TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages

Experimental data

TCGA data are accessible via the TCGA data portal and the Broad Institute’s GDAC Firehose. The data are provided as different levels or tiers: Level 1 (Raw Data), Level 2 (Processed Data), Level 3 (Segmented or Interpreted Data) and Level 4 (Region of Interest Data). While the TCGA data portal provides level 1 to 3 data, Firehose only provides level 3 and 4. An explanation of the different levels can be found at TCGA Wikipedia. The data provided by TCGA data portal can be accessed using Bioconductor package TCGAbiolinks, while the data provided by Firehose can be accessed by Bioconductor package RTCGAtoolbox.

The next steps describes how one could use TCGAbiolinks & RTCGAtoolbox to download clinical, genomics, transcriptomics, epigenomics data, as well as subtype information and GISTIC results (identified genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth). Just to reiterate, the data used in this workflow are published data and freely available.

Downloading data from TCGA data portal. The Bioconductor package TCGAbiolinks⁷ has three main functions TGCAquery, TCGAdownload and TCGAprepare that should sequentially be used to respectively search, download and load the data as an R object.

TGCAquery searches in a pre-processed TCGA database and returns a summary table with the found files, samples, version and other useful information. The most important TGCAquery arguments are tumor which receives one or multiple tumor types (USC, LGG, SKCM, KICH, CHO, etc), platform which receives the platform (HumanMethylation27, Genome_Wide_SNP_6, IlluminaHiSeq_RNASeqV2, etc), version which receives the version of the data to be downloaded if the user wants an older version and samples which receives a list of TCGA barcodes (ex. "TCGA-CS-4938") to filter the search results. A complete list of possible entries for arguments can be found in the TCGAbiolinks vignette. Lines 6 and 13 of Listing 1 show an example of this function.

After searching, the user will be able to download the data with TCGAdownload. An important feature of this function is the ability to filter the data using the arguments type if the user wants to specify file tumor type and samples if user wants to specify samples (list of TCGA barcodes). For example, lines 15 and 18 of Listing 1 are used to select a specific tumor type to download and prepare the data respectively. The platforms and their possible inputs for the type argument is shown below:

Finally, TCGAprepare transforms the downloaded data into a summarizedExperiment object or a data frame. If summarizedExperiment is set to TRUE, TCGAbiolinks will add metadata to the object in order to help the user when working with the data. Also, if the user sets the argument add.subtype to TRUE the summarizedExperiment will receive subtype information defined by The Cancer Genome Atlas (TCGA) Research Network reports (the full list of papers can be seen in TCGAquery_subtype section in TCGAbiolinks vignette), Likewise, if the user sets the argument add.clinical to TRUE the summarizedExperiment will receive clinical information. Lines 8–11 and 18–22 of Listing 1 illustrates this function.

 1 library(TCGAbiolinks)
 2
 3 # Download the DNA methylation data: HumanMethylation450 LGG and GBM.
 4 path <– "."
 5
 6 query.met <– TCGAquery(tumor = c("LGG","GBM"),"HumanMethylation450", level = 3)
 7 TCGAdownload(query.met, path = path )
 8 met <– TCGAprepare(query = query.met,dir = path,
 9                      add.subtype = TRUE, add.clinical = TRUE,
10                    summarizedExperiment = TRUE,
11                      save = TRUE, filename = "lgg_gbm_met.rda")
12
13 # Download the expression data: IlluminaHiSeq_RNASeqV2 LGG and GBM.
14 query.exp <– TCGAquery(tumor = c("lgg","gbm"), platform = "IlluminaHiSeq_RNASeqV2",level = 3)
15
16 TCGAdownload(query.exp,path = path, type = "rsem.genes.normalized_results")
17
18 exp <– TCGAprepare(query = query.exp, dir = path,
19                    summarizedExperiment = TRUE,
20                      add.subtype = TRUE, add.clinical = TRUE,
21                    type = "rsem.genes.normalized_results",
22                      save = T,filename = "lgg_gbm_exp.rda")

Listing 1. Downloading DNA methylation and gene expression data from TCGA with TCGAbiolinks

If a summarizedExperiment object was chosen, the data can be accessed with three different accessors: assay for the data information, rowRanges to gets the range of values in each row and colData to get the sample information (patient, batch, sample type, etc)^8,9. An example is shown in Listing 2.

1 library(summarizedExperiment)
2 # get expression matrix
3 data <– assay(exp)
4
5 # get sample information
6 sample.info <– colData(exp)
7
8 # get genes information
9 genes.info <– rowRanges(exp)

Listing 2. summarizedExperiment accessors

Clinical data can be obtained using the function TCGAquery_clinical which can be used as described in Listing 3. This function has three arguments tumor, clinical_data_type and samples. The clinical_data_type argument is always required and should be accompanied by at least one of the other two parameters. Examples for the argument clinical_data_type are: “clinical_drug”, “clinical_patient”, and “clinical_radiation” (a complete list and description can be found in the section ‘Working with clinical data.’ of the TCGAbiolinks vignette).

An important note about the clinical data is that follow-up data for TCGA patients are contained in the ‘clinical_follow_up’ files for each cancer type and to obtain all available disease progression information, the users should use all the follow_up files in your analyses, not just the latest version.

 1 # get clinical patient data for GBM samples
 2 gbm_clin <– TCGAquery_clinic("gbm","clinical_patient")
 3
 4 # get clinical patient data for LGG samples
 5 lgg_clin <– TCGAquery_clinic("lgg","clinical_patient")
 6
 7 # Bind the results, as the columns might not be the same,
 8 # we will plyr rbind.fill , to have all columns from both files
 9 clinical <– plyr::rbind.fill(gbm_clin ,lgg_clin)
10
11 # Other clinical files can be downloaded,
12 # Use ?TCGAquery_clinic for more information
13 clin_radiation <– TCGAquery_clinic("lgg","clinical_radiation")
14
15 # Also, you can get clinical information from different tumor types.
16 # For example sample 1 is GBM, sample 2 and 3 are TGCT
17 data <– TCGAquery_clinic(clinical_data_type = "clinical_patient",
18 			    samples = c("TCGA-06-5416-01A-01D-1481-05",
19					  "TCGA-2G-AAEW-01A-11D-A42Z-05",
20					  "TCGA-2G-AAEX-01A-11D-A42Z-05"))

Listing 3. Downloading clinical data with TCGAbiolinks

Mutation information is stored in Mutation Annotation Format (MAF) files which contain different mutation types (somatic or germline) and states (validated or putative). A summary of all the Mutation Annotation Format (MAF) can be accessed at TCGA wiki. To download these data using TCGAbiolinks, TCGAquery_maf function is provided. It will download the non-obsolete tables from TCGA wiki, remove the protected entries and ask the user which file s/he wants to download (see Listing 4). It will then download and return a data frame with the data.

 1   > mutation <– TCGAquery_maf(tumor = "lgg")
 2   Getting maf tables
 3   Source: https://wiki.nci.nih.gov/display/TCGA/TCGA+MAF+Files
 4   We found these maf files below:
 5 							       MAF.File.Name
 6   2 		             hgsc.bcm.edu_LGG.IlluminaGA_DNASeq.1.somatic.maf
 7
 8   3 LGG_FINAL_ANALYSIS.aggregated.capture.tcga.uuid.curated.somatic.maf
 9
10						       Archive.Name Deploy.Date
11   2   hgsc.bcm.edu_LGG.IlluminaGA_DNASeq_automated.Level_2.1.0.0   10-DEC-13
12   3    broad.mit.edu_LGG.IlluminaGA_DNASeq_curated.Level_2.1.3.0   24-DEC-14
13
14   Please, select the line that you want to download: 3

Listing 4. Downloading mutation data with TCGAbiolinks

Finally, the Cancer Genome Atlas (TCGA) Research Network has reported integrated genome-wide studies of various diseases, in what is called ‘PanCan’. TCGAqueryPrepare function can automatically import the subtypes defined by these reports and incorporate them into a summarizedExperiment object. The subtypes can also be accessed using TCGAquery_subtype function. The subtypes include: LGG¹⁰, GBM¹⁰, STAD¹¹, BRCA¹², READ¹³, COAD¹³ and LUAD¹⁴.

1 gbm.subtypes <− TCGAquery_subtype(tumor = "gbm")
2 brca.subtypes <− TCGAquery_subtype(tumor = "brca")

Listing 5. summarizedExperiment accessors

Downloading data from Broad TCGA GDAC. The Bioconductor package RTCGAtoolbox¹⁵ provides access to Firehose Level 3 and 4 data through the function getFirehoseData. The following arguments allows users to select the version and tumor type of interest:

These arguments can be used to select the data type to download: RNAseq_Gene, Clinic, miRNASeq_Gene, ccRNAseq2_Gene_Norm, CNA_SNP, CNV_SNP, CNA_Seq, CNA_CGH, Methylation, Mutation, mRNA_Array, miRNA_Array, and RPPA.

By default, RTCGAtoolbox allows users to download up to 500 MB worth of data. To increase the size of the download, users are encouraged to use fileSizeLimit argument. An example is found in Listing 6. The getData function allow users to access the downloaded data (see lines 22–24 of Listing 6) as a S4Vector object.

 1 library(RTCGAToolbox)
 2
 3 # Get the last run dates
 4 lastRunDate <− getFirehoseRunningDates()[1]
 5 lastAnalyseDate <− getFirehoseAnalyzeDates(1)
 6
 7 # get DNA methylation data, RNAseq2 and clinical data for LGG
 8 lgg.data <− getFirehoseData(dataset = "LGG",
 9			       gistic2_Date = getFirehoseAnalyzeDates(1), runDate = lastRunDate,
10			       Methylation = TRUE, RNAseq2_Gene_Norm = TRUE, Clinic = TRUE,
11			       Mutation = T,
12			       fileSizeLimit = 10000)
13
14 # get DNA methylation data, RNAseq2 and clinical data for GBM
15 gbm.data <− getFirehoseData(dataset = "GBM",
16			       runDate = lastDate, gistic2_Date = getFirehoseAnalyzeDates(1),
17			       Methylation = TRUE, Clinic = TRUE, RNAseq2_Gene_Norm = TRUE,
18			       fileSizeLimit = 10000)
19
20 # To access the data you should use the getData function
21 # or simply access with @ (for example gbm.data@Clinical)
22 gbm.mut <− getData(gbm.data,"Mutations")
23 gbm.clin <− getData(gbm.data,"Clinical")
24 gbm.gistic <− getData(gbm.data,"GISTIC")

Listing 6. Downloading TCGA data files with RTCGAtoolbox

Finnaly, RTCGAtoolbox can access level 4 data, which can be handy when the user requires GISTIC results. GISTIC is used to identify genes targeted by somatic copy-number alterations (SCNAs)¹⁶ (see Listing 7).

1 # Download GISTIC results
2 gistic <− getFirehoseData("GBM",gistic2_Date ="20141017" )
3
4 # get GISTIC results
5 gistic.allbygene <− gistic@GISTIC@AllByGene
6 gistic.thresholedbygene <− gistic@GISTIC@ThresholedByGene

Listing 7. Using RTCGAToolbox to get the GISTIC results

Genomic analysis

Copy number variations (CNV) has a critical role in cancer development and progression. A chromosomal segment can be deleted or amplified as a result of genomic rearrangements, such as deletions, duplications, insertions and translocations. CNV are genomic regions greater than 1 kb with an alteration of copy number between two conditions, e.g. Tumor versus Normal.

