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---
title: "TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages"
author: Tiago C. Silva, Antonio Colaprico, Catharina Olsen, Fulvio D’Angelo, Gianluca Bontempi Michele Ceccarelli, and Houtan Noushmehr
date: "`r Sys.Date()`"
output:
rmdformats::readthedown:
toc_depth: 3
number_sections: true
bibliography: bibliography.bib
---
```{r setup, include=FALSE, echo=FALSE, warning=FALSE, message=FALSE}
knitr::opts_chunk$set(message = FALSE,
warning = FALSE,
error = FALSE,
tidy = FALSE,
fig.align="center",
dpi = 150,
cache = FALSE,
progress=FALSE,
quite = TRUE)
```
```{r, echo=FALSE, results="hide", warning=FALSE,message=FALSE}
suppressPackageStartupMessages(library(TCGAWorkflow))
```
# About
This workflow is based on the article: [TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages](https://f1000research.com/articles/5-1542/v2) [@10.12688/f1000research.8923.2].
Due to time and space contraints we downloaded only a subset of the data, for a real analysis please use all data available. The data used in the examples are available in the package `TCGAWorkflowData`.
## Installation
To be able to execute all the steps of this workflow please install it with the following code:
```{R, eval=FALSE, include=TRUE}
source("https://bioconductor.org/biocLite.R")
deps <- c("pathview", "clusterProfiler", "ELMER", "DO.db","GO.db",
"ComplexHeatmap", "EDASeq", "TCGAbiolinks", "AnnotationHub",
"gaia","ChIPseeker", "minet","BSgenome.Hsapiens.UCSC.hg19",
"MotifDb", "MotIV", "rGADEM", "motifStack", "RTCGAToolbox")
for(pkg in deps) if (!pkg %in% installed.packages()) biocLite(pkg, dependencies = TRUE)
deps <- c("devtools","DT","pbapply","readr","circlize")
for(pkg in deps) if (!pkg %in% installed.packages()) install.packages(pkg,dependencies = TRUE)
devtools::install_github("BioinformaticsFMRP/TCGAWorkflowData")
devtools::install_github("BioinformaticsFMRP/TCGAWorkflow", dependencies = TRUE)
```
## Loading packages
At the beginning of each section, the packages required to execute the code will be loaded. However the following packages are required for all sections.
- TCGAWorkflowData: this package contains the data necessary to execute each of the analysis
steps. This is a subset of the downloaded to make the example faster. For a real analysis, please
use all the data available.
- DT: we will use it to visualize the results
```{R, eval=TRUE, message=FALSE, warning=FALSE, include=TRUE}
library(TCGAWorkflowData)
library(DT)
```
```{R, eval=TRUE, message=FALSE, warning=FALSE, include=FALSE}
library(TCGAbiolinks)
```
# Abstract
Biotechnological advances in sequencing have led to an explosion of
publicly available data via large international consortia such as [The
Cancer Genome Atlas (TCGA)](http://cancergenome.nih.gov/), [The
Encyclopedia of DNA Elements (ENCODE)](http://www.encodeproject.org/),
and [The NIH Roadmap Epigenomics Mapping Consortium
(Roadmap)](http://www.roadmapepigenomics.org/). These projects have
provided unprecedented opportunities to interrogate the epigenome of
cultured cancer cell lines as well as normal and tumor tissues with high
genomic resolution. The [Bioconductor](http://www.bioconductor.org/)
project offers more than 1,000 open-source software and statistical
packages to analyze high-throughput genomic data. However, most packages
are designed for specific data types (e.g. expression, epigenetics,
genomics) and there is no one comprehensive tool that provides a
complete integrative analysis of the resources and data provided by all
three public projects. A need to create an integration of these
different analyses was recently proposed. In this workflow, we provide a
series of biologically focused integrative analyses of different
molecular data. We describe how to download, process and prepare TCGA
data and by harnessing several key Bioconductor packages, we describe
how to extract biologically meaningful genomic and epigenomic data.
Using Roadmap and ENCODE data, we provide a work plan to identify
biologically relevant functional epigenomic elements associated with
cancer.
To illustrate our workflow, we analyzed two types of brain
tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma
multiform or GBM).
All the package landing pages used in this workflow can be found through the [biocViews interface](http://www.bioconductor.org/packages/release/BiocViews.html#___Software).
**Keywords:** Epigenomics, Genomics, Cancer, non-coding, TCGA, ENCODE, Roadmap, Bioinformatics.
# Introduction
Cancer is a complex genetic disease spanning multiple molecular events
such as point mutations, structural variations, translocations and
activation of epigenetic and transcriptional signatures and networks.
The effects of these events take place at different spatial and temporal
scales with interlayer communications and feedback mechanisms creating a
highly complex dynamic system. To gain insight into the biology of
tumours most of the research in cancer genomics is aimed at the
integration of the observations at multiple molecular scales and the
analysis of their interplay. Even if many tumors share similar recurrent
genomic events, their relationships with the observed phenotype are
often not understood. For example, although we know that the majority of
the most aggressive form of brain tumours such as glioma harbour the
mutation of a single gene (IDH), the mechanistic explanation of the
activation of its characteristic epigenetic and transcriptional
signatures are still far to be well characterized. Moreover,
network-based strategies have recently emerged as an effective framework
for the discovery functional disease drivers that act as main regulators
of cancer phenotypes. Here we describe a comprehensice workflow that
integrates many Bioconductor packages in order to analyze and integrate
the molteplicity of molecular observation layers in large scale cancer
dataset.
Indeed, recent technological developments allowed the deposition of
large amounts of genomic and epigenomic data, such as gene expression,
DNA methylation, and genomic localization of transcription factors, into
freely available public international consortia like The Cancer Genome
Atlas ([TCGA](http://cancergenome.nih.gov/)), The Encyclopedia of DNA
Elements ([ENCODE](http://www.encodeproject.org/)), and The NIH Roadmap
Epigenomics Mapping Consortium
([Roadmap](http://www.roadmapepigenomics.org/)) [@Hawkins]. An overview
of the three consortia is described below:
- **The Cancer Genome Atlas (TCGA):** The TCGA consortium, which is a
National Institute of Health (NIH) initiative, makes publicly
available molecular and clinical information for more than 30 types
of human cancers including exome (variant analysis), single
nucleotide polymorphism (SNP), DNA methylation, transcriptome
(mRNA), microRNA (miRNA) and proteome . Sample types available at
TCGA are: primary solid tumors, recurrent solid tumors, blood
derived normal and tumor, metastatic, and solid tissue normal
[@weinstein2013cancer].
- **The Encyclopedia of DNA Elements (ENCODE):** Found in 2003 by the
National Human Genome Research Institute (NHGRI), the project aims
to build a comprehensive list of functional elements that have an
active role in the genome, including regulatory elements that govern
gene expression. Biosamples includes immortalized cell lines,
tissues, primary cells and stem cells [@encode2011user].
- **The NIH Roadmap Epigenomics Mapping Consortium:** This was
launched with the goal of producing a public resource of human
epigenomic data in order to analyze biology and disease-oriented
research. Roadmap maps DNA methylation, histone modifications,
chromatin accessibility, and small RNA transcripts in stem cells and
primary ex vivo tissues [@Fingerman; @Bernstein].
Briefly, these three consortia provide large scale epigenomic data onto
a variety of microarrays and next-generation sequencing (NGS) platforms.
Each consortium encompasses specific types of biological information on
specific type of tissue or cell and when analyzed together, it provides
an invaluable opportunity for research laboratories to better understand
the developmental progression of normal cells to cancer state at the
molecular level and importantly, correlate these phenotypes with tissue
of origins.
Although there exists a wealth of possibilities [@kannan2015public] in
accessing cancer associated data,
[Bioconductor](http://www.bioconductor.org/) represent the most
comprehensive set of open source, updated and integrated professional
tools for the statistical analysis of large scale genomic data. Thus, we
propose our workflow within Bioconductor to describe how to download,
process, analyze and integrate cancer data to understand specific
cancer-related specific questions. However, there is no tool that solves
the issue of integration in a comprehensive sequence and mutation
information, epigenomic state and gene expression within the context of
gene regulatory networks to identify oncogenic drivers and characterize
altered pathways during cancer progression. Therefore, our workflow
presents several [Bioconductor](http://www.bioconductor.org/) packages
to work with genomic and epigenomics data.
# Methods
## Access to the data
TCGA data is accessible via the the NCI Genomic Data Commons (GDC) [data
portal](https://gdc-portal.nci.nih.gov/), [GDC Legacy
Archive](https://gdc-portal.nci.nih.gov/legacy-archive/search/f) and the
[Broad Institute’s GDAC Firehose](gdac.broadinstitute.org). The GDC Data
Portal provides access to the subset of TCGA data that has been
harmonized against GRCh38 (hg38) using GDC Bioinformatics Pipelines
which provides methods to the standardization of biospecimen and
clinical data, the re-alignment of DNA and RNA sequence data against a
common reference genome build GRCh38, and the generation of derived
data. Whereas the GDC Legacy Archive provides access to an unmodified
copy of data that was previously stored in
[CGHub](https://cghub.ucsc.edu/)[@wilks2014cancer] and in the TCGA Data
Portal hosted by the TCGA Data Coordinating Center (DCC), in which uses
as references GRCh37 (hg19) and GRCh36 (hg18).
The previously stored data in CGHub, TCGA Data Portal and Broad
Institute’s GDAC Firehose, were provided as different levels or tiers
that were defined in terms of a specific combination of both processing
level (raw, normalized, integrated) and access level (controlled or open
access). Level 1 indicated raw and controlled data, level 2 indicated
processed and controlled data, level 3 indicated Segmented or
Interpreted Data and open access and level 4 indicated region of
interest and open access data. While the TCGA data portal provided level
1 to 3 data, Firehose only provides level 3 and 4. An explanation of the
different levels can be found at [TCGA
Wiki](https://wiki.nci.nih.gov/display/TCGA/Data+level). However, the
GDC data portal no longer uses this based classification model in
levels. Instead a new data model was created, its documentation can be
found in [GDC
documentation](https://gdc.nci.nih.gov/developers/gdc-data-model/gdc-data-model-components).
In this new model, data can be open or controlled access. While the GDC
open access data does not require authentication or authorization to
access it and generally includes high level genomic data that is not
individually identifiable, as well as most clinical and all biospecimen
data elements, the GDC controlled access data requires dbGaP
authorization and eRA Commons authentication and generally includes
individually identifiable data such as low level genomic sequencing
data, germline variants, SNP6 genotype data, and certain clinical data
elements. The process to obtain access to controlled data is found in
[GDC web
site](https://gdc.nci.nih.gov/access-data/obtaining-access-controlled-data).
Finally, the data provided by [GDC data
portal](https://gdc-portal.nci.nih.gov/) and [GDC Legacy
Archive](https://gdc-portal.nci.nih.gov/legacy-archive/search/f) can be
accessed using Bioconductor package
[TCGAbiolinks](http://bioconductor.org/packages/TCGAbiolinks/), while
the data provided by Firehose can be accessed by Bioconductor package
[RTCGAToolbox](http://bioconductor.org/packages/RTCGAToolbox/).
