aux <- subset(coad.exp,
                       select = colData(coad.exp)$methylation_subtype %in% c("CIMP.H","CIMP.L"))

idx <- colData(coad.exp.aux)$methylation_subtype %in% c("CIMP.H")
idx2 <- colData(coad.exp.aux)$methylation_subtype %in% c("CIMP.L")

dataPrep <- TCGAanalyze_Preprocessing(object = coad.exp.aux,
                                      cor.cut = 0.6)

dataNorm <- TCGAanalyze_Normalization(tabDF = dataPrep,
                                      geneInfo = geneInfo,
                                      method = "gcContent")

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

dataDEGs <- TCGAanalyze_DEA(mat1 = dataFilt[,idx],
                            mat2 = dataFilt[,idx2],
                            Cond1type = "CIMP.H",
                            Cond2type = "CIMP.l",
                            method = "glmLRT")

TCGAVisualize_volcano(dataDEGs$logFC,dataDEGs$FDR,
                      filename = "Case3_volcanoexp.png",
                      x.cut = 3,
                      y.cut = 10^-4,
                      names = rownames(dataDEGs),
                      xlab = " Gene expression fold change (Log2)",
                      legend = "State",
                      title = "Volcano plot (CIMP.l vs CIMP.H)")

#------------------------------------------
# 2.4 - Starburst plot
# -----------------------------------------
starburst <- TCGAvisualize_starburst(coad.aux, dataDEGs,"CIMP.H","CIMP.L",
                                     filename = "starburst.png",
                                     met.p.cut = 10^-5,
                                     exp.p.cut = 10^-4,
                                     diffmean.cut = 0.25,
                                     logFC.cut = 3,
                                     names = TRUE)

The figures resulted from the code above are shown below.

Case study n. 4: Elmer pipeline - KIRC

One of the biggest problems related to the study data is the preparation phase, which often consists of successive steps in order to prepare it to a format acceptable by certain algorithms and software.

With the object of assisting users in this arduous step, TCGAbiolinks offers in data preparation stage, the toPackage argument, which aims to prepare the data in order to obtain the correct format for different packages.

An example of package to perform DNA methylation and expression analysis is ELMER (Huber, Wolfgang and Carey, Vincent J and Gentleman, Robert and Anders, Simon and Carlson, Marc and Carvalho, Benilton S and Bravo, Hector Corrada and Davis, Sean and Gatto, Laurent and Girke, Thomas and others 2015). We will present this case study the study KIRC by TCGAbiolinks and ELMER integration. For more information, please consult ELMER package.

#-----------------------------------
# STEP 1: Search, download, prepare |
#-----------------------------------
# Step 1.1 download methylation data|
# -----------------------------------
path <-  "."
query <- TCGAquery(tumor = "KIRC",level = 3, platform = "HumanMethylation450")
TCGAdownload(query, path = path)
kirc.met <- TCGAprepare(query,dir = path,
                        save = TRUE,
                        filename = "metKirc.rda",
                        summarizedExperiment = FALSE)

kirc.met <- TCGAprepare_elmer(kirc.met,
                              platform = "HumanMethylation450",
                              save = TRUE,
                              met.na.cut = 0.2)

# Step 1.2 download expression data
query.rna <- TCGAquery(tumor="KIRC",level=3, platform="IlluminaHiSeq_RNASeqV2")
TCGAdownload(query.rna,path=path,type = "rsem.genes.normalized_results")
kirc.exp <- TCGAprepare(query.rna, dir=path, save = TRUE,
                        type = "rsem.genes.normalized_results",
                        filename = "expKirc.rda", summarizedExperiment = FALSE)

kirc.exp <- TCGAprepare_elmer(kirc.exp,
                              save = TRUE,
                              platform = "IlluminaHiSeq_RNASeqV2")
#-----------------------------------
# STEP 2: ELMER integration         |
#-----------------------------------
# Step 2.1 prepare mee object       |
# -----------------------------------
library(ELMER)
library(parallel)

geneAnnot <- txs()
geneAnnot$GENEID <- paste0("ID",geneAnnot$GENEID)
geneInfo <- promoters(geneAnnot,upstream = 0, downstream = 0)
probe <- get.feature.probe()
mee <- fetch.mee(meth = kirc.met, exp = kirc.exp, TCGA = TRUE,
                 probeInfo = probe, geneInfo = geneInfo)

