---
title: "RTCGAToolbox"
author: "Mehmet Kemal Samur"
date: "`r Sys.Date()`"
output:
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    toc: true
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references:
- id: ref1
  title: Comprehensive genomic characterization defines human glioblastoma genes and core pathways
  author:
  - family: Cancer Genome Atlas Research Network
    given:
  journal: Nature
  volume: 455
  number: 7216
  pages: 1061-1068
  issued:
    year: 2008

- id: ref2
  title: GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers
  author:
  - family: Mermel, C. H. and Schumacher, S. E. and Hill, B. and Meyerson, M. L. and Beroukhim, R. and Getz, G
    given:
  journal: Genome Biol
  volume: 12
  number: 4
  pages: R41
  issued:
    year: 2011

- id: ref3
  title: Linear models and empirical bayes methods for assessing differential expression in microarray experiments
  author:
  - family: Smyth, G. K
    given:
  journal: Stat Appl Genet Mol Biol
  volume: 3
  number:
  pages: Article3
  issued:
    year: 2004

- id: ref4
  title: voom\:\ Precision weights unlock linear model analysis tools for RNA-seq read counts
  author:
  - family: Law, C. W. and Chen, Y. and Shi, W. and Smyth, G. K
    given:
  journal: Stat Appl Genet Mol Biol
  volume: 15
  number: 2
  pages: R29
  issued:
    year: 2014

- id: ref5
  title: RCircos\:\ an R package for Circos 2D track plots
  author:
  - family: Zhang, H. and Meltzer, P. and Davis, S
    given:
  journal: BMC Bioinformatics
  volume: 14
  number:
  pages: 244
  issued:
    year: 2013

- id: ref6
  title: RTCGAToolbox\:\ A New Tool for Exporting TCGA Firehose Data
  author:
  - family: Samur MK.
    given:
  journal: Plos ONE
  volume: 9
  number: 9
  pages: e106397
  issued:
    year: 2014

vignette: >
  %\VignetteIndexEntry{Vignette Title}
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---

# Introduction

Managing data from large scale projects such as The Cancer Genome Atlas
(TCGA)[@ref1] for further analysis is an important and time consuming step for
research projects. Several efforts, such as Firehose project, make TCGA
pre-processed data publicly available via web services and data portals but it
requires managing, downloading and preparing the data for following steps. We
developed an open source and extensible R based data client for Firehose Level
3 and Level 4 data and demonstrated its use with sample case studies.
RTCGAToolbox could improve data management for researchers who are interested
with TCGA data. In addition, it can be integrated with other analysis
pipelines for further data analysis.

RTCGAToolbox is open-source and licensed under the GNU General Public License
Version 2.0. All documentation and source code for RTCGAToolbox is freely
available.

Currently, following functions are provided to access datasets and process
datasets.

* Control functions:
    + getFirehoseRunningDates: This function can be called to access valid
stddata run dates. To access data, users have to provide valid dates.
    + getFirehoseAnalyzeDates: This function can be called to access valid
analyze run dates. To access data, users have to provide valid dates. This
function only affects the GISTIC2 [@ref2] processed copy estimate matrices.
    + getFirehoseDatasets: This function can be called to access valid dataset
aliases.
* Data client function:
    + getFirehoseData: This is the core function of the package. Users can
access Firehose processed data via this function. Once it is called, several
steps are realized by the library to access data. Finally this function
returns an S4 object that keeps all the downloaded data.
* Analysis Functions:
    + getDiffExpressedGenes: This function takes "FirehoseData" object as an
input and uses differential gene expression analysis to compare cancer and
normal samples. Function takes "limma"[3-4] package advantages for performing
analysis. In addition, sample and normal population is obtained from TCGA
sample barcodes.
    + getCNGECorrelation: This function calculates the correlation between
gene expression values and copy number data. Users have to download GISTIC2
[@ref2] copy number estimates, as well as the expression data from at least
one platform.
    + getMutationRate: From all samples that have mutation information, this
function calculates the genes' mutation frequency.
    + getSurvival: Performs an univariate survival comparison for individual
genes between high and low expressed sample groups.
    + getReport: Creates a circular pdf figure from differential gene
expression, copy number and mutation information.


