Package 'TCGAbiolinks'

Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data
Description: The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.
Authors: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen, Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot, Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr
Maintainer: Tiago Chedraoui Silva <[email protected]>, Antonio Colaprico <[email protected]>
License: GPL (>= 3)
Version: 2.35.0
Built: 2024-12-19 04:45:39 UTC
Source: https://github.com/bioc/TCGAbiolinks

Help Index

Create samples information matrix for GDC samples

Description

Create samples information matrix for GDC samples add subtype information

Usage

colDataPrepare(barcode)

Arguments

barcode

TCGA or TARGET barcode

Examples

metadata <- colDataPrepare(c("TCGA-OR-A5K3-01A","C3N-00321-01"))
 metadata <- colDataPrepare(c("BLGSP-71-06-00157-01A",
                              "BLGSP-71-22-00332-01A"))

Perform non-parametrix wilcoxon test

Description

Perform non-parametrix wilcoxon test

Usage

dmc.non.parametric(
  matrix,
  idx1 = NULL,
  idx2 = NULL,
  paired = FALSE,
  adj.method = "BH",
  alternative = "two.sided",
  cores = 1
)

Arguments

matrix

A matrix
idx1

Index columns group1
idx2

Index columns group2
paired

Do a paired wilcoxon test? Default: True
adj.method

P-value adjustment method. Default:"BH" Benjamini-Hochberg
alternative

wilcoxon test alternative
cores

Number of cores to be used

Value

Data frame with p-values and diff mean

Examples

nrows <- 200; ncols <- 20
 counts <- matrix(
   runif(nrows * ncols, 1, 1e4), nrows,
   dimnames = list(paste0("cg",1:200),paste0("S",1:20))
 )
 TCGAbiolinks:::dmc.non.parametric(counts,1:10,11:20)

Creates a plot for GAIA output (all significant aberrant regions.)

Description

This function is a auxiliary function to visualize GAIA output (all significant aberrant regions.)

Usage

gaiaCNVplot(calls, threshold = 0.01)

Arguments

calls

A matrix with the following columns: Chromossome, Aberration Kind Region Start, Region End, Region Size and score
threshold

Score threshold (orange horizontal line in the plot)

Value

A plot with all significant aberrant regions.

Examples

call <- data.frame("Chromossome" = rep(9,100),
                   "Aberration Kind" = rep(c(-2,-1,0,1,2),20),
                   "Region Start [bp]" = 18259823:18259922,
                   "Region End [bp]" = 18259823:18259922,
                   "score" = rep(c(1,2,3,4),25))
 gaiaCNVplot(call,threshold = 0.01)
 call <- data.frame("Chromossome" = rep(c(1,9),50),
                    "Aberration Kind" = rep(c(-2,-1,0,1,2),20),
                    "Region Start [bp]" = 18259823:18259922,
                    "Region End [bp]" = 18259823:18259922,
                    "score" = rep(c(1,2,3,4),25))
 gaiaCNVplot(call,threshold = 0.01)

Download GDC data

Description

Uses GDC API or GDC transfer tool to download gdc data The user can use query argument The data from query will be save in a folder: project/data.category

Usage

GDCdownload(
  query,
  token.file,
  method = "api",
  directory = "GDCdata",
  files.per.chunk = NULL
)

Arguments

query

A query for GDCquery function
token.file

Token file to download controlled data (only for method = "client")
method

Uses the API (POST method) or gdc client tool. Options "api", "client". API is faster, but the data might get corrupted in the download, and it might need to be executed again
directory

Directory/Folder where the data was downloaded. Default: GDCdata
files.per.chunk

This will make the API method only download n (files.per.chunk) files at a time. This may reduce the download problems when the data size is too large. Expected a integer number (example files.per.chunk = 6)

Value

Shows the output from the GDC transfer tools

Author(s)

Tiago Chedraoui Silva

Examples

## Not run: 
# Download clinical data from XML
query <- GDCquery(project = "TCGA-COAD", data.category = "Clinical")
GDCdownload(query, files.per.chunk = 200)
query <- GDCquery(
  project = "TARGET-AML",
  data.category = "Transcriptome Profiling",
  data.type = "miRNA Expression Quantification",
  workflow.type = "BCGSC miRNA Profiling",
  barcode = c("TARGET-20-PARUDL-03A-01R", "TARGET-20-PASRRB-03A-01R")
)
# data will be saved in:
# example_data_dir/TARGET-AML/harmonized/Transcriptome_Profiling/miRNA_Expression_Quantification
GDCdownload(query, method = "client", directory = "example_data_dir")
query_acc_gbm <- GDCquery(
  project = c("TCGA-ACC", "TCGA-GBM"),
  data.category = "Transcriptome Profiling",
  data.type = "Gene Expression Quantification",
  workflow.type = "STAR - Counts"
)
GDCdownload(
  query = query_acc_gbm,
  method = "api",
  directory = "example",
  files.per.chunk = 50
)

## End(Not run)

Prepare GDC data

Description

Reads the data downloaded and prepare it into an R object

Usage

GDCprepare(
  query,
  save = FALSE,
  save.filename,
  directory = "GDCdata",
  summarizedExperiment = TRUE,
  remove.files.prepared = FALSE,
  add.gistic2.mut = NULL,
  mutant_variant_classification = c("Frame_Shift_Del", "Frame_Shift_Ins",
    "Missense_Mutation", "Nonsense_Mutation", "Splice_Site", "In_Frame_Del",
    "In_Frame_Ins", "Translation_Start_Site", "Nonstop_Mutation")
)

Arguments

query

A query for GDCquery function
save

Save result as RData object?
save.filename

Name of the file to be save if empty an automatic will be created
directory

Directory/Folder where the data was downloaded. Default: GDCdata
summarizedExperiment

Create a summarizedExperiment? Default TRUE (if possible)
remove.files.prepared

Remove the files read? Default: FALSE This argument will be considered only if save argument is set to true
add.gistic2.mut

If a list of genes (gene symbol) is given, columns with gistic2 results from GDAC firehose (hg19) and a column indicating if there is or not mutation in that gene (hg38) (TRUE or FALSE - use the MAF file for more information) will be added to the sample matrix in the summarized Experiment object.
mutant_variant_classification

List of mutant_variant_classification that will be consider a sample mutant or not. Default: "Frame_Shift_Del", "Frame_Shift_Ins", "Missense_Mutation", "Nonsense_Mutation", "Splice_Site", "In_Frame_Del", "In_Frame_Ins", "Translation_Start_Site", "Nonstop_Mutation"

Value

A summarizedExperiment or a data.frame

Author(s)

Tiago Chedraoui Silva

Examples

## Not run: 
query <- GDCquery(
  project = "TCGA-KIRP",
  data.category = "Simple Nucleotide Variation",
  data.type = "Masked Somatic Mutation"
)
GDCdownload(query, method = "api", directory = "maf")
maf <- GDCprepare(query, directory = "maf")


## End(Not run)

Parsing clinical xml files

Description

This function receives the query argument and parses the clinical xml files based on the desired information

Usage

GDCprepare_clinic(query, clinical.info, directory = "GDCdata")

Arguments

query

Result from GDCquery, with data.category set to Clinical
clinical.info

Which information should be retrieved. Options Clinical: drug, admin, follow_up,radiation, patient, stage_event or new_tumor_event Options Biospecimen: protocol, admin, aliquot, analyte, bio_patient, sample, portion, slide
directory

Directory/Folder where the data was downloaded. Default: GDCdata

Value

A data frame with the parsed values from the XML

Examples

query <- GDCquery(
  project = "TCGA-COAD",
  data.category = "Clinical",
  data.format = "bcr xml",
  barcode = c("TCGA-RU-A8FL","TCGA-AA-3972")
)
GDCdownload(query)
clinical <- GDCprepare_clinic(query,"patient")
clinical.drug <- GDCprepare_clinic(query,"drug")
clinical.radiation <- GDCprepare_clinic(query,"radiation")
clinical.admin <- GDCprepare_clinic(query,"admin")
## Not run: 
query <- GDCquery(
   project = "TCGA-COAD",
   data.category = "Biospecimen",
   data.format = "bcr xml",
   data.type = "Biospecimen Supplement",
   barcode = c("TCGA-RU-A8FL","TCGA-AA-3972")
)
GDCdownload(query)
clinical <- GDCprepare_clinic(query,"admin")
clinical.drug <- GDCprepare_clinic(query,"sample")
clinical.radiation <- GDCprepare_clinic(query,"portion")
clinical.admin <- GDCprepare_clinic(query,"slide")

## End(Not run)

Query GDC data

Description

Uses GDC API to search for search, it searches for both controlled and open-access data. For GDC data arguments project, data.category, data.type and workflow.type should be used Please, see the vignette for a table with the possibilities.

Usage

GDCquery(
  project,
  data.category,
  data.type,
  workflow.type,
  access,
  platform,
  barcode,
  data.format,
  experimental.strategy,
  sample.type
)

Arguments

project

A list of valid project (see list with TCGAbiolinks:::getGDCprojects()$project_id)]

  • BEATAML1.0-COHORT

  • BEATAML1.0-CRENOLANIB

  • CGCI-BLGSP

  • CPTAC-2

  • CPTAC-3

  • CTSP-DLBCL1

  • FM-AD

  • HCMI-CMDC

  • MMRF-COMMPASS

  • NCICCR-DLBCL

  • OHSU-CNL

  • ORGANOID-PANCREATIC

  • TARGET-ALL-P1

  • TARGET-ALL-P2

  • TARGET-ALL-P3

  • TARGET-AML

  • TARGET-CCSK

  • TARGET-NBL

  • TARGET-OS

  • TARGET-RT

  • TARGET-WT

  • TCGA-ACC

  • TCGA-BLCA

  • TCGA-BRCA

  • TCGA-CESC

  • TCGA-CHOL

  • TCGA-COAD

  • TCGA-DLBC

  • TCGA-ESCA

  • TCGA-GBM

  • TCGA-HNSC

  • TCGA-KICH

  • TCGA-KIRC

  • TCGA-KIRP

  • TCGA-LAML

  • TCGA-LGG

  • TCGA-LIHC

  • TCGA-LUAD

  • TCGA-LUSC

  • TCGA-MESO

  • TCGA-OV

  • TCGA-PAAD

  • TCGA-PCPG

  • TCGA-PRAD

  • TCGA-READ

  • TCGA-SARC

  • TCGA-SKCM

  • TCGA-STAD

  • TCGA-TGCT

  • TCGA-THCA

  • TCGA-THYM

  • TCGA-UCEC

  • TCGA-UCS

  • TCGA-UVM

  • VAREPOP-APOLLO

data.category

A valid project (see list with TCGAbiolinks:::getProjectSummary(project)) For the complete list please check the vignette. List for harmonized database:

  • Biospecimen

  • Clinical

  • Copy Number Variation

  • DNA Methylation

  • Sequencing Reads

  • Simple Nucleotide Variation

  • Transcriptome Profiling

data.type

A data type to filter the files to download For the complete list please check the vignette.
workflow.type

GDC workflow type
access

Filter by access type. Possible values: controlled, open
platform

Example:

CGH- 1x1M_G4447A IlluminaGA_RNASeqV2
AgilentG4502A_07 IlluminaGA_mRNA_DGE
Human1MDuo HumanMethylation450
HG-CGH-415K_G4124A IlluminaGA_miRNASeq
HumanHap550 IlluminaHiSeq_miRNASeq
ABI H-miRNA_8x15K
HG-CGH-244A SOLiD_DNASeq
IlluminaDNAMethylation_OMA003_CPI IlluminaGA_DNASeq_automated
IlluminaDNAMethylation_OMA002_CPI HG-U133_Plus_2
HuEx- 1_0-st-v2 Mixed_DNASeq
H-miRNA_8x15Kv2 IlluminaGA_DNASeq_curated
MDA_RPPA_Core IlluminaHiSeq_TotalRNASeqV2
HT_HG-U133A IlluminaHiSeq_DNASeq_automated
diagnostic_images microsat_i
IlluminaHiSeq_RNASeq SOLiD_DNASeq_curated
IlluminaHiSeq_DNASeqC Mixed_DNASeq_curated
IlluminaGA_RNASeq IlluminaGA_DNASeq_Cont_automated
IlluminaGA_DNASeq IlluminaHiSeq_WGBS
pathology_reports IlluminaHiSeq_DNASeq_Cont_automated
Genome_Wide_SNP_6 bio
tissue_images Mixed_DNASeq_automated
HumanMethylation27 Mixed_DNASeq_Cont_curated
IlluminaHiSeq_RNASeqV2 Mixed_DNASeq_Cont
barcode