TCGA collects copy number data and allows the CNV profiling of cancer. Tumor and paired-normal DNA samples were analyzed for CNV detection using microarray- and sequencing-based technologies. Level 3 processed data are the aberrant regions along the genome resulting from CNV segmentation, and they are available for all copy number technologies.

In this section, we will show how to analyze CNV level 3 data from TCGA to identify recurrent alterations in cancer genome. We analyzed GBM and LGG segmented CNV from SNP array (Affymetrix Genome-Wide Human SNP Array 6.0).

Pre-Processing Data. The only CNV platform available for both LGG and GBM in TCGA is "Affymetrix Genome-Wide Human SNP Array 6.0". Using TCGAbiolinks, we queried for CNV SNP6 level 3 data for primary solid tumor samples. Data for selected samples were downloaded and prepared in two separate rse objects (RangedSummarizedExperiment).

 1 #############################
 2 ## CNV data pre–processing ##
 3 #############################
 4 library(TCGAbiolinks)
 5
 6 # Select available copy number platform for GBM and LGG.
 7 PanCancer <– c("LGG","GBM")
 8 PlatformCancer <– "Genome_Wide_SNP_6"
 9 dataType <– "nocnv_hg19"
10
11 for(tumor in PanCancer){
12   pathCancer <– paste0("../data",tumor)
13
14   datQuery <– TCGAquery(tumor = tumor, platform = PlatformCancer, level = "3")
15   lsSample <– TCGAquery_samplesfilter(query = datQuery)
16
17    # Select primary solid tumor ("TP" 01)
18   selected <– TCGAquery_SampleTypes(barcode = lsSample$Genome_Wide_SNP_6, typesample = "TP")
19
20   TCGAdownload(data = datQuery, path = pathCancer, type = dataType, samples = selected)
21
22   dataAssay <– TCGAprepare(query = datQuery, dir = pathCancer, type = dataType,
23                              save = TRUE,  summarizedExperiment = TRUE,
24                           samples = selected)
25    save(PlatformCancer, tumor, pathCancer, selected, dataAssay,
26         file = paste0(tumor,"_",PlatformCancer,".rda"))
27 }

Listing 8. Searching, downloading and preparing CNV data with TCGAbiolinks

Identification of recurrent CNV in cancer. Cancer related CNV have to be present in many of the analyzed genomes. The most significant recurrent CNV were identified using GAIA¹⁷, an iterative procedure where a statistical hypothesis framework is extended to take into account within-sample homogeneity. GAIA is based on a conservative permutation test allowing the estimation of the probability distribution of the contemporary mutations expected for non-driver markers. Segmented data retrieved from TCGA were used to generate a matrix including all needed information about the observed aberrant regions. Furthermore, GAIA requires genomic probes metadata (specific for each CNV technology), that can be downloaded from broadinstitute website.

 1 ##################################
 2 ## Recurrent CNV identification ##
 3 ##################################
 4
 5 for(cancer in c("LGG","GBM"){
 6
 7      load(paste0(cancer,"_Genome_Wide_SNP_6.rda"))
 8
 9      # Prepare CNV matrix
10     cnvMatrix <– dataAssay
11      # Add label (0 for loss, 1 for gain)
12     cnvMatrix <– cbind(cnvMatrix,Label=NA)
13     cnvMatrix[cnvMatrix[,"Segment_Mean"] < –0.3,"Label"] <– 0
14     cnvMatrix[cnvMatrix[,"Segment_Mean"] > 0.3,"Label"] <– 1
15     cnvMatrix <– cnvMatrix[!is.na(cnvMatrix$Label),]
16      # Remove "Segment_Mean" and change col.names
17     cnvMatrix <– cnvMatrix[,–6]
18      colnames(cnvMatrix) <– c("Sample.Name", "Chromosome", "Start", "End", "Num.of.Markers", "
        Aberration")
19      # Substitute Chromosomes "X" and "Y" with "23" and "24"
20     xidx <– which(cnvMatrix$Chromosome=="X")
21     yidx <– which(cnvMatrix$Chromosome=="Y")
22     cnvMatrix[xidx,"Chromosome"] <– 23
23     cnvMatrix[yidx,"Chromosome"] <– 24
24     cnvMatrix$Chromosome <– sapply(cnvMatrix$Chromosome,as.integer)
25
26      # Recurrent CNV identification with GAIA
27
28      # Retrieve probes meta file from broadinstitute website
29      # Recurrent CNV identification with GAIA
30     gdac.root <– "ftp://ftp.broadinstitute.org/pub/GISTIC2.0/hg19_support/"
31      # Retrieve probes meta file from broadinstitute website
32     markersMatrix <– read.delim(paste0(gdac.root,"genome.info.6.0_hg19.na31_minus_frequent_nan_
          probes_sorted_2.1.txt"),
33                                       as.is=TRUE, header=FALSE)
34      colnames(markersMatrix) <– c("Probe.Name", "Chromosome", "Start")
35      unique(markersMatrix$Chromosome)
36     xidx <– which(markersMatrix$Chromosome=="X")
37     yidx <– which(markersMatrix$Chromosome=="Y")
38     markersMatrix[xidx,"Chromosome"] <– 23
39     markersMatrix[yidx,"Chromosome"] <– 24
40     markersMatrix$Chromosome <– sapply(markersMatrix$Chromosome,as.integer)
41     markerID <– apply(markersMatrix,1,function(x) paste0(x[2],":",x[3]))
42      table(duplicated(markerID))
43      ## FALSE    TRUE
44      ## 1831041     186
45      # There are 186 duplicated markers
46      table(duplicated(markersMatrix$Probe.Name))
47      ## FALSE
48      ## 1831227
49      # ... with different names!
50      # Removed duplicates
51     markersMatrix <– markersMatrix[–which(duplicated(markerID)),]
52      #Filter markersMatrix for common CNV
53     markerID <– apply(markersMatrix,1,function(x) paste0(x[2],":",x[3]))
54     commonCNV <– read.delim(paste0(gdac.root,"CNV.hg19.bypos.111213.txt"), as.is=TRUE)
55     commonCNV[,2] <– sapply(commonCNV[,2], as.integer)
56     commonCNV[,3] <– sapply(commonCNV[,3], as.integer)
57     commonID <– apply(commonCNV,1,function(x) paste0(x[2],":",x[3]))
58      table(commonID %in% markerID)
59      table(markerID %in% commonID)
60     markersMatrix_fil <– markersMatrix[!markerID %in% commonID,]
61
62     markers_obj <– load_markers(markersMatrix_fil)
63
64     cnv_obj <– load_cnv(cnvMatrix, markers_obj, length(selected))
65     results <– runGAIA(cnv_obj, markers_obj, output_file_name=paste0("GAIA_",cancer,"_",
        PlatformCancer,"_flt.txt"), aberrations = –1,
66                        chromosomes = –1, num_iterations = 10, threshold = 0.25)
67
68      # Set q–value threshold
69     threshold <– 0.0001
70
71      # Plot the results
72     RecCNV <– t(apply(results,1,as.numeric))
73      colnames(RecCNV)<– colnames(results)
74     RecCNV <– cbind(RecCNV, score=0)
75     minval <– format(min(RecCNV[RecCNV[,"q–value"]!=0,"q–value"]),scientific=FALSE)
76     minval <– substring(minval,1, nchar(minval)–1)
77     RecCNV[RecCNV[,"q–value"]==0,"q–value"] <– as.numeric(minval)
78     RecCNV[,"score"] <– sapply(RecCNV[,"q–value"],function(x) –log10(as.numeric(x)))
79     RecCNV[RecCNV[,"q–value"]==as.numeric(minval),]
80
81      source("gaiaCNVplot.R")
82     gaiaCNVplot(RecCNV,cancer,threshold)
83
84      save(results, RecCNV, threshold, file = paste0(cancer,"_CNV_results.rda"))
85 }

Listing 9.Recurrent CNV identification in cancer with GAIA

Recurrent amplifications and deletions were identified for both LGG (Figure 1a) and GBM (Figure 1b), and represented in chromosomal overview plots by a statistical score (—log₁₀ corrected p-value for amplifications and log₁₀ corrected p-value for deletions). Genomic regions identified as significantly altered in copy number (corrected p-value < 10^–4) were then annotated to report amplified and deleted genes potentially related with cancer.

Figure 1. Recurrent CNV (|score threshold| = 4).

Gene annotation of recurrent CNV. The aberrant recurrent genomic regions in cancer, as identified by GAIA, have to be annotated to verify which genes are significantly amplified or deleted. Using biomaRt we retrieved the genomic ranges of all human genes and we compared them with significant aberrant regions to select full length genes. An example of the result is shown in Table 1.

 1 ##############################
 2 ## Recurrent CNV annotation ##
 3 ##############################
 4 for(cancer in c("LGG","GBM"){
 5   load(paste0(cancer,"_CNV_results.rda" ))
 6     mart <– useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")
 7     genes <– getBM(attributes = c("hgnc_symbol", "chromosome_name","start_position","end_position"),
        mart=mart)
 8     genes <– genes[genes[,1]!="" & genes[,2]%in%c(1:22,"X","Y"),]
 9     xidx <– which(genes[,2]=="X")
10     yidx <– which(genes[,2]=="Y")
11     genes[xidx, 2] <– 23
12     genes[yidx, 2] <– 24
13     genes[,2] <– sapply(genes[,2],as.integer)
14     genes <– genes[order(genes[,3]),]
15     genes <– genes[order(genes[,2]),]
16      colnames(genes) <– c("GeneSymbol","Chr","Start","End")
17     genes_GR <– makeGRangesFromDataFrame(genes,keep.extra.columns = TRUE)
18
19     sCNV <– RecCNV[RecCNV[,"q–value"]<=threshold,c(1:4,6)]
20     sCNV <– sCNV[order(sCNV[,3]),]
21     sCNV <– sCNV[order(sCNV[,1]),]
22      colnames(sCNV) <– c("Chr","Aberration","Start","End","q–value")
23     sCNV_GR <– makeGRangesFromDataFrame(sCNV,keep.extra.columns = TRUE)
24
25     hits <– findOverlaps(genes_GR, sCNV_GR, type="within")
26     sCNV_ann <– cbind(sCNV[subjectHits(hits),],genes[queryHits(hits),])
27     AberrantRegion <– paste0(sCNV_ann[,1],":",sCNV_ann[,3],"–",sCNV_ann[,4])
28     GeneRegion <– paste0(sCNV_ann[,7],":",sCNV_ann[,8],"–",sCNV_ann[,9])
29     AmpDel_genes <– cbind(sCNV_ann[,c(6,2,5)],AberrantRegion,GeneRegion)
30     AmpDel_genes[AmpDel_genes[,2]==0,2] <– "Del"
31     AmpDel_genes[AmpDel_genes[,2]==1,2] <– "Amp"
32      rownames(AmpDel_genes) <– NULL
33
34      save(RecCNV, AmpDel_genes, file = paste0(cancer,"_CNV_results.rda"))
35 }

Listing 10. Gene annotation of recurrent CNV

Visualizing multiple genomic alteration events. In order to visualize multiple genomic alteration events we recommend using OncoPrint plot which is provided by bioconductor package complexHeatmap¹⁸. The Listing 11 shows how to download mutation data using TCGAquery_maf (line 4), then we filtered the genes to obtain genes with mutations found among glioma specific pathways (lines 6 – 12). The following steps prepared the data into a matrix to fit oncoPrint function. We defined SNPs as blue, insertions as green and deletions as red. The upper barplot indicates the number of genetic mutation per patient, while the right barplot shows the number of genetic mutations per gene. Also, it is possible to add annotations to rows or columns. In the columns case, if an insertion is made at the top, will remove the barplot. The final result for adding the annotation to the bottom is highlighted in Figure 2.