# Installation

To install RTCGAToolbox, you can use Bioconductor. Source code is also
available on GitHub. First time users use the following code snippet to
install the package

```{r eval=FALSE}
source("http://bioconductor.org/biocLite.R")
biocLite("RTCGAToolbox")
```

# Data Client
Before getting the data from Firehose pipelines, users have to check valid
dataset aliases, stddata run dates and analyze run dates. To provide valid
information RTCGAToolbox comes with three control functions. Users can list
datasets with "getFirehoseDatasets" function. In addition, users have to
provide stddata run date or/and analyze run date for client function. Valid
dates are accessible via "getFirehoseRunningDates" and
"getFirehoseAnalyzeDates" functions. Below code chunk shows how to list
datasets and dates.

```{r}
library(RTCGAToolbox)

# Valid aliases
getFirehoseDatasets()
```
```{r}
# Valid stddata runs
stddata <- getFirehoseRunningDates()
stddata
```
```{r}
# Valid analysis running dates (will return 3 recent date)
gisticDate <- getFirehoseAnalyzeDates(last=3)
gisticDate
```
When the dates and datasets are determined users can call data client function
("getFirehoseData") to access data. Current version can download multiple data
types except ISOFORM and exon level data due to their huge data size. Below
code chunk will download READ dataset with clinical and mutation data.

```{r eval=TRUE,message=FALSE}
# READ mutation data and clinical data
brcaData <- getFirehoseData(dataset="READ", runDate="20150402",
    forceDownload=TRUE, clinical=TRUE, Mutation=TRUE)
```
Users have to set several parameters to get data they need. Below
"getFirehoseData" options has been explained:

* dataset: Users should set cohort code for the dataset they would like to
download. List can be accessiable via getFirehoseDatasets() like as explained
above.
* runDate: Firehose project provides different data point for cohorts. Users
can list dates by using function above. "getFirehoseRunningDates()"
* gistic2Date: Just like cohorts Firehose project runs their analysis
pipelines to process copy number data with GISTIC2 [@ref2]. Users who want to
get GISTIC2 processed copy number data should set this date. List can be
accessible via "getFirehoseAnalyzeDates()"

Following logic keys are provided for different data types. By default client
only download  clinical data.

* RNAseqGene
* clinical
* miRNASeqGene
* RNAseq2GeneNorm
* CNASNP
* CNVSNP
* CNASeq
* CNACGH
* Methylation
* Mutation
* mRNAArray
* miRNAArray
* RPPAArray

Users can also set following parameters to set client behavior.

* forceDownload: By default RTCGAToolbox checks your working directory before
download data. If you have data in the working directory from previous run it
loads data by using these exports. If you would like to suppress  this and re
download data you can force RTCGAToolbox.
* fileSizeLimit: If you would like to set a limit for downloaded file size you
can use this parameter. Huge data files require longer download time and
memory to load. By default his parameter set as 500MB.
* getUUIDs: Firehose provides TCGA barcodes for every sample. In some cases
users may want to use UUIDs for samples. If this parameter set, then after
processing data RTCGAToolbox gets UUIDs for each barcode.


# Post analysis functions
RTCGAToolbox has analyze functions to provide basic information about
datasets. Analyze function includes differential gene expression analyze,
correlation analysis between CN and GE data, univariate survival analysis,
mutation frequency table and report
figure.

## Toy dataset
Since downloading new dataset takes some time and requires enough space on
hard drive, I will use nonsense data set for following steps.

You can use following code snippet to load toy dataset.
```{r}
data(RTCGASample)
RTCGASample
```

## Differential gene expression
RTCGAToolbox hires voom[@ref4] and limma[@ref3] package functions to preform
differential gene expression analysis between "Normal" and "Cancer" tissue
samples. Every sample which is processed by TCGA project[@ref1] has a
structured barcode number which includes the source of the tissue.
RTCGAToolbox uses the barcode information to divide samples into "Normal" or
"Tumor" groups and perform DGE analysis. Since "voom"[@ref4] requires raw
count for RNASeq data, normalized RNASeq data cannot be used for the analysis.
This function uses all gene expression datasets and returns a list which each
member is "DGEResult" object. Each result object keeps top table from the
genes that have 2 log fold change expression difference and significant
adjusted p value.
This function filters the results as a deafult behaviour using raw p value,
adjusted p value and log fold change. Users can change "adj.pval", "raw.pval"
and "logFC" parameters to refine their results. Also function uses Benjamini &
Hochberg adjustment for p values. For more details about adjment users can
check base adjustment methods by calling "?p.adjust". In addition to filter as
a default behaviour function only draws heatmap for top 100 up and down
regulated genes. Users can also adjust these values by using "hmTopUpN" and
"hmTopDownN" parameters.

```{r}
# Differential gene expression analysis for gene level RNA data.
diffGeneExprs <- getDiffExpressedGenes(dataObject=RTCGASample, DrawPlots=TRUE,
    adj.method="BH", adj.pval=0.05, raw.pval=0.05, logFC=2, hmTopUpN=10,
    hmTopDownN=10)
# Show head for expression outputs
diffGeneExprs
showResults(diffGeneExprs[[1]])
toptableOut <- showResults(diffGeneExprs[[1]])
```
If "DrawPlots" set as FALSE, running code above won't provide any output figure.