A list of barcodes to filter the files to download
data.format

Data format filter ("VCF", "TXT", "BAM","SVS","BCR XML","BCR SSF XML", "TSV", "BCR Auxiliary XML", "BCR OMF XML", "BCR Biotab", "MAF", "BCR PPS XML", "XLSX")
experimental.strategy

Filter to experimental strategy. Harmonized: WXS, RNA-Seq, miRNA-Seq, Genotyping Array.
sample.type

A sample type to filter the files to download

Value

A data frame with the results and the parameters used

Author(s)

Tiago Chedraoui Silva

Examples

query <- GDCquery(
   project = "TCGA-ACC",
   data.category = "Copy Number Variation",
   data.type = "Copy Number Segment"
)
## Not run: 
query <- GDCquery(
  project = "TARGET-AML",
  data.category = "Transcriptome Profiling",
  data.type = "miRNA Expression Quantification",
  workflow.type = "BCGSC miRNA Profiling",
  barcode = c("TARGET-20-PARUDL-03A-01R","TARGET-20-PASRRB-03A-01R")
)
query <- GDCquery(
   project = "TARGET-AML",
   data.category = "Transcriptome Profiling",
   data.type = "Gene Expression Quantification",
   workflow.type = "STAR - Counts",
   barcode = c("TARGET-20-PADZCG-04A-01R","TARGET-20-PARJCR-09A-01R")
)
query <- GDCquery(
   project = "TCGA-ACC",
   data.category =  "Copy Number Variation",
   data.type = "Masked Copy Number Segment",
   sample.type = c("Primary Tumor")
)

## End(Not run)

Retrieve open access ATAC-seq files from GDC server

Description

Retrieve open access ATAC-seq files from GDC server https://gdc.cancer.gov/about-data/publications/ATACseq-AWG Manifest available at: https://gdc.cancer.gov/files/public/file/ATACseq-AWG_Open_GDC-Manifest.txt

Usage

GDCquery_ATAC_seq(tumor = NULL, file.type = NULL)

Arguments

tumor

a valid tumor
file.type

Write maf file into a csv document

Value

A data frame with the maf file information

Examples

query <- GDCquery_ATAC_seq(file.type = "txt")
## Not run: 
   GDCdownload(query)

## End(Not run)
query <- GDCquery_ATAC_seq(tumor = "BRCA",file.type = "bigWigs")
## Not run: 
   GDCdownload(query,method = "client")

## End(Not run)

Get GDC clinical data

Description

GDCquery_clinic will download all clinical information from the API as the one with using the button from each project

Usage

GDCquery_clinic(project, type = "clinical", save.csv = FALSE)

Arguments

project

A valid project (see list with getGDCprojects()$project_id)]

  • BEATAML1.0-COHORT

  • BEATAML1.0-CRENOLANIB

  • CGCI-BLGSP

  • CPTAC-2

  • CPTAC-3

  • CTSP-DLBCL1

  • FM-AD

  • HCMI-CMDC

  • MMRF-COMMPASS

  • NCICCR-DLBCL

  • OHSU-CNL

  • ORGANOID-PANCREATIC

  • TARGET-ALL-P1

  • TARGET-ALL-P2

  • TARGET-ALL-P3

  • TARGET-AML

  • TARGET-CCSK

  • TARGET-NBL

  • TARGET-OS

  • TARGET-RT

  • TARGET-WT

  • TCGA-ACC

  • TCGA-BLCA

  • TCGA-BRCA

  • TCGA-CESC

  • TCGA-CHOL

  • TCGA-COAD

  • TCGA-DLBC

  • TCGA-ESCA

  • TCGA-GBM

  • TCGA-HNSC

  • TCGA-KICH

  • TCGA-KIRC

  • TCGA-KIRP

  • TCGA-LAML

  • TCGA-LGG

  • TCGA-LIHC

  • TCGA-LUAD

  • TCGA-LUSC

  • TCGA-MESO

  • TCGA-OV

  • TCGA-PAAD

  • TCGA-PCPG

  • TCGA-PRAD

  • TCGA-READ

  • TCGA-SARC

  • TCGA-SKCM

  • TCGA-STAD

  • TCGA-TGCT

  • TCGA-THCA

  • TCGA-THYM

  • TCGA-UCEC

  • TCGA-UCS

  • TCGA-UVM

  • VAREPOP-APOLLO

type

A valid type. Options "clinical", "Biospecimen" (see list with getGDCprojects()$project_id)]
save.csv

Write clinical information into a csv document

Value

A data frame with the clinical information

Author(s)

Tiago Chedraoui Silva

Examples

clinical <- GDCquery_clinic(
   project = "TCGA-ACC",
   type = "clinical",
   save.csv = FALSE
 )
clinical <- GDCquery_clinic(
   project = "TCGA-ACC",
   type = "biospecimen",
   save.csv = FALSE
)
## Not run: 
clinical_cptac_3 <- GDCquery_clinic(
   project = "CPTAC-3",
   type = "clinical"
)
clinical_cptac_2 <- GDCquery_clinic(
   project = "CPTAC-2",
   type = "clinical"
)
clinical_HCMI_CMDC <- GDCquery_clinic(
   project = "HCMI-CMDC",
   type = "clinical"
)
clinical_GCI_HTMCP_CC <- GDCquery_clinic(
   project = "CGCI-HTMCP-CC",
   type = "clinical"
)
clinical <- GDCquery_clinic(
   project = "NCICCR-DLBCL",
   type = "clinical"
)
clinical <- GDCquery_clinic(
   project = "ORGANOID-PANCREATIC",
   type = "clinical"
)

## End(Not run)

Extract information from TCGA barcodes.

Description

get_IDs allows user to extract metadata from barcodes. The dataframe returned has columns for 'project', 'tss','participant', 'sample', "portion", "plate", and "center"

Usage

get_IDs(data)

Arguments

data

numeric matrix, each row represents a gene, each column represents a sample

Value

data frame with columns 'project', 'tss','participant', 'sample', "portion", "plate", "center", "condition"

Get hg19 gene annotation or hg38 (gencode v36)

Description

Get hg19 (from biomart) or hg38 (gencode v36) gene annotation

Usage

get.GRCh.bioMart(genome = c("hg19", "hg38"), as.granges = FALSE)

Arguments

genome

hg38 or hg19
as.granges

Output as GRanges or data.frame

Get a matrix of interactions of genes from biogrid

Description

Using biogrid database, it will create a matrix of gene interactions. If columns A and row B has value 1, it means the gene A and gene B interacts.

Usage

getAdjacencyBiogrid(tmp.biogrid, names.genes = NULL)

Arguments

tmp.biogrid

Biogrid table
names.genes

List of genes to filter from output. Default: consider all genes

Value

A matrix with 1 for genes that interacts, 0 for no interaction.

Examples

names.genes.de <- c("PLCB1","MCL1","PRDX4","TTF2","TACC3", "PARP4","LSM1")
tmp.biogrid <- data.frame("Official.Symbol.Interactor.A" = names.genes.de,
                          "Official.Symbol.Interactor.B" = rev(names.genes.de))
net.biogrid.de <- getAdjacencyBiogrid(tmp.biogrid, names.genes.de)
## Not run: 
  file <- paste0("http://thebiogrid.org/downloads/archives/",
                 "Release%20Archive/BIOGRID-3.4.133/BIOGRID-ALL-3.4.133.tab2.zip")
  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)
  names.genes.de <- c("PLCB1","MCL1","PRDX4","TTF2","TACC3", "PARP4","LSM1")
  net.biogrid.de <- getAdjacencyBiogrid(tmp.biogrid, names.genes.de)

## End(Not run)

Create a Summary table for each sample in a project saying if it contains or not files for a certain data category

Description

Create a Summary table for each sample in a project saying if it contains or not files for a certain data category

Usage

getDataCategorySummary(project)

Arguments

project

A GDC project

Value

A data frame

Author(s)

Tiago Chedraoui Silva

Examples

summary <- getDataCategorySummary("TCGA-ACC")

Check GDC server status

Description

Check GDC server status using the api https://api.gdc.cancer.gov/status

Usage

getGDCInfo()

Value

Return true all status

Examples

info <- getGDCInfo()

Retrieve all GDC projects

Description

getGDCprojects uses the following api to get projects https://api.gdc.cancer.gov/projects

Usage

getGDCprojects()

Value

A data frame with last GDC projects

Examples

projects <- getGDCprojects()

Download GISTIC data from firehose

Description

Download GISTIC data from firehose from http://gdac.broadinstitute.org/runs/analyses__latest/data/

Usage

getGistic(disease, type = "thresholded")

Arguments

disease

TCGA disease. Option available in http://gdac.broadinstitute.org/runs/analyses__latest/data/
type

Results type: thresholded or data

Retrieve linkedOmics data

Description

Retrieve linkedOmics data from http://linkedomics.org/

Usage

getLinkedOmicsData(project, dataset)

Arguments

project

A linkedOmics project:

  • TCGA-ACC

  • TCGA-BLCA

  • TCGA-BRCA

  • TCGA-CESC

  • TCGA-CHOL

  • TCGA-COADREAD

  • TCGA-DLBC

  • TCGA-ESCA

  • TCGA-GBM

  • TCGA-GBMLGG

  • TCGA-HNSC

  • TCGA-KICH

  • TCGA-KIPAN

  • TCGA-KIRC

  • TCGA-KIRP

  • TCGA-LAML

  • TCGA-LGG

  • TCGA-LIHC

  • TCGA-LUAD

  • TCGA-LUSC

  • TCGA-MESO

  • TCGA-OV

  • TCGA-PAAD

  • TCGA-PCPG

  • TCGA-PRAD

  • TCGA-SARC

  • TCGA-SKCM

  • TCGA-STAD

  • TCGA-STES

  • TCGA-TGCT

  • TCGA-THCA

  • TCGA-THYM

  • TCGA-UCEC

  • TCGA-UCS

  • TCGA-UVM

  • CPTAC-COAD

dataset

A dataset from the list below

  • Annotated mutation

  • Clinical

  • Glycoproteome (Gene level)

  • Glycoproteome (Site level)

  • Methylation (CpG-site level, HM27)

  • Methylation (CpG-site level, HM450K)

  • Methylation (Gene level, HM27)

  • Methylation (Gene level, HM450K)

  • miRNA (GA, Gene level)

  • miRNA (GA, Isoform level)

  • miRNA (GA, miRgene level)

  • miRNA (Gene level)

  • miRNA (HiSeq, Gene level)

  • miRNA (HiSeq, miRgene level)

  • miRNA (isoform level)

  • miRNA (miRgene level)

  • Mutation (Gene level)

  • Mutation (Site level)

  • Mutation raw file (Somatic and MSIndel)

  • Phosphoproteome (Gene level)

  • Phosphoproteome (Site level)

  • Phosphoproteomics (Normal)

  • Phosphoproteomics (Tumor)

  • Proteome (Gene level)

  • Proteome (Gene Level)

  • Proteome (JHU, Gene level)

  • Proteome (PNNL, Gene level, Normal TMT Unshared Log Ratio)

  • Proteome (PNNL, Gene level, Tumor TMT Unshared Log Ratio)

  • Proteome (PNNL, Gene level)

  • Proteome (VU, Gene level, Label-free Unshared Counts)

  • RNAseq (GA, Gene level)

  • RNAseq (HiSeq, Gene level)

  • RPPA (Analyte level)

  • RPPA (Analyte Level)

  • RPPA (Gene level)