Figure 2. Oncoprint for LGG samples.

Blue defines SNP, green defines insertions and red defines deletions. The upper barplot shows the number of these genetic mutation for each patient, while the right barplot shows the number of genetic mutations for each gene. The bottom bar shows the group of each sample.

 1 library(ComplexHeatmap) # Version 1.10.2
 2 library(TCGAbiolinks)
 3
 4 LGGmut <– TCGAquery_maf(tumor = "LGG",archive.name="LGG.IlluminaGA_DNASeq_curated.Level_2.1.4.0")
 5 GBMmut <– TCGAquery_maf(tumor = "GBM", archive.name = "ucsc.edu_GBM.IlluminaGA_DNASeq_automated.Level
         _2.1.1.0")
 6 mut <– plyr::rbind.fill(LGGmut,GBMmut)
 7
 8 # Filtering mutations in gliomas
 9 EA_pathways <– TCGAbiolinks:::listEA_pathways
10 Glioma_pathways <– EA_pathways[grep("glioma", tolower(EA_pathways$Pathway)),]
11 Glioma_signaling <– Glioma_pathways[Glioma_pathways$Pathway == "Glioma Signaling",]
12 Glioma_signaling_genes <– unlist(strsplit(as.character(Glioma_signaling$Molecules),","))
13
14 mut <– mut[mut$Hugo_Symbol %in% Glioma_signaling_genes,]
15
16 samples <– unique(mut$Tumor_Sample_Barcode)
17 genes <– unique(mut$Hugo_Symbol)
18 mat <– Matrix(0,length(genes),length(samples))
19 colnames(mat) <– samples
20 rownames(mat) <– genes
21
22 pb <– txtProgressBar(min = 0, max = nrow(mat), style = 3)
23
24 for (i in 1:nrow(mat)) {
25     curGene <– rownames(mat)[i]
26     setTxtProgressBar(pb, i)
27      for (j in 1:ncol(mat)) {
28         curSample <– colnames(mat)[j]
29
30           if (length(intersect(mut$Tumor_Sample_Barcode, curSample))==1){
31             mat1 <– mut[mut$Tumor_Sample_Barcode == curSample,]
32               if (length(intersect(mat1$Hugo_Symbol, curGene))==1){
33                 mat3 <– mat1[mat1$Hugo_Symbol == curGene,]
34                   mat[curGene,curSample]<– as.character(mat3$Variant_Type)[1]
35             }
36         }
37    }
38 }
39 close(pb)
40
41 mat[mat==0] <– ""
42 colnames(mat) <– substr(colnames(mat),1,12)
43
44 mat[is.na(mat)] = ""
45 #mat = t(as.Matrix(mat))
46 mat[1:3, 1:3]
47
48 alter_fun = list(
49     background = function (x, y, w, h) {
50           grid.rect(x, y, w–unit(0.5, "mm"), h–unit(0.5, "mm"), gp = gpar(fill = "#CCCCCC", col = NA))
51     } ,
52     SNP = function(x, y, w, h) {
53           grid.rect(x, y, w–unit(0.5, "mm"), h–unit(0.5, "mm"), gp = gpar(fill = "blue", col = NA))
54     },
55     DEL = function(x, y, w, h) {
56           grid.rect(x, y, w-unit(0.5, "mm"), h-unit(0.5, "mm"), gp = gpar(fill = "red", col = NA))
57     },
58     INS = function(x, y, w, h) {
59           grid.rect(x, y, w-unit(0.5, "mm"), h∗0.33, gp = gpar(fill = "#008000", col = NA))
60     }
61 )
62
63 col = c("INS" = "#008000", "DEL" = "red", "SNP" = "blue")
64
65 clin.gbm <– TCGAquery_clinic("gbm", "clinical_patient")
66 clin.lgg <– TCGAquery_clinic("lgg", "clinical_patient")
67 clinical <– plyr::rbind.fill(clin.lgg,clin.gbm)
68 annotation <– clinical[match(colnames(mat),clinical$bcr_patient_barcode),c("disease","radiation_
          therapy")]
69 annotation <– HeatmapAnnotation(annotation_height = rep(unit(0.3, "cm"),ncol(annotation)),
70	                                df = annotation,
71      	                        col = list(disease = c("LGG"="green", "GBM"="orange"),
72	                                     radiation_therapy = c("YES"="blue","NO"="red","[Unknown]"=
          "yellow","[Not Available]"="grey")),
73       	                   annotation_legend_param = list(title_gp = gpar(fontsize = 16,
        fontface = "bold"),
74	                                                                          labels_gp = gpar(fontsize = 16), #
          size labels
75	                                                                          grid_height = unit(8, "mm")))
76
77 pdf("LGG_GBM_oncoprint.pdf",width = 20,height = 20)
78 p <– oncoPrint(mat, get_type = function(x) strsplit(x, ";")[[1]],
79              remove_empty_columns = FALSE,
80           column_order = NULL, # Do not sort the columns
81           alter_fun = alter_fun, col = col,
82              row_names_gp = gpar(fontsize = 16), # set size for row names
83           pct_gp = gpar(fontsize = 16), # set size for percentage labels
84              axis_gp = gpar(fontsize = 16),# size of axis
85           column_title = "OncoPrint for TCGA LGG, genes in Glioma signaling",
86           column_title_gp = gpar(fontsize = 22),
87           pct_digits = 2,
88              row_barplot_width = unit(4, "cm"), #size barplot
89           bottom_annotation = annotation,
90           heatmap_legend_param = list(title = "Mutations", at = c("DEL", "INS", "SNP"),
91                                            labels = c("DEL", "INS", "SNP"),
92                                            title_gp = gpar(fontsize = 16, fontface = "bold"),
93                                            labels_gp = gpar(fontsize = 16), # size labels
94                                            grid_ height = unit(8, "mm")
95           )
96 )
97 draw(p, annotation_legend_side = "bottom")
98 dev.off()

Listing 11. Oncoprint

Overview of genomic alterations by circos plot

Genomic alterations in cancer, including CNV and mutations, can be represented in an effective overview plot named circos. We used circlize CRAN package to represent significant CNV (resulting from GAIA analysis) and recurrent mutations (selecting curated genetic variations retrieved from TCGA that are identified in at least two tumor samples) in LGG (see Listing 13). Circos plot can illustrate molecular alterations genome-wide or only in one or more selected chromosomes. The Figure 3 shows the resulting circos plot for all chromosomes, while the Figure 4 shows the plot for only the chromosome 17.

  1 ###############################################
  2 ## Genomic aberration overview - Circos plot ##
  3 ###############################################
  4
  5 # Retrieve curated mutations for selected cancer (e.g. "LGG")
  6 library(TCGAbiolinks)
  7 mut <− TCGAquery_maf(tumor = "LGG",archive.name="LGG.IlluminaGA_DNASeq_curated.Level_2.1.4.0")
  8 # Select only potentially damaging mutations
  9 mut <− mut[mut$Variant_Classification %in% c("Missense_Mutation","Nonsense_Mutation","Nonstop_
           Mutation","Frame_Shift_Del","Frame_Shift_Ins"),]
 10 # Select recurrent mutations (identified in at least two samples)
 11 mut.id <− paste0(mut$Chromosome,":",mut$Start_position,"-",mut$End_position,"|",mut$Reference_Allele,
           "/",mut$Tumor_Seq_Allele2)
 12 mut <− cbind(mut.id, mut)
 13 numSamples <− table(mut.id)
 14 s.mut <− names(which(numSamples>=2))
 15 # Prepare selected mutations data for circos plot
 16 s.mut <− mut[mut$mut.id %in% s.mut,]
 17 s.mut <− s.mut[,c("Chromosome","Start_position","End_position","Variant_Classification","Hugo_Symbol"
           )]
 18 s.mut <− unique(s.mut)
 19 Chromosome <− sapply(s.mut[,1],function(x) paste0("chr",x))
 20 s.mut <− cbind(Chromosome,s.mut[,−1])
 21 s.mut[,1] <− as.character(s.mut[,1])
 22 s.mut[,4] <− as.character(s.mut[,4])
 23 s.mut[,5] <− as.character(s.mut[,5])
 24 typeNames <− unique(s.mut[,4])
 25 type <− c(4:1)
 26 names(type) <− typeNames[1:4]
 27 Type <− type[s.mut[,4]]
 28 s.mut <− cbind(s.mut,Type)
 29 s.mut <− s.mut[,c(1:3,6,4,5)]
 30
 31 # Load recurrent CNV data for selected cancer (e.g. "LGG")
 32 load("LGG_CNV_results.rda")
 33 # Prepare selected sample CNV data for circos plot
 34 s.cnv <− as.data.frame(RecCNV[RecCNV[,"q—value"]<=10^—4,c(1:4,6)])
 35 s.cnv <− s.cnv[,c(1,3,4,2)]
 36 xidx <− which(s.cnv$Chromosome==23)
 37 yidx <− which(s.cnv$Chromosome==24)
 38 s.cnv[xidx,"Chromosome"] <− "X"
 39 s.cnv[yidx,"Chromosome"] <− "Y"
 40 Chromosome <− sapply(s.cnv[,1],function(x) paste0("chr",x))
 41 s.cnv <− cbind(Chromosome, s.cnv[,−1])
 42 s.cnv[,1] <− as.character(s.cnv[,1])
 43 s.cnv[,4] <− as.character(s.cnv[,4])
 44 s.cnv <− cbind(s.cnv,CNV=1)
 45 colnames(s.cnv) <− c("Chromosome","Start_position","End_position","Aberration_Kind","CNV")
 46
 47 # Draw genomic circos plot
 48 library(circlize)
 49 pdf("CircosPlot.pdf",width=15,height=15)
 50 par(mar=c(1,1,1,1), cex=1)
 51 circos.initializeWithIdeogram()
 52 # Add CNV results
 53 colors <− c("forestgreen","firebrick")
 54 names(colors) <− c(0,1)
 55 circos.genomicTrackPlotRegion(s.cnv,  ylim = c(0,1.2),
 56                                   panel.fun = function(region, value, ...) {
 57				       circos.genomicRect(region, value, ytop.column = 2, ybottom = 0,
 58   								     col = colors[value[[1]]],
 59						       border="white")
 60				    cell.xlim = get.cell.meta.data("cell.xlim")
 61				    circos.lines(cell.xlim, c(0, 0), lty = 2, col = "#00000040")
 62				  })
 63 # Add mutation results
 64 colors <− c("blue","green","red","gold")
 65 names(colors) <− typeNames[1:4]
 66 circos.genomicTrackPlotRegion(s.mut, ylim = c(1.2,4.2),
 67				      panel.fun = function(region, value, ...) {
 68				       circos.genomicPoints(region, value, cex = 0.8, pch = 16, col = colors
           [value[[2]]], ...)
 69				 })
 70
 71 circos.clear()
 72
 73 legend(−0.2, 0.2, bty="n", y.intersp=1, c("Amp","Del"), pch=15, col=c("firebrick","forestgreen"), 
          title="CNVs", text.font=3, cex=1.2, title.adj=0)
 74 legend(−0.2, 0, bty="n", y.intersp=1, names(colors), pch=16, col=colors, title="Mutations", text.font
         =3, cex=1.2, title.adj=0)
 75 dev.off()
 76
 77 # Draw single chromosome circos plot (e.g. "Chr 17")
 78 pdf("CircosPlotChr17.pdf",width=18,height=13)
 79 par(mar=c(1,1,1,1),cex=1.5)
 80 circos.par("start.degree" = 90, canvas.xlim = c(0, 1), canvas.ylim = c(0, 1),
 81            gap.degree = 270, cell.padding = c(0, 0, 0, 0), track.margin = c(0.005, 0.005))
 82 circos.initializeWithIdeogram(chromosome.index = "chr17")
 83 circos.par(cell.padding = c(0, 0, 0, 0))
 84 # Add CNV results
 85 colors <− c("forestgreen","firebrick")
 86 names(colors)  <− c(0,1)
 87 circos.genomicTrackPlotRegion(s.cnv,  ylim = c(0,1.2),
 88				       panel.fun = function(region, value, ...) {
 89                                   circos.genomicRect(region, value, ytop.column = 2, ybottom = 0,
 90							  	     col = colors[value[[1]]],
 91						       border="white")
 92				    cell.xlim = get.cell.meta.data("cell.xlim")
 93				    circos.lines(cell.xlim, c(0, 0), lty = 2, col = "#00000040")
 94				 })
 95
 96 # Add mutation results representing single genes
 97 genes.mut <− paste0(s.mut$Hugo_Symbol,"−",s.mut$Type)
 98 s.mutt <− cbind(s.mut,genes.mut)
 99 n.mut <− table(genes.mut)
100 idx <− !duplicated(s.mutt$genes.mut)
101 s.mutt <− s.mutt[idx,]
102 s.mutt <− cbind(s.mutt,num=n.mut[s.mutt$genes.mut])
103 genes.num <− paste0(s.mutt$Hugo_Symbol," (",s.mutt$num,")")
104 s.mutt <− cbind(s.mutt[,−c(6:8)],genes.num)
105 s.mutt[,6] <− as.character(s.mutt[,6])
106 s.mutt[,4] <− s.mutt[,4]/2
107
108 colors <− c("blue","green","red","gold")
109 names(colors)  <− typeNames[1:4]
110 circos.genomicTrackPlotRegion(s.mutt, ylim = c(0.3,2.2), track.height = 0.05,
111                                    panel.fun = function(region, value, ...) {
112                                    circos.genomicPoints(region, value, cex = 0.8, pch = 16, col = colors
           [value[[2]]], ...)
113                               })
114
115 circos.genomicTrackPlotRegion(s.mutt, ylim = c(0, 1), track.height = 0.1, bg.border = NA)
116 i_track = get.cell.meta.data("track.index")
117
118 circos.genomicTrackPlotRegion(s.mutt, ylim = c(0,1),
119                                    panel.fun = function(region, value, ...) {
120                                    circos.genomicText(region, value,
121                                                    y = 1,
122                                                                 labels.column = 3,
123                                                                 col = colors[value[[2]]],
124                                                    facing = "clockwise", adj = c(1, 0.5),
125                                                    posTransform = posTransform.text, cex = 1.5,
         niceFacing = T)
126                               }, track.height = 0.1, bg.border = NA)
127
128 circos.genomicPosTransformLines(s.mutt,
129                                 posTransform = function(region, value)
130                                   posTransform.text(region,
131                                                     y = 1,
132                                                                  labels = value[[3]],
133                                                     cex = 0.8, track.index = i_track+1),
134                                 direction = "inside", track.index = i_track)
135
136 circos.clear()
137
138 legend(0.25, 0.2, bty="n", y.intersp=1, c("Amp","Del"), pch=15, col=c("firebrick","forestgreen"),
           title="CNVs", text.font=3, cex=1.3,  title.adj=0)
139 legend(0, 0.2, bty="n", y.intersp=1, names(colors), pch=16, col=colors, title="Mutations", text.font
         =3, cex=1.3, title.adj=0)
140 dev.off()