The next steps describes how one could use
[TCGAbiolinks](http://bioconductor.org/packages/TCGAbiolinks/) &
[RTCGAToolbox](http://bioconductor.org/packages/RTCGAToolbox/) to
download clinical, genomics, transcriptomics, epigenomics data, as well
as subtype information and GISTIC results (i.e., identified genes
targeted by somatic copy-number alterations (SCNAs) that drive cancer
growth). All the data used in this workflow has as reference the Genome
Reference Consortium human genome (build 37 - hg19).
### Downloading data from TCGA data portal
The Bioconductor package
[TCGAbiolinks](http://bioconductor.org/packages/TCGAbiolinks/)
[@TCGAbiolinks] has three main functions *GDCquery*, *GDCdownload* and
*GDCprepare* that should sequentially be used to respectively search,
download and load the data as an R object.
*GDCquery* uses [GDC
API](https://gdc.cancer.gov/developers/gdc-application-programming-interface-api)
to search the data for a given project and data category and filters the
results by samples, sample type, file type and others features if
requested by the user. This function returns a object with a summary
table with the results found (samples, files and other useful
information) and the arguments used in the query. The most important
*GDCquery* arguments are *project* which receives a GDC project
(TCGA-USC, TCGA-LGG, TARGET-AML, etc), *data.category* which receives a
data category (Transcriptome Profiling, Copy Number Variation, DNA
methylation, Gene expression, etc), *data.type* which receives a data
type (Gene expression quantification, Isoform Expression Quantification,
miRNA Expression Quantification, Copy Number Segment, Masked Copy Number
Segment, etc), *workflow.type*, which receives a GDC workflow type
(HTSeq - Counts, HTSeq - FPKM-UQ, HTSeq - FPKM), *legacy*, which selects
to use the legacy database or the harmonized database, *file.type*,
which receives a file type for the searches in the legacy database
(hg18.seg, hg19.seg, nocnv\_,hg18.seg, nocnv\_hg19.seg,
rsem.genes.results, rsem.genes.normalized\_results, etc) and *platform*,
which receives a the platform for the searches in the legacy database
(HumanMethylation27, Genome\_Wide\_SNP\_6, IlluminaHiSeq\_RNASeqV2,
etc). A complete list of possible entries for arguments can be found in
the [TCGAbiolinks
vignette](https://www.bioconductor.org/packages/3.3/bioc/vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.html#harmonized-data-1).
Listing 1 shows an example of this function.
After the search step, the user will be able to download the data using
the *GDCdownload* function which can use either the GDC API to download
the samples, or the [gdc client
tools](https://gdc.cancer.gov/access-data/gdc-data-transfer-tool). The
downloaded data will be saved in a directory with the project name and a
sub-folder with the data.category, for example
"TCGA-GBM/DNA\_methylation".
Finally, *GDCprepare* transforms the downloaded data into a
[summarizedExperiment](http://bioconductor.org/packages/SummarizedExperiment/)
object [@huber2015orchestrating] or a data frame. If
*SummarizedExperiment* is set to TRUE, TCGAbiolinks will add to the
object sub-type information, which was defined by The Cancer Genome
Atlas (TCGA) Research Network reports (the full list of papers can be
seen in [TCGAquery\_subtype
section](http://bioconductor.org/packages/devel/bioc/vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.html#tcgaquery_subtype-working-with-molecular-subtypes-data.)
in TCGAbiolinks vignette), and clinical information. Listing
1 shows how to use these functions to download DNA
methylation and gene expression data from the GDC legacy database and
2 shows how to download copy number
variation from harmonized data portal. Other examples, that access the
harmonized data can be found in the [TCGAbiolinks
vignette](https://www.bioconductor.org/packages/3.3/bioc/vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.html).
```{R, eval=FALSE, include=TRUE}
library(TCGAbiolinks)
# Obs: The data in the legacy database has been aligned to hg19
query.met.gbm <- GDCquery(project = "TCGA-GBM",
legacy = TRUE,
data.category = "DNA methylation",
platform = "Illumina Human Methylation 450",
barcode = c("TCGA-76-4926-01B-01D-1481-05", "TCGA-28-5211-01C-11D-1844-05"))
GDCdownload(query.met.gbm)
met.gbm.450 <- GDCprepare(query = query.met.gbm,
save = TRUE,
save.filename = "gbmDNAmet450k.rda",
summarizedExperiment = TRUE)
query.met.lgg <- GDCquery(project = "TCGA-LGG",
legacy = TRUE,
data.category = "DNA methylation",
platform = "Illumina Human Methylation 450",
barcode = c("TCGA-HT-7879-01A-11D-2399-05", "TCGA-HT-8113-01A-11D-2399-05"))
GDCdownload(query.met.lgg)
met.lgg.450 <- GDCprepare(query = query.met.lgg,
save = TRUE,
save.filename = "lggDNAmet450k.rda",
summarizedExperiment = TRUE)
met.gbm.lgg <- SummarizedExperiment::cbind(met.lgg.450, met.gbm.450)
query.exp.lgg <- GDCquery(project = "TCGA-LGG",
legacy = TRUE,
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results",
sample.type = "Primary solid Tumor")
GDCdownload(query.exp.lgg)
exp.lgg <- GDCprepare(query = query.exp.lgg, save = TRUE, save.filename = "lggExp.rda")
query.exp.gbm <- GDCquery(project = "TCGA-GBM",
legacy = TRUE,
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results",
sample.type = "Primary solid Tumor")
GDCdownload(query.exp.gbm)
exp.gbm <- GDCprepare(query = query.exp.gbm, save = TRUE, save.filename = "gbmExp.rda")
exp.gbm.lgg <- SummarizedExperiment::cbind(exp.lgg, exp.gbm)
```
```{R, eval=FALSE, include=TRUE}
#-----------------------------------------------------------------------------
# Data.category: Copy number variation aligned to hg38
#-----------------------------------------------------------------------------
query <- GDCquery(project = "TCGA-ACC",
data.category = "Copy Number Variation",
data.type = "Copy Number Segment",
barcode = c( "TCGA-OR-A5KU-01A-11D-A29H-01", "TCGA-OR-A5JK-01A-11D-A29H-01"))
GDCdownload(query)
data <- GDCprepare(query)
query <- GDCquery("TCGA-ACC",
"Copy Number Variation",
data.type = "Masked Copy Number Segment",
sample.type = c("Primary solid Tumor")) # see the barcodes with getResults(query)$cases
GDCdownload(query)
data <- GDCprepare(query)
```
If a [SummarizedExperiment object](http://www.nature.com/nmeth/journal/v12/n2/fig_tab/nmeth.3252_F2.html) 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)
[@huber2015orchestrating; @SummarizedExperiment]. An example is shown in
listing below.
```{R, eval=TRUE, include=TRUE}
library(SummarizedExperiment)
# Load object from TCGAWorkflowData package
# THis object will be created in the further sections,
data(GBMIllumina_HiSeq)
# get expression matrix
data <- assay(gbm.exp)
data[1:5,1:5]
# get genes information
genes.info <- rowRanges(gbm.exp)
genes.info
# get sample information
sample.info <- colData(gbm.exp)
head(sample.info)
```
The clinical data can be obtained using TCGAbiolinks through two
methods. The first one will download only the indexed GDC clinical data
which includes diagnoses (vital status, days to death, age at diagnosis,
days to last follow up, days to recurrence), treatments (days to
treatment, treatment id, therapeutic agents, treatment intent type),
demographic (gender, race, ethnicity) and exposures (cigarettes per day,
weight, height, alcohol history) information. This indexed clinical data
can be obtained using the function *GDCquery\_clinical* which can be
used as described in listing below. This function has two
arguments *project* ("TCGA-GBM","TARGET-AML",etc) and *type* ("Clinical"
or "Biospecimen"). The second method will download the xml files with
all clinical data for the patient and retrieve the desired information
from it. This will give access to all clinical data available which
includes patient (tumor tissue site, histological type, gender, vital
status, days to birth, days to last follow up, etc), drug (days to drug
therapy start, days to drug therapy end, therapy types, drug name),
radiation (days to radiation therapy start, days to radiation therapy
end, radiation type, radiation dosage ), new tumor event (days to new
tumor event after initial treatment, new neoplasm event type, additional
pharmaceutical therapy), follow up (primary therapy outcome success,
follow up treatment success, vital status, days to last follow up, date
of form completion), stage event (pathologic stage, tnm categories),
admin (batch number, project code, disease code, Biospecimen Core
Resource).
```{R, eval=TRUE, include=TRUE}
# get indexed clinical patient data for GBM samples
gbm_clin <- GDCquery_clinic(project = "TCGA-GBM", type = "Clinical")
# get indexed clinical patient data for LGG samples
lgg_clin <- GDCquery_clinic(project = "TCGA-LGG", type = "Clinical")
# Bind the results, as the columns might not be the same,
# we will will plyr rbind.fill, to have all columns from both files
clinical <- plyr::rbind.fill(gbm_clin,lgg_clin)
```
```{r echo = TRUE, message = FALSE, warning = FALSE}
head(clinical)
```
```{r results = 'hide', echo=TRUE, message=FALSE, warning=FALSE}
# Fetch clinical data directly from the clinical XML files.
# if barcode is not set, it will consider all samples.
# We only set it to make the example faster
query <- GDCquery(project = "TCGA-GBM",
data.category = "Clinical",
barcode = c("TCGA-08-0516","TCGA-02-0317"))
GDCdownload(query)
clinical <- GDCprepare_clinic(query, clinical.info = "patient")
```
```{r echo = TRUE, message = FALSE, warning = FALSE}
head(clinical)
```
```{r results = 'hide', echo=TRUE, message=FALSE, warning=FALSE}
clinical.drug <- GDCprepare_clinic(query, clinical.info = "drug")
```
```{r echo = TRUE, message = FALSE, warning = FALSE}
head(clinical.drug)
```
```{r results = 'hide', echo=TRUE, message=FALSE, warning=FALSE}
clinical.radiation <- GDCprepare_clinic(query, clinical.info = "radiation")
```
```{r echo = TRUE, message = FALSE, warning = FALSE}
head(clinical.radiation)
```
```{r results = 'hide', echo=TRUE, message=FALSE, warning=FALSE}
clinical.admin <- GDCprepare_clinic(query, clinical.info = "admin")
```
```{r echo = TRUE, message = FALSE, warning = FALSE}
head(clinical.admin)
```
Mutation information is stored in two types of Mutation Annotation
Format (MAF): Protected and Somatic (or Public) MAF files, which are
derived from the GDC annotated VCF files. Annotated VCF files often have
variants reported on multiple transcripts whereas the protected MAF
(\*protected.maf) only reports the most critically affected one and the
Somatic MAFs (\*somatic.maf) are further processed to remove low quality
and potential germline variants. To download Somatic MAFs data using
[TCGAbiolinks](http://bioconductor.org/packages/TCGAbiolinks/),
*GDCquery\_maf* function is provided (see listing below).