  • RPPA (Gene Level)

  • SCNV (Focal level, log-ratio)

  • SCNV (Focal level, Thresholded)

  • SCNV (Gene level, log ratio)

  • SCNV (Gene level, log-ratio)

  • SCNV (Gene level, Thresholded)

  • SCNV (Segment level)

Value

A matrix with the data

Examples

## Not run: 
TCGA_COAD_protein <- getLinkedOmicsData(
  project = "TCGA-COADREAD",
  dataset = "Proteome (Gene level)"
)
TCGA_COAD_RNASeq_hiseq <- getLinkedOmicsData(
  project = "TCGA-COADREAD",
  dataset = "RNAseq (HiSeq, Gene level)"
)
TCGA_COAD_RNASeq_ga <- getLinkedOmicsData(
  project = "TCGA-COADREAD",
  dataset = "RNAseq (GA, Gene level)"
)
TCGA_COAD_RPPA <- getLinkedOmicsData(
  project = "TCGA-COADREAD",
  dataset = "RPPA (Gene level)"
)

## End(Not run)

Get a Manifest from GDCquery output that can be used with GDC-client

Description

Get a Manifest from GDCquery output that can be used with GDC-client

Usage

getManifest(query, save = FALSE)

Arguments

query

A query for GDCquery function
save

Write Manifest to a txt file (tab separated)

Examples

query <- GDCquery(
  project = "TARGET-AML",
  data.category = "Transcriptome Profiling",
  data.type = "Gene Expression Quantification",
  workflow.type = "STAR - Counts",
  barcode = c("TARGET-20-PADZCG-04A-01R", "TARGET-20-PARJCR-09A-01R")
)
getManifest(query)

Retrieve open access mc3 MAF file from GDC server

Description

Download data from https://gdc.cancer.gov/about-data/publications/mc3-2017 https://gdc-docs.nci.nih.gov/Data/Release_Notes/Data_Release_Notes/

Usage

getMC3MAF()

Value

A data frame with the MAF file information from https://gdc.cancer.gov/about-data/publications/mc3-2017

Examples

## Not run: 
   maf <- getMC3MAF()

## End(Not run)

Get Number of cases in GDC for a project

Description

Get Number of cases in GDC for a project

Usage

getNbCases(project, data.category)

Arguments

project

A GDC project
data.category

A GDC project data category

Author(s)

Tiago Chedraoui Silva

Examples

## Not run: 
getNbCases("TCGA-ACC","Clinical")
getNbCases("CPTAC-2","Clinical")

## End(Not run)

Get Number of files in GDC for a project

Description

Get Number of files in GDC for a project

Usage

getNbFiles(project, data.category)

Arguments

project

A GDC project
data.category

A GDC project data category

Author(s)

Tiago Chedraoui Silva

Examples

## Not run: 
getNbFiles("TCGA-ACC","Clinical")
getNbFiles("CPTAC-2","Clinical")

## End(Not run)

Get Project Summary from GDC

Description

Get Project Summary from GDC

Usage

getProjectSummary(project)

Arguments

project

A GDC project

Author(s)

Tiago Chedraoui Silva

Examples

getProjectSummary("TCGA-ACC")
## Not run: 
getProjectSummary("CPTAC-2")

## End(Not run)

Get the results table from query

Description

Get the results table from query, it can select columns with cols argument and return a number of rows using rows argument.

Usage

getResults(query, rows, cols)

Arguments

query

A object from GDCquery
rows

Rows identifiers (row numbers)
cols

Columns identifiers (col names)

Value

Table with query results

Examples

query <- GDCquery(
  project = "TCGA-GBM",
  data.category = "Transcriptome Profiling",
  data.type = "Gene Expression Quantification",
  workflow.type = "STAR - Counts",
  barcode = c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01")
)
results <- getResults(query)

Retrieve summary of files per sample in a project

Description

Retrieve the numner of files under each data_category + data_type + experimental_strategy + platform Almost like https://portal.gdc.cancer.gov/exploration

Usage

getSampleFilesSummary(project, files.access = NA)

Arguments

project

A GDC project
files.access

Filter by file access ("open" or "controlled"). Default: no filter

Value

A data frame with the maf file information

Author(s)

Tiago Chedraoui Silva

Examples

summary <- getSampleFilesSummary("TCGA-UCS")
## Not run: 
   summary <- getSampleFilesSummary(c("TCGA-OV","TCGA-ACC"))

## End(Not run)

getTSS to fetch GENCODE gene annotation (transcripts level) from Bioconductor package biomaRt If upstream and downstream are specified in TSS list, promoter regions of GENCODE gene will be generated.

Description

getTSS to fetch GENCODE gene annotation (transcripts level) from Bioconductor package biomaRt If upstream and downstream are specified in TSS list, promoter regions of GENCODE gene will be generated.

Usage

getTSS(
  genome = c("hg38", "hg19"),
  TSS = list(upstream = NULL, downstream = NULL)
)

Arguments

genome

Which genome build will be used: hg38 (default) or hg19.
TSS

A list. Contains upstream and downstream like TSS=list(upstream, downstream). When upstream and downstream is specified, coordinates of promoter regions with gene annotation will be generated.

Value

GENCODE gene annotation if TSS is not specified. Coordinates of GENCODE gene promoter regions if TSS is specified.

Examples

# get GENCODE gene annotation (transcripts level)
## Not run: 
    getTSS <- getTSS()
    getTSS <- getTSS(genome.build = "hg38", TSS=list(upstream=1000, downstream=1000))

## End(Not run)

Gliomar classifier

Description

Classify DNA methylation gliomas using data from https://doi.org/10.1016/j.cell.2015.12.028

Usage

gliomaClassifier(data)

Arguments

data

DNA methylation matrix or Summarized Experiments with samples on columns and probes on the rows

Value

A list of 3 data frames: 1) Sample final classification 2) Each model final classification 3) Each class probability of classification

Author(s)

Tiago Chedraoui Silva, Tathiane Malta, Houtan Noushmehr

Examples

## Not run: 
query <- GDCquery(
   project= "TCGA-GBM",
   data.category = "DNA methylation",
   barcode = c("TCGA-06-0122","TCGA-14-1456"),
   platform = "Illumina Human Methylation 27",
   legacy = TRUE
)
GDCdownload(query)
data.hg19 <- GDCprepare(query)
classification <- gliomaClassifier(data.hg19)

# Comparing reslts
TCGAquery_subtype("GBM") %>%
dplyr::filter(patient %in% c("TCGA-06-0122","TCGA-14-1456")) %>%
dplyr::select("patient","Supervised.DNA.Methylation.Cluster")

## End(Not run)

Check GDC server status is OK

Description

Check GDC server status using the api https://api.gdc.cancer.gov/status

Usage

isServeOK()

Value

Return true if status is ok

Examples

status <- isServeOK()

Get GDC primary tumors samples with both DNA methylation (HM450K) and Gene expression data

Description

For a given TCGA project it gets the primary tumors samples (barcode) with both DNA methylation and Gene expression data from GDC database

Usage

matchedMetExp(project, n = NULL)

Arguments

project

A GDC project
n

Number of samples to return. If NULL return all (default)

Value

A vector of barcodes

Examples

# Get ACC samples with both  DNA methylation (HM450K) and gene expression aligned to hg19
samples <- matchedMetExp("TCGA-UCS")

Retrieve table with TCGA molecular subtypes

Description

PanCancerAtlas_subtypes is a curated table with molecular subtypes for 24 TCGA cancer types

Usage

PanCancerAtlas_subtypes()

Value

a data.frame with barcode and molecular subtypes for 24 cancer types

Examples

molecular.subtypes <- PanCancerAtlas_subtypes()

internal function to break a huge API call into smaller ones so it repects the max character limit of a string

Description

internal function to break a huge API call into smaller ones so it repects the max character limit of a string

Usage

splitAPICall(FUN, step = 20, items)

Arguments

FUN

function that calls the API
step

How many items to be evaluated per API call
items

vector of items to be using within the function (list of barcodes, aliquot ids, etc)

Author(s)

Tiago Chedraoui Silva

TCGA samples with their Pam50 subtypes

Description

A dataset containing the Sample Ids from TCGA and PAM50 subtyping attributes of 4768 tumor patients

Usage

TabSubtypesCol_merged

Format

A data frame with 4768 rows and 3 variables:

samples

Sample ID from TCGA barcodes, character string

subtype

Pam50 classification, character string

color

color, character string

...

Retrieve molecular subtypes for given TCGA barcodes

Description

TCGA_MolecularSubtype Retrieve molecular subtypes from TCGA consortium for a given set of barcodes

Usage

TCGA_MolecularSubtype(barcodes)

Arguments

barcodes

is a vector of TCGA barcodes

Value

List with $subtypes attribute as a dataframe with barcodes, samples, subtypes, and colors. The $filtered attribute is returned as filtered samples with no subtype info

Examples

TCGA_MolecularSubtype("TCGA-60-2721-01A-01R-0851-07")

Generate network

Description

TCGAanalyze_analyseGRN perform gene regulatory network.

Usage

TCGAanalyze_analyseGRN(TFs, normCounts, kNum)

Arguments

TFs

a vector of genes.
normCounts

is a matrix of gene expression with genes in rows and samples in columns.
kNum

the number of nearest neighbors to consider to estimate the mutual information. Must be less than the number of columns of normCounts.

Value

an adjacent matrix

Hierarchical cluster analysis

Description

Hierarchical cluster analysis using several methods such as ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

Usage

TCGAanalyze_Clustering(tabDF, method, methodHC = "ward.D2")

Arguments

tabDF

is a dataframe or numeric matrix, each row represents a gene, each column represents a sample come from TCGAPrepare.
method

is method to be used for generic cluster such as 'hclust' or 'consensus'
methodHC

is method to be used for Hierarchical cluster.

Value

object of class hclust if method selected is 'hclust'. If method selected is 'Consensus' returns a list of length maxK (maximum cluster number to evaluate.). Each element is a list containing consensusMatrix (numerical matrix), consensusTree (hclust), consensusClass (consensus class assignments). ConsensusClusterPlus also produces images.

Differential expression analysis (DEA) using edgeR or limma package.

Description

TCGAanalyze_DEA allows user to perform Differentially expression analysis (DEA), using edgeR package or limma to identify differentially expressed genes (DEGs). It is possible to do a two-class analysis.

TCGAanalyze_DEA performs DEA using following functions from edgeR:

  1. edgeR::DGEList converts the count matrix into an edgeR object.

  2. edgeR::estimateCommonDisp each gene gets assigned the same dispersion estimate.

  3. edgeR::exactTest performs pair-wise tests for differential expression between two groups.

  4. edgeR::topTags takes the output from exactTest(), adjusts the raw p-values using the False Discovery Rate (FDR) correction, and returns the top differentially expressed genes.

TCGAanalyze_DEA performs DEA using following functions from limma:

  1. limma::makeContrasts construct matrix of custom contrasts.

  2. limma::lmFit Fit linear model for each gene given a series of arrays.

  3. limma::contrasts.fit Given a linear model fit to microarray data, compute estimated coefficients and standard errors for a given set of contrasts.

  4. limma::eBayes Given a microarray linear model fit, compute moderated t-statistics, moderated F-statistic, and log-odds of differential expression by empirical Bayes moderation of the standard errors towards a common value.

  5. limma::toptable Extract a table of the top-ranked genes from a linear model fit.