#--------------------------------------
# STEP 3: Analysis                     |
#--------------------------------------
# Step 3.1: Get diff methylated probes |
#--------------------------------------
Sig.probes <- get.diff.meth(mee ,cores=detectCores(),
                            dir.out ="kirc",diff.dir="hypo",
                            pvalue = 0.01)

#--------------------------------------------------
# Step 3.2: Identifying putative probe-gene pairs |
#--------------------------------------------------
# Collect nearby 20 genes for Sig.probes
nearGenes <- GetNearGenes(TRange=getProbeInfo(mee, probe=Sig.probes),
                          cores=detectCores(),
                          geneAnnot=getGeneInfo(mee))

# Identify significant probe-gene pairs
Hypo.pair <- get.pair(mee=mee,
                      probes=Sig.probes,
                      nearGenes=nearGenes,
                      permu.dir="./kirc/permu",
                      dir.out="./kirc/",
                      cores=detectCores(),
                      label= "hypo",
                      permu.size=10000,
                      Pe = 0.001)

Sig.probes.paired <- fetch.pair(pair=Hypo.pair,
                                probeInfo = getProbeInfo(mee),
                                geneInfo = getGeneInfo(mee))

#-------------------------------------------------------------
# Step 3.3: Motif enrichment analysis on the selected probes |
#-------------------------------------------------------------
enriched.motif <- get.enriched.motif(probes=Sig.probes.paired,
                                     dir.out="kirc", label="hypo",
                                     background.probes = probe$name)

#-------------------------------------------------------------
# Step 3.4: Identifying regulatory TFs                        |
#-------------------------------------------------------------
TF <- get.TFs(mee=mee,
              enriched.motif=enriched.motif,
              dir.out="kirc",
              cores=detectCores(), label= "hypo")

From this analysis it is possible to verify the relation between a probe and nearby genes. The result of this is show by the ELMER scatter plot.

Each scatter plot showing the average methylation level of sites with the AP1 motif in all KIRC samples plotted against the expression of the transcription factor CEBPB and GFI1 respectively.

The schematic plot shows probe colored in blue and the location of nearby 20 genes, The genes significantly linked to the probe were shown in red.

The plot shows the odds ratio (x axis) for the selected motifs with OR above 1.3 and lower boundary of OR above 1.3. The range shows the 95% confidence interval for each Odds Ratio.

sessionInfo()

R version 3.2.4 Revised (2016-03-16 r70336)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.4 LTS

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      stats4    parallel  tools     stats     graphics  grDevices
 [8] utils     datasets  methods   base     

other attached packages:
 [1] png_0.1-7                  SummarizedExperiment_1.0.2
 [3] Biobase_2.30.0             GenomicRanges_1.22.4      
 [5] GenomeInfoDb_1.6.3         IRanges_2.4.8             
 [7] S4Vectors_0.8.11           BiocGenerics_0.16.1       
 [9] stringr_1.0.0              TCGAbiolinks_1.0.10       
[11] BiocStyle_1.8.0           