```{R, eval=FALSE, include=TRUE}
LGGmut <- GDCquery_Maf(tumor = "LGG", pipelines = "mutect2")
```
```{r echo = TRUE, message = FALSE, warning = FALSE}
data(mafMutect2LGGGBM)
head(LGGmut)
```
Finally, the Cancer Genome Atlas (TCGA) Research Network has reported
integrated genome-wide studies of various diseases (ACC
[@zheng2016comprehensive], BRCA [@cancer2012comprehensive_brca], COAD
[@cancer2012comprehensive_colon], GBM [@Cell], HNSC
[@cancer2015comprehensive], KICH [@davis2014somatic], KIRC
[@cancer2013comprehensive], KIRP [@cancer2016comprehensive], LGG
[@Cell], LUAD [@cancer2014comprehensive_lung], LUSC
[@cancer2012comprehensive_lusc], PRAD [@cancer2015molecular_prad], READ
[@cancer2012comprehensive_colon], SKCM [@cancer2015genomic_skcm], STAD
[@cancer2014comprehensive_stad], THCA [@cancer2014integrated_thca] and
UCEC [@cancer2014comprehensive_gastric]) which classified them in
different subtypes. This classification can be retrieved using the
*TCGAquery\_subtype* function or by accessing the samples information in
the SummarizedExperiment object that created by the *GDCprepare*
function, which automatically incorporates it into the object.
```{R, eval=TRUE, include=TRUE}
gbm.subtypes <- TCGAquery_subtype(tumor = "gbm")
```
```{r echo = TRUE, message = FALSE, warning = FALSE}
head(gbm.subtypes)
```
### Downloading data from Broad TCGA GDAC
The Bioconductor package
[RTCGAToolbox](http://bioconductor.org/packages/RTCGAToolbox/)
[@samur2014rtcgatoolbox] 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:
- dataset - Tumor to download. A complete list of possibilities can be
viewed with *getFirehoseDatasets* function.
- runDate - Stddata run dates. Dates can be viewed with
*getFirehoseRunningDates* function.
- gistic2\_Date - Analyze run dates. Dates can viewed with
*getFirehoseAnalyzeDates* function.
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](http://bioconductor.org/packages/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 listingbelow. The *getData* function
allow users to access the downloaded data (see lines 22-24 of listing
below as a S4Vector object.
```{R, eval=FALSE, include=TRUE}
library(RTCGAToolbox)
```
```{R, eval=FALSE, include=TRUE}
# Get the last run dates
lastRunDate <- getFirehoseRunningDates()[1]
# get DNA methylation data, RNAseq2 and clinical data for GBM
gbm.data <- getFirehoseData(dataset = "GBM",
runDate = lastRunDate, gistic2_Date = getFirehoseAnalyzeDates(1),
Methylation = FALSE, Clinic = TRUE,
RNAseq2_Gene_Norm = FALSE, Mutation = TRUE,
fileSizeLimit = 10000)
gbm.mut <- getData(gbm.data,"Mutations")
gbm.clin <- getData(gbm.data,"Clinical")
```
Finnaly, [RTCGAToolbox](http://bioconductor.org/packages/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) [@mermel2011gistic2].
```{R, eval=FALSE, message=FALSE, warning=FALSE, include=TRUE}
# Download GISTIC results
lastAnalyseDate <- getFirehoseAnalyzeDates(1)
gistic <- getFirehoseData("GBM",gistic2_Date = lastAnalyseDate)
# get GISTIC results
gistic.allbygene <- getData(gistic,type = "GISTIC", CN = "All")
gistic.thresholedbygene <- getData(gistic,type = "GISTIC", CN = "Thresholed")
```
```{R, eval=TRUE, message=FALSE, warning=FALSE, include=TRUE}
data(GBMGistic)
gistic.allbygene[1:5,1:5]
gistic.thresholedbygene[1:5,1:5]
```
## Genomic analysis
Copy number variations (CNVs) have 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. CNVs 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 analysed 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
segmented CNV from SNP array (Affymetrix Genome-Wide Human SNP Array
6.0).
### Pre-Processing Data {#pre-processing-data .unnumbered}
The only CNV platform available for GBM in TCGA is
"Affymetrix Genome-Wide Human SNP Array 6.0". Using TCGAbiolinks, we
queried for CNV SNP6 level 3 data for 20 primary solid tumor samples in the
legacy database. Data for selected samples were downloaded and prepared
in a rse objects (RangedSummarizedExperiment).
```{r results='hide', eval=FALSE, message=FALSE, warning=FALSE, include=TRUE}
library(TCGAbiolinks)
# Select common CN technology available for GBM and LGG
#############################
## CNV data pre-processing ##
#############################
query.gbm.nocnv <- GDCquery(project = "TCGA-GBM",
data.category = "Copy number variation",
legacy = TRUE,
file.type = "nocnv_hg19.seg",
sample.type = c("Primary solid Tumor"))
# to reduce time we will select only 20 samples
query.gbm.nocnv$results[[1]] <- query.gbm.nocnv$results[[1]][1:20,]
GDCdownload(query.gbm.nocnv, chunks.per.download = 100)
gbm.nocnv <- GDCprepare(query.gbm.nocnv, save = TRUE, save.filename = "GBMnocnvhg19.rda")
```
### 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
[GAIA](http://bioconductor.org/packages/gaia/) [@morganellagaia], 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.
```{r results='hide', eval=FALSE, message=FALSE, warning=FALSE, include=TRUE}
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-==--=--==---=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-==--=--==--
# Retrieve probes meta file from broadinstitute website for hg19
# For hg38 analysis please take a look on:
# https://gdc.cancer.gov/about-data/data-harmonization-and-generation/gdc-reference-files
# File: SNP6 GRCh38 Liftover Probeset File for Copy Number Variation Analysis
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-==--=--==---=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-==--=--==--
gdac.root <- "ftp://ftp.broadinstitute.org/pub/GISTIC2.0/hg19_support/"
file <- paste0(gdac.root, "genome.info.6.0_hg19.na31_minus_frequent_nan_probes_sorted_2.1.txt")
if(!file.exists(basename(file))) downloader::download(file, basename(file))
markersMatrix <- readr::read_tsv(basename(file), col_names = FALSE, col_types = "ccn", progress = FALSE)
save(markersMatrix, file = "markersMatrix.rda", compress = "xz")
```
```{r, echo=TRUE, message=FALSE,warning=FALSE, include=TRUE}
cancer <- "GBM"
message(paste0("Starting ", cancer))
# get objects created above
data(GBMnocnvhg19)
data(markersMatrix)
# Add label (0 for loss, 1 for gain)
cnvMatrix <- cbind(cnvMatrix,Label=NA)
cnvMatrix[cnvMatrix[,"Segment_Mean"] < -0.3,"Label"] <- 0
cnvMatrix[cnvMatrix[,"Segment_Mean"] > 0.3,"Label"] <- 1
cnvMatrix <- cnvMatrix[!is.na(cnvMatrix$Label),]
# Remove "Segment_Mean" and change col.names
cnvMatrix <- cnvMatrix[,-6]
colnames(cnvMatrix) <- c("Sample.Name", "Chromosome", "Start", "End", "Num.of.Markers", "Aberration")
# Substitute Chromosomes "X" and "Y" with "23" and "24"
cnvMatrix[cnvMatrix$Chromosome == "X","Chromosome"] <- 23
cnvMatrix[cnvMatrix$Chromosome == "Y","Chromosome"] <- 24
cnvMatrix$Chromosome <- as.integer(cnvMatrix$Chromosome)
# Recurrent CNV identification with GAIA
colnames(markersMatrix) <- c("Probe.Name", "Chromosome", "Start")
unique(markersMatrix$Chromosome)
markersMatrix[markersMatrix$Chromosome == "X","Chromosome"] <- 23
markersMatrix[markersMatrix$Chromosome == "Y","Chromosome"] <- 24
markersMatrix$Chromosome <- as.integer(markersMatrix$Chromosome)
markerID <- paste(markersMatrix$Chromosome,markersMatrix$Start, sep = ":")
# Removed duplicates
markersMatrix <- markersMatrix[!duplicated(markerID),]
# Filter markersMatrix for common CNV
markerID <- paste(markersMatrix$Chromosome,markersMatrix$Start, sep = ":")
file <- "ftp://ftp.broadinstitute.org/pub/GISTIC2.0/hg19_support/CNV.hg19.bypos.111213.txt"
if(!file.exists(basename(file))) downloader::download(file, basename(file))
commonCNV <- readr::read_tsv(basename(file), progress = FALSE)
commonID <- paste(commonCNV$Chromosome,commonCNV$Start, sep = ":")
markersMatrix_fil <- markersMatrix[!markerID %in% commonID,]
library(gaia)
set.seed(200)
markers_obj <- load_markers(as.data.frame(markersMatrix_fil))
nbsamples <- length(unique(cnvMatrix$Sample))
cnv_obj <- load_cnv(cnvMatrix, markers_obj, nbsamples)
suppressWarnings({
results <- runGAIA(cnv_obj,
markers_obj,
output_file_name = paste0("GAIA_",cancer,"_flt.txt"),
aberrations = -1, # -1 to all aberrations
chromosomes = 9, # -1 to all chromosomes
approximation = TRUE, # Set to TRUE to speed up the time requirements
num_iterations = 5000, # Reduced to speed up the time requirements
threshold = 0.25)
})
# Set q-value threshold
# Use a smalled value for your analysis. We set this as high values
# due to the small number of samples which did not reproduced
# results with smaller q-values
threshold <- 0.3
# Plot the results
RecCNV <- t(apply(results,1,as.numeric))
colnames(RecCNV) <- colnames(results)
RecCNV <- cbind(RecCNV, score = 0)
minval <- format(min(RecCNV[RecCNV[,"q-value"] != 0,"q-value"]), scientific = FALSE)
minval <- substring(minval,1, nchar(minval) - 1)
RecCNV[RecCNV[,"q-value"] == 0,"q-value"] <- as.numeric(minval)
RecCNV[,"score"] <- sapply(RecCNV[,"q-value"],function(x) -log10(as.numeric(x)))
RecCNV[RecCNV[,"q-value"] == as.numeric(minval),]
gaiaCNVplot(RecCNV,threshold)
save(results, RecCNV, threshold, file = paste0(cancer,"_CNV_results.rda"))
```
Recurrent amplifications and deletions were identified for GBM, and
represented in chromosomal overview plots by a statistical score
(*$-log_{10}$ corrected p-value* for amplifications and *$log_{10}$
corrected p-value* for deletions). Genomic regions identified as
significantly altered in copy number (*corrected p-value* &lt;
$10^{-4}$) were then annotated to report amplified and deleted genes
potentially related with cancer.