Usage

TCGAanalyze_DEA(
  mat1,
  mat2,
  metadata = TRUE,
  Cond1type,
  Cond2type,
  pipeline = "edgeR",
  method = "exactTest",
  fdr.cut = 1,
  logFC.cut = 0,
  batch.factors = NULL,
  ClinicalDF = data.frame(),
  paired = FALSE,
  log.trans = FALSE,
  voom = FALSE,
  trend = FALSE,
  MAT = data.frame(),
  contrast.formula = "",
  Condtypes = c()
)

Arguments

mat1

numeric matrix, each row represents a gene, each column represents a sample with Cond1type
mat2

numeric matrix, each row represents a gene, each column represents a sample with Cond2type
metadata

Add metadata
Cond1type

a string containing the class label of the samples in mat1 (e.g., control group)
Cond2type

a string containing the class label of the samples in mat2 (e.g., case group)
pipeline

a string to specify which package to use ("limma" or "edgeR")
method

is 'glmLRT' (1) or 'exactTest' (2) used for edgeR (1) Fit a negative binomial generalized log-linear model to the read counts for each gene (2) Compute genewise exact tests for differences in the means between two groups of negative-binomially distributed counts.
fdr.cut

is a threshold to filter DEGs according their p-value corrected
logFC.cut

is a threshold to filter DEGs according their logFC
batch.factors

a vector containing strings to specify options for batch correction. Options are "Plate", "TSS", "Year", "Portion", "Center", and "Patients"
ClinicalDF

a dataframe returned by GDCquery_clinic() to be used to extract year data
paired

boolean to account for paired or non-paired samples. Set to TRUE for paired case
log.trans

boolean to perform log cpm transformation. Set to TRUE for log transformation
voom

boolean to perform voom transformation for limma-voom pipeline. Set to TRUE for voom transformation
trend

boolean to perform limma-trend pipeline. Set to TRUE to go through limma-trend
MAT

matrix containing expression set as all samples in columns and genes as rows. Do not provide if mat1 and mat2 are used
contrast.formula

string input to determine coefficients and to design contrasts in a customized way
Condtypes

vector of grouping for samples in MAT

Value

table with DEGs containing for each gene logFC, logCPM, pValue,and FDR, also for each contrast

Examples

dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)
dataFilt <- TCGAanalyze_Filtering(tabDF = dataBRCA, method = "quantile", qnt.cut =  0.25)
samplesNT <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))
samplesTP <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))
dataDEGs <- TCGAanalyze_DEA(
   mat1 = dataFilt[,samplesNT],
   mat2 = dataFilt[,samplesTP],
   Cond1type = "Normal",
   Cond2type = "Tumor"
)

Differentially expression analysis (DEA) using limma package.

Description

Differentially expression analysis (DEA) using limma package.

Usage

TCGAanalyze_DEA_Affy(AffySet, FC.cut = 0.01)

Arguments

AffySet

A matrix-like data object containing log-ratios or log-expression values for a series of arrays, with rows corresponding to genes and columns to samples
FC.cut

write

Value

List of list with tables in 2 by 2 comparison of the top-ranked genes from a linear model fitted by DEA's limma

Examples

## Not run: 
to add example

## End(Not run)

Differentially methylated regions Analysis

Description

This function will search for differentially methylated CpG sites, which are regarded as possible functional regions involved in gene transcriptional regulation.

In order to find these regions we use the beta-values (methylation values ranging from 0.0 to 1.0) to compare two groups.

Firstly, it calculates the difference between the mean methylation of each group for each probes. Secondly, it calculates the p-value using the wilcoxon test using the Benjamini-Hochberg adjustment method. The default parameters will require a minimum absolute beta values delta of 0.2 and a false discovery rate (FDR)-adjusted Wilcoxon rank-sum P-value of < 0.01 for the difference.

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

If the calculus already exists in the object it will not recalculated. You should set overwrite parameter to TRUE to force it, or remove the columns with the results from the object.

Usage

TCGAanalyze_DMC(
  data,
  groupCol = NULL,
  group1 = NULL,
  group2 = NULL,
  alternative = "two.sided",
  diffmean.cut = 0.2,
  paired = FALSE,
  adj.method = "BH",
  plot.filename = "methylation_volcano.pdf",
  ylab = expression(paste(-Log[10], " (FDR corrected P-values)")),
  xlab = expression(paste("DNA Methylation difference (", beta, "-values)")),
  title = NULL,
  legend = "Legend",
  color = c("black", "red", "darkgreen"),
  label = NULL,
  xlim = NULL,
  ylim = NULL,
  p.cut = 0.01,
  probe.names = FALSE,
  cores = 1,
  save = TRUE,
  save.directory = ".",
  filename = NULL
)

Arguments

data

SummarizedExperiment obtained from the TCGAPrepare
groupCol

Columns with the groups inside the SummarizedExperiment object. (This will be obtained by the function colData(data))
group1

In case our object has more than 2 groups, you should set the name of the group
group2

In case our object has more than 2 groups, you should set the name of the group
alternative

wilcoxon test alternative
diffmean.cut

diffmean threshold. Default: 0.2
paired

Wilcoxon paired parameter. Default: FALSE
adj.method

Adjusted method for the p-value calculation
plot.filename

Filename. Default: volcano.pdf, volcano.svg, volcano.png. If set to FALSE, there will be no plot.
ylab

y axis text
xlab

x axis text
title

main title. If not specified it will be "Volcano plot (group1 vs group2)
legend

Legend title
color

vector of colors to be used in graph
label

vector of labels to be used in the figure. Example: c("Not Significant","Hypermethylated in group1", "Hypomethylated in group1"))
xlim

x limits to cut image
ylim

y limits to cut image
p.cut

p values threshold. Default: 0.01
probe.names

is probe.names
cores

Number of cores to be used in the non-parametric test Default = groupCol.group1.group2.rda
save

Save object with results? Default: TRUE
save.directory

Directory to save the files. Default: working directory
filename

Name of the file to save the object.

Value

Volcano plot saved and the given data with the results (diffmean.group1.group2,p.value.group1.group2, p.value.adj.group1.group2,status.group1.group2) in the rowRanges where group1 and group2 are the names of the groups

Examples

nrows <- 200; ncols <- 20
 counts <- matrix(
   runif(nrows * ncols, 1, 1e4), nrows,
   dimnames = list(paste0("cg",1:200),paste0("S",1:20))
)
rowRanges <- GenomicRanges::GRanges(
  rep(c("chr1", "chr2"), c(50, 150)),
  IRanges::IRanges(floor(runif(200, 1e5, 1e6)), width = 100),
  strand = sample(c("+", "-"), 200, TRUE),
  feature_id = sprintf("ID%03d", 1:200)
)
names(rowRanges) <-  paste0("cg",1:200)
colData <- S4Vectors::DataFrame(
  Treatment = rep(c("ChIP", "Input"), 5),
  row.names = paste0("S",1:20),
  group = rep(c("group1","group2"),c(10,10))
)
data <- SummarizedExperiment::SummarizedExperiment(
         assays=S4Vectors::SimpleList(counts=counts),
         rowRanges = rowRanges,
         colData = colData
)
SummarizedExperiment::colData(data)$group <- c(rep("group 1",ncol(data)/2),
                         rep("group 2",ncol(data)/2))
hypo.hyper <- TCGAanalyze_DMC(data, p.cut = 0.85,"group","group 1","group 2")
SummarizedExperiment::colData(data)$group2 <- c(rep("group_1",ncol(data)/2),
                         rep("group_2",ncol(data)/2))
hypo.hyper <- TCGAanalyze_DMC(
  data = data,
  p.cut = 0.85,
  groupCol = "group2",
  group1 = "group_1",
  group2 = "group_2"
)

Enrichment analysis of a gene-set with GO [BP,MF,CC] and pathways.

Description

The rational behind a enrichment analysis ( gene-set, pathway etc) is to compute statistics of whether the overlap between the focus list (signature) and the gene-set is significant. ie the confidence that overlap between the list is not due to chance. The Gene Ontology project describes genes (gene products) using terms from three structured vocabularies: biological process, cellular component and molecular function. The Gene Ontology Enrichment component, also referred to as the GO Terms" component, allows the genes in any such "changed-gene" list to be characterized using the Gene Ontology terms annotated to them. It asks, whether for any particular GO term, the fraction of genes assigned to it in the "changed-gene" list is higher than expected by chance (is over-represented), relative to the fraction of genes assigned to that term in the reference set. In statistical terms it perform the analysis tests the null hypothesis that, for any particular ontology term, there is no difference in the proportion of genes annotated to it in the reference list and the proportion annotated to it in the test list. We adopted a Fisher Exact Test to perform the EA.

Usage

TCGAanalyze_EA(
  GeneName,
  RegulonList,
  TableEnrichment,
  EAGenes,
  GOtype,
  FDRThresh = 0.01,
  GeneSymbolsTable = FALSE
)

Arguments

GeneName

is the name of gene signatures list
RegulonList

is a gene signature (lisf of genes) in which perform EA.
TableEnrichment

is a table related to annotations of gene symbols such as GO[BP,MF,CC] and Pathways. It was created from DAVID gene ontology on-line.
EAGenes

is a table with informations about genes such as ID, Gene, Description, Location and Family.
GOtype

is type of gene ontology Biological process (BP), Molecular Function (MF), Cellular componet (CC)
FDRThresh

pvalue corrected (FDR) as threshold to selected significant BP, MF,CC, or pathways. (default FDR < 0.01)
GeneSymbolsTable

if it is TRUE will return a table with GeneSymbols in common GO or pathways.

Value

Table with enriched GO or pathways by selected gene signature.

Examples

## Not run: 
EAGenes <- get("EAGenes")
RegulonList <- rownames(dataDEGsFiltLevel)
ResBP <- TCGAanalyze_EA(
  GeneName="DEA genes Normal Vs Tumor",
  RegulonList = RegulonList,
  TableEnrichment = DAVID_BP_matrix,
   EAGenes = EAGenes,
   GOtype = "DavidBP"
 )

## End(Not run)

Enrichment analysis for Gene Ontology (GO) [BP,MF,CC] and Pathways

Description

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

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

Usage

TCGAanalyze_EAcomplete(TFname, RegulonList)

Arguments

TFname

is the name of the list of genes or TF's regulon.
RegulonList

List of genes such as TF's regulon or DEGs where to find enrichment.

Value

Enrichment analysis GO[BP,MF,CC] and Pathways complete table enriched by genelist.

Examples

Genelist <- c("FN1","COL1A1")
ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes Normal Vs Tumor",Genelist)
## Not run: 
Genelist <- rownames(dataDEGsFiltLevel)
system.time(ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes Normal Vs Tumor",Genelist))

## End(Not run)

Filtering mRNA transcripts and miRNA selecting a threshold.

Description

TCGAanalyze_Filtering allows user to filter mRNA transcripts and miRNA, samples, higher than the threshold defined quantile mean across all samples.

Usage

TCGAanalyze_Filtering(
  tabDF,
  method,
  qnt.cut = 0.25,
  var.func = IQR,
  var.cutoff = 0.75,
  eta = 0.05,
  foldChange = 1
)

Arguments

tabDF

is a dataframe or numeric matrix, each row represents a gene, each column represents a sample come from TCGAPrepare
method

is method of filtering such as 'quantile', 'varFilter', 'filter1', 'filter2'
qnt.cut

is threshold selected as mean for filtering
var.func

is function used as the per-feature filtering statistic. See genefilter documentation
var.cutoff

is a numeric value. See genefilter documentation
eta

is a parameter for filter1. default eta = 0.05.
foldChange

is a parameter for filter2. default foldChange = 1.

Value

A filtered dataframe or numeric matrix where each row represents a gene, each column represents a sample

Examples

dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)
dataNorm <- TCGAanalyze_Normalization(tabDF = dataBRCA,
geneInfo = geneInfo,
method = "geneLength")
dataFilt <- TCGAanalyze_Filtering(tabDF = dataNorm, method = "quantile", qnt.cut = 0.25)

Adding information related to DEGs genes from DEA as mean values in two conditions.

Description

TCGAanalyze_LevelTab allows user to add information related to DEGs genes from Differentially expression analysis (DEA) such as mean values and in two conditions.