Listing 12. Genomic aberration overview by circos plot

Figure 3. Circos plot of recurrent CNV and mutations in LGG.

Figure 4. Circos plot of chromosome 17 recurrent CNV and mutations in LGG.

Transcriptomic analysis

Pre-Processing Data. The LGG and GBM data used for following transcriptomic analysis were downloaded using TCGAbiolinks. We downloaded only primary solid tumor (TP) samples, which resulted in 516 LGG samples and 156 GBM samples, then prepared it in two separate rse object (RangedSummarizedExperiment) saving them as an R object with a filename including both the name of the cancer and the name of the plaftorm used for gene expression data (see Listing 13).

 1 library(TCGAbiolinks)
 2
 3 # defining common parameters
 4 PanCancer <– c("LGG","GBM")
 5 PlatformCancer <– "IlluminaHiSeq_RNASeqV2"
 6 dataType <– "rsem.genes.results"
 7 
 8 for(tumor in PanCancer){
 9    pathCancer <– paste0("../data",tumor)
10    datQuery <– TCGAquery(tumor = tumor, platform = PlatformCancer, level = "3")
11 
12    # get only primary solid Tumor
13    lsSample <– TCGAquery_samplesfilter(query = datQuery)
14    dataSmTP <– TCGAquery_SampleTypes(barcode = lsSample$IlluminaHiSeq_RNASeqV2, typesample ="TP")
15 
16    TCGAdownload(data = datQuery, path = pathCancer, type = dataType, samples = c(dataSmTP))
17    dataAssy <– TCGAprepare(query = datQuery, dir = pathCancer, type = dataType,
18                               save = TRUE, summarizedExperiment = TRUE,
19                               samples = c(dataSmTP),
20                               filename = paste0(tumor,"_",PlatformCancer,".rda"))
21  }

Listing 13. Searching, downloading and preparing RNA-seq data with TCGAbiolinks

To pre-process the data, first, we searched for possible outliers using the TCGAanalyze_Preprocessing function, which performs an Array Array Intensity correlation AAIC (lines 14-17 and 26-29 of Listing 14). In this way we defined a square symmetric matrix of Pearson correlation among all samples in each cancer type (LGG or GBM). This matrix found 0 samples with low correlation (cor.cut = 0.6) that can be identified as possible outliers.

Second, using the TCGAanalyze_Normalization function, which encompasses the functions of the EDASeq package, we normalized mRNA transcripts.

This function implements Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization¹⁹ and between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization²⁰.

 1 library(TCGAbiolinks)
 2
 3 # loading LGG and GBM rse data
 4
 5 load("LGG_IlluminaHiSeq_RNASeqV2.rda")
 6 cancer <–   "LGG"
 7 pathCancer <– paste0("../data",cancer)
 8 PlatformCancer <– "IlluminaHiSeq_RNASeqV2"
 9
10 dataClin_LGG <– TCGAquery_clinic(tumor = "LGG",
11                                     clinical_data_type = "clinical_patient")
12 
13 
14 dataPrep_LGG <– TCGAanalyze_Preprocessing(object = rse,
15                                                cor.cut = 0.6,
16                                                filename = "LGG_IlluminaHiSeq_RNASeqV2.png")
17 
18 load("GBM_IlluminaHiSeq_RNASeqV2.rda")
19 cancer <–   "GBM"
20 pathCancer <– paste0("../data",cancer)
21 PlatformCancer <– "IlluminaHiSeq_RNASeqV2"
22 
23 dataClin_GBM <– TCGAquery_clinic(tumor = "GBM",
24                                      clinical_data_type = "clinical_patient")
25 
26 dataPrep_GBM <– TCGAanalyze_Preprocessing(object = rse,
27                                                     cor.cut = 0.6,
28                                                     filename = "GBM_IlluminaHiSeq_RNASeqV2.png")
29 
30 dataNorm <– TCGAanalyze_Normalization(tabDF = cbind(dataPrep_LGG, dataPrep_GBM),
31                                            geneInfo = geneInfo,
32                                            method = "gcContent") #18323   672
33 
34 dataFilt <– TCGAanalyze_Filtering(tabDF = dataNorm,
35                                       method = "quantile",
36                                       qnt.cut =  0.25)   #13742	672
37 
38 save(dataFilt, file = paste0("LGG_GBM_Norm_",PlatformCancer,".rda"))
39 
40 dataFiltLGG <– subset(dataFilt, select = substr(colnames(dataFilt),1,12) %in% dataClin_LGG$bcr_
	  patient_barcode)
41 dataFiltGBM <– subset(dataFilt, select = substr(colnames(dataFilt),1,12) %in% dataClin_GBM$bcr_
	  patient_barcode)
42
43 dataDEGs <– TCGAanalyze_DEA(mat1 = dataFiltLGG,
44                             mat2 = dataFiltGBM,
45                             Cond1type = "LGG",
46                             Cond2type = "GBM",
47                             fdr.cut = 0.01 ,
48                             logFC.cut = 1,
49                             method ="glmLRT")

Listing 14. Normalizing mRNA transcripts and differentially expression analysis with TCGAbiolinks

Using TCGAanalyze_DEA, we identified 2,901 differentially expressed genes (DEG)(log fold change >=1 and FDR < 1%) between 515 LGG and 155 GBM samples.

EA: enrichment analysis. In order to understand the underlying biological process from DEGs we performed an enrichment analysis using TCGAanalyze_EA_complete function (see Listing 15).

 1 ansEA <– TCGAanalyze_EAcomplete(TFname="DEA genes LGG Vs GBM", RegulonList = rownames(dataDEGs))
 2
 3 TCGAvisualize_EAbarplot(tf = rownames(ansEA$ResBP),
 4                         GOBPTab = ansEA$ResBP, GOCCTab = ansEA$ResCC,
 5                         GOMFTab = ansEA$ResMF, PathTab = ansEA$ResPat,
 6                         nRGTab = rownames(dataDEGs),
 7                         nBar = 20)

Listing 15. Enrichment analysis

TCGAanalyze_EAbarplot outputs a bar chart as shown in Figure 5 with the number of genes for the main categories of three ontologies (GO:biological process, GO:cellular component, and GO:molecular function.

The Figure 5 shows canonical pathways significantly overrepresented (enriched) by the DEGs. The most statistically significant canonical pathways identified in DEGs list are listed according to their p-value corrected FDR (-Log10) (colored bars) and the ratio of list genes found in each pathway over the total number of genes in that pathway (ratio, red line).

PEA: Pathways enrichment analysis. To verify if the genes found have a specific role in a pathway, the bioconductor package pathview²¹ can be used. Listing 16 shows an example how to use it. It can receive, for example, a named vector of gene with the expression level, the pathway.id which can be found in KEGG database, the species ('hsa' for Homo sapiens) and the limits for the gene expression.