Voom + limma: To voom (variance modeling at the observational level) is to
estimate the mean-variance relationship robustly and non-parametrically from
the data. Voom works with log-counts that are normalized for sequence depth,
in particular with log-counts per million (log-cpm). The mean-variance is
fitted to the gene-wise standard deviations of the log-cpm, as a function of
the average log-count. This method incorporates the mean-variance trend into a
precision weight for each individual normalized observation. The normalized
log-counts and associated precision weights can then be entered into the limma
analysis pipeline, or indeed into any statistical pipeline for microarray data
that is precision weight aware[@ref3; @ref4]. Users can check the following
publications for more information about methods:

[limma : Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, Vol. 3, No. 1, Article 3.](http://www.ncbi.nlm.nih.gov/pubmed/16646809)

[Voom: Law, CW, Chen, Y, Shi, W, Smyth, GK (2014). Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology15, R29.](http://www.ncbi.nlm.nih.gov/pubmed/24485249)

## Correlation between gene expression and copy number data

"getCNGECorrelation" function provides correlation coefficient and adjusted p
value between copy number and gene expression data for each dataset. This
function takes main dataobject as an input (uses gene copy number estimates
from GISTIC2 [@ref2] algorithm and gen expression values from every platform
(RNAseq and arrays) to prepare return lists. List object stores "CorResult"
object that contains results for each comparison.)

```{r}
#Correlation between gene expression values and copy number
corrGECN <- getCNGECorrelation(dataObject=RTCGASample, adj.method="BH",
    adj.pval=0.9, raw.pval=0.05)
corrGECN
showResults(corrGECN[[1]])
corRes <- showResults(corrGECN[[1]])
```

If the dataset has RNASeq data, data will be normalized for correlation
analysis. Correlation function uses Benjamini & Hochberg adjustment for p
values. For more details about adjment users can check base adjustment methods
by calling "?p.adjust". In addition, to filter results adjusted and raw p
values are used. Users can change "adj.pval" and "raw.pval" parameters to
refine results.

The RTCGAToolbox uses one of Pearson's product moment correlation coefficient
to test for associations between paired samples. Measures of association, all
in the range [-1, 1] with 0 indicating no association, shows how copy number
alterations affect gene expression changes. The test statistic follows a
t-distribution, with length (x)-2 degrees of freedom if the samples follow
independent normal distributions. Users can get detailed information by
calling `?cor.test` function

## Mutation frequencies

"getMutationRate" function gets the data frame that stores mutation frequency
for the genes. This function gets the mutation information for each sample
that has data and calculates frequency for each gene.

```{r}
# Mutation frequencies
mutFrq <- getMutationRate(dataObject=RTCGASample)
head(mutFrq[order(mutFrq[, 2], decreasing=TRUE), ])
```

## Univariate survival analysis
Survival analysis is considered as one of the methods that can provide
clinically valuable information. To provide this information, the function
creates 2 or 3 groups based on expression data.(If the dataset has RNASeq
data, data will be normalized for survival analysis.). If function is
triggered with 2 groups, RTCGAToolbox creates groups using the median
expression level of individual genes. If group number is set to be 3, then the
groups will be defined as: the samples in 1st. quartile (expression < 1st Q),
the samples those have higher expression (expression > 3rd Q) and the samples
lying in between these 2 groups.

This function also needs a survival data, which can be obtained using clinical
data frame. Clinical data frames can be obtained from main data downloads.
First column of the survival data frame should be sample barcodes, second
column should be time and the last column should be event data. Below code
chunk shows how survival data frame can be obtained from clinical data and how
survival analysis can be done.

```{r fig.width=6,fig.height=6,fig.align='center'}
# Creating survival data frame and running analysis for
# FCGBP which is one of the most frequently mutated gene in the toy data
# Running following code will provide following KM plot.
clinicData <- getData(RTCGASample,"clinical")
head(clinicData)
clinicData <- clinicData[, 3:5]
clinicData[is.na(clinicData[, 3]), 3] <- clinicData[is.na(clinicData[, 3]), 2]
survData <- data.frame(Samples=rownames(clinicData),
    Time=as.numeric(clinicData[, 3]), Censor=as.numeric(clinicData[, 1]))
getSurvival(dataObject=RTCGASample, geneSymbols=c("FCGBP"), sampleTimeCensor=survData)
```