### 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.
```{r , eval = TRUE, message=FALSE,warning=FALSE, include=FALSE}
library(GenomicRanges)
```
```{r , eval = FALSE, message=FALSE,warning=FALSE, include=TRUE}
library(GenomicRanges)
# Get gene information from GENCODE using biomart
genes <- TCGAbiolinks:::get.GRCh.bioMart(genome = "hg19")
genes <- genes[genes$external_gene_id != "" & genes$chromosome_name %in% c(1:22,"X","Y"),]
genes[genes$chromosome_name == "X", "chromosome_name"] <- 23
genes[genes$chromosome_name == "Y", "chromosome_name"] <- 24
genes$chromosome_name <- sapply(genes$chromosome_name,as.integer)
genes <- genes[order(genes$start_position),]
genes <- genes[order(genes$chromosome_name),]
genes <- genes[,c("external_gene_id", "chromosome_name", "start_position","end_position")]
colnames(genes) <- c("GeneSymbol","Chr","Start","End")
genes_GR <- makeGRangesFromDataFrame(genes,keep.extra.columns = TRUE)
save(genes_GR,genes,file = "genes_GR.rda", compress = "xz")
```
```{r , echo=TRUE, message=FALSE,warning=FALSE, include=TRUE}
##############################
## Recurrent CNV annotation ##
##############################
# Get gene information from GENCODE using biomart
data(genes_GR) # downloaded in the previous step (available in TCGAWorkflowData)
load(paste0(cancer,"_CNV_results.rda"))
sCNV <- RecCNV[RecCNV[,"q-value"] <= threshold,c(1:4,6)]
sCNV <- sCNV[order(sCNV[,3]),]
sCNV <- sCNV[order(sCNV[,1]),]
colnames(sCNV) <- c("Chr","Aberration","Start","End","q-value")
sCNV_GR <- makeGRangesFromDataFrame(sCNV,keep.extra.columns = TRUE)
hits <- findOverlaps(genes_GR, sCNV_GR, type = "within")
sCNV_ann <- cbind(sCNV[subjectHits(hits),],genes[queryHits(hits),])
AberrantRegion <- paste0(sCNV_ann[,1],":",sCNV_ann[,3],"-",sCNV_ann[,4])
GeneRegion <- paste0(sCNV_ann[,7],":",sCNV_ann[,8],"-",sCNV_ann[,9])
AmpDel_genes <- cbind(sCNV_ann[,c(6,2,5)],AberrantRegion,GeneRegion)
AmpDel_genes[AmpDel_genes[,2] == 0,2] <- "Del"
AmpDel_genes[AmpDel_genes[,2] == 1,2] <- "Amp"
rownames(AmpDel_genes) <- NULL
save(RecCNV, AmpDel_genes, file = paste0(cancer,"_CNV_results.rda"))
```
```{r results='asis', echo=FALSE, message=FALSE, warning=FALSE}
knitr::kable(head(AmpDel_genes), caption = "Chromosome 9 recurrent deleted genes in LGG")
```
### Visualizing multiple genomic alteration events
In order to visualize multiple genomic alteration events we recommend
using OncoPrint plot which is provided by Bioconductor package
[complexHeatmap](http://bioconductor.org/packages/ComplexHeatmap/)
[@ComplexHeatmap]. The listing below shows how to download
mutation data using *GDCquery\_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. For the columns, an insertion made at the top will
remove the barplot. The final result for adding the annotation to the
bottom is highlighted in the figure .
```{r results='hide', echo=TRUE, message=FALSE, warning=FALSE, eval = FALSE}
LGGmut <- GDCquery_Maf(tumor = "LGG", pipelines = "mutect2")
GBMmut <- GDCquery_Maf(tumor = "GBM", pipelines = "mutect2")
mut <- plyr::rbind.fill(LGGmut, GBMmut)
save(mut,file ="mafMutect2LGGGBM.rda")
```
```{r results='hide', echo=TRUE, message=FALSE, warning=FALSE}
library(ComplexHeatmap)
# recovering data from TCGAWorkflowData package.
data(mafMutect2LGGGBM)
# Filtering mutations in gliomas
EA_pathways <- TCGAbiolinks:::listEA_pathways
Glioma_pathways <- EA_pathways[grep("glioma", tolower(EA_pathways$Pathway)),]
Glioma_signaling <- Glioma_pathways[Glioma_pathways$Pathway == "Glioma Signaling",]
Glioma_signaling_genes <- unlist(strsplit(as.character(Glioma_signaling$Molecules),","))[1:10]
mut <- mut[mut$Hugo_Symbol %in% Glioma_signaling_genes,]
clinical <- plyr::rbind.fill(gbm_clin,lgg_clin)
```
```{r, echo=TRUE, message=FALSE, warning=FALSE}
TCGAvisualize_oncoprint(mut = mut,
gene = Glioma_signaling_genes,
annotation = clinical[, c("bcr_patient_barcode","disease","vital_status","ethnicity")],
annotation.position = "bottom",
rm.empty.columns = FALSE)
```
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.
**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](https://cran.r-project.org/web/packages/circlize/index.html)
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.
Circos plot can illustrate molecular alterations genome-wide or only in
one or more selected chromosomes.
```{r results='hide', eval = FALSE, echo=TRUE, message=FALSE,warning=FALSE}
LGGmut <- GDCquery_Maf(tumor = "LGG", pipelines = "mutect2")
```
```{r results='hide', echo=TRUE, message=FALSE,warning=FALSE}
###############################################
## Genomic aberration overview - Circos plot ##
###############################################
# Retrieve curated mutations for selected cancer (e.g. "LGG")
data(mafMutect2LGGGBM)
# Select only potentially damaging mutations
LGGmut <- LGGmut[LGGmut$Variant_Classification %in% c("Missense_Mutation",
"Nonsense_Mutation",
"Nonstop_Mutation",
"Frame_Shift_Del",
"Frame_Shift_Ins"),]
# Select recurrent mutations (identified in at least two samples)
mut.id <- paste0(LGGmut$Chromosome,":",LGGmut$Start_Position,"-",LGGmut$End_Position,"|",LGGmut$Reference_Allele)
mut <- cbind(mut.id, LGGmut)
# Prepare selected mutations data for circos plot
s.mut <- mut[mut$mut.id %in% unique(mut.id[duplicated(mut.id)]),]
s.mut <- s.mut[,c("Chromosome","Start_Position","End_Position","Variant_Classification","Hugo_Symbol")]
s.mut <- unique(s.mut)
typeNames <- unique(s.mut[,4])
type <- c(4:1)
names(type) <- typeNames[1:4]
Type <- type[s.mut[,4]]
s.mut <- cbind(s.mut,Type)
s.mut <- s.mut[,c(1:3,6,4,5)]
# Load recurrent CNV data for selected cancer (e.g. "LGG")
load("GBM_CNV_results.rda")
# Prepare selected sample CNV data for circos plot
s.cnv <- as.data.frame(RecCNV[RecCNV[,"q-value"] <= threshold,c(1:4,6)])
s.cnv <- s.cnv[,c(1,3,4,2)]
s.cnv[s.cnv$Chromosome == 23,"Chromosome"] <- "X"
s.cnv[s.cnv$Chromosome == 24,"Chromosome"] <- "Y"
Chromosome <- paste0("chr",s.cnv[,1])
s.cnv <- cbind(Chromosome, s.cnv[,-1])
s.cnv[,1] <- as.character(s.cnv[,1])
s.cnv[,4] <- as.character(s.cnv[,4])
s.cnv <- cbind(s.cnv,CNV=1)
colnames(s.cnv) <- c("Chromosome","Start_position","End_position","Aberration_Kind","CNV")
library(circlize)
# Draw genomic circos plot
par(mar=c(1,1,1,1), cex=1)
circos.initializeWithIdeogram()
# Add CNV results
colors <- c("forestgreen","firebrick")
names(colors) <- c(0,1)
circos.genomicTrackPlotRegion(s.cnv, ylim = c(0,1.2),
panel.fun = function(region, value, ...) {
circos.genomicRect(region, value, ytop.column = 2, ybottom = 0,
col = colors[value[[1]]],
border="white")
cell.xlim = get.cell.meta.data("cell.xlim")
circos.lines(cell.xlim, c(0, 0), lty = 2, col = "#00000040")
})
# Add mutation results
colors <- c("blue","green","red","gold")
names(colors) <- typeNames[1:4]
circos.genomicTrackPlotRegion(s.mut, ylim = c(1.2,4.2),
panel.fun = function(region, value, ...) {
circos.genomicPoints(region, value, cex = 0.8, pch = 16, col = colors[value[[2]]], ...)
})
circos.clear()
legend(-0.2, 0.2, bty="n", y.intersp=1, c("Amp","Del"), pch=15,
col=c("firebrick","forestgreen"), title="CNVs", text.font=1, cex=0.4, title.adj=0)
legend(-0.2, 0, bty="n", y.intersp=1, names(colors), pch=16,
col=colors, title="Mutations", text.font=1, cex=0.4, title.adj=0)
```
```{r results='asis', echo=TRUE, message=FALSE,warning=FALSE}
par(mar=c(1,1,1,1),cex=1.5)
circos.par("start.degree" = 90, canvas.xlim = c(0, 1), canvas.ylim = c(0, 1),
gap.degree = 270, cell.padding = c(0, 0, 0, 0), track.margin = c(0.005, 0.005))
circos.initializeWithIdeogram(chromosome.index = "chr17")
circos.par(cell.padding = c(0, 0, 0, 0))
# Add CNV results
colors <- c("forestgreen","firebrick")
names(colors) <- c(0,1)
circos.genomicTrackPlotRegion(s.cnv, ylim = c(0,1.2),
panel.fun = function(region, value, ...) {
circos.genomicRect(region, value, ytop.column = 2, ybottom = 0,
col = colors[value[[1]]],
border="white")
cell.xlim = get.cell.meta.data("cell.xlim")
circos.lines(cell.xlim, c(0, 0), lty = 2, col = "#00000040")
})
# Add mutation results representing single genes
genes.mut <- paste0(s.mut$Hugo_Symbol,"-",s.mut$Type)
s.mutt <- cbind(s.mut,genes.mut)
n.mut <- table(genes.mut)
idx <- !duplicated(s.mutt$genes.mut)
s.mutt <- s.mutt[idx,]
s.mutt <- cbind(s.mutt,num=n.mut[s.mutt$genes.mut])
genes.num <- paste0(s.mutt$Hugo_Symbol," (",s.mutt$num.Freq,")")
s.mutt <- cbind(s.mutt[,-c(6:8)],genes.num)
s.mutt[,6] <- as.character(s.mutt[,6])
s.mutt[,4] <- s.mutt[,4]/2
s.mutt$num.Freq <- NULL
colors <- c("blue","green","red","gold")
names(colors) <- typeNames[1:4]
circos.genomicTrackPlotRegion(s.mutt, ylim = c(0.3,2.2), track.height = 0.05,
panel.fun = function(region, value, ...) {
circos.genomicPoints(region, value, cex = 0.4, pch = 16, col = colors[value[[2]]], ...)