Usage

TCGAanalyze_LevelTab(
  FC_FDR_table_mRNA,
  typeCond1,
  typeCond2,
  TableCond1,
  TableCond2,
  typeOrder = TRUE
)

Arguments

FC_FDR_table_mRNA

Output of dataDEGs filter by abs(LogFC) >=1
typeCond1

a string containing the class label of the samples in TableCond1 (e.g., control group)
typeCond2

a string containing the class label of the samples in TableCond2 (e.g., case group)
TableCond1

numeric matrix, each row represents a gene, each column represents a sample with Cond1type
TableCond2

numeric matrix, each row represents a gene, each column represents a sample with Cond2type
typeOrder

typeOrder

Value

table with DEGs, log Fold Change (FC), false discovery rate (FDR), the gene expression level for samples in Cond1type, and Cond2type, and Delta value (the difference of gene expression between the two conditions multiplied logFC)

Examples

dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)
dataFilt <- TCGAanalyze_Filtering(tabDF = dataBRCA, method = "quantile", qnt.cut =  0.25)
samplesNT <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))
samplesTP <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))
dataDEGs <- TCGAanalyze_DEA(
  dataFilt[,samplesNT],
  dataFilt[,samplesTP],
  Cond1type = "Normal",
  Cond2type = "Tumor"
)
dataDEGsFilt <- dataDEGs[abs(dataDEGs$logFC) >= 1,]
dataTP <- dataFilt[,samplesTP]
dataTN <- dataFilt[,samplesNT]
dataDEGsFiltLevel <- TCGAanalyze_LevelTab(
  FC_FDR_table_mRNA = dataDEGsFilt,
  typeCond1 = "Tumor",
  typeCond2 = "Normal",
  TableCond1 = dataTP,
  TableCond2 = dataTN
)

infer gene regulatory networks

Description

TCGAanalyze_networkInference taking expression data as input, this will return an adjacency matrix of interactions

Usage

TCGAanalyze_networkInference(data, optionMethod = "clr")

Arguments

data

expression data, genes in columns, samples in rows
optionMethod

inference method, chose from aracne, c3net, clr and mrnet

Value

an adjacent matrix

normalization mRNA transcripts and miRNA using EDASeq package.

Description

TCGAanalyze_Normalization allows user to normalize mRNA transcripts and miRNA, using EDASeq package.

Normalization for RNA-Seq Numerical and graphical summaries of RNA-Seq read data. 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 (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010).

For istance returns all mRNA or miRNA with mean across all samples, higher than the threshold defined quantile mean across all samples.

TCGAanalyze_Normalization performs normalization using following functions from EDASeq

  1. EDASeq::newSeqExpressionSet

  2. EDASeq::withinLaneNormalization

  3. EDASeq::betweenLaneNormalization

  4. EDASeq::counts

Usage

TCGAanalyze_Normalization(tabDF, geneInfo, method = "geneLength")

Arguments

tabDF

Rnaseq numeric matrix, each row represents a gene, each column represents a sample
geneInfo

Information matrix of 20531 genes about geneLength and gcContent. Two objects are provided: TCGAbiolinks::geneInfoHT,TCGAbiolinks::geneInfo
method

is method of normalization such as 'gcContent' or 'geneLength'

Value

Rnaseq matrix normalized with counts slot holds the count data as a matrix of non-negative integer count values, one row for each observational unit (gene or the like), and one column for each sample.

Examples

dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)

Generate pathview graph

Description

TCGAanalyze_Pathview pathway based data integration and visualization.

Usage

TCGAanalyze_Pathview(dataDEGs, pathwayKEGG = "hsa05200")

Arguments

dataDEGs

dataDEGs
pathwayKEGG

pathwayKEGG

Value

an adjacent matrix

Examples

## Not run: 
  dataDEGs <- data.frame(mRNA = c("TP53","TP63","TP73"), logFC = c(1,2,3))
  TCGAanalyze_Pathview(dataDEGs)

## End(Not run)

Array Array Intensity correlation (AAIC) and correlation boxplot to define outlier

Description

TCGAanalyze_Preprocessing perform Array Array Intensity correlation (AAIC). It defines a square symmetric matrix of spearman correlation among samples. According this matrix and boxplot of correlation samples by samples it is possible to find samples with low correlation that can be identified as possible outliers.

Usage

TCGAanalyze_Preprocessing(
  object,
  cor.cut = 0,
  datatype = names(assays(object))[1],
  filename = NULL,
  width = 1000,
  height = 1000
)

Arguments

object

gene expression of class RangedSummarizedExperiment from TCGAprepare
cor.cut

is a threshold to filter samples according their spearman correlation in samples by samples. default cor.cut is 0
datatype

is a string from RangedSummarizedExperiment assay
filename

Filename of the image file
width

Image width
height

Image height

Value

Plot with array array intensity correlation and boxplot of correlation samples by samples

Generate Stemness Score based on RNASeq (mRNAsi stemness index) Malta et al., Cell, 2018

Description

TCGAanalyze_Stemness generate the mRNAsi score

Usage

TCGAanalyze_Stemness(stemSig, dataGE, colname.score = "stemness_score")

Arguments

stemSig

is a vector of the stemness Signature generated using gelnet package. Please check the data from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5902191/

dataGE

is a matrix of Gene expression (genes in rows, samples in cols) from TCGAprepare
colname.score

Column name of the output. Default "stemness_score"

Value

table with samples and selected score

Examples

# Selecting TCGA breast cancer (10 samples) for example stored in dataBRCA
 dataNorm <- TCGAanalyze_Normalization(
     tabDF = dataBRCA,
     geneInfo =  geneInfo
 )

 # quantile filter of genes
 dataFilt <- TCGAanalyze_Filtering(
   tabDF = dataNorm,
   method = "quantile",
   qnt.cut =  0.25
 )
 Stemness_score <- TCGAanalyze_Stemness(
     stemSig = SC_PCBC_stemSig,
     dataGE = dataFilt,
     colname.score = "SC_PCBC_stem_score"
  )
 ECTO_score <- TCGAanalyze_Stemness(
     stemSig = ECTO_PCBC_stemSig,
     dataGE = dataFilt,
     colname.score = "ECTO_PCBC_stem_score"
  )
  MESO_score <- TCGAanalyze_Stemness(
     stemSig = MESO_PCBC_stemSig,
     dataGE = dataFilt,
     colname.score = "MESO_PCBC_stem_score"
  )

Creates survival analysis

Description

Creates a survival plot from TCGA patient clinical data using survival library. It uses the fields days_to_death and vital, plus a columns for groups.

Usage

TCGAanalyze_survival(
  data,
  clusterCol = NULL,
  legend = "Legend",
  labels = NULL,
  risk.table = TRUE,
  xlim = NULL,
  main = "Kaplan-Meier Overall Survival Curves",
  ylab = "Probability of survival",
  xlab = "Time since diagnosis (days)",
  filename = "survival.pdf",
  color = NULL,
  height = 8,
  width = 12,
  dpi = 300,
  pvalue = TRUE,
  conf.int = TRUE,
  ...
)

Arguments

data

TCGA Clinical patient with the information days_to_death
clusterCol

Column with groups to plot. This is a mandatory field, the caption will be based in this column
legend

Legend title of the figure
labels

labels of the plot
risk.table

show or not the risk table
xlim

x axis limits e.g. xlim = c(0, 1000). Present narrower X axis, but not affect survival estimates.
main

main title of the plot
ylab

y axis text of the plot
xlab

x axis text of the plot
filename

The name of the pdf file.
color

Define the colors/Pallete for lines.
height

Image height
width

Image width
dpi

Figure quality
pvalue

show p-value of log-rank test
conf.int

show confidence intervals for point estimates of survival curves.
...

Further arguments passed to ggsurvplot.

Value

Survival plot

Examples

# clin <- GDCquery_clinic("TCGA-BRCA","clinical")
 clin <- data.frame(
      vital_status = c("alive","alive","alive","dead","alive",
                       "alive","dead","alive","dead","alive"),
      days_to_death = c(NA,NA,NA,172,NA,NA,3472,NA,786,NA),
      days_to_last_follow_up = c(3011,965,718,NA,1914,423,NA,5,656,1417),
      gender = c(rep("male",5),rep("female",5))
 )
TCGAanalyze_survival(clin, clusterCol="gender")
TCGAanalyze_survival(clin, clusterCol="gender", xlim = 1000)
TCGAanalyze_survival(clin,
                     clusterCol="gender",
                     risk.table = FALSE,
                     conf.int = FALSE,
                     color = c("pink","blue"))
TCGAanalyze_survival(clin,
                     clusterCol="gender",
                     risk.table = FALSE,
                     xlim = c(100,1000),
                     conf.int = FALSE,
                     color = c("Dark2"))

survival analysis (SA) univariate with Kaplan-Meier (KM) method.

Description

TCGAanalyze_SurvivalKM perform an univariate Kaplan-Meier (KM) survival analysis (SA). It performed Kaplan-Meier survival univariate using complete follow up with all days taking one gene a time from Genelist of gene symbols. For each gene according its level of mean expression in cancer samples, defining two thresholds for quantile expression of that gene in all samples (default ThreshTop=0.67,ThreshDown=0.33) it is possible to define a threshold of intensity of gene expression to divide the samples in 3 groups (High, intermediate, low). TCGAanalyze_SurvivalKM performs SA between High and low groups using following functions from survival package

  1. survival::Surv

  2. survival::survdiff

  3. survival::survfit

Usage

TCGAanalyze_SurvivalKM(
  clinical_patient,
  dataGE,
  Genelist,
  Survresult = FALSE,
  ThreshTop = 0.67,
  ThreshDown = 0.33,
  p.cut = 0.05,
  group1,
  group2
)

Arguments

clinical_patient

is a data.frame using function 'clinic' with information related to barcode / samples such as bcr_patient_barcode, days_to_death , days_to_last_follow_up , vital_status, etc
dataGE

is a matrix of Gene expression (genes in rows, samples in cols) from TCGAprepare
Genelist

is a list of gene symbols where perform survival KM.
Survresult

is a parameter (default = FALSE) if is TRUE will show KM plot and results.
ThreshTop

is a quantile threshold to identify samples with high expression of a gene
ThreshDown

is a quantile threshold to identify samples with low expression of a gene
p.cut

p.values threshold. Default: 0.05
group1

a string containing the barcode list of the samples in in control group
group2

a string containing the barcode list of the samples in in disease group

Value

table with survival genes pvalues from KM.

Examples

# Selecting only 20 genes for example
 dataBRCAcomplete <- log2(dataBRCA[1:20,] + 1)

 # clinical_patient_Cancer <- GDCquery_clinic("TCGA-BRCA","clinical")
 clinical_patient_Cancer <- data.frame(
      bcr_patient_barcode = substr(colnames(dataBRCAcomplete),1,12),
      vital_status = c(rep("alive",3),"dead",rep("alive",2),rep(c("dead","alive"),2)),
      days_to_death = c(NA,NA,NA,172,NA,NA,3472,NA,786,NA),
      days_to_last_follow_up = c(3011,965,718,NA,1914,423,NA,5,656,1417)
 )

 group1 <- TCGAquery_SampleTypes(colnames(dataBRCAcomplete), typesample = c("NT"))
 group2 <- TCGAquery_SampleTypes(colnames(dataBRCAcomplete), typesample = c("TP"))

 tabSurvKM <- TCGAanalyze_SurvivalKM(
    clinical_patient = clinical_patient_Cancer,
    dataGE = dataBRCAcomplete,
    Genelist = rownames(dataBRCAcomplete),
    Survresult = FALSE,
    p.cut = 0.4,
    ThreshTop = 0.67,
    ThreshDown = 0.33,
    group1 = group1, # Control group
    group2 = group2
  ) # Disease group

 # If the groups are not specified group1 == group2 and all samples are used
 ## Not run: 
 tabSurvKM <- TCGAanalyze_SurvivalKM(
   clinical_patient_Cancer,
   dataBRCAcomplete,
   Genelist = rownames(dataBRCAcomplete),
   Survresult = TRUE,
   p.cut = 0.2,
    ThreshTop = 0.67,
    ThreshDown = 0.33
  )

## End(Not run)

Batch correction using ComBat and Voom transformation using limma package.

Description

TCGAbatch_correction allows user to perform a Voom correction on gene expression data and have it ready for DEA. One can also use ComBat for batch correction for exploratory analysis. If batch.factor or adjustment argument is "Year" please provide clinical data. If no batch factor is provided, the data will be voom corrected only

TCGAanalyze_DEA performs DEA using following functions from sva and limma:

  1. limma::voom Transform RNA-Seq Data Ready for Linear Modelling.

