BRCA_rnaseqv2

Description

BRCA_rnaseqv2

Format

A data frame with 200 rows (genes) and 1172 variables (samples)

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

Value

Shows the output from the GDC transfer tools

Examples

query <- GDCquery(project = "TCGA-ACC",
data.category =  "Copy number variation",
legacy = TRUE,
file.type = "hg19.seg",
barcode = c("TCGA-OR-A5LR-01A-11D-A29H-01", "TCGA-OR-A5LJ-10A-01D-A29K-01"))
# data will be saved in  GDCdata/TCGA-ACC/legacy/Copy_number_variation/Copy_number_segmentation
GDCdownload(query, method = "api")
# 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")
acc.gbm <- GDCquery(project =  c("TCGA-ACC","TCGA-GBM"),
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "HTSeq - Counts")
GDCdownload(acc.gbm, method = "api", directory = "example", files.per.chunk = 50)

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, mut.pipeline = "mutect2",
  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

Value

A summarizedExperiment or a data.frame

Examples

query <- GDCquery(project = "TCGA-KIRP",
data.category = "Simple Nucleotide Variation",
data.type = "Masked Somatic Mutation",
workflow.type = "MuSE Variant Aggregation and Masking")
GDCdownload(query, method = "api", directory = "maf")
maf <- GDCprepare(query, directory = "maf")

# Get GISTIC values
gistic.query <- GDCquery(project = "TCGA-ACC",
data.category = "Copy Number Variation",
data.type = "Gene Level Copy Number Scores",
access="open")
GDCdownload(gistic.query)
gistic <- GDCprepare(gistic.query)

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

Value

A data frame with the parsed values from the XML

Examples

query <- GDCquery(project = "TCGA-COAD",
data.category = "Clinical",
file.type = "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")
query <- GDCquery(project = "TCGA-COAD",
data.category = "Biospecimen",
file.type = "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")

Query GDC data

Description

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

Usage

GDCquery(project, data.category, data.type, workflow.type,
  legacy = FALSE, access, platform, file.type, barcode,
  experimental.strategy, sample.type)

Arguments

|platform | Example: list(list("ll"), list(" ", "CGH- 1x1M_G4447A ", list(), " IlluminaGA_RNASeqV2 ", list(), " ", "AgilentG4502A_07 ", list(), " IlluminaGA_mRNA_DGE ", list(), " ", "Human1MDuo ", list(), " HumanMethylation450 ", list(), " ", "HG-CGH-415K_G4124A ", list(), " IlluminaGA_miRNASeq ", list(), " ", "HumanHap550 ", list(), " IlluminaHiSeq_miRNASeq ", list(), " ", "ABI ", |

list(), " H-miRNA_8x15K  ", list(), "

", "HG-CGH-244A ", list(), " SOLiD_DNASeq ", list(), " ", "IlluminaDNAMethylation_OMA003_CPI ", list(), " IlluminaGA_DNASeq_automated ", list(), " ", "IlluminaDNAMethylation_OMA002_CPI ", list(), " HG-U133_Plus_2 ", list(), " ", "HuEx- 1_0-st-v2 ", list(), " Mixed_DNASeq ", list(), " ", "H-miRNA_8x15Kv2 ", list(), " IlluminaGA_DNASeq_curated ", list(), " ", "MDA_RPPA_Core ",

list(), " IlluminaHiSeq_TotalRNASeqV2    ", list(), "

", "HT_HG-U133A ", list(), " IlluminaHiSeq_DNASeq_automated ", list(), " ", "diagnostic_images ", list(), " microsat_i ", list(), " ", "IlluminaHiSeq_RNASeq ", list(), " SOLiD_DNASeq_curated ", list(), " ", "IlluminaHiSeq_DNASeqC ", list(), " Mixed_DNASeq_curated ", list(), " ", "IlluminaGA_RNASeq ", list(), " IlluminaGA_DNASeq_Cont_automated ",

list(), "

", "IlluminaGA_DNASeq ", list(), " IlluminaHiSeq_WGBS ", list(), " ", "pathology_reports ", list(), " IlluminaHiSeq_DNASeq_Cont_automated", list(), " ", "Genome_Wide_SNP_6 ", list(), " bio ", list(), " ", "tissue_images ", list(), " Mixed_DNASeq_automated ", list(), " ", "HumanMethylation27 ", list(), " Mixed_DNASeq_Cont_curated ", list(), " ",