loaded via a namespace (and not attached):
  [1] TH.data_1.0-7                          
  [2] colorspace_1.2-6                       
  [3] rjson_0.2.15                           
  [4] hwriter_1.3.2                          
  [5] modeltools_0.2-21                      
  [6] futile.logger_1.4.1                    
  [7] XVector_0.10.0                         
  [8] roxygen2_5.0.1                         
  [9] hexbin_1.27.1                          
 [10] affyio_1.40.0                          
 [11] AnnotationDbi_1.32.3                   
 [12] mvtnorm_1.0-5                          
 [13] coin_1.1-2                             
 [14] xml2_0.1.2                             
 [15] codetools_0.2-14                       
 [16] splines_3.2.4                          
 [17] R.methodsS3_1.7.1                      
 [18] doParallel_1.0.10                      
 [19] DESeq_1.22.1                           
 [20] geneplotter_1.48.0                     
 [21] knitr_1.12.3                           
 [22] heatmap.plus_1.3                       
 [23] Rsamtools_1.22.0                       
 [24] annotate_1.48.0                        
 [25] cluster_2.0.3                          
 [26] R.oo_1.20.0                            
 [27] supraHex_1.8.0                         
 [28] graph_1.48.0                           
 [29] httr_1.1.0                             
 [30] assertthat_0.1                         
 [31] Matrix_1.2-4                           
 [32] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [33] limma_3.26.9                           
 [34] formatR_1.3                            
 [35] htmltools_0.3.5                        
 [36] igraph_1.0.1                           
 [37] gtable_0.2.0                           
 [38] affy_1.48.0                            
 [39] dplyr_0.4.3                            
 [40] ShortRead_1.28.0                       
 [41] Rcpp_0.12.4                            
 [42] Biostrings_2.38.4                      
 [43] gdata_2.17.0                           
 [44] ape_3.4                                
 [45] preprocessCore_1.32.0                  
 [46] nlme_3.1-126                           
 [47] rtracklayer_1.30.4                     
 [48] iterators_1.0.8                        
 [49] CNTools_1.26.0                         
 [50] rvest_0.3.1                            
 [51] gtools_3.5.0                           
 [52] devtools_1.10.0                        
 [53] XML_3.98-1.4                           
 [54] edgeR_3.12.0                           
 [55] zlibbioc_1.16.0                        
 [56] MASS_7.3-45                            
 [57] zoo_1.7-12                             
 [58] scales_0.4.0                           
 [59] aroma.light_3.0.0                      
 [60] BiocInstaller_1.20.1                   
 [61] sandwich_2.3-4                         
 [62] lambda.r_1.1.7                         
 [63] RColorBrewer_1.1-2                     
 [64] yaml_2.1.13                            
 [65] memoise_1.0.0                          
 [66] ggplot2_2.1.0                          
 [67] downloader_0.4                         
 [68] biomaRt_2.26.1                         
 [69] reshape_0.8.5                          
 [70] latticeExtra_0.6-28                    
 [71] stringi_1.0-1                          
 [72] RSQLite_1.0.0                          
 [73] highr_0.5.1                            
 [74] genefilter_1.52.1                      
 [75] foreach_1.4.3                          
 [76] GenomicFeatures_1.22.13                
 [77] caTools_1.17.1                         
 [78] BiocParallel_1.4.3                     
 [79] chron_2.3-47                           
 [80] matrixStats_0.50.1                     
 [81] bitops_1.0-6                           
 [82] dnet_1.0.7                             
 [83] evaluate_0.8.3                         
 [84] lattice_0.20-33                        
 [85] GenomicAlignments_1.6.3                
 [86] GGally_1.0.1                           
 [87] plyr_1.8.3                             
 [88] magrittr_1.5                           
 [89] R6_2.1.2                               
 [90] gplots_3.0.0                           
 [91] multcomp_1.4-4                         
 [92] DBI_0.3.1                              
 [93] withr_1.0.1                            
 [94] survival_2.38-3                        
 [95] RCurl_1.95-4.8                         
 [96] EDASeq_2.4.1                           
 [97] futile.options_1.0.0                   
 [98] KernSmooth_2.23-15                     
 [99] rmarkdown_0.9.5                        
[100] data.table_1.9.6                       
[101] Rgraphviz_2.14.0                       
[102] ConsensusClusterPlus_1.24.0            
[103] digest_0.6.9                           
[104] xtable_1.8-2                           
[105] R.utils_2.2.0                          
[106] munsell_0.4.3                          
[107] cghMCR_1.28.0