  2. sva::ComBat Adjust for batch effects using an empirical Bayes framework.

Usage

TCGAbatch_Correction(
  tabDF,
  batch.factor = NULL,
  adjustment = NULL,
  ClinicalDF = data.frame(),
  UnpublishedData = FALSE,
  AnnotationDF = data.frame()
)

Arguments

tabDF

numeric matrix, each row represents a gene, each column represents a sample
batch.factor

a string containing the batch factor to use for correction. Options are "Plate", "TSS", "Year", "Portion", "Center"
adjustment

vector containing strings for factors to adjust for using ComBat. Options are "Plate", "TSS", "Year", "Portion", "Center"
ClinicalDF

a dataframe returned by GDCquery_clinic() to be used to extract year data
UnpublishedData

if TRUE perform a batch correction after adding new data
AnnotationDF

a dataframe with column Batch indicating different batches of the samples in the tabDF

Value

data frame with ComBat batch correction applied

Prepare CEL files into an AffyBatch.

Description

Prepare CEL files into an AffyBatch.

Usage

TCGAprepare_Affy(ClinData, PathFolder, TabCel)

Arguments

ClinData

write
PathFolder

write
TabCel

write

Value

Normalized Expression data from Affy eSets

Examples

## Not run: 
to add example

## End(Not run)

Retrieve multiple tissue types from the same patients.

Description

TCGAquery_MatchedCoupledSampleTypes

Usage

TCGAquery_MatchedCoupledSampleTypes(barcode, typesample)

Arguments

barcode

barcode
typesample

typesample

Value

a list of samples / barcode filtered by type sample selected

Examples

TCGAquery_MatchedCoupledSampleTypes(c("TCGA-B0-4698-01Z-00-DX1",
                             "TCGA-B0-4698-02Z-00-DX1"),
                             c("TP","TR"))
 barcode <- c("TARGET-20-PANSBH-02A-02D","TARGET-20-PANSBH-01A-02D",
              "TCGA-B0-4698-01Z-00-DX1","TCGA-CZ-4863-02Z-00-DX1",
              "TARGET-20-PANSZZ-02A-02D","TARGET-20-PANSZZ-11A-02D",
              "TCGA-B0-4699-01Z-00-DX1","TCGA-B0-4699-02Z-00-DX1"
              )
  TCGAquery_MatchedCoupledSampleTypes(barcode,c("TR","TP"))

Query gene counts of TCGA and GTEx data from the Recount2 project

Description

TCGArecount2_query queries and downloads data produced by the Recount2 project. User can specify which project and which tissue to query

Usage

TCGAquery_recount2(project, tissue = c())

Arguments

project

is a string denoting which project the user wants. Options are "tcga" and "gtex"
tissue

a vector of tissue(s) to download. Options are "adipose tissue", "adrenal gland", "bladder","blood", "blood vessel", "bone marrow", "brain", "breast","cervix uteri", "colon", "esophagus", "fallopian tube","heart", "kidney", "liver", "lung", "muscle", "nerve", "ovary","pancreas", "pituitary", "prostate", "salivary gland", "skin", "small intestine", "spleen", "stomach", "testis", "thyroid", "uterus", "vagina"

Value

List with $subtypes attribute as a dataframe with barcodes, samples, subtypes, and colors. The $filtered attribute is returned as filtered samples with no subtype info

Examples

## Not run: 
 brain.rec<-TCGAquery_recount2(project = "gtex", tissue = "brain")

## End(Not run)

Retrieve multiple tissue types not from the same patients.

Description

TCGAquery_SampleTypes for a given list of samples and types, return the union of samples that are from theses type.

Usage

TCGAquery_SampleTypes(barcode, typesample)

Arguments

barcode

is a list of samples as TCGA barcodes
typesample

a character vector indicating tissue type to query. Example:

TP PRIMARY SOLID TUMOR
TR RECURRENT SOLID TUMOR
TB Primary Blood Derived Cancer-Peripheral Blood
TRBM Recurrent Blood Derived Cancer-Bone Marrow
TAP Additional-New Primary
TM Metastatic
TAM Additional Metastatic
THOC Human Tumor Original Cells
TBM Primary Blood Derived Cancer-Bone Marrow
NB Blood Derived Normal
NT Solid Tissue Normal
NBC Buccal Cell Normal
NEBV EBV Immortalized Normal
NBM Bone Marrow Normal

Value

a list of samples / barcode filtered by type sample selected

Examples

# selection of normal samples "NT"
barcode <- c("TCGA-B0-4698-01Z-00-DX1","TCGA-CZ-4863-02Z-00-DX1")
# Returns the second barcode
 TCGAquery_SampleTypes(barcode,"TR")
 # Returns both barcode
 TCGAquery_SampleTypes(barcode,c("TR","TP"))
 barcode <- c("TARGET-20-PANSBH-14A-02D","TARGET-20-PANSBH-01A-02D",
              "TCGA-B0-4698-01Z-00-DX1","TCGA-CZ-4863-02Z-00-DX1")
  TCGAquery_SampleTypes(barcode,c("TR","TP"))

Retrieve molecular subtypes for a given tumor

Description

TCGAquery_subtype Retrieve molecular subtypes for a given tumor

Usage

TCGAquery_subtype(tumor)

Arguments

tumor

is a cancer Examples:

lgg gbm luad stad brca
coad read

Value

a data.frame with barcode and molecular subtypes

Examples

dataSubt <- TCGAquery_subtype(tumor = "lgg")

Filters TCGA barcodes according to purity parameters

Description

TCGAtumor_purity Filters TCGA samples using 5 estimates from 5 methods as thresholds.

Usage

TCGAtumor_purity(barcodes, estimate, absolute, lump, ihc, cpe)

Arguments

barcodes

is a vector of TCGA barcodes
estimate

uses gene expression profiles of 141 immune genes and 141 stromal genes
absolute

which uses somatic copy-number data (estimations were available for only 11 cancer types)
lump

(leukocytes unmethylation for purity), which averages 44 non-methylated immune-specific CpG sites
ihc

as estimated by image analysis of haematoxylin and eosin stain slides produced by the Nationwide Childrens Hospital Biospecimen Core Resource
cpe

CPE is a derived consensus measurement as the median purity level after normalizing levels from all methods to give them equal means and s.ds

Value

List with $pure_barcodes attribute as a vector of pure samples and $filtered attribute as filtered samples with no purity info

Examples

dataTableSubt <- TCGAtumor_purity("TCGA-60-2721-01A-01R-0851-07",
                          estimate = 0.6,
                          absolute = 0.6,
                          ihc = 0.8,
                          lump = 0.8,
                          cpe = 0.7)

Barplot of subtypes and clinical info in groups of gene expression clustered.

Description

Barplot of subtypes and clinical info in groups of gene expression clustered.

Usage

TCGAvisualize_BarPlot(
  DFfilt,
  DFclin,
  DFsubt,
  data_Hc2,
  Subtype,
  cbPalette,
  filename,
  width,
  height,
  dpi
)

Arguments

DFfilt

write
DFclin

write
DFsubt

write
data_Hc2

write
Subtype

write
cbPalette

Define the colors of the bar.
filename

The name of the pdf file
width

Image width
height

Image height
dpi

Image dpi

Value

barplot image in pdf or png file

barPlot for a complete Enrichment Analysis

Description

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

Usage

TCGAvisualize_EAbarplot(
  tf,
  GOMFTab,
  GOBPTab,
  GOCCTab,
  PathTab,
  nBar,
  nRGTab,
  filename = "TCGAvisualize_EAbarplot_Output.pdf",
  text.size = 1,
  mfrow = c(2, 2),
  xlim = NULL,
  fig.width = 30,
  fig.height = 15,
  color = c("orange", "cyan", "green", "yellow")
)

Arguments

tf

is a list of gene symbols
GOMFTab

is results from TCGAanalyze_EAcomplete related to Molecular Function (MF)
GOBPTab

is results from TCGAanalyze_EAcomplete related to Biological Process (BP)
GOCCTab

is results from TCGAanalyze_EAcomplete related to Cellular Component (CC)
PathTab

is results from TCGAanalyze_EAcomplete related to Pathways EA
nBar

is the number of bar histogram selected to show (default = 10)
nRGTab

is the gene signature list with gene symbols.
filename

Name for the pdf. If null it will return the plot.
text.size

Text size
mfrow

Vector with number of rows/columns of the plot. Default 2 rows/2 columns "c(2,2)"
xlim

Upper limit of the x-axis.
fig.width

Default 30
fig.height

Default 15
color

A vector of colors for each barplot. Deafult: c("orange", "cyan","green","yellow")

Value

Complete barPlot from Enrichment Analysis showing significant (default FDR < 0.01) BP,CC,MF and pathways enriched by list of genes.

Examples

Genelist <- c("FN1","COL1A1")
ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes Normal Vs Tumor",Genelist)
TCGAvisualize_EAbarplot(tf = rownames(ansEA$ResBP),
         GOBPTab = ansEA$ResBP,
         GOCCTab = ansEA$ResCC,
         GOMFTab = ansEA$ResMF,
        PathTab = ansEA$ResPat,
         nRGTab = Genelist,
         nBar = 10,
         filename="a.pdf")
## Not run: 
Genelist <- rownames(dataDEGsFiltLevel)
system.time(ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes Normal Vs Tumor",Genelist))
# Enrichment Analysis EA (TCGAVisualize)
# Gene Ontology (GO) and Pathway enrichment barPlot
TCGAvisualize_EAbarplot(tf = rownames(ansEA$ResBP),
         GOBPTab = ansEA$ResBP,
         GOCCTab = ansEA$ResCC,
         GOMFTab = ansEA$ResMF,
        PathTab = ansEA$ResPat,
         nRGTab = Genelist,
         nBar = 10)

## End(Not run)

Heatmap with more sensible behavior using heatmap.plus

Description

Heatmap with more sensible behavior using heatmap.plus

Usage

TCGAvisualize_Heatmap(
  data,
  col.metadata,
  row.metadata,
  col.colors = NULL,
  row.colors = NULL,
  show_column_names = FALSE,
  show_row_names = FALSE,
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  sortCol,
  extremes = NULL,
  rownames.size = 12,
  title = NULL,
  color.levels = NULL,
  values.label = NULL,
  filename = "heatmap.pdf",
  width = 10,
  height = 10,
  type = "expression",
  scale = "none",
  heatmap.legend.color.bar = "continuous"
)

Arguments

data

The object to with the heatmap data (expression, methylation)
col.metadata

Metadata for the columns (samples). It should have on of the following columns: barcode (28 characters) column to match with the samples. It will also work with "bcr_patient_barcode"(12 chars),"patient"(12 chars),"sample"(16 chars) columns but as one patient might have more than one sample, this coul lead to errors in the annotation. The code will throw a warning in case two samples are from the same patient.
row.metadata

Metadata for the rows genes (expression) or probes (methylation)
col.colors

A list of names colors
row.colors

A list of named colors
show_column_names

Show column names names? Default: FALSE
show_row_names

Show row names? Default: FALSE
cluster_rows

Cluster rows ? Default: FALSE
cluster_columns

Cluster columns ? Default: FALSE
sortCol

Name of the column to be used to sort the columns
extremes

Extremes of colors (vector of 3 values)
rownames.size

Rownames size
title

Title of the plot
color.levels

A vector with the colors (low level, middle level, high level)
values.label

Text of the levels in the heatmap
filename

Filename to save the heatmap. Default: heatmap.png
width

figure width
height

figure height
type

Select the colors of the heatmap values. Possible values are "expression" (default), "methylation"
scale

Use z-score to make the heatmap? If we want to show differences between genes, it is good to make Z-score by samples (force each sample to have zero mean and standard deviation=1). If we want to show differences between samples, it is good to make Z-score by genes (force each gene to have zero mean and standard deviation=1). Possibilities: "row", "col". Default "none"
heatmap.legend.color.bar