"IlluminaHiSeq_RNASeqV2            ", list(), " Mixed_DNASeq_Cont

")) |file.type | To be used in the legacy database for some platforms, to define which file types to be used.| |barcode | A list of barcodes to filter the files to download| |experimental.strategy | Filter to experimental stratey. Harmonized: WXS, RNA-Seq, miRNA-Seq, Genotyping Array. Legacy: WXS, RNA-Seq, miRNA-Seq, Genotyping Array, DNA-Seq, Methylation array, Protein expression array, WXS,CGH array, VALIDATION, Gene expression array,WGS, MSI-Mono-Dinucleotide Assay, miRNA expression array, Mixed strategies, AMPLICON, Exon array, Total RNA-Seq, Capillary sequencing, Bisulfite-Seq| |sample.type | A sample type to filter the files to download|

Value

A data frame with the results and the parameters used

Examples

query <- GDCquery(project = "TCGA-ACC",
data.category = "Copy Number Variation",
data.type = "Copy Number Segment")
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 = "HTSeq - 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 solid Tumor"))
query.met <- GDCquery(project = c("TCGA-GBM","TCGA-LGG"),
legacy = TRUE,
data.category = "DNA methylation",
platform = "Illumina Human Methylation 450")
query <- GDCquery(project = "TCGA-ACC",
data.category =  "Copy number variation",
legacy = TRUE,
file.type = "hg19.seg",
barcode = c("TCGA-OR-A5LR-01A-11D-A29H-01"))

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

Value

A data frame with the maf file information

Examples

query <- GDCquery_ATAC_seq(file.type = "txt")
GDCdownload(query)
query <- GDCquery_ATAC_seq(file.type = "bigWigs")
GDCdownload(query)

Retrieve open access maf files from GDC server

Description

GDCquery_Maf uses the following guide to download maf files https://gdc-docs.nci.nih.gov/Data/Release_Notes/Data_Release_Notes/

Usage

GDCquery_Maf(tumor, save.csv = FALSE, directory = "GDCdata",
  pipelines = NULL)

Arguments

Value

A data frame with the maf file information

Examples

acc.muse.maf <- GDCquery_Maf("ACC", pipelines = "muse")
acc.varscan2.maf <- GDCquery_Maf("ACC", pipelines = "varscan2")
acc.somaticsniper.maf <- GDCquery_Maf("ACC", pipelines = "somaticsniper")
acc.mutect.maf <- GDCquery_Maf("ACC", pipelines = "mutect2")

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

Value

A data frame with the clinical information

Examples

clin <- GDCquery_clinic("TCGA-ACC", type = "clinical", save.csv = TRUE)
clin <- GDCquery_clinic("TCGA-ACC", type = "biospecimen", save.csv = TRUE)

GeneSplitRegulon

Description

GeneSplitRegulon

Usage

GeneSplitRegulon(Genelist, Sep)

Arguments

Value

GeneSplitRegulon

GenesCutID

Description

GenesCutID

Usage

GenesCutID(GeneList)

Arguments

Value

list of gene symbol without IDs

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()

Creates a volcano plot for DNA methylation or expression

Description

Creates a volcano plot from the expression and 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

Details

Creates a volcano plot from the expression and methylation analysis. Please see the vignette for more information Observation: This function automatically is called by TCGAanalyse_DMR

Value

Saves the volcano plot in the current folder

Examples

x <- runif(200, -1, 1)
y <- runif(200, 0.01, 1)
TCGAVisualize_volcano(x,y)
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")
while (!(is.null(dev.list()["RStudioGD"]))){dev.off()}

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

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")

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

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 asssignments). 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:

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

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

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

• 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:

• limma::makeContrasts construct matrix of custom contrasts.

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

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

• 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.

• 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, elementsRatio = 30000, batch.factors = NULL,
  ClinicalDF = data.frame(), paired = FALSE, log.trans = FALSE,
  voom = FALSE, trend = FALSE, MAT = data.frame(),
  contrast.formula = "", Condtypes = c())

Arguments

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

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

to add example

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 collumns with the results from the object.