})
circos.genomicTrackPlotRegion(s.mutt, ylim = c(0, 1), track.height = 0.1, bg.border = NA)
i_track = get.cell.meta.data("track.index")
circos.genomicTrackPlotRegion(s.mutt, ylim = c(0,1),
panel.fun = function(region, value, ...) {
circos.genomicText(region, value,
y = 1,
labels.column = 3,
col = colors[value[[2]]],
facing = "clockwise", adj = c(1, 0.5),
posTransform = posTransform.text, cex = 0.4, niceFacing = TRUE)
}, track.height = 0.1, bg.border = NA)
circos.genomicPosTransformLines(s.mutt,
posTransform = function(region, value)
posTransform.text(region,
y = 1,
labels = value[[3]],
cex = 0.4, track.index = i_track+1),
direction = "inside", track.index = i_track)
circos.clear()
legend(0.25, 0.2, bty="n", y.intersp=1, c("Amp","Del"), pch=15,
col=c("firebrick","forestgreen"), title="CNVs", text.font=1, cex=0.4, title.adj=0)
legend(0, 0.2, bty="n", y.intersp=1, names(colors), pch=16,
col=colors, title="Mutations", text.font=1, cex=0.4, title.adj=0)
```
## Transcriptomic analysis
### Pre-Processing Data
The
[LGG](https://gdc-portal.nci.nih.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-LGG%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22files.data_category%22,%22value%22:%5B%22Gene%20expression%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22files.data_type%22,%22value%22:%5B%22Gene%20expression%20quantification%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22files.platform%22,%22value%22:%5B%22Illumina%20HiSeq%22%5D%7D%7D%5D%7D)
and
[GBM](https://gdc-portal.nci.nih.gov/legacy-archive/search/f?filters=%7B%22op%22:%22and%22,%22content%22:%5B%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22cases.project.project_id%22,%22value%22:%5B%22TCGA-GBM%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22files.data_category%22,%22value%22:%5B%22Gene%20expression%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22files.data_type%22,%22value%22:%5B%22Gene%20expression%20quantification%22%5D%7D%7D,%7B%22op%22:%22in%22,%22content%22:%7B%22field%22:%22files.platform%22,%22value%22:%5B%22Illumina%20HiSeq%22%5D%7D%7D%5D%7D)
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 objects (*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
below).
```{R, eval=FALSE, include=TRUE}
query <- GDCquery(project = "TCGA-GBM",
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results",
sample.type = c("Primary solid Tumor"),
legacy = TRUE)
# We will use only 20 samples to make the example faster
query$results[[1]] <- query$results[[1]][1:20,]
GDCdownload(query)
gbm.exp <- GDCprepare(query,
save = TRUE,
summarizedExperiment = TRUE,
save.filename = "GBMIllumina_HiSeq.rda")
query <- GDCquery(project = "TCGA-LGG",
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results",
sample.type = c("Primary solid Tumor"),
legacy = TRUE)
# We will use only 20 samples to make the example faster
query$results[[1]] <- query$results[[1]][1:20,]
GDCdownload(query)
lgg.exp <- GDCprepare(query,
save = TRUE,
summarizedExperiment = TRUE,
save.filename = "LGGIllumina_HiSeq.rda")
```
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
below). 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](http://bioconductor.org/packages/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
normalization [@risso2011gc] and between-lane normalization procedures
to adjust for distributional differences between lanes (e.g., sequencing
depth): global-scaling and full-quantile normalization
[@bullard2010evaluation].
```{r results='asis', echo=TRUE, message=FALSE,warning=FALSE}
data("LGGIllumina_HiSeq")
data("GBMIllumina_HiSeq")
dataPrep_LGG <- TCGAanalyze_Preprocessing(object = lgg.exp,
cor.cut = 0.6,
datatype = "raw_count",
filename = "LGG_IlluminaHiSeq_RNASeqV2.png")
dataPrep_GBM <- TCGAanalyze_Preprocessing(object = gbm.exp,
cor.cut = 0.6,
datatype = "raw_count",
filename = "GBM_IlluminaHiSeq_RNASeqV2.png")
dataNorm <- TCGAanalyze_Normalization(tabDF = cbind(dataPrep_LGG, dataPrep_GBM),
geneInfo = TCGAbiolinks::geneInfo,
method = "gcContent") #18323 672
dataFilt <- TCGAanalyze_Filtering(tabDF = dataNorm,
method = "quantile",
qnt.cut = 0.25) # 13742 672
save(dataFilt, file = paste0("LGG_GBM_Norm_IlluminaHiSeq.rda"))
dataFiltLGG <- subset(dataFilt, select = substr(colnames(dataFilt),1,12) %in% lgg_clin$bcr_patient_barcode)
dataFiltGBM <- subset(dataFilt, select = substr(colnames(dataFilt),1,12) %in% gbm_clin$bcr_patient_barcode)
dataDEGs <- TCGAanalyze_DEA(mat1 = dataFiltLGG,
mat2 = dataFiltGBM,
Cond1type = "LGG",
Cond2type = "GBM",
fdr.cut = 0.01 ,
logFC.cut = 1,
method = "glmLRT")
```
```{r results='asis', echo=TRUE, message=FALSE,warning=FALSE}
# Number of differentially expressed genes (DEG)
nrow(dataDEGs)
```
### EA: enrichment analysis
In order to understand the underlying biological process of DEGs we
performed an enrichment analysis using *TCGAanalyze\_EA\_complete*
function.
```{r results='hide', echo=TRUE, message=FALSE,warning=FALSE,fig.height=10}
#------------------- 4.2 EA: enrichment analysis --------------------
ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes LGG Vs GBM", RegulonList = rownames(dataDEGs))
TCGAvisualize_EAbarplot(tf = rownames(ansEA$ResBP),
filename = NULL,
GOBPTab = ansEA$ResBP,
GOCCTab = ansEA$ResCC,
GOMFTab = ansEA$ResMF,
PathTab = ansEA$ResPat,
nRGTab = rownames(dataDEGs),
nBar = 20)
```
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).]
*TCGAanalyze\_EAbarplot* outputs a bar chart as shown in figure
\ref{eabarplot} with the number of genes for the main categories of three
ontologies (i.e., GO biological process, GO cellular component, and
GO molecular function).
The Figure shows canonical pathways significantly
overrepresented (enriched) by the DEGs. The most statistically
significant canonical pathways identified in the DEGs are ranked
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
[pathview](http://bioconductor.org/packages/pathview/)
[@luo2013pathview] can be used. Listing below 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](http://www.genome.jp/kegg/pathway.html), the species
(’hsa’ for Homo sapiens) and the limits for the gene expression.
```{r results='asis', echo=TRUE, message=FALSE,warning=FALSE}
library(SummarizedExperiment)
GenelistComplete <- rownames(assay(lgg.exp,1))
# DEGs TopTable
dataDEGsFiltLevel <- TCGAanalyze_LevelTab(dataDEGs,"LGG","GBM",
dataFilt[,colnames(dataFiltLGG)],
dataFilt[,colnames(dataFiltGBM)])
dataDEGsFiltLevel$GeneID <- 0
library(clusterProfiler)
# Converting Gene symbol to geneID
eg = as.data.frame(bitr(dataDEGsFiltLevel$mRNA,
fromType="SYMBOL",
toType="ENTREZID",
OrgDb="org.Hs.eg.db"))
eg <- eg[!duplicated(eg$SYMBOL),]
dataDEGsFiltLevel <- dataDEGsFiltLevel[dataDEGsFiltLevel$mRNA %in% eg$SYMBOL,]
dataDEGsFiltLevel <- dataDEGsFiltLevel[order(dataDEGsFiltLevel$mRNA,decreasing=FALSE),]
eg <- eg[order(eg$SYMBOL,decreasing=FALSE),]
# table(eg$SYMBOL == dataDEGsFiltLevel$mRNA) should be TRUE
all(eg$SYMBOL == dataDEGsFiltLevel$mRNA)
dataDEGsFiltLevel$GeneID <- eg$ENTREZID
dataDEGsFiltLevel_sub <- subset(dataDEGsFiltLevel, select = c("GeneID", "logFC"))
genelistDEGs <- as.numeric(dataDEGsFiltLevel_sub$logFC)
names(genelistDEGs) <- dataDEGsFiltLevel_sub$GeneID
```
```{r, eval=FALSE}
library(pathview)
# pathway.id: hsa05214 is the glioma pathway
# limit: sets the limit for gene expression legend and color
hsa05214 <- pathview::pathview(
gene.data = genelistDEGs,
pathway.id = "hsa05214",
species = "hsa",
limit = list(gene=as.integer(max(abs(genelistDEGs)))))
```
The red genes are up-regulated and the green genes are down-regulated in
the LGG samples compared to GBM.
![\label{gliomapathview} Pathways enrichment analysis : glioma pathway. Red defines genes that are up-regulated and green defines genes that are down-regulated.](hsa05214.pathview.png)
### 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: ARACNE [@margolin2006aracne], CLR [@faith2007large], MRNET
[@meyer2007information] and C3NET [@altay2010inferring]. 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
([MINET](http://bioconductor.org/packages/minet/) [@Meyer2008] and
[c3net](https://cran.r-project.org/web/packages/c3net/index.html),
[@altay2010inferring] respectively). Many gene regulatory interactions
have been experimentally validated and published. These ’known’
interactions can be accessed using different tools and databases such as
BioGrid [@Stark01012006] or GeneMANIA [@montojo2010genemania]. 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 below.
validated not validated/non-existing
-------------- ----------- ----------------------------
inferred TP FP
not inferred FN TN
: Confusion matrix, comparing inferred network to network of validated
interactions.<span data-label="table::confusion_matrix"></span>
Different quality measures can then be computed such as the false
positive rate $$fpr=\frac{FP}{FP+TN},$$ the true positive rate (also
called recall) $$tpr=\frac{TP}{TP+FN}$$ and the precision
$$p=\frac{TP}{TP+FP}.$$ 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](http://thebiogrid.org/).
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 one gene set for the LGG data.
```{R, eval=FALSE, include=TRUE}
### read biogrid info (available in TCGAWorkflowData as "biogrid")
### Check last version in https://thebiogrid.org/download.php
file <- "http://thebiogrid.org/downloads/archives/Release%20Archive/BIOGRID-3.4.146/BIOGRID-ALL-3.4.146.tab2.zip"
if(!file.exists(gsub("zip","txt",basename(file)))){
downloader::download(file,basename(file))
unzip(basename(file),junkpaths =TRUE)
}
tmp.biogrid <- read.csv(gsub("zip","txt",basename(file)), header=TRUE, sep="\t", stringsAsFactors=FALSE)
```
```{R, eval=TRUE, include=TRUE}
### plot details (colors & symbols)
mycols <- c('#e41a1c','#377eb8','#4daf4a','#984ea3','#ff7f00','#ffff33','#a65628')
### load network inference libraries
library(minet)
library(c3net)
### deferentially identified genes using TCGAbiolinks
# we will use only a subset (first 50 genes) of it to make the example faster
names.genes.de <- rownames(dataDEGs)[1:30]
data(biogrid)
net.biogrid.de <- getAdjacencyBiogrid(tmp.biogrid, names.genes.de)
mydata <- dataFiltLGG[names.genes.de, ]
### infer networks
t.mydata <- t(mydata)
net.aracne <- minet(t.mydata, method = "aracne")
net.mrnet <- minet(t.mydata)
net.clr <- minet(t.mydata, method = "clr")
net.c3net <- c3net(mydata)
### validate compared to biogrid network
tmp.val <- list(validate(net.aracne, net.biogrid.de),
validate(net.mrnet, net.biogrid.de),
validate(net.clr, net.biogrid.de),
validate(net.c3net, net.biogrid.de))
### plot roc and compute auc for the different networks
dev1 <- show.roc(tmp.val[[1]],cex=0.3,col=mycols[1],type="l")
res.auc <- auc.roc(tmp.val[[1]])
for(count in 2:length(tmp.val)){
show.roc(tmp.val[[count]],device=dev1,cex=0.3,col=mycols[count],type="l")
res.auc <- c(res.auc, auc.roc(tmp.val[[count]]))
}
legend("bottomright", legend=paste(c("aracne","mrnet","clr","c3net"), signif(res.auc,4), sep=": "),
col=mycols[1:length(tmp.val)],lty=1, bty="n" )
# Please, uncomment this line to produce the pdf files.
# dev.copy2pdf(width=8,height=8,device = dev1, file = paste0("roc_biogrid_",cancertype,".pdf"))
```
![ROC with corresponding AUC for inferred GBM networks compared to
BioGrid interactions\label{fig::roc_GBM}](roc_biogrid_GBM.png)
In Figure above, 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.