 1 GenelistComplete <– rownames(assay(rse,1))
 2
 3 # DEGs TopTable
 4 dataDEGsFiltLevel <– TCGAanalyze_LevelTab(dataDEGs,"LGG","GBM",
 5                                          dataFilt[,colnames(dataFiltLGG)],
 6                                          dataFilt[,colnames(dataFiltGBM)])
 7
 8 dataDEGsFiltLevel$GeneID <– 0
 9
10 # Converting Gene symbol to geneID
11 library(clusterProfiler)
12 eg = as.data.frame(bitr(dataDEGsFiltLevel$mRNA,
13                         fromType="SYMBOL",
14                         toType="ENTREZID",
15                         annoDb="org.Hs.eg.db"))
16 eg <– eg[!duplicated(eg$SYMBOL),]
17
18 dataDEGsFiltLevel <– dataDEGsFiltLevel[dataDEGsFiltLevel$mRNA %in% eg$SYMBOL,]
19
20 dataDEGsFiltLevel <– dataDEGsFiltLevel[order(dataDEGsFiltLevel$mRNA,decreasing=FALSE),]
21 eg <– eg[order(eg$SYMBOL,decreasing=FALSE),]
22
23 # table(eg$SYMBOL == dataDEGsFiltLevel$mRNA) should be TRUE
24 all(eg$SYMBOL == dataDEGsFiltLevel$mRNA)
25 dataDEGsFiltLevel$GeneID <– eg$ENTREZID
26
27 dataDEGsFiltLevel_sub <– subset(dataDEGsFiltLevel, select = c("GeneID", "logFC"))
28 genelistDEGs <– as.numeric(dataDEGsFiltLevel_sub$logFC)
29 names(genelistDEGs) <– dataDEGsFiltLevel_sub$GeneID
30
31 require("pathview")
32 # pathway.id: hsa05214 is the glioma pathway
33 # limit: sets the limit for gene expression legend and color
34 hsa05214  <–  pathview(gene.data = genelistDEGs,
35                       pathway.id = "hsa05214",
36                       species    = "hsa",
37                       limit      = list(gene=as.integer(max(abs(genelistDEGs)))))

Listing 16. Pathways enrichment analysis with pathview package

Figure 5. The plot shows canonical pathways significantly overrepresented (enriched) by the DEGs (differentially expressed genes) with the number of genes for the main categories of three ontologies (GO:biological process, GO:cellular component, and GO:molecular function, respectively).

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).

The red genes are up-regulated and the green genes are down-regulated in the LGG samples compared to GBM.

Inference of gene regulatory networks. Starting with the set of differentially expressed genes, we infer gene regulatory networks using the following state-of-the art inference algorithms: ARACNE²², CLR²³, MRNET²⁴ and C3NET²⁵. These methods are based on mutual inference and use different heuristics to infer the edges in the network. These methods have been made available via Bioconductor/CRAN packages (MINET²⁶, and c3net,²⁵ respectively).

Many gene regulatory interactions have been experimentally validated and published. These ‘known’ interactions can be accessed using different tools and databases such as BioGrid²⁷ or GeneMANIA²⁸. However, this knowledge is far from complete and in most cases only contains a small subset of the real interactome. The quality assessment of the inferred networks can be carried out by comparing the inferred interactions to those that have been validated. This comparison results in a confusion matrix as presented in Table 2. Different quality measures can then be computed such as the false positive rate

Figure 6. Pathways enrichment analysis : glioma pathway.

Red defines genes that are up-regulated and green defines genes that are down-regulated.

The performance of an algorithm can then be summarized using ROC (false positive rate versus true positive rate) or PR (precision versus recall) curves.

A weakness of this type of comparison is that an edge that is not present in the set of known interactions can either mean that an experimental validation has been tried and did not show any regulatory mechanism or (more likely) has not yet been attempted.

In the following, we ran the nce on i) the 2,901 differentially expressed genes identified in Section “Transcriptomic analysis”.

Retrieving known interactions

We obtained a set of known interactions from the BioGrid database.

 1 get.adjacency.biogrid <– function(tmp.biogrid, names.genes = NULL){
 2
 3   if(is.null(names.genes)){
 4      names.genes <– sort(union(unique(tmp.biogrid[,"Official.Symbol.Interactor.A"]),
 5      unique(tmp.biogrid[,"Official.Symbol.Interactor.B"])))
 6     ind <– seq(1,nrow(tmp.biogrid))
 7   }else{
 8     ind.A <– which(tmp.biogrid[,"Official.Symbol.Interactor.A"]%in%names.genes)
 9     ind.B <– which(tmp.biogrid[,"Official.Symbol.Interactor.B"]%in%names.genes)
10
11     ind <– intersect(ind.A, ind.B)
12   }
13
14    mat.biogrid <– matrix(0, nrow=length(names.genes), ncol=length(names.genes), dimnames=list(names.
        genes, names.genes))
15
16    for(i in ind){
17       mat.biogrid[tmp.biogrid[i,"Official.Symbol.Interactor.A"], tmp.biogrid[i,"Official.Symbol.
         Interactor.B"]] <– mat.biogrid[tmp.biogrid[i,"Official.Symbol.Interactor.B"], tmp.biogrid[i,"
         Official.Symbol.Interactor.A"]] <– 1
18   }
19    diag(mat.biogrid) <– 0
20
21    return(mat.biogrid)
22 }

There are 3,941 unique interactions between the 2,901 differentially expressed genes.

Using differentially expressed genes from TCGAbiolinks workflow

We start this analysis by inferring two gene regulatory networks (the corresponding number of edges are presented in Table 3) for the GBM data set and for the LGG data set using one gene set.

 1 ### plot details (colors & symbols)
 2 mycols<–c(’#e41a1c’,’#377eb8’,’#4daf4a’,’#984ea3’,’#ff7f00’,’#ffff33’,’#a65628’)
 3
 4 ### load network inference libraries
 5 library(minet)
 6 library(c3net)
 7
 8 ### deferentially identified genes using TCGAbiolinks
 9 names.genes.de <– rownames(dataDEGs)
10
11 ### read biogrid info
12 library(downloader)
13 file <– "http://the biogrid.org/downloads/archives/Release%20Archive/BIOGRID–3.4.133/BIOGRID–ALL
        –3.4.133.tab2.zip"
14 download(file ,basename(file))
15 unzip(basename(file),junkpaths =T)
16 tmp.biogrid <– read.csv(gsub("zip","txt",basename(file)), header=TRUE, sep="\t", stringsAsFactors=
        FALSE)
17 net.biogrid.de <– get.adjacency.biogrid(tmp.biogrid, names.genes.de)
18
19 for (cancertype in c("LGG", "GBM")) {
20
21      if(cancertype == "GBM"){
22         mydata <– dataFiltGBM[names.genes.de, ]
23     } else if(cancertype == "LGG"){
24         mydata <– dataFiltLGG[names.genes.de, ]
25     }
26      ### infer networks
27     net.aracne <– minet(t(mydata), method = "aracne")
28     net.mrnet <– minet(t(mydata))
29     net.clr <– minet(t(mydata), method = "clr")
30     net.c3net <– c3net(mydata)
31
32      ### validate compared to biogrid network
33     tmp.val <– list(validate(net.aracne, net.biogrid.de), validate(net.mrnet, net.biogrid.de),
34                     validate(net.clr, net.biogrid.de), validate(net.c3net, net.biogrid.de))
35
36      ### plot roc and compute auc for the different networks
37     dev1 <– show.roc(tmp.val[[1]],cex=0.3,col=mycols[1],type="l")
38     res.auc <– auc.roc(tmp.val[[1]])
39	 for(count in 2:length(tmp.val)){
40           show.roc(tmp.val[[count]],device=dev1,cex=0.3,col=mycols[count],type="l")
41         res.auc <– c(res.auc, auc.roc(tmp.val[[count]]))
42     }
43
44      legend("bottomright", legend=paste(c("aracne","mrnet","clr","c3net"), signif(res.auc,4), sep=": "
          ),
45               col=mycols[1:length(tmp.val)],lty=1, bty="n" )
46      dev.copy2pdf(width=8,height=8,device = dev1, file = paste0("roc_biogrid_",cancertype,".pdf"))
47      save(net.aracne, net.mrnet, net.clr, net.c3net, file=paste0("nets_",cancertype,".RData"))
48
49 }

In Figure 7, the obtained ROC curve and the corresponding area under curve (AUC) are presented. It can be observed that CLR and MRNET perform best when comparing the inferred network with known interactions from the BioGrid database.

Figure 7. ROC with corresponding AUC for inferred GBM networks compared to BioGrid interactions using 2901 genes.

Epigenetic analysis

The DNA methylation is an important component in numerous cellular processes, such as embryonic development, genomic imprinting, X-chromosome inactivation, and preservation of chromosome stability²⁹.

In mammals, DNA methylation is found sparsely but globally, distributed in definite CpG sequences throughout the entire genome. There is however an exception, the CpG islands (CGIs) which are short interspersed DNA sequences with are enriched for GC. These CpG islands are normally found in sites of transcription initiation and their methylation can lead to gene silencing³⁰

Thus, the investigation of the DNA methylation is crucial to understanding regulatory gene networks in cancer as the DNA methylation represses transcription³¹. Therefore, the DMR (differentially Methylation Region) detection can help us investigate regulatory gene networks.

This section describes the analysis of DNA methylation using the bioconductor package TCGAbiolinks⁷. For this analysis, and due to the time required to perform it, we selected only 10 LGG samples and 10 GBM samples that have both DNA methylation data from Infinium HumanMethylation450 and gene expression from Illumina HiSeq 2000 RNA Sequencing Version 2 analysis (lines 1–7 of the Listing 17 describes how to make the data acquisition). We started by checking the mean DNA methylation of different groups of samples, then a DMR (Differentially methylated region) analysis is performed in which we search for regions that have possible biological significance, for example, regions that are methylated in one group and unmethylated in the other. After finding these regions, they can be visualized using heatmaps.

Visualizing the mean DNA methylation of each patient. It should be highlighted that some pre-processing of the DNA methylation data was done. The DNA methylation data from the 450k platform have three types of probes cg (CpG loci) , ch (non-CpG loci) and rs (SNP assay). The last type of probe can be used for sample identification and tracking and should be excluded for differential methylation analysis according to the ilumina manual. Therefore, the rs probes were removed (see Listing 17 lines 43). Also, probes in chromosomes X, Y were removed to eliminate potential artifacts originating from the presence of a different proportion of males and females³². The last pre-processing steps were to remove probes with at least one NA value (see Listing 17 lines 40).

After this pre-processing step, using the function TCGAvisualize_meanMethylation provided we can take a look at the mean DNA methylation of each patient in each group. It receives as argument a summarizedExperiment object with the DNA methylation data, and the arguments groupCol and subgroupCol which should be two columns from the sample information matrix of the summarizedExperiment object (accessed by the colData function) (see Listing 17 lines 46-50).