Usage

TCGAanalyze_DMR(data, groupCol = NULL, group1 = NULL, group2 = NULL,
  calculate.pvalues.probes = "all",
  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, diffmean.cut = 0.2,
  paired = FALSE, adj.method = "BH", overwrite = FALSE, cores = 1,
  save = TRUE, save.directory = ".", filename = NULL)

Arguments

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)
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"), 5),
row.names=LETTERS[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)$group <- c(rep("group 1",ncol(data)/2),
rep("group 2",ncol(data)/2))
hypo.hyper <- TCGAanalyze_DMR(data, p.cut = 0.85,"group","group 1","group 2")
SummarizedExperiment::colData(data)$group2 <- c(rep("group_1",ncol(data)/2),
rep("group_2",ncol(data)/2))
hypo.hyper <- TCGAanalyze_DMR(data, p.cut = 0.85,"group2","group_1","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 peform the analysis tests the null hypothesis that, for any particular ontology term, there is no diffeerence 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)

Arguments

Value

Table with enriched GO or pathways by selected gene signature.

Examples

EAGenes <- get("EAGenes")
RegulonList <- rownames(dataDEGsFiltLevel)
ResBP <- TCGAanalyze_EA(GeneName="DEA genes Normal Vs Tumor",
RegulonList,DAVID_BP_matrix,
EAGenes,GOtype = "DavidBP")

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

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)
Genelist <- rownames(dataDEGsFiltLevel)
system.time(ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes Normal Vs Tumor",Genelist))

Filtering mRNA transcripts and miRNA selecting a threshold.

Description

TCGAanalyze_Filtering allows user to filter mRNA transcripts and miRNA, selecting a threshold. For istance returns all mRNA or miRNA with mean across all 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

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

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(dataDEGsFilt,"Tumor","Normal",
dataTP,dataTN)

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

• EDASeq::newSeqExpressionSet

• EDASeq::withinLaneNormalization

• EDASeq::betweenLaneNormalization

• EDASeq::counts

Usage

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

Arguments

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

Value

an adjacent matrix

Examples

dataDEGs <- data.frame(mRNA = c("TP53","TP63","TP73"), logFC = c(1,2,3))
TCGAanalyze_Pathview(dataDEGs)

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, filename = NULL,
  width = 1000, height = 1000, datatype = names(assays(object))[1])

Arguments

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)

Arguments

Value

table with samples and stemness 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)
dataBRCA_stemness <- TCGAanalyze_Stemness(stemSig = PCBC_stemSig, dataGE = dataFilt)

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

• survival::Surv

• survival::survdiff

• survival::survfit

Usage

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

Arguments

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_Cancer,
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
tabSurvKM <- TCGAanalyze_SurvivalKM(clinical_patient_Cancer,
dataBRCAcomplete,
Genelist = rownames(dataBRCAcomplete),
Survresult = TRUE,
p.cut = 0.2,
ThreshTop = 0.67,
ThreshDown = 0.33)

Generate network

Description

TCGAanalyze_analyseGRN perform gene regulatory network.

Usage

TCGAanalyze_analyseGRN(TFs, normCounts, kNum)

Arguments

Value

an adjacent matrix

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

Value

an adjacent matrix

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

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"))

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:

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

• 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

Value

data frame with ComBat batch correction applied

The aim of TCGAbiolinks is : i) facilitate the TCGA 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) allow the user to download a specific version of the data and thus 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.

Description

The functions you're likely to need from TCGAbiolinks is GDCdownload , GDCquery . Otherwise refer to the vignettes to see how to format the documentation.

Prepare CEL files into an AffyBatch.

Description

Prepare CEL files into an AffyBatch.

Usage

TCGAprepare_Affy(ClinData, PathFolder, TabCel)

Arguments

Value

Normalizd Expression data from Affy eSets

Examples

to add example

Retrieve multiple tissue types from the same patients.

Description

TCGAquery_MatchedCoupledSampleTypes

Usage

TCGAquery_MatchedCoupledSampleTypes(barcode, typesample)

Arguments

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"))

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

|typesample | a character vector indicating tissue type to query. Example: list(list("ll"), list(" ", "TP ", list(), " PRIMARY SOLID TUMOR ", list(), " ", "TR ", list(), " RECURRENT SOLID TUMOR ", list(), " ", "TB ", list(), " Primary Blood Derived Cancer-Peripheral Blood ", list(), " ", "TRBM ", list(), " Recurrent Blood Derived Cancer-Bone Marrow ", list(), " ", "TAP ", list(), " Additional-New Primary ", list(), " ", "TM ", list(), " Metastatic ", list(), " ", "TAM ", list(), " Additional Metastatic ", list(), " ", "THOC ", list(), " Human Tumor Original Cells ", |

list(), "