# Data Export
You can export downloaded data from FirehoseData object by using 'getData()' function.
```{r}
# Note: This function is provided for real dataset test since the toy dataset is small.
RTCGASample
```
```{r message=FALSE}
RTCGASampleClinical <- getData(RTCGASample, "clinical")
RTCGASampleRNAseqCounts <- getData(RTCGASample, "RNASeqGene")
RTCGASampleCN <- getData(RTCGASample, "GISTIC", "AllByGene")
```
# Reproducing BRCA results from original manuscript
Following code block is provided to reproduce case study in the RTCGAToolbox paper[@ref6].

```{r eval=FALSE}
# BRCA data with mRNA (Both array and RNASeq), GISTIC processed copy number data
# mutation data and clinical data
# (Depends on bandwidth this process may take long time)
brcaData <- getFirehoseData (dataset="BRCA", runDate="20140416",
    gistic2Date="20140115", clinic=TRUE, RNAseqGene=TRUE, mRNAArray=TRUE,
    Mutation=TRUE)

# Differential gene expression analysis for gene level RNA data.
# Heatmaps are given below.
diffGeneExprs <- getDiffExpressedGenes(dataObject=brcaData,DrawPlots=TRUE,
    adj.method="BH", adj.pval=0.05, raw.pval=0.05, logFC=2, hmTopUpN=100,
    hmTopDownN=100)
# Show head for expression outputs
diffGeneExprs
showResults(diffGeneExprs[[1]])
toptableOut <- showResults(diffGeneExprs[[1]])

# Correlation between expresiion profiles and copy number data
corrGECN <- getCNGECorrelation(dataObject=brcaData, adj.method="BH",
    adj.pval=0.05, raw.pval=0.05)

corrGECN
showResults(corrGECN[[1]])
corRes <- showResults(corrGECN[[1]])

# Gene mutation frequincies in BRCA dataset
mutFrq <- getMutationRate(dataObject=brcaData)
head(mutFrq[order(mutFrq[,2],decreasing=TRUE),])

# PIK3CA which is one of the most frequently mutated gene in BRCA dataset
# KM plot is given below.
clinicData <- getData(brcaData,"clinical")
head(clinicData)
clinicData <- clinicData[, 3:5]
clinicData[is.na(clinicData[, 3]), 3] <- clinicData[is.na(clinicData[, 3]), 2]
survData <- data.frame(Samples=rownames(clinicData),
    Time=as.numeric(clinicData[, 3]), Censor=as.numeric(clinicData[, 1]))
getSurvival(dataObject=brcaData, geneSymbols=c("PIK3CA"),
    sampleTimeCensor=survData)
```

Differentially expressed genes.</br>
<img src="Figure2.png" height="300px" width="600px" />
<br/>
KM plot for PIK3CA on BRCA dataset.<br/>
<img src="Figure3.png" height="400px" width="400px" />

## Report figure
This function provides an overall circle figure for the dataset by using the
RCircos[@ref5]. This function uses differential gene expression analysis
results (max results for 2 different platforms), copy number data estimates
from GISTIC2 [@ref2] and mutation data.

Outer circle shows the gene symbols that have mutation in at least 5% of the
samples. Inner tracks show the significantly altered gene expressions as fold
change and copy number changes where blue represents the deletions and red
represents the amplifications.

This function needs a genes location data frame, which can be obtained from
"hg19.ucsc.gene.locations" data object. Please see the next section.

```{r eval=FALSE}
# Creating dataset analysis summary figure with getReport.
# Figure will be saved as PDF file.
library("Homo.sapiens")
locations <- genes(Homo.sapiens, columns="SYMBOL")
locations <- as.data.frame(locations)
locations <- locations[,c(6,1,5,2:3)]
locations <- locations[!is.na(locations[,1]), ]
locations <- locations[!duplicated(locations[,1]), ]
rownames(locations) <- locations[,1]
getReport(dataObject=brcaData, DGEResult1=diffGeneExprs[[1]],
    DGEResult2=diffGeneExprs[[2]], geneLocations=locations)
```
Running code above will provide following circle plot.
<img src="Figure4.png" height="600px" width="600px" />

# Data Objects

RTCGAToolbox provides toy data object for testing functions.

* "RTCGASample" data is a FirehoseData object that stores RNAseq, copy number,
mutation, clinical data for artificially created dataset.

```{r}
data(RTCGASample)
```

******
```{r}
sessionInfo()
```

# References