 1 #––––––––––––––––––––––––––––
 2 # Obtaining DNA methylation
 3 #––––––––––––––––––––––––––––
 4 library(TCGAbiolinks)
 5 library(stringr)
 6 # Samples
 7 matched_met_exp <– function(tumor, n = NULL){
 8      # get primary solid tumor samples: DNA methylation
 9     met450k <– TCGAquery(tumor = tumor,"HumanMethylation450", level = 3)
10     met450k.tp <– TCGAquery_SampleTypes(unique(unlist(stringr::str_split(met450k$barcode,","))),c("TP
        "))
11
12      # get primary solid tumor samples: RNAseq
13     rnaseq <– TCGAquery(tumor = tumor,c("IlluminaHiSeq_RNASeqV2"),level = 3)
14     rnaseq.tp <– TCGAquery_SampleTypes(unique(unlist(stringr::str_split(rnaseq$barcode,","))),c("TP")
         )
15
16      # Get patients with samples in both platforms
17     patients <– unique(substr(rnaseq.tp,1,15)[substr(rnaseq.tp,1,12) %in% substr(met450k.tp,1,12)] )
18      if(!is.null(n)) patients <– patients[1:n] # get only n samples
19      return(patients)
20 }
21 lgg.samples <– matched_met_exp("LGG", n = 10)
22 gbm.samples <– matched_met_exp("GBM", n = 10)
23 samples <– c(lgg.samples,gbm.samples)
24
25 #–––––––––––––––––––––––––––––––––––
26 # 1 – methylation
27 # ––––––––––––––––––––––––––––––––––
28 # For methylation it is quicker in this case to download the tar.gz file
29 # and get the samples we want instead of downloading files by files
30 query.met <– TCGAquery(tumor = c("GBM","LGG"), platform = "HumanMethylation450", level = 3, samples =
         samples)
31 TCGAdownload(query.met, samples = samples)
32 met <– TCGAprepare(query.met,dir = ".", save = FALSE,samples = samples)
33 #––––––––––––––––––––––––––––
34 # Mean methylation
35 #––––––––––––––––––––––––––––
36 # Plot a barplot for the groups in the disease column in the
37 # summarizedExperiment object
38
39 # remove probes with NA (similar to na.omit)
40 met <– subset(met,subset = (rowSums(is.na(assay(met))) == 0))
41
42 # remove probes in chromossomes X, Y and NA
43 met <– subset(met,subset = !as.character(seqnames(met)) %in% c("chrNA","chrX","chrY"))
44
45
46 TCGAvisualize_meanMethylation(met,
47                               groupCol = "disease",
48                               group.legend  = "Groups",
49                               filename = "mean_lgg_gbm.png",
50                                       print.pvalue = TRUE)

Listing 17. Visualizing the DNA mean methylation of groups

Figure 8 illustrates a mean DNA methylation plot for each sample in the GBM group (140 samples) and a mean DNA methylation for each sample in the LGG group. Genome-wide view of the data highlights a difference between the groups of tumors (p-value = 6.1 × 10⁻⁰⁶ ).

Searching for differentially methylated CpG sites. The next step is to define differentially methylated CpG sites between the two groups. This can be done using the TCGAanalyze_DMR function (see Listing 18). The DNA methylation data (level 3) is presented in the form of beta-values that uses a scale ranging from 0.0 (probes completely unmethylated ) up to 1.0 (probes completely methylated).

To find these differentially methylated CpG sites, first, it calculates the difference between the mean DNA methylation (mean of the beta-values) of each group for each probe. Second, it tests for differential expression between two groups using the Wilcoxon test adjusting by the Benjamini-Hochberg method. Arguments of TCGAanalyze_DMR was set to require a minimum absolute beta-values difference of 0.25 and an adjusted p-value of less than 10⁻².

Figure 8. Boxplot of mean DNA methylation of each sample (black dots).

After these tests, a volcano plot (x-axis: difference of mean DNA methylation, y-axis: statistical significance) is created to help users identify the differentially methylated CpG sites and return the object with the results in the rowRanges. Figure 9 shows the volcano plot produced by Listing 18. This plot aids the user in selecting relevant thresholds, as we search for candidate biological DMRs.

 1 # Becareful! Depending on the number of probes and samples this function might take some days.
 2 # To make this example faster we used only the chromosome 9
 3 # This should take some minutes
 4 met.chr9 <– subset(met,subset = as.character(seqnames(met)) %in% c("chr9"))
 5
 6 met.chr9 <– TCGAanalyze_DMR(met.chr9,
 7                        groupCol = "disease", # a column in the colData matrix
 8                        group1 = "GBM",         # a type of the the groupCol column
 9                        group2="LGG",           # a type of the the groupCol column
10                        p.cut = 10^–2,
11                        diffmean.cut = 0.25,
12                           legend = "State",
13                           plot.filename = "LGG_GBM_metvolcano.png",
14                        cores = 1 # if set to 1 there will be a progress bar
15)

Listing 18. Finding differentially methylated CpG sites

Figure 9. Volcano plot: searching for differentially methylated CpG sites (x-axis: difference of mean DNA methylation, y-axis: statistical significance).

To visualize the level of DNA methylation of these probes across all samples, we use heatmaps that can be generate by the bioconductor package complexHeatmap¹⁸. To create a heatmap using the complexHeatmap package, the user should provide at least one matrix with the DNA methylation levels. Also, annotation layers can be added and placed at the bottom, top, left side and right side of the heatmap to provide additional metadata description. The Listing 19 shows the code to produce the heatmap of a DNA methylation data (Figure 10).

 1 #––––––––––––––––––––––––––
 2 # DNA methylation heatmap
 3 #–––––––––––––––––––––––––
 4 library(ComplexHeatmap)
 5
 6 # get the probes that are Hypermethylated or Hypomethylated
 7 # met is the same object of the section ’DNA methylation analysis’
 8 sig.met <– met.chr9[values(met.chr9)$status.GBM.LGG %in% c("Hypermethylated","Hypomethylated"),]
 9
10 # To speed up the example, let take a look on the first 100 probes
11 sig.met.100 <– sig.met[1:100,]
12
13 # top annotation, which sampples are LGG and GBM
14 # We will add clinical data as annotation of the samples
15 # we will sort the clinical data to have the same order of the DNA methylation matrix
16 clinical.order <– clinical[match(substr(colnames(sig.met.100),1,12),clinical$bcr_patient_barcode),]
17 ta = HeatmapAnnotation(df = clinical.order[,c("disease","gender","icd_o_3_histology","tumor_tissue_
          site")],
18                           col = list(disease = c("LGG" = "grey", "GBM" = "black"),
19                                      gender = c("MALE"="blue","FEMALE"="pink")))
20
21 # row annotation: add the status for LGG in relation to GBM
22 # For exmaple: status.gbm.lgg Hypomethyated means that the mean DNA methylation of
23 # probes for lgg are hypomethylated compared to GBM ones.
24 ra = rowAnnotation(df = values(sig.met.100)["status.GBM.LGG" ],
25                         col = list(status.GBM.LGG = c("Hypomethylated" = "orange",
26                     					        "Hypermethylated" = "darkgreen")),
27                    width = unit(1, "cm"))
28
29 heatmap <– Heatmap(assay(sig.met.100),
30                    name = "DNA methylation",
31                    top_annotation = ta,
32                       col = matlab::jet.colors(200),
33                       show_row_names = F,
34                    cluster_rows = T,
35                    cluster_columns = F,
36			  show_column_names = F,
37                    column_ title = "DNA methylation") + ra
38 png("heatmap.png",width = 600, height = 400); draw(heatmap); dev.off()

Listing 19. Creating heatmaps for DNA methylation using ComplexHeatmap

Figure 10. Heatmap of DNA methylation in probes.

Rows are probes and columns are samples (patients). The DNA methylation values ranges from 0.0 (completely DNA unmethylated, blue) to 1.0 (completely DNA methylated, red). The groups of each sample were annotated in the top bar and the DNA methylation status for each probe was annotated in the right bar.

Motif analysis. Motif discovery is the attempt to extract small sequence signals hidden within largely non-functional intergenic sequences. These sequence short nucleotide sequences (6–15 bp) might have a biological significance as it can be used to control the expression of genes. These sequences are called Regulatory motifs. The bioconductor package rGADEM^33,34 provides an efficient de novo motif discovery algorithm for large-scale genomic sequence data.

The user may be interested in looking for unique signatures in the regions defined by ‘differentially methylated’ to identify candidate transcription factors that could bind to these elements affected by the accumulation or absence of DNA methylation. For this analysis we use a sequence of 100 bases before and after the probe location (See lines 6–8 in the Listing 20). An object will be returned which contains all relevant information about your motif analysis (sequence consensus, pwm, chromosome, pvalue…).

Using bioconductor package motifStack³⁵ it is possible to generate a graphic representation of multiple motifs with different similarity scores (see Figure 11).

 1 library(rGADEM)
 2 library(BSgenome.Hsapiens.UCSC.hg19)
 3 library(motifStack)
 4
 5 probes <– rowRanges(met.chr9)[values(met.chr9)$status.GBM.LGG %in% c("Hypermethylated" ,"
        Hypomethylated") ,]
 6 # Get hypo/hyper methylated probes and make a 200bp window surrounding each probe.
 7 sequence <– RangedData(space=as .character(probes@seqnames),
 8                       IRanges(start=probes@ranges@start – 100,
 9                                end=probes@ranges@start + 100), strand="∗")
10 #look for motifs
11 gadem <– GADEM(sequence, verbose=1, genome=Hsapiens)
12
13 # How many motifs were found?
14 length(gadem@motifList)
15
16 # get the number of occurences
17 nOccurrences(gadem)
18
19 # view all sequences consensus
20 consensus(gadem)
21
22 # print the first two motif logos.
23 plot(gadem@motifList[[1]])
24 pwm <– getPWM(gadem)
25 pfm <– new("pfm",mat=pwm[[1]],name="Novel Site 1")
26 plotMotifLogo(pfm)
27
28 # Number of instances of motif 1?
29 length(gadem@motifList[[1]]@alignList)

Listing 20. rGADEM: de novo motif discovery

Figure 11. Motif logos found during de-novo motif analysis.

After rGADEM returns it’s results, the user can use MotIV package^36–39 to start the motif matching analysis (line 4 of Listing 21). The result is shown in Figure 12.

 1 library(MotIV)
 2
 3 analysis.jaspar <– motifMatch(pwm)
 4 summary(analysis.jaspar)
 5 plot(analysis.jaspar, ncol=1, top=5, rev=FALSE, main="", bysim=TRUE, cex=0.3)
 6
 7 # visualize the quality of the results around the alignments
 8 # E–value give an estimation of the match.
 9 alignment <– viewAlignments(analysis.jaspar )
10 print(alignment[[1]])

Listing 21. MotIV: motifs matches analysis

Figure 12. Identified transcription factors: the sequence logo, the name of the motif match and the p-value of the alignment.

Integrative (Epigenomic & Transcriptomic) analysis

Recent studies have shown that providing a deep integrative analysis can aid researchers in identifying and extracting biological insight from high through put data^29,40,41. In this section, we will introduce a bioconductor package called ELMER to identify regulatory enhancers using gene expression + DNA methylation data + motif analysis. In addition, we show how to integrate the results from the previous sections with important epigenomic data derived from both the ENCODE and Roadmap.

Integration of DNA methylation & gene expression data. After finding differentially methylated CpG sites, one possible question one might ask is whether nearby genes also undergo a change in its expression, either an increase or a decrease. DNA methylation at promoters of genes have been shown to be associated with silencing of the respective gene.

The starburst plot is proposed to combine information from two volcano plots, and is applied for a study of DNA methylation and gene expression⁴². Even though, being desirable that both gene expression and DNA methylation data are from the same samples, the starburst plot can be applied as a meta-analysis tool, combining data from different samples⁴³.