", "TBM ", list(), " Primary Blood Derived Cancer-Bone Marrow ", list(), " ", "NB ", list(), " Blood Derived Normal ", list(), " ", "NT ", list(), " Solid Tissue Normal ", list(), " ", "NBC ", list(), " Buccal Cell Normal ", list(), " ", "NEBV ", list(), " EBV Immortalized Normal ", list(), " ", "NBM ", list(), " Bone Marrow Normal ", list(), " "))

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"))

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

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

brain.rec<-TCGAquery_recount2(project = "gtex", tissue = "brain")

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: list(list("lllll"), list(" ", "lgg ", list(), " gbm ", list(), " luad ", list(), " stad ", list(), " brca", list(), " ", "coad ", list(), " read ", list(), " ", list(), " ", list(), " "))|

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

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

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, color = c("orange",
  "cyan", "green", "yellow"))

Arguments

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$ResPat,
nRGTab = Genelist,
nBar = 10,
filename="a.pdf")
while (!(is.null(dev.list()["RStudioGD"]))){dev.off()}
Genelist <- rownames(dataDEGsFiltLevel)
system.time(ansEA <- TCGAanalyze_EAcomplete(TFname="DEA genes Normal Vs Tumor",Genelist))
# Enrichment Analysis EA (TCGAVisualize)
# Gene Ontology (GO) and Pathway enrichment barPlot
TCGAvisualize_EAbarplot(tf = rownames(ansEA$ResBP),
GOBPTab = ansEA$ResBP,
GOCCTab = ansEA$ResCC,
GOMFTab = ansEA$ResMF,
PathTab = ansEA$ResPat,
nRGTab = Genelist,
nBar = 10)

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, extrems = 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

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)
if (!(is.null(dev.list()["RStudioGD"]))){dev.off()}

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

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)
if (!(is.null(dev.list()["RStudioGD"]))){dev.off()}

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:

• survival::coxph

• igraph::subgraph.edges

• igraph::layout.fruchterman.reingold

• igraph::spinglass.community

• igraph::communities

• dnet::dRDataLoader

• dnet::dNetInduce

• dnet::dNetPipeline

• dnet::visNet

• dnet::dCommSignif

Usage

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

Arguments

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.

• Cox regression survival analysis to obtain hazard ratio (HR) and pvaules

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

• Network comunites

• 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.

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

• extract network that only contains genes in pvals

• Identification of gene-active network

• visualisation of the gene-active network itself

• the layout of the network visualisation (fixed in different visuals)

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

• node sizes according to degrees

• highlight different communities

• visualise the subnetwork

Value

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

Mean methylation boxplot

Description

Creates a mean methylation boxplot for groups (groupCol), subgroups will be highlited 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

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")
if (!(is.null(dev.list()["RStudioGD"]))){dev.off()}

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

Value

A oncoprint plot

Examples

mut <- GDCquery_Maf(tumor = "ACC", pipelines = "mutect")
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)

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, genome, names = FALSE,
  names.fill = TRUE, filename = "starburst.pdf", 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

Details

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

Value

Save a starburst plot

Examples

library(SummarizedExperiment)
met <- TCGAbiolinks:::getMetPlatInfo(genome = "hg38",platform = "27K")
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)
# It can also receive a data frame as input
result <- TCGAvisualize_starburst(SummarizedExperiment::values(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)

TCGA samples with their Pam50 subtypes

Description

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

Format

A data frame with 4768 rows and 3 variables: list(" ", " ", list(list("samples"), list("Sample ID from TCGA barcodes, character string")), " ", " ", list(list("subtype"), list("Pam50 classification, character string")), " ", " ", list(list("color"), list("color, character string")), " ", " ... ")

Usage

TabSubtypesCol_merged

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

Format

A data frame with 9364 rows and 7 variables: list(" ", " ", list(list("Sample.ID"), list("Sample ID from TCGA barcodes, character string")), " ", " ", list(list("Cancer.type"), list("Cancer type, character string")), " ", " ", list(list("ESTIMATE"), list("uses gene expression profiles of 141 immune genes and 141 stromal genes, 0-1 value")), " ", " ", list(list("ABSOLUTE"), list("uses somatic copy-number data (estimations were available for only 11 cancer types), 0-1 value")), " ", " ", list(list("LUMP"), list("(leukocytes unmethylation for purity), which averages 