## 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 stability
[@phillips2008role].
In mammals DNA methylation is found sparsely but globally, distributed
in definite CpG sequences throughout the entire genome; however, there
is an exception. CpG islands (CGIs) which are short interspersed DNA
sequences that are enriched for GC. These islands are normally found in
sites of transcription initiation and their methylation can lead to gene
silencing [@deaton2011cpg].
Thus, the investigation of the DNA methylation is crucial to
understanding regulatory gene networks in cancer as the DNA methylation
represses transcription [@robertson2005dna]. 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
[TCGAbiolinks](http://bioconductor.org/packages/TCGAbiolinks/)
[@TCGAbiolinks]. 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 1-56 of the listing below describes how to make the
data acquisition). We started by checking the mean DNA methylation of
different groups of samples, then performed a DMR in which we search for
regions of possible biological significance, (e.g., 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 has 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](https://support.illumina.com/content/dam/illumina-support/documents/myillumina/90666eaa-0c66-48b4-8199-3be99b2b3ef9/genomestudio_methylation_v1.8_user_guide_11319130_b.pdf).
Therefore, the rs probes were removed (see listing below lines
68). Also, probes in chromosomes X, Y were removed to eliminate
potential artifacts originating from the presence of a different
proportion of males and females [@marabita2013evaluation]. The last
pre-processing steps were to remove probes with at least one NA value
(see listing below lines 65).
After this pre-processing step and using the function
*TCGAvisualize\_meanMethylation* function, we can 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 below lines
70-74).
```{R, eval=FALSE, include=TRUE}
#----------------------------
# Obtaining DNA methylation
#----------------------------
# Samples
lgg.samples <- matchedMetExp("TCGA-LGG", n = 10)
gbm.samples <- matchedMetExp("TCGA-GBM", n = 10)
samples <- c(lgg.samples,gbm.samples)
#-----------------------------------
# 1 - Methylation
# ----------------------------------
# For methylation it is quicker in this case to download the tar.gz file
# and get the samples we want instead of downloading files by files
query <- GDCquery(project = c("TCGA-LGG","TCGA-GBM"),
data.category = "DNA methylation",
platform = "Illumina Human Methylation 450",
legacy = TRUE,
barcode = samples)
GDCdownload(query)
met <- GDCprepare(query, save = FALSE)
# We will use only chr9 to make the example faster
met <- subset(met,subset = as.character(seqnames(met)) %in% c("chr9"))
# This data is avaliable in the package
save(met, file = "met20SamplesGBMLGGchr9.rda")
```
```{r echo=TRUE, message=FALSE,warning=FALSE}
data(met20SamplesGBMLGGchr9)
#----------------------------
# Mean methylation
#----------------------------
# Plot a barplot for the groups in the disease column in the
# summarizedExperiment object
# remove probes with NA (similar to na.omit)
met <- subset(met,subset = (rowSums(is.na(assay(met))) == 0))
TCGAvisualize_meanMethylation(met,
groupCol = "disease_type",
group.legend = "Groups",
filename = NULL,
print.pvalue = TRUE)
```
The figure above 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.
### 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 below). 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, the function
calculates the difference between the mean DNA methylation (mean of the
beta-values) of each group for each probe. Second, it test for
differential expression between two groups using the wilcoxon test
adjusting by the Benjamini-Hochberg method. Arguments of
TCGAanalyze\_DMR was set to require a minimum absolute beta-values
difference of 0.15 and an adjusted p-value of less than $0.05$.
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.
```{r results='hide', echo=TRUE, message=FALSE,warning=FALSE}
#------- Searching for differentially methylated CpG sites ----------
met <- TCGAanalyze_DMR(met,
groupCol = "disease_type", # a column in the colData matrix
group1 = "Glioblastoma Multiforme", # a type of the disease type column
group2 = "Brain Lower Grade Glioma", # a type of the disease column
p.cut = 0.05,
diffmean.cut = 0.15,
save = FALSE,
legend = "State",
plot.filename = "LGG_GBM_metvolcano.png",
cores = 1
)
```
Figure below shows the
volcano plot produced by listing below. This plot aids the
user in selecting relevant thresholds, as we search for candidate
biological DMRs.
![Volcano plot: searching for differentially methylated
CpG sites (x-axis:difference of mean DNA methylation, y-axis:
statistical
significance)](LGG_GBM_metvolcano.png)
To visualize the level of DNA methylation of these probes across all
samples, we use heatmaps that can be generate by the Bioconductor
package
[complexHeatmap](http://bioconductor.org/packages/ComplexHeatmap/)
[@ComplexHeatmap]. To create a heatmap using the
[complexHeatmap](http://bioconductor.org/packages/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 below shows
the code to produce the heatmap of a DNA methylation datum.
```{r results='hide', echo=TRUE, message=FALSE,warning=FALSE}
#--------------------------
# DNA Methylation heatmap
#-------------------------
library(ComplexHeatmap)
clinical <- plyr::rbind.fill(gbm_clin,lgg_clin)
# get the probes that are Hypermethylated or Hypomethylated
# met is the same object of the section 'DNA methylation analysis'
status.col <- "status.Glioblastoma.Multiforme.Brain.Lower.Grade.Glioma"
sig.met <- met[values(met)[,status.col] %in% c("Hypermethylated","Hypomethylated"),]
# top annotation, which sampples are LGG and GBM
# We will add clinical data as annotation of the samples
# we will sort the clinical data to have the same order of the DNA methylation matrix
clinical.order <- clinical[match(substr(colnames(sig.met),1,12),clinical$bcr_patient_barcode),]
ta = HeatmapAnnotation(df = clinical.order[,c("disease","gender","vital_status","race")],
col = list(disease = c("LGG" = "grey", "GBM" = "black"),
gender = c("male" = "blue","female" = "pink")))
# row annotation: add the status for LGG in relation to GBM
# For exmaple: status.gbm.lgg Hypomethyated means that the
# mean DNA methylation of probes for lgg are hypomethylated
# compared to GBM ones.
ra = rowAnnotation(df = values(sig.met)[status.col],
col = list("status.Glioblastoma.Multiforme.Brain.Lower.Grade.Glioma" =
c("Hypomethylated" = "orange",
"Hypermethylated" = "darkgreen")),
width = unit(1, "cm"))
heatmap <- Heatmap(assay(sig.met),
name = "DNA methylation",
col = matlab::jet.colors(200),
show_row_names = FALSE,
cluster_rows = TRUE,
cluster_columns = FALSE,
show_column_names = FALSE,
bottom_annotation = ta,
column_title = "DNA Methylation")
# Save to pdf
png("heatmap.png",width = 600, height = 400)
draw(heatmap, annotation_legend_side = "bottom")
dev.off()
```
![](heatmap.png)
### Motif analysis
Motif discovery is the attempt to extract small sequence signals hidden
within largely non-functional intergenic sequences. These small sequence
nucleotide signals (6-15 bp) might have a biological significance as
they can be used to control the expression of genes. These sequences are
called Regulatory motifs. The Bioconductor package
[rGADEM](http://bioconductor.org/packages/rGADEM/)
[@droit2015rgadem; @li2009gadem] 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 below). An object will be returned which
contains all relevant information about your motif analysis (i.e.,
sequence consensus, pwm, chromosome, p-value, etc).
Using Bioconductor package
[motifStack](http://bioconductor.org/packages/motifStack/)
[@ou2013motifstack] it is possible to generate a graphic representation
of multiple motifs with different similarity scores.
```{r results='asis', echo=TRUE, message=FALSE,warning=FALSE}
library(rGADEM)
library(BSgenome.Hsapiens.UCSC.hg19)
library(motifStack)
probes <- rowRanges(met)[values(met)[,status.col] %in% c("Hypermethylated" ,"Hypomethylated") ,]
# Get hypo/hyper methylated probes and make a 200bp window
# surrounding each probe.
sequence <- RangedData(space = as.character(seqnames(probes)),
IRanges(start = start(ranges(probes)) - 100,
end = start(ranges(probes)) + 100), strand = "*")
#look for motifs
gadem <- GADEM(sequence, verbose = FALSE, genome = Hsapiens)
# How many motifs were found?
nMotifs(gadem)
# get the number of occurences
nOccurrences(gadem)
# view all sequences consensus
consensus(gadem)
# Print motif
pwm <- getPWM(gadem)
pfm <- new("pfm",mat=pwm[[1]],name="Novel Site 1")
plotMotifLogo(pfm)
# Number of instances of motif 1?
length(gadem@motifList[[1]]@alignList)
```
After [rGADEM](http://bioconductor.org/packages/rGADEM/) returns it’s
results, the user can use
[MotIV](http://bioconductor.org/packages/MotIV/) package
[@mercier2014motiv; @mahony2007dna; @mahony2007stamp; @mercier2011integrated]
to start the motif matching analysis (line 4 of listing below).
```{r results='asis', echo=TRUE,message=FALSE,warning=FALSE}
library(MotIV)
# motif matching analysis
analysis.jaspar <- motifMatch(pwm)
summary(analysis.jaspar)
plot(analysis.jaspar, ncol=2, top=5, rev=FALSE, main="", bysim=TRUE, cex=0.4)
# visualize the quality of the results looking into the alignments
# E-value give an estimation of the match.
alignment <- viewAlignments(analysis.jaspar)
print(alignment[[1]])
```
## 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
[@phillips2008role; @shi2014integrative; @rhodes2005integrative]. In
this section, we will introduce a Bioconductor package called
[ELMER](http://bioconductor.org/packages/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](http://www.encodeproject.org/) and
[Roadmap](http://www.roadmapepigenomics.org/).
### Integration of DNA methylation & gene expression data
After finding differentially methylated CpG sites, one might ask whether
nearby genes also undergo a change in expression either an increase or a
decrease. DNA methylation at promoters of genes has 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 applicable
for studies of DNA methylation and gene expression
[@noushmehr2010identification]. 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 samples [@siegmund2011statistical]. We used the
*TCGAvisualize\_starburst* function to create a starburst plot. The
$log_{10}$ (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. 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 below).
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.