The function TCGAvisualize_starburst creates a Starburst plot for comparison of DNA methylation and gene expression. The log₁₀ (FDR-corrected P value) for DNA methylation is plotted on the x axis, and for gene expression on the y axis, for each gene. The horizontal black dashed line shows the FDR-adjusted P value of 10⁻² for the expression data and the vertical ones shows the FDR-adjusted P value of 10⁻² for the DNA methylation data. The Starburst plot for the Listing 22 is shown in Figure 13. While the argument met.p.cut and exp.p.cut controls the black dashed lines, the arguments diffmean.cut and logFC.cut will be used to highlight the genes that respects these parameters (circled genes in Figure 13). For the example below we set higher p.cuts trying to get the most significant list of pair gene/probes. But for the next sections we will use exp.p.cut = 0.01 and logFC.cut = 1 as the previous sections.

 1 #––––––––––––––––––– Starburst plot ––––––––––––––––––––––––––––––
 2 starburst <– TCGAvisualize_starburst(met.chr9,     # DNA methylation with results
 3				       dataDEGs,     # DEG results
 4                                              "GBM", "LGG", # Groups
 5                                     filename = "starburst.png",
 6                                     met.p.cut = 10^–2,
 7                                              exp.p.cut = 10^–2,
 8                                     diffmean.cut = 0.25,
 9                                     logFC.cut = 1,
10                                     width = 15, height = 10,
11                                              names = TRUE)

Listing 22. Starburst plot for comparison of DNA methylation and gene expression

Figure 13. Starburst plot: x-axis is the log10 of the correct P-value for DNA methylation and the y-axis is the log10 of the correct P-value for the expression data.

The starburst plot highlights nine distinct quadrants. Highlighted genes might have the potential for activation due to epigenetic alterations.

ChIP-seq analysis. ChIP-seq is used primarily to determine how transcription factors and other chromatin-associated proteins influence phenotype-affecting mechanisms. Determining how proteins interact with DNA to regulate gene expression is essential for fully understanding many biological processes and disease states. The aim is to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation. A summary of the association of each histone mark is shown in Table 4. Besides, ChIP-seq data exist in the ROADMAP database and can be obtained through the AnnotationHub package⁴⁴ or from Roadmap web portal. The Table 5 shows the description for all the roadmap files that are available through AnnotationHub.

After obtaining the ChIP-seq data, we can then identify overlapping regions with the regions identified in the starburst plot. The narrowPeak files are the ones selected for this step.

For a complete pipeline with Chip-seq data, bioconductor provides excellent tutorials to work with ChIP-seq and we encourage our readers to review the following article⁵³.

The first step shown in Listing 23 is to download the Chip-seq data. The function query received as argument the annotationHub database (ah) and a list of keywords to be used for searching the data, EpigenomeRoadmap is selecting the roadmap database, consolidated is selecting only the consolidate epigenomes, brain is selecting the brain samples, E068 is one of the epigenomes for brain (a table for the list is found in this summary table)⁵⁴.and narrowPeak is selecting the type of file. The data downloaded are processed data from an integrative Analysis of 111 reference human epigenomes⁵⁴.

 1 library(AnnotationHub)
 2 library(pbapply)
 3 #–––––––––––––––––– Working with ChipSeq data –––––––––––––––
 4 # Step 1: download histone marks for a brain samples.
 5 #––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
 6 ah = AnnotationHub() # loading annotation hub database
 7
 8 # Searching for brain consolidated epigenomes in the roadmap database
 9 bpChipEpi_brain <– query(ah, c("EpigenomeRoadMap","narrowPeak","chip","consolidated","brain","E068"))
10
11 # Get chip–seq data
12 histone.marks <– pblapply(names(bpChipEpi_brain), function(x){ah[[x]]})
13 names(histone.marks) <– names(bpChipEpi_brain)

Listing 23. Download chip-seq data

The Chipseeker package⁵⁵ implements functions that uses Chip-seq data to retrieve the nearest genes around the peak, to annotate genomic region of the peak, among others. Also, it provides several visualization functions to summarize the coverage of the peak, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS and overlap of peaks or genes.

After downloading the histone marks (see Listing 23, it is useful to verify the average profile of peaks binding to hypomethylated and hypermethylated regions, which will help the user understand better the regions found. Listing 24 shows an example of code to plot the average profile. Figure 14 shows the result.

Figure 14. Average profiles for histone marks H3K27ac, H3K27me3, H3K36me3, H3K4me1, H3K4me3, H3K9ac, H3K9me3.

The figure indicates that the differentially methylated regions overlaps regions of enhancers, promoters and increased activation of genomic elements.

To help the user understand better the regions found in the DMR analysis, we downloaded histone marks specific for brain tissue, which was done using the AnnotationHub package that can access Roadmap datababse (Listing 23). After, the Chipseeker was used to visualize how histone modifications are enriched to to hypomethylated and hypermethylated regions, (Listing 24). The enrichment heatmap and the average profile of peaks binding to those region is shown in Figure 14 and Figure 15 respectively.

Figure 15. Heatmap of histone marks H3K4me1, H3K4me3, H3K27ac, H3K9ac, H3K9me3, H3K27me3 and H3K36me3 for brain tissues.

The figure indicates that the most of the peaks that overlaps the probes are not brain specific.

The hypomethylated and hypermethylated regions are enriched for H3K4me3, H3K9ac, H3K27ac and H3K4me1 which indicates regions of enhancers, promoters and increased activation of genomic elements. However, these regions are not associated neither with transcribed regions nor Polycomb repression as the H3K36me3 and H3K27me3 heatmaps does not show an enrichment nearby the position 0, and the average profile also does not show a peak at position 0.

 1 library(ChIPseeker)
 2 library(pbapply)
 3 library(SummarizedExperiment)
 4 library(GenomeInfoDb)
 5 library(ggplot2)
 6 library(AnnotationHub)
 7
 8 # Create a GR object based on the hypo/ hypermethylated probes.
 9 probes <– keepStandardChromosomes(rowRanges(met.chr9)[values(met.chr9)$status.GBM.LGG %in% c("
        Hypermethylated", "Hypomethylated"),])
10 # Defining a window of 3kbp – 3kbp_probe_3kbp
11 probes@ranges <– IRanges(start = c(probes@ranges@start – 3000), end = c(probes@ranges@start + 3000))
12
13 ### Profile of ChIP peaks binding to TSS regions
14 # First of all, for calculate the profile of ChIP peaks binding to TSS regions, we should
15 # prepare the TSS regions, which are defined as the flanking sequence of the TSS sites.
16 # Then align the peaks that are mapping to these regions, and generate the tagMatrix.
17 tagMatrixList <– pblapply(histone.marks, function(x) {
18    getTagMatrix(keepStandardChromosomes(x), windows = probes, weightCol = "score")
19 })
20 names(tagMatrixList) <– basename(bpChipEpi_brain $title)
21 names(tagMatrixList) <– gsub(".narrowPeak.gz","",names(tagMatrixList)) # remove file type from name
22 names(tagMatrixList) <– gsub("E068–","",names(tagMatrixList)) # remove file type from name
23
24 pdf("chip_heatmap.pdf", height = 5, width = 10)
25 tagHeatmap(tagMatrixList, xlim=c(–3000, 3000),color = NULL)
26 dev.off()
27
28 p <– plotAvgProf(tagMatrixList, xlim = c(–3000,3000), xlab = "Genomic Region (5’–>3’, centered on CpG
        )")
29 # We are centreing in the CpG instead of the TSS. So we’ll change the labels manually
30 p <– p + scale_x_continuous(breaks=c(–3000,–1500,0,1500,3000),labels=c(–3000,–1500,"CpG",1500,3000))
31 library(ggthemes)
32 pdf("chip–seq.pdf", height = 5, width = 7)
33 p + theme_few() + scale_colour_few(name="Histone marks") +guides(colour = guide_legend(override.aes =
           list(size=4)))
34 dev.off()

Listing 24. Average profile plot

To annotate the location of a given peak in terms of genomic features, annotatePeak assigns peaks to genomic annotation in “annotation” column of the output, which includes whether a peak is in the TSS, Exon, 5’ UTR, 3’ UTR, Intronic or Intergenic.

1 require(TxDb.Hsapiens.UCSC.hg19.knownGene)
2 txdb <– TxDb.Hsapiens.UCSC.hg19.knownGene
3 peakAnno <– annotatePeak(probes, tssRegion=c(–3000, 3000), TxDb=txdb, annoDb="org.Hs.eg.db")
4 plotAnnoPie(peakAnno)

Listing 25. Annotate the location of a given peak in terms of genomic features

Identification of Regulatory Enhancers. Recently, many studies suggest that enhancers play a major role as regulators of cell-specific phenotypes leading to alteration in transcriptomes related to diseases^56–59. In order to investigate regulatory enhancers that can be located at long distances upstream or downstream of target genes bioconductor offers the Enhancer Linking by Methylation/Expression Relationship (ELMER) package. This package is designed to combine DNA methylation and gene expression data from human tissues to infer multi-level cis-regulatory networks. It uses DNA methylation to identify enhancers and correlates their state with expression of nearby genes to identify one or more transcriptional targets. Transcription factor (TF) binding site analysis of enhancers is coupled with expression analysis of all TFs to infer upstream regulators. This package can be easily applied to TCGA public available cancer data sets and custom DNA methylation and gene expression data sets⁶⁰.

Figure 16. Feature distribution: annotation the region of the probes closer to the H3K4me3 peaks.

ELMER analysis has five main steps:

This section shows how to use ELMER to analyze TCGA data using as example LGG and GBM samples.

Preparing the data for ELMER package. After downloading the data with TCGAbiolinks package, some steps are still required to use TCGA data with ELMER. These steps can be done with the function TCGAprepare_elmer. This function for the DNA methylation data will remove probes with NA values in more than 20% samples and remove the annotation data, for RNA expression data it will take the log2(expression + 1) of the expression matrix in order to linearize the relation between DNA methylation and expression. Also, it will prepare the row names of the matrix as required by the package.

The Listing 26 shows how to use TCGAbiolinks⁷ to search, download and prepare the data for the ELMER package. Due to time and memory constraints, we will use in this example only data from 10 LGG patients and 10 GBM patients that have both DNA methylation and gene expression data. These samples are the same used in the previous steps.