Heatmap legends values type. Options: "continuous", "discrete"

Value

Heatmap plotted in the device

Examples

row.mdat <- matrix(c("FALSE","FALSE",
                     "TRUE","TRUE",
                     "FALSE","FALSE",
                     "TRUE","FALSE",
                     "FALSE","TRUE"
                ),
              nrow = 5, ncol = 2, byrow = TRUE,
              dimnames = list(
                  c("probe1", "probe2","probe3","probe4","probe5"),
                  c("duplicated", "Enhancer region")))
dat <- matrix(c(0.3,0.2,0.3,1,1,0.1,1,1,0, 0.8,1,0.7,0.7,0.3,1),
             nrow = 5, ncol = 3, byrow = TRUE,
               dimnames = list(
               c("probe1", "probe2","probe3","probe4","probe5"),
               c("TCGA-DU-6410",
                 "TCGA-DU-A5TS",
                 "TCGA-HT-7688")))

mdat <- data.frame(patient=c("TCGA-DU-6410","TCGA-DU-A5TS","TCGA-HT-7688"),
                   Sex=c("Male","Female","Male"),
                   COCCluster=c("coc1","coc1","coc1"),
                   IDHtype=c("IDHwt","IDHMut-cod","IDHMut-noncod"))

TCGAvisualize_Heatmap(dat,
                    col.metadata = mdat,
                    row.metadata = row.mdat,
                    row.colors = list(duplicated = c("FALSE" = "pink",
                                                     "TRUE"="green"),
                                     "Enhancer region" = c("FALSE" = "purple",
                                                            "TRUE"="grey")),
                    col.colors = list(Sex = c("Male" = "blue", "Female"="red"),
                                      COCCluster=c("coc1"="grey"),
                                      IDHtype=c("IDHwt"="cyan",
                                      "IDHMut-cod"="tomato"
                                      ,"IDHMut-noncod"="gold")),
                    type = "methylation",
                    show_row_names=TRUE)

Mean methylation boxplot

Description

Creates a mean methylation boxplot for groups (groupCol), subgroups will be highlighted as shapes if the subgroupCol was set.

Observation: Data is a summarizedExperiment.

Usage

TCGAvisualize_meanMethylation(
  data,
  groupCol = NULL,
  subgroupCol = NULL,
  shapes = NULL,
  print.pvalue = FALSE,
  plot.jitter = TRUE,
  jitter.size = 3,
  filename = "groupMeanMet.pdf",
  ylab = expression(paste("Mean DNA methylation (", beta, "-values)")),
  xlab = NULL,
  title = "Mean DNA methylation",
  labels = NULL,
  group.legend = NULL,
  subgroup.legend = NULL,
  color = NULL,
  y.limits = NULL,
  sort,
  order,
  legend.position = "top",
  legend.title.position = "top",
  legend.ncols = 3,
  add.axis.x.text = TRUE,
  width = 10,
  height = 10,
  dpi = 600,
  axis.text.x.angle = 90
)

Arguments

data

SummarizedExperiment object obtained from TCGAPrepare
groupCol

Columns in colData(data) that defines the groups. If no columns defined a columns called "Patients" will be used
subgroupCol

Columns in colData(data) that defines the subgroups.
shapes

Shape vector of the subgroups. It must have the size of the levels of the subgroups. Example: shapes = c(21,23) if for two levels
print.pvalue

Print p-value for two groups
plot.jitter

Plot jitter? Default TRUE
jitter.size

Plot jitter size? Default 3
filename

The name of the pdf that will be saved
ylab

y axis text in the plot
xlab

x axis text in the plot
title

main title in the plot
labels

Labels of the groups
group.legend

Name of the group legend. DEFAULT: groupCol
subgroup.legend

Name of the subgroup legend. DEFAULT: subgroupCol
color

vector of colors to be used in graph
y.limits

Change lower/upper y-axis limit
sort

Sort boxplot by mean or median. Possible values: mean.asc, mean.desc, median.asc, median.desc
order

Order of the boxplots
legend.position

Legend position ("top", "right","left","bottom")
legend.title.position

Legend title position ("top", "right","left","bottom")
legend.ncols

Number of columns of the legend
add.axis.x.text

Add text to x-axis? Default: FALSE
width

Plot width default:10
height

Plot height default:10
dpi

Pdf dpi default:600
axis.text.x.angle

Angle of text in the x axis

Value

Save the pdf survival plot

Examples

nrows <- 200; ncols <- 21
counts <- matrix(runif(nrows * ncols, 0, 1), nrows)
rowRanges <- GenomicRanges::GRanges(rep(c("chr1", "chr2"), c(50, 150)),
                   IRanges::IRanges(floor(runif(200, 1e5, 1e6)), width=100),
                    strand=sample(c("+", "-"), 200, TRUE),
                    feature_id=sprintf("ID%03d", 1:200))
colData <- S4Vectors::DataFrame(Treatment=rep(c("ChIP", "Input","Other"), 7),
                    row.names=LETTERS[1:21],
                    group=rep(c("group1","group2","group3"),c(7,7,7)),
                    subgroup=rep(c("subgroup1","subgroup2","subgroup3"),7))
data <- SummarizedExperiment::SummarizedExperiment(
         assays=S4Vectors::SimpleList(counts=counts),
         rowRanges=rowRanges,
         colData=colData)
TCGAvisualize_meanMethylation(data,groupCol  = "group")
# change lower/upper y-axis limit
TCGAvisualize_meanMethylation(data,groupCol  = "group", y.limits = c(0,1))
# change lower y-axis limit
TCGAvisualize_meanMethylation(data,groupCol  = "group", y.limits = 0)
TCGAvisualize_meanMethylation(data,groupCol  = "group", subgroupCol="subgroup")
TCGAvisualize_meanMethylation(data,groupCol  = "group")
TCGAvisualize_meanMethylation(data,groupCol  = "group",sort="mean.desc",filename="meandesc.pdf")
TCGAvisualize_meanMethylation(data,groupCol  = "group",sort="mean.asc",filename="meanasc.pdf")
TCGAvisualize_meanMethylation(data,groupCol  = "group",sort="median.asc",filename="medianasc.pdf")
TCGAvisualize_meanMethylation(data,groupCol  = "group",sort="median.desc",filename="mediandesc.pdf")

Creating a oncoprint

Description

Creating a oncoprint

Usage

TCGAvisualize_oncoprint(
  mut,
  genes,
  filename,
  color,
  annotation.position = "bottom",
  annotation,
  height,
  width = 10,
  rm.empty.columns = FALSE,
  show.column.names = FALSE,
  show.row.barplot = TRUE,
  label.title = "Mutation",
  column.names.size = 8,
  label.font.size = 16,
  rows.font.size = 16,
  dist.col = 0.5,
  dist.row = 0.5,
  information = "Variant_Type",
  row.order = TRUE,
  col.order = TRUE,
  heatmap.legend.side = "bottom",
  annotation.legend.side = "bottom"
)

Arguments

mut

A dataframe from the mutation annotation file (see TCGAquery_maf from TCGAbiolinks)
genes

Gene list
filename

name of the pdf
color

named vector for the plot
annotation.position

Position of the annotation "bottom" or "top"
annotation

Matrix or data frame with the annotation. Should have a column bcr_patient_barcode with the same ID of the mutation object
height

pdf height
width

pdf width
rm.empty.columns

If there is no alteration in that sample, whether remove it on the oncoprint
show.column.names

Show column names? Default: FALSE
show.row.barplot

Show barplot annotation on rows?
label.title

Title of the label
column.names.size

Size of the fonts of the columns names
label.font.size

Size of the fonts
rows.font.size

Size of the fonts
dist.col

distance between columns in the plot
dist.row

distance between rows in the plot
information

Which column to use as information from MAF. Options: 1) "Variant_Classification" (The information will be "Frame_Shift_Del", "Frame_Shift_Ins", "In_Frame_Del", "In_Frame_Ins", "Missense_Mutation", "Nonsense_Mutation", "Nonstop_Mutation", "RNA", "Silent" , "Splice_Site", "Targeted_Region", "Translation_Start_Site") 2) "Variant_Type" (The information will be INS,DEL,SNP)
row.order

Order the genes (rows) Default:TRUE. Genes with more mutations will be in the first rows
col.order

Order columns. Default:TRUE.
heatmap.legend.side

Position of the heatmap legend
annotation.legend.side

Position of the annotation legend

Value

A oncoprint plot

Examples

## Not run: 
library(dplyr)
query <- GDCquery(
   project = "TCGA-CHOL",
   data.category = "Simple Nucleotide Variation",
   access = "open",
   legacy = FALSE,
   data.type = "Masked Somatic Mutation",
   workflow.type = "Aliquot Ensemble Somatic Variant Merging and Masking"
)
GDCdownload(query)
mut <- GDCprepare(query)
TCGAvisualize_oncoprint(mut = mut, genes = mut$Hugo_Symbol[1:10], rm.empty.columns = TRUE)
TCGAvisualize_oncoprint(
  mut = mut, genes = mut$Hugo_Symbol[1:10],
  filename = "onco.pdf",
  color = c("background"="#CCCCCC","DEL"="purple","INS"="yellow","SNP"="brown")
)
clin <- GDCquery_clinic("TCGA-ACC","clinical")
clin <- clin[,c("bcr_patient_barcode","disease","gender","tumor_stage","race","vital_status")]
TCGAvisualize_oncoprint(
   mut = mut, genes = mut$Hugo_Symbol[1:20],
   filename = "onco.pdf",
   annotation = clin,
   color=c("background"="#CCCCCC","DEL"="purple","INS"="yellow","SNP"="brown"),
   rows.font.size=10,
   heatmap.legend.side = "right",
   dist.col = 0,
   label.font.size = 10
)

## End(Not run)

Principal components analysis (PCA) plot

Description

TCGAvisualize_PCA performs a principal components analysis (PCA) on the given data matrix and returns the results as an object of class prcomp, and shows results in PCA level.

Usage

TCGAvisualize_PCA(dataFilt, dataDEGsFiltLevel, ntopgenes, group1, group2)

Arguments

dataFilt

A filtered dataframe or numeric matrix where each row represents a gene, each column represents a sample from function TCGAanalyze_Filtering
dataDEGsFiltLevel

table with DEGs, log Fold Change (FC), false discovery rate (FDR), the gene expression level, etc, from function TCGAanalyze_LevelTab.
ntopgenes

number of DEGs genes to plot in PCA
group1

a string containing the barcode list of the samples in in control group
group2

a string containing the barcode list of the samples in in disease group the name of the group

Value

principal components analysis (PCA) plot of PC1 and PC2

Examples

# normalization of genes
dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(tabDF = dataBRCA, geneInfo = geneInfo,
method = "geneLength")
# quantile filter of genes
dataFilt <- TCGAanalyze_Filtering(tabDF = dataBRCA, method = "quantile", qnt.cut =  0.25)
# Principal Component Analysis plot for ntop selected DEGs
    # selection of normal samples "NT"
    group1 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))
    # selection of normal samples "TP"
    group2 <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))
pca <- TCGAvisualize_PCA(dataFilt,dataDEGsFiltLevel, ntopgenes = 200, group1, group2)

Create starburst plot

Description

Create Starburst plot for comparison of DNA methylation and gene expression. The log10 (FDR-corrected P value) is plotted for beta value for DNA methylation (x axis) and gene expression (y axis) for each gene.

The black dashed line shows the FDR-adjusted P value of 0.01.

You can set names to TRUE to get the names of the significant genes.

Candidate biologically significant genes will be circled in the plot.