```{R, eval=FALSE, include=TRUE}
#------------------- Starburst plot ------------------------------
starburst <- TCGAvisualize_starburst(met, # DNA methylation with results
dataDEGs, # DEG results
group1 = "Glioblastoma Multiforme",
group2 = "Brain Lower Grade Glioma",
filename = "starburst.png",
met.p.cut = 0.05,
exp.p.cut = 10^-2,
diffmean.cut = 0.15,
logFC.cut = 1,
width = 15,height = 10,
names = TRUE)
```
```{r results='asis', echo=FALSE, message=FALSE, warning=FALSE}
#------------------- Starburst plot ------------------------------
starburst <- TCGAvisualize_starburst(met, # DNA methylation with results
dataDEGs, # DEG results
group1 = "Glioblastoma Multiforme",
group2 = "Brain Lower Grade Glioma",
filename = "starburst.png",
met.p.cut = 0.05,
exp.p.cut = 10^-2,
diffmean.cut = 0.15,
logFC.cut = 1,
width = 15,height = 10,
names = TRUE)
```
![](starburst.png)
Due to the time constraints, with the reduced number of samples, genes and probes,
the result showed previously might not be very descriptive. A results with a
large data set can be found in [TCGAbiolinks vignette](https://bioconductor.org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.html#tcgavisualize_starburst:_integration_of_gene_expression_and_dna_methylation_data).
### 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 the table below.
```{r table4, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'}
tabl <- "
| Histone marks | Role |
|:----------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|
| Histone H3 lysine 4 trimethylation (H3K4me3) | Promoter regions [@heintzman2007distinct,@bernstein2005genomic] |
| Histone H3 lysine 4 monomethylation (H3K4me1) | Enhancer regions [@heintzman2007distinct] |
| Histone H3 lysine 36 trimethylation (H3K36me3) | Transcribed regions |
| Histone H3 lysine 27 trimethylation (H3K27me3) | Polycomb repression [@bonasio2010molecular] |
| Histone H3 lysine 9 trimethylation (H3K9me3) | Heterochromatin regions [@peters2003partitioning] |
| Histone H3 acetylated at lysine 27 (H3K27ac) | Increase activation of genomic elements [@heintzman2009histone,@rada2011unique,@creyghton2010histone] |
| Histone H3 lysine 9 acetylation (H3K9ac) | Transcriptional activation [@nishida2006histone] |
"
cat(tabl)
```
Besides, ChIP-seq data exists in the ROADMAP database and can be obtained through the
[AnnotationHub](http://bioconductor.org/packages/AnnotationHub/) package [@annotationHub]
or from [Roadmap web portal](http://egg2.wustl.edu/roadmap/webportal/processed_data.html).
The table below shows the description for all the roadmap files that are available through AnnotationHub.
```{r table5, echo=FALSE, message=FALSE, warnings=FALSE, results='asis'}
tabl <- "
| File | Description |
|:----------------------------------:|:----------------------------------------------------------------------:|
| fc.signal.bigwig | Bigwig File containing fold enrichment signal tracks |
| pval.signal.bigwig | Bigwig File containing -log10(p-value) signal tracks |
| hotspot.fdr0.01.broad.bed.gz | Broad domains on enrichment for DNase-seq for consolidated epigenomes |
| hotspot.broad.bed.gz | Broad domains on enrichment for DNase-seq for consolidated epigenomes |
| broadPeak.gz | Broad ChIP-seq peaks for consolidated epigenomes |
| gappedPeak.gz | Gapped ChIP-seq peaks for consolidated epigenomes |
| narrowPeak.gz | Narrow ChIP-seq peaks for consolidated epigenomes |
| hotspot.fdr0.01.peaks.bed.gz | Narrow DNasePeaks for consolidated epigenomes |
| hotspot.all.peaks.bed.gz | Narrow DNasePeaks for consolidated epigenomes |
| .macs2.narrowPeak.gz | Narrow DNasePeaks for consolidated epigenomes |
| coreMarks_mnemonics.bed.gz | 15 state chromatin segmentations |
| mCRF_FractionalMethylation.bigwig | MeDIP/MRE(mCRF) fractional methylation calls |
| RRBS_FractionalMethylation.bigwig | RRBS fractional methylation calls |
| WGBS_FractionalMethylation.bigwig | Whole genome bisulphite fractional methylation calls |
"
cat(tabl)
```
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 article [@ChIP-seqbioc]. The first step shown in listing
below 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 (keywords can be seen in the [summary
table](https://docs.google.com/spreadsheets/d/1yikGx4MsO9Ei36b64yOy9Vb6oPC5IBGlFbYEt-N6gOM/edit#gid=15))
and narrowPeak is selecting the type of file. The data downloaded is a
processed data from an integrative Analysis of 111 reference human
epigenomes [@kundaje2015integrative].
```{r results='hide', eval=TRUE, echo=TRUE, message=FALSE,warning=FALSE}
library(ChIPseeker)
library(pbapply)
library(ggplot2)
```
```{r results='hide', eval=FALSE, echo=TRUE, message=FALSE,warning=FALSE}
#------------------ Working with ChipSeq data ---------------
# Step 1: download histone marks for a brain and non-brain samples.
#------------------------------------------------------------
# loading annotation hub database
library(AnnotationHub)
setAnnotationHubOption("CACHE", "/liftrroot/")
ah = AnnotationHub()
# Searching for brain consolidated epigenomes in the roadmap database
bpChipEpi_brain <- query(ah , c("EpigenomeRoadMap", "narrowPeak", "chip", "consolidated","brain","E068"))
# Get chip-seq data
histone.marks <- pblapply(names(bpChipEpi_brain), function(x) {ah[[x]]})
names(histone.marks) <- names(bpChipEpi_brain)
# OBS: histone.marks is available in TCGAWorkflowData package
```
The [Chipseeker](http://bioconductor.org/packages/ChIPseeker/) package
[@yu2015chipseeker] 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,
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 below
shows an example of code to plot the average profile.
To help the user better understand the regions found in the DMR
analysis, we downloaded histone marks specific for brain tissue using
the AnnotationHub package that can access the Roadmap database.
Next, the Chipseeker was used to visualize how
histone modifications are enriched onto hypomethylated and
hypermethylated regions, (listing below). The
enrichment heatmap and the average profile of peaks binding to those
regions.
```{r results='hide', echo=TRUE, message=FALSE,warning=FALSE}
data(histoneMarks)
# Create a GR object based on the hypo/hypermethylated probes.
probes <- keepStandardChromosomes(rowRanges(met)[values(met)[,status.col] %in% c("Hypermethylated","Hypomethylated"),])
# Defining a window of 3kbp - 3kbp_probe_3kbp
ranges(probes) <- IRanges(start = c(start(ranges(probes)) - 3000), end = c(start(ranges(probes)) + 3000))
### Profile of ChIP peaks binding to TSS regions
# First of all, for calculate the profile of ChIP peaks binding to TSS regions, we should
# prepare the TSS regions, which are defined as the flanking sequence of the TSS sites.
# Then align the peaks that are mapping to these regions, and generate the tagMatrix.
tagMatrixList <- pbapply::pblapply(histone.marks, function(x) {
getTagMatrix(keepStandardChromosomes(x), windows = probes, weightCol = "score")
})
# change nanmes retrieved with the following command: basename(bpChipEpi_brain$title)
names(tagMatrixList) <- c("H3K4me1","H3K4me3", "H3K9ac", "H3K9me3", "H3K27ac", "H3K27me3", "H3K36me3")
```
To plot the enrichment heatmap use the function `tagHeatmap`
```{R, eval=FALSE, include=TRUE}
tagHeatmap(tagMatrixList, xlim=c(-3000, 3000),color = NULL)
```
```{r results='asis', echo=FALSE, message=FALSE,warning=FALSE}
tagHeatmap(tagMatrixList, xlim=c(-3000, 3000),color = NULL)
```
To plot the average profile of peaks binding to those region use `plotAvgProf`:
```{R, eval=FALSE, include=TRUE}
p <- plotAvgProf(tagMatrixList, xlim = c(-3000,3000), xlab = "Genomic Region (5'->3', centered on CpG)")
# We are centreing in the CpG instead of the TSS. So we'll change the labels manually
p <- p + scale_x_continuous(breaks=c(-3000,-1500,0,1500,3000),labels=c(-3000,-1500,"CpG",1500,3000))
library(ggthemes)
p + theme_few() + scale_colour_few(name="Histone marks") + guides(colour = guide_legend(override.aes = list(size=4)))
```
```{r results='asis', echo=FALSE, message=FALSE,warning=FALSE}
p <- plotAvgProf(tagMatrixList, xlim = c(-3000,3000), xlab = "Genomic Region (5'->3', centered on CpG)")
# We are centreing in the CpG instead of the TSS. So we'll change the labels manually
if (requireNamespace("ggplot2", quietly = TRUE))
p <- p + ggplot2::scale_x_continuous(breaks=c(-3000,-1500,0,1500,3000),
labels=c(-3000,-1500,"CpG",1500,3000))
if (requireNamespace("ggthemes", quietly = TRUE))
p + ggthemes::theme_few() +
ggthemes::scale_colour_few(name="Histone marks") +
ggplot2::guides(colour = guide_legend(override.aes = list(size=4)))
```
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 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.
Recently, many studies suggest that enhancers play a major role as
regulators of cell-specific phenotypes leading to alteration in
transcriptomes realated to diseases
[@giorgio2015large; @groschel2014single; @sur2012mice; @yao2015demystifying].
In order to investigate regulatory enhancers that can be located at long
distances upstream or downstream of target genes Bioconductor offer the
[Enhancer Linking by Methylation/Expression Relationship
(ELMER)](http://bioconductor.org/packages/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
sets [@yao2015inferring].
[ELMER](http://bioconductor.org/packages/ELMER/) analysis have 5 main
steps:
1. Identify distal enhancer probes on HM450K.
2. Identify distal enhancer probes with significantly different DNA
methyaltion level in control group and experiment group.
3. Identify putative target genes for differentially methylated distal
enhancer probes.
4. Identify enriched motifs for the distal enhancer probes which are
significantly differentially methylated and linked to putative
target gene.
5. Identify regulatory TFs whose expression associate with DNA
methylation at motifs.
This section shows how to use ELMER to analyze TCGA data using as
example LGG and GBM samples.
### Preparing the data for ELMER package {#preparing-the-data-for-elmer-package .unnumbered}
After downloading the data with TCGAbiolinks package, some steps are
still required to use TCGA data with
[ELMER](http://bioconductor.org/packages/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 below shows how to use
[TCGAbiolinks](http://bioconductor.org/packages/TCGAbiolinks/)
[@TCGAbiolinks] to search, download and prepare the data for the
[ELMER](http://bioconductor.org/packages/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. This samples are the same used in the previous steps.