 1 #––––––––––––––––––––– 8.3 Identification of Regulatory Enhancers ––––––––––––––––––––––––––––
 2 library(TCGAbiolinks)
 3 library(stringr)
 4 # Samples: primary solid tumor with both DNA methylation and gene expression
 5 matched_met_exp <– function(tumor, n = NULL){
 6      # get primary solid tumor samples: DNA methylation
 7     met450k <– TCGAquery(tumor = tumor,"HumanMethylation450", level = 3)
 8     met450k.tp <– TCGAquery_SampleTypes(unique(unlist(stringr::str_split(met450k$barcode,","))),c("TP
        "))
 9
10      # get primary solid tumor samples: RNAseq
11     rnaseq <– TCGAquery(tumor = tumor,c("IlluminaHiSeq _RNASeqV2"),level = 3)
12     rnaseq.tp <– TCGAquery_SampleTypes(unique(unlist(stringr::str_split(rnaseq$barcode,","))),c("TP")
       )
13
14      # Get samples with samples in both platforms
15     samples <– unique(substr(rnaseq.tp,1,15)[substr(rnaseq.tp,1,12) %in% substr(met450k.tp,1,12)] )
16      if(!is.null(n)) samples <– samples [1:n] # get only n samples
17      return(samples)
18 }
19 lgg.samples <– matched_met_exp("LGG", n = 10)
20 gbm.samples <– matched_met_exp("GBM", n = 10)
21 samples <– c(lgg.samples, gbm.samples)
22
23 #–––––––––––––––––––––––––––––––––––
24 # 1 – Methylation
25 # ––––––––––––––––––––––––––––––––––
26 # For methylation it is quicker in this case to download the tar.gz file
27 # and get the samples we want instead of downloading files by files
28 query.met <– TCGAquery(tumor = c("GBM","LGG"), platform = "HumanMethylation450", level = 3, samples =
         samples)
29 TCGAdownload(query.met, samples = samples)
30 met.elmer <– TCGAprepare(query.met,dir = ".", save = FALSE,samples = samples)
31 met.elmer <– TCGAprepare_elmer(met.elmer, platform = "HumanMethylation450")
32
33 #–––––––––––––––––––––––––––––––––––
34 # 2 – Expression
35 # ––––––––––––––––––––––––––––––––––
36 query.rna <– TCGAquery(tumor = c("GBM","LGG"), platform = "IlluminaHiSeq_RNASeqV2", level = 3)
37 TCGAdownload(query.rna, samples = samples, type = "rsem.genes.normalized_results")
38 exp.elmer <– TCGAprepare(query.rna,dir = ".", ,type = "rsem.genes.normalized_results",
39                          samples = samples, save = FALSE)
40 exp.elmer <– TCGAprepare_elmer(exp.elmer, platform = "IlluminaHiSeq_RNASeqV2")

Listing 26. Preparing TCGA data for ELMER’s mee object

Finally, the ELMER input is a mee object that contains a DNA methylation matrix, a gene expression matrix, a probe information GRanges, the gene information GRanges and a data frame summarizing the data. It should be highlighted that samples without both the gene expression and DNA methylation data will be removed from the mee object.

By default the function fetch.mee that is used to create the mee will separate the samples into two groups, the control group (normal samples) and the experiment group (tumor samples), but the user can relabel the samples to compare different groups. For the next sections, we will work with two groups the experiment group (LGG) and control samples (GBM).

 1 library(ELMER)
 2 geneAnnot <– txs()
 3 geneAnnot$GENEID <– paste0("ID",geneAnnot$GENEID)
 4 geneInfo <– promoters(geneAnnot,upstream = 0, downstream = 0)
 5 probe <– get.feature.probe()
 6
 7 # create mee object, use @ to access the matrices inside the object
 8 mee <– fetch.mee(meth = met, exp = exp, TCGA = TRUE, probeInfo = probe, geneInfo = geneInfo)
 9
10 # Get gbm barcodes
11 samples <– unlist(str_split(TCGAquery("gbm", "HumanMethylation450", level = 3)$barcode,","))
12
13 # Relabel GBM samples in the mee object: GBM is control
14 mee@sample$TN[mee@sample$ID %in% lgg.samples] <– "Control"

Listing 27. Creating mee object with TCGA data to be used in ELMER

ELMER analysis. After preparing the data into a mee object, we executed the five ELMER steps for both the hypo (distal enhancer probes hypomethylated in the LGG group) and hyper (distal enhancer probes hypermethylated in the LGG group) direction. The code is shown below. A description of how these distal enhancer probes are identified is found in the ELMER.data vignette.

 1 library(parallel)
 2 # Available directions are hypo and hyper, we will use only hypo
 3 # due to speed constraint
 4 direction <– c("hypo")
 5
 6 for(j in direction){
 7     print(j)
 8     dir.out <– paste0("elmer/",j)
 9     dir.create(dir.out, recursive = TRUE)
10     #––––––––––––––––––––––––––––––––––––––
11     # STEP 3: Analysis                     |
12     #––––––––––––––––––––––––––––––––––––––
13     # Step 3.1: Get diff methylated probes |
14     #––––––––––––––––––––––––––––––––––––––
15    Sig.probes <– get.diff.meth(mee, cores=detectCores(),
16     				          dir.out =dir.out,
17     				          diff.dir=j,
18     				  pvalue = 0.01)
19
20     #–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
21     # Step 3.2: Identify significant probe–gene pairs            |
22     #–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
23     # Collect nearby 20 genes for Sig.probes
24    nearGenes <– GetNearGenes(TRange=getProbeInfo(mee, probe=Sig.probes$probe),
25    				cores=detectCores(),
26    				geneAnnot=getGeneInfo(mee))
27
28     pair <– get.pair(mee=mee,
29                      probes=Sig.probes$probe,
30                      nearGenes=nearGenes,
31                      permu.dir=paste0(dir.out,"/permu"),
32                         dir.out=dir.out,
33                      cores=detectCores(),
34                      label= j,
35                      permu.size=100, # For significant results use 10000
36                      Pe = 0.01) # For significant results use 0.001
37
38     Sig.probes.paired <– fetch.pair(pair=pair,
39   		    		       probeInfo = getProbeInfo(mee),
40      			       geneInfo = getGeneInfo(mee))
41     Sig.probes.paired <–read.csv(paste0(dir.out,"/getPair.",j,".pairs.significant.csv"),
42     				    stringsAsFactors=FALSE)[,1]
43
44
45     #–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
46     # Step 3.3: Motif enrichment analysis on the selected probes |
47     #–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
48     if(length(Sig.probes.paired) > 0 ){
49          #–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
50          # Step 3.3: Motif enrichment analysis on the selected probes |
51          #–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
52        enriched.motif <– get.enriched.motif(probes=Sig.probes.paired,
53     						          dir.out=dir.out, label=j,
54     					       background.probes = probe$name)
55        motif.enrichment <– read.csv(paste0(dir.out,"/getMotif.",j,".motif.enrichment.csv"),
56     				       stringsAsFactors=FALSE)
57          if(length(enriched.motif) > 0){
58               #–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
59               # Step 3.4: Identifying regulatory TFs |
60               #–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––
61               print("get.TFs")
62
63            TF <– get.TFs(mee = mee,
64     			      enriched.motif = enriched.motif,
65     			 dir.out = dir.out,
66     			      cores = detectCores(), label = j)
67            TF.meth.cor <– get(load(paste0(dir.out,"/getTF.",j,".TFs.with.motif.pvalue.rda")))
68              save(TF, enriched.motif, Sig.probes.paired,
69                  pair, nearGenes, Sig.probes, motif.enrichment, TF.meth.cor,
70                     file=paste0(dir.out,"/ELMER_results_",j,".rda"))
71         }
72     }
73 }

Listing 28. Running ELMER analysis

When ELMER identifies the enriched motifs for the distal enhancer probes which are significantly differentially methylated and linked to putative target gene, it will plot the Odds Ratio (x axis) for the each motifs found.

The list of motifs found for the hyper direction (probes hypomethylated in LGG group compared to the GBM group) is found in Figure 17. To select the motifs we select the motifs that had a minimum incidence of 10 in the given probes set and the smallest lower boundary of 95% confidence interval for Odds Ratio of 1.1. These values are the default from the ELMER package.

Figure 17. The plot shows the Odds Ratio (x axis) for the selected motifs.

The range shows the 95% confidence interval for each Odds Ratio.

The analysis found 14 enriched motifs for the hyper direction and no enriched motifs for the hypo direction.

After finding these list of enriched motifs, ELMER identifies regulatory TFs whose expression associate with DNA methylation at motifs and for each enriched motif a TF ranking plot is created automatically by ELMER. This plot shows the TF ranking plots based on the score (−log(Pvalue)) of association between TF expression and DNA methylation of the motif. We can see in Figure 18 that the top three associated TFs that are associated with that AP1 motif are POLR3K, DLX3 and NEUROD2.

Figure 18. TF ranking plots based on the score (−log(Pvalue)) of association between TF expression and DNA methylation of the AP1 motif.

The dashed line indicates the boundary of the top 5% association score and the TF within this boundary were considered candidate upstream regulators. The top 3 associated TFs and the TF family members (dots in red) that are associated with that specific motif are labeled in the plot.

The output of this step is a data frame with the following columns:

Also, for each motif we can take a look on the three most relevant transcription factors. For example, for the AP1 motif the average DNA methylation level of sites with the AP1 motif plotted against the expression of the transcription factors WT1, ZNF208, ATF4 and DDX5 is show in Figure 19. We can see that the experiment group (GBM samples) has a lower average methylation level of sites with the AP1 motif plotted and a higher expression of the transcription factors.

1 scatter.plot(mee, category="TN", save=T, lm_line=TRUE,
2              byTF=list(TF=c("HOXA5","TGIF1","HOXA6","FOSL1"), probe=enriched.motif[["AP1"]]))

Listing 29. Visualizing the average DNA methylation level of sites with a chosen motif vs TF expression

Figure 19. Each scatter plot shows the average DNA methylation level of sites with the AP1 motif plotted against the expression of the transcription factors HOXA5, TGIF1, HOXA6 and FOSL1 respectively.

And for each relevant TF we will use the clinical data to analyze the survival curves for the 30% patients with higher expression of that transcription factor versus the 30% with lower expression. The code below shows that analysis.

 1 TCGAsurvival_TFplot <– function(TF,mee, clinical, percentage = 0.3){
 2
 3     # For the transcription factor, gets it getGeneID
 4    gene <– getGeneID(mee,symbol=TF)
 5     # Get the expression values for the genes.
 6     #(getExp is a ELMER function)
 7     exp <– getExp(mee,geneID=gene)
 8
 9     # Get the names of the 30% patients with lower expression
10    g1 <– names(sort(exp)[1:ceiling(length(exp) ∗ percentage)])
11
12     # Get the names of the 30% patients with higher expression
13    g2 <– names(sort(exp, decreasing = T)[1:ceiling(length(exp)∗percentage)])
14
15     # get the data of only these patients
16    clinical <– clinical[clinical$bcr_patient_barcode %in% substr(c(g1, g2),1,12),]
17
18     # Create the labels for each sample
19    clinical$tf_groups <– "high"
20    clinical[clinical$bcr_patient_barcode %in% substr(c(g1) ,1 ,12),]$tf_groups <– "low"
21
22     # Use TCGAbiolinks to create the survival curve
23    TCGAanalyze_survival(clinical, "tf_groups",
24                            legend=paste0(TF," Exp level"),
25                         filename = paste0(TF,".png"))
26 }
27
28 # get clinical patient data for GBM samples
29 gbm_clin <– TCGAquery_clinic("gbm","clinical_patient")
30
31 # get clinical patient data for LGG samples
32 lgg_clin <– TCGAquery_clinic("lgg","clinical_patient")
33
34 # Bind the results, as the columns might not be the same,
35 # we will will plyr rbind.fill, to have all columns from both files
36 clinical <– plyr::rbind.fill(gbm_clin,lgg_clin)
37 # Call the function we created
38 TCGAsurvival_TFplot("FOXP4",mee,clinical)
39 TCGAsurvival_TFplot("FOXE3",mee,clinical)

Listing 30. Survival analysis for samples with lower expression of regulatory TF and higher expression

The Figures 20, shows that the samples with lower expression of these TFs have a better survival than those with higher expression.

Figure 20.

A) Survival plot for the 30% patients with high expression and low expression of FOXP4 TF. B) Survival plot for the 30% patients with high expression and low expression of FOXE3 TF.