Candidate biologically significant are the genes that respect the expression (logFC.cut), DNA methylation (diffmean.cut) and significance thresholds (exp.p.cut, met.p.cut)

Usage

TCGAvisualize_starburst(
  met,
  exp,
  group1 = NULL,
  group2 = NULL,
  exp.p.cut = 0.01,
  met.p.cut = 0.01,
  diffmean.cut = 0,
  logFC.cut = 0,
  met.platform = c("Illumina Human Methylation 450", "Illumina Human Methylation 27",
    "Illumina Methylation Epic"),
  genome,
  names = FALSE,
  names.fill = TRUE,
  filename = "starburst.png",
  return.plot = FALSE,
  ylab = expression(atop("Gene Expression", paste(-Log[10],
    " (FDR corrected P values)"))),
  xlab = expression(atop("DNA Methylation", paste(-Log[10],
    " (FDR corrected P values)"))),
  title = "Starburst Plot",
  legend = "DNA Methylation/Expression Relation",
  color = NULL,
  label = c("Not Significant", "Up regulated & Hypo methylated",
    "Down regulated & Hypo methylated", "hypo methylated", "hyper methylated",
    "Up regulated", "Down regulated", "Up regulated & Hyper methylated",
    "Down regulated & Hyper methylated"),
  xlim = NULL,
  ylim = NULL,
  height = 10,
  width = 20,
  dpi = 600
)

Arguments

met

A SummarizedExperiment with methylation data obtained from the TCGAPrepare or Data frame from DMR_results file. Expected colData columns: diffmean, p.value.adj and p.value Execute volcanoPlot function in order to obtain these values for the object.
exp

Object obtained by DEArnaSEQ function
group1

The name of the group 1 Obs: Column p.value.adj.group1.group2 should exist
group2

The name of the group 2. Obs: Column p.value.adj.group1.group2 should exist
exp.p.cut

expression p value cut-off
met.p.cut

methylation p value cut-off
diffmean.cut

If set, the probes with diffmean higher than methylation cut-off will be highlighted in the plot. And the data frame return will be subseted.
logFC.cut

If set, the probes with expression fold change higher than methylation cut-off will be highlighted in the plot. And the data frame return will be subseted.
met.platform

DNA methylation platform "Illumina Human Methylation 450", "Illumina Human Methylation 27", "Illumina Methylation Epic"
genome

Genome of reference ("hg38" or "hg19") used to identify nearest probes TSS
names

Add the names of the significant genes? Default: FALSE
names.fill

Names should be filled in a color box? Default: TRUE
filename

The filename of the file (it can be pdf, svg, png, etc)
return.plot

If true only plot object will be returned (pdf will not be created)
ylab

y axis text
xlab

x axis text
title

main title
legend

legend title
color

vector of colors to be used in graph
label

vector of labels to be used in graph
xlim

x limits to cut image
ylim

y limits to cut image
height

Figure height
width

Figure width
dpi

Figure dpi

Details

Input: data with gene expression/methylation expression Output: starburst plot

Value

Save a starburst plot

Examples

## Not run: 
library(SummarizedExperiment)
met <- TCGAbiolinks:::getMetPlatInfo(
   genome = "hg38",
   platform = "Illumina Human Methylation 27"
 )
values(met) <- NULL
met$probeID <- names(met)
nrows <- length(met); ncols <- 20
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
colData <- S4Vectors::DataFrame(
  Treatment = rep(c("ChIP", "Input"), 5),
  row.names = LETTERS[1:20],
  group = rep(c("group1","group2"),c(10,10))
)
met <- SummarizedExperiment::SummarizedExperiment(
         assays = S4Vectors::SimpleList(counts=counts),
         rowRanges = met,
         colData = colData
)
rowRanges(met)$diffmean.g1.g2 <- c(runif(nrows, -0.1, 0.1))
rowRanges(met)$diffmean.g2.g1 <- -1*(rowRanges(met)$diffmean.g1.g2)
rowRanges(met)$p.value.g1.g2 <- c(runif(nrows, 0, 1))
rowRanges(met)$p.value.adj.g1.g2 <- c(runif(nrows, 0, 1))
exp <- TCGAbiolinks:::get.GRCh.bioMart("hg38")
exp$logFC <- runif(nrow(exp), -5, 5)
exp$FDR <- runif(nrow(exp), 0.01, 1)

result <- TCGAvisualize_starburst(
  met,
  exp,
  exp.p.cut = 0.05,
  met.p.cut = 0.05,
  logFC.cut = 2,
  group1 = "g1",
  group2 = "g2",
  genome = "hg38",
  met.platform = "27k",
  diffmean.cut = 0.0,
  names  = TRUE
)

## End(Not run)

Survival analysis with univariate Cox regression package (dnet)

Description

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

  1. survival::coxph

  2. igraph::subgraph.edges

  3. igraph::layout.fruchterman.reingold

  4. igraph::spinglass.community

  5. igraph::communities

  6. dnet::dRDataLoader

  7. dnet::dNetInduce

  8. dnet::dNetPipeline

  9. dnet::visNet

  10. dnet::dCommSignif

Usage

TCGAvisualize_SurvivalCoxNET(
  clinical_patient,
  dataGE,
  Genelist,
  org.Hs.string,
  scoreConfidence = 700,
  titlePlot = "TCGAvisualize_SurvivalCoxNET Example"
)

Arguments

clinical_patient

is a data.frame using function 'clinic' with information related to barcode / samples such as bcr_patient_barcode, days_to_death , days_to_last_followup , vital_status, etc
dataGE

is a matrix of Gene expression (genes in rows, samples in cols) from TCGAprepare
Genelist

is a list of gene symbols where perform survival KM.
org.Hs.string

an igraph object that contains a functional protein association network in human. The network is extracted from the STRING database (version 10).
scoreConfidence

restrict to those edges with high confidence (eg. score>=700)
titlePlot

is the title to show in the final plot.

Details

TCGAvisualize_SurvivalCoxNET allow user to perform the complete workflow using coxph and dnet package related to survival analysis with an identification of gene-active networks from high-throughput omics data using gene expression and clinical data.

  1. Cox regression survival analysis to obtain hazard ratio (HR) and p-values

  2. fit a Cox proportional hazards model and ANOVA (Chisq test)

  3. Network comunites

  4. An igraph object that contains a functional protein association network in human. The network is extracted from the STRING database (version 9.1). Only those associations with medium confidence (score>=400) are retained.

  5. restrict to those edges with high confidence (score>=700)

  6. extract network that only contains genes in pvals

  7. Identification of gene-active network

  8. visualisation of the gene-active network itself

  9. the layout of the network visualisation (fixed in different visuals)

  10. color nodes according to communities (identified via a spin-glass model and simulated annealing)

  11. node sizes according to degrees

  12. highlight different communities

  13. visualize the subnetwork

Value

net IGRAPH with related Cox survival genes in community (same pval and color) and with interactions from STRING database.

Creates a volcano plot for DNA methylation or gene expression

Description

Creates a volcano plot from the gene expression and DNA methylation analysis.

Usage

TCGAVisualize_volcano(
  x,
  y,
  filename = "volcano.pdf",
  ylab = expression(paste(-Log[10], " (FDR corrected P-values)")),
  xlab = NULL,
  title = "Volcano plot",
  legend = NULL,
  label = NULL,
  xlim = NULL,
  ylim = NULL,
  color = c("black", "red", "green"),
  names = NULL,
  names.fill = TRUE,
  show.names = "significant",
  x.cut = 0,
  y.cut = 0.01,
  height = 5,
  width = 10,
  highlight = NULL,
  highlight.color = "orange",
  names.size = 4,
  dpi = 300
)

Arguments

x

x-axis data (i.e. Diff mean beta-values or Log2FC).
y

FDR adjusted p-value (q-value). This data will be transformed to -log10 values.
filename

File name: volcano.pdf, volcano.svg, volcano.png. If NULL returns the ggplot object.
ylab

y axis text. Default: -Log10 FDR corrected P-values
xlab

x axis text. Default: No text. Examples of input: expression(paste(Log[2], "FoldChange"))
title

main title. If not specified it will be "Volcano plot (group1 vs group2)
legend

Legend title
label

vector of labels to be used in the figure. Example: c("Not Significant","Hypermethylated in group1", "Hypomethylated in group1"))#'
xlim

x limits to cut image (i.e. c(-4,4))
ylim

y limits to cut image (i.e. c(-1,10))
color

vector of colors to be used in graph
names

Names to be plotted if significant. Should be the same size of x and y
names.fill

Names should be filled in a color box? Default: TRUE
show.names

What names will be showed? Possibilities: "both", "significant", "highlighted"
x.cut

x-axis threshold. Default: 0.0 If you give only one number (e.g. 0.2) the cut-offs will be -0.2 and 0.2. Or you can give different cut-offs as a vector (e.g. c(-0.3,0.4))
y.cut

q-values threshold (i.e. 0.01, 10^-10)
height

Figure height
width

Figure width
highlight

List of genes/probes to be highlighted. It should be in the names argument.
highlight.color

Color of the points highlighted
names.size

Size of the names text
dpi

Figure dpi

Details

Creates a volcano plot from the gene expression and DNA methylation analysis. Please see the vignette for more information

Value

Saves the volcano plot in the current folder

Examples

log2_foldchange <- runif(200, -2, 2)
fdr <- runif(200, 0.01, 1)
TCGAVisualize_volcano(
    x = log2_foldchange,
    y = fdr,
    x.cut = 1.5,
    y.cut = 0.01,
    title = "Title example",
    xlab = expression(paste(Log[2], "FoldChange"))
)
## Not run: 
beta_diff <- runif(200, -1, 1)
fdr <- runif(200, 0.01, 1)
TCGAVisualize_volcano(
    x = beta_diff,
    y = fdr,
    x.cut = 1.5,
    y.cut = 0.01,
    title = "Title example",
    xlab = expression(paste("DNA Methylation difference (", beta, "-values)"))
)
TCGAVisualize_volcano(
  x,
  y,
  filename = NULL,
  y.cut = 10000000,
  x.cut=0.8,
   names = rep("AAAA",length(x)),
   legend = "Status",
   names.fill = FALSE
)

TCGAVisualize_volcano(
  x,
  y,
  filename = NULL,
  y.cut = 10000000,
  x.cut = 0.8,
   names = as.character(1:length(x)),
   legend = "Status",
   names.fill = TRUE, highlight = c("1","2"),
   show = "both"
)
TCGAVisualize_volcano(
  x,
  y,
  filename = NULL,
  y.cut = 10000000,
  x.cut = c(-0.3,0.8),
  names = as.character(1:length(x)),
  legend = "Status",
  names.fill = TRUE,
  highlight = c("1","2"),
  show = "both"
)

## End(Not run)
while (!(is.null(dev.list()["RStudioGD"]))){dev.off()}

TCGA samples with their Tumor Purity measures

Description

A dataset containing the Sample Ids from TCGA tumor purity measured according to 4 estimates attributes of 9364 tumor patients

Usage

Tumor.purity

Format

A data frame with 9364 rows and 7 variables:

Sample.ID

Sample ID from TCGA barcodes, character string

Cancer.type

Cancer type, character string

ESTIMATE

uses gene expression profiles of 141 immune genes and 141 stromal genes, 0-1 value

ABSOLUTE

uses somatic copy-number data (estimations were available for only 11 cancer types), 0-1 value

LUMP

(leukocytes unmethylation for purity), which averages 44 non-methylated immune-specific CpG sites, 0-1value

IHC

as estimated by image analysis of haematoxylin and eosin stain slides produced by the Nationwide Childrens Hospital Biospecimen Core Resource, 0-1 value

CPE

derived consensus measurement as the median purity level after normalizing levels from all methods to give them equal means and s.ds, 0-1 value

...

Source

https://images.nature.com/original/nature-assets/ncomms/2015/151204/ncomms9971/extref/ncomms9971-s2.xlsx

Use raw count from the DataPrep object which genes are removed by normalization and filtering steps.

Description

function to keep raw counts after filtering and/or normalizing.

Usage

UseRaw_afterFilter(DataPrep, DataFilt)

Arguments

DataPrep

DataPrep object returned by TCGAanalyze_Preprocessing()
DataFilt

Filtered data frame containing samples in columns and genes in rows after normalization and/or filtering steps

Value

Filtered return object similar to DataPrep with genes removed after normalization and filtering process.

Examples

## Not run: 
  dataPrep_raw <- UseRaw_afterFilter(dataPrep, dataFilt)

## End(Not run)