```{R, eval=FALSE, include=TRUE}
#----------- 8.3 Identification of Regulatory Enhancers -------
library(TCGAbiolinks)
# Samples: primary solid tumor w/ DNA methylation and gene expression
lgg.samples <- matchedMetExp("TCGA-LGG", n = 10)
gbm.samples <- matchedMetExp("TCGA-GBM", n = 10)
samples <- c(lgg.samples,gbm.samples)
#-----------------------------------
# 1 - Methylation
# ----------------------------------
query.met <- GDCquery(project = c("TCGA-LGG","TCGA-GBM"),
data.category = "DNA methylation",
platform = "Illumina Human Methylation 450",
legacy = TRUE,
barcode = samples)
GDCdownload(query.met)
met <- GDCprepare(query.met, save = FALSE)
met <- subset(met,subset = as.character(GenomicRanges::seqnames(met)) %in% c("chr9"))
met.elmer <- TCGAprepare_elmer(met, platform = "HumanMethylation450")
#-----------------------------------
# 2 - Expression
# ----------------------------------
query.exp <- GDCquery(project = c("TCGA-LGG","TCGA-GBM"),
data.category = "Gene expression",
data.type = "Gene expression quantification",
platform = "Illumina HiSeq",
file.type = "results",
legacy = TRUE,
barcode = samples)
GDCdownload(query.exp)
exp.lgg <- GDCprepare(query.exp, save = FALSE)
exp.elmer <- TCGAprepare_elmer(exp.lgg, platform = "IlluminaHiSeq_RNASeqV2")
save(exp.elmer, met.elmer, gbm.samples, lgg.samples, file = "elmer.example.rda", compress = "xz")
```
Finally, the [ELMER](http://bioconductor.org/packages/ELMER/) input is a
mee object that contains a DNA methylation matrix, an 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
object 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 both the experimental group (LGG) and
control group (GBM).
```{r, message=FALSE,warning=FALSE}
library(ELMER)
geneAnnot <- txs()
geneAnnot$GENEID <- paste0("ID",geneAnnot$GENEID)
geneInfo <- promoters(geneAnnot,upstream = 0, downstream = 0)
probe <- get.feature.probe()
# Recover the data created in the last step
data(elmerExample)
# create mee object, use @ to access the matrices inside the object
mee <- fetch.mee(meth = met.elmer[1:800,],
exp = exp.elmer,
TCGA = TRUE,
probeInfo = probe,
geneInfo = geneInfo)
# Relabel GBM samples in the mee object: GBM is control
mee@sample$TN[mee@sample$ID %in% gbm.samples] <- "Control"
```
## ELMER analysis
After preparing the data into a mee object, we executed the five
[ELMER](http://bioconductor.org/packages/ELMER/) steps for both the hypo
(distal enhancer probes hypomethylated in the LGG group) and hyper
(distal enhancer probes hypermethylated in the LGG group) direction. The
code is shown below. A description of how these distal enhacer probes
are identified is found in the
[ELMER.data](https://bioconductor.org/packages/release/data/experiment/vignettes/ELMER.data/inst/doc/vignettes.pdf)
vignette.
```{r, warning=FALSE}
cores <- 1 # you can use more cores if you want
# Available directions are hypo and hyper, we will use only hypo
# due to speed constraint
direction <- c("hyper")
dir.out <- paste0("elmer/",direction)
dir.create(dir.out, showWarnings = FALSE, recursive = TRUE)
#--------------------------------------
# STEP 3: Analysis |
#--------------------------------------
# Step 3.1: Get diff methylated probes |
#--------------------------------------
message("Get diff methylated probes")
Sig.probes <- get.diff.meth(mee,
cores = cores,
dir.out = dir.out,
diff.dir = direction,
save = FALSE,
pvalue = 0.1) # Please a more strigent pvalue
#-------------------------------------------------------------
# Step 3.2: Identify significant probe-gene pairs |
#-------------------------------------------------------------
# Collect nearby 20 genes for Sig.probes
message("Get nearby genes")
nearGenes <- GetNearGenes(TRange=getProbeInfo(mee, probe=Sig.probes$probe),
cores=cores,
geneNum = 4, # defaul is 20
geneAnnot=getGeneInfo(mee))
message("Get anti correlated probes-genes")
pair <- get.pair(mee = mee,
probes = na.omit(Sig.probes$probe),
nearGenes = nearGenes,
permu.dir = paste0(dir.out,"/permu"),
dir.out = dir.out,
cores = cores,
label = direction,
permu.size = 5, # For significant results use 10000
Pe = 0.5) # For significant results use 0.001
Sig.probes.paired <- pair$Probe
#-------------------------------------------------------------
# Step 3.3: Motif enrichment analysis on the selected probes |
#-------------------------------------------------------------
if(length(Sig.probes.paired) > 0 ){
#-------------------------------------------------------------
# Step 3.3: Motif enrichment analysis on the selected probes |
#-------------------------------------------------------------
enriched.motif <- get.enriched.motif(probes=Sig.probes.paired,
dir.out=dir.out,
# Default value for min.incidence is 10
# we set 6 because we have only a subset
# of probes (only chr 9)
min.incidence = 5,
label=direction,
background.probes = probe$name)
# One of the output from the previous function is a file with the motif, OR and Number of probes
# It will be used for plotting purposes
motif.enrichment <- read.csv(paste0(dir.out,"/getMotif.",direction, ".motif.enrichment.csv"))
if(length(enriched.motif) > 0){
#-------------------------------------------------------------
# Step 3.4: Identifying regulatory TFs |
#-------------------------------------------------------------
message("get.TFs")
TF <- get.TFs(mee = mee,
enriched.motif = enriched.motif,
dir.out = dir.out,
cores = cores,
label = direction)
# One of the output from the previous function is a file with the raking of TF,
# for each motif. It will be used for plotting purposes
TF.meth.cor <- get(load(
paste0(dir.out,"/getTF.",direction,".TFs.with.motif.pvalue.rda")
))
}
}
```
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 for the hyper direction (probes hypermethylated in
LGG group compared to the GBM group) is found in the Figure
below.
```{r, echo=TRUE, message=FALSE, warnings=FALSE, results='asis',fig.height=10}
motif.enrichment.plot(motif.enrichment = motif.enrichment, save = FALSE)
```
We selected 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 both values are the
default from the ELMER package.
```{r tableTF, echo=FALSE, message=FALSE, warning=FALSE, results='asis'}
as.data.frame(TF)
```
After finding the enriched motifs,
[ELMER](http://bioconductor.org/packages/ELMER/) identifies regulatory
transcription factors (TFs) whose expression is associated with DNA
methylation at motifs. [ELMER](http://bioconductor.org/packages/ELMER/)
automatically creates a TF ranking plot for each enriched motif. This
plot shows the TF ranking plots based on the association score
$(-log(P value))$ between TF expression and DNA methylation of the
motif. We can see in Figure below that the top three TFs that are
associated with a motif found.
```{R, eval=FALSE, include=TRUE}
grid:TF.rank.plot(motif.pvalue=TF.meth.cor, motif="AP1", save=FALSE)
```
```{r results='asis', echo=FALSE, message=FALSE,warning=FALSE, fig.align='center',fig.height=8,fig.width=8}
gg <- TF.rank.plot(motif.pvalue=TF.meth.cor, motif=TF$motif[1], save=FALSE)
grid::grid.draw(gg[[1]])
```
The output of this step is a data frame with the following columns:
1. motif: the names of motif.
2. top.potential.TF: the highest ranking upstream TFs which are known
recognized the motif.
3. potential.TFs: TFs which are within top 5% list and are known
recognized the motif. top5percent: all TFs which are within top 5%
list considered candidate upstream regulators
Also, for each motif we can take a look at the three most relevant TFs.
For example, Figure below shows the average DNA methylation level
of sites with the first motif plotted against the expression of some top TFs
associated with it. We can see that the experiment
group (LGG samples) has a lower average methylation level of sites with
the motif plotted and a higher average expression of the TFs.
```{r results='asis', echo=TRUE, message=FALSE, warning=FALSE}
png("TF.png",width = 800, height = 400)
scatter.plot(mee, category="TN", save=FALSE, lm_line=TRUE,
byTF=list(TF=unlist(stringr::str_split(TF[1,"top_5percent"],";"))[1:4],
probe=enriched.motif[[TF$motif[1]]]))
dev.off()
```
![](TF.png)
# Conclusion
This workflow outlines how one can use specific Bioconductor packages
for the analysis of cancer genomics and epigenomics data derived from
the TCGA. In addition, we highlight the importance of using ENCODE and
Roadmap data to inform on the biology of the non-coding elements defined
by functional roles in gene regulation. We introduced
[TCGAbiolinks](http://bioconductor.org/packages/TCGAbiolinks/) and
[RTCGAToolbox](http://bioconductor.org/packages/RTCGAToolbox/)
Bioconductor packages in order to illustrate how one can acquire TCGA
specific data, followed by key steps for genomics analysis using
[GAIA](http://bioconductor.org/packages/gaia/) package, for
transcriptomic analysis using
[TCGAbiolinks](http://bioconductor.org/packages/TCGAbiolinks/),
[pathview](http://bioconductor.org/packages/pathview/) packages and for
DNA methylation analysis using
[TCGAbiolinks](http://bioconductor.org/packages/TCGAbiolinks/) package.
An inference of gene regulatory networks was also introduced by
[MINET](http://bioconductor.org/packages/minet/) package. Finally, we
introduced Bioconductor packages
[AnnotationHub](http://bioconductor.org/packages/AnnotationHub/),
[ChIPSeeker](http://bioconductor.org/packages/ChIPseeker/),
[ComplexHeatmap](http://bioconductor.org/packages/ComplexHeatmap/), and
[ELMER](http://bioconductor.org/packages/ELMER/) to illustrate how one
can acquire ENCODE/Roadmap data and integrate these data with the
results obtained from analyzing TCGA data to identify and characterize
candidate regulatory enhancers associated with cancer.
```{r}
sessionInfo()
```
# Author contributions {#author-contributions .unnumbered}
HN conceived the study. HN, MC and GB provided direction on the design
of the Transcriptomics, Genomics, master regulatory networks and DNA
methylation workflows. TCS developed and tested sections "Experimental
data", "DNA methylation analysis", "Motif analysis" and "Integrative
analysis". AC developed and tested section "Transcriptomic analysis". CO
developed and tested the section "Inference of gene regulatory
networks". FDA developed and tested section "Genomic analysis". TCS, AC,
CO, and FDA prepared the first draft of the manuscript. All authors were
involved in the revision of the draft manuscript and have agreed to the
final content. Also, AC, TS, CO, MC, GB and HN are authors of the
TCGAbiolinks package and MC is author of the GAIA package.
# Competing interests {#competing-interests .unnumbered}
No competing interests were disclosed.
# Grant information {#grant-information .unnumbered}
The project was supported by the São Paulo Research Foundation
([FAPESP](http://www.fapesp.br/)) (2015/02844-7 and 2016/01389-7 to
T.C.S. & H.N. and 2015/07925-5 to H.N.), the [BridgeIRIS
project](http://mlg.ulb.ac.be/BridgeIRIS), funded by INNOVIRIS, Region
de Bruxelles Capitale, Brussels, Belgium, and by GENomic profiling of
Gastrointestinal Inflammatory-Sensitive CANcers
([GENGISCAN](http://mlg.ulb.ac.be/GENGISCAN)), Belgian FNRS PDR
(T100914F to G.B.). Funding for open access charge: São Paulo Research
Foundation (FAPESP) (2015/07925-5).
# Acknowledgements {#acknowledgements .unnumbered}
We are grateful to all the authors of the packages used in this article.
Also, we would like to thank The GDC Support Team for the provided help,
which was necessary in order to update the TCGAbiolinks package to use
GDC API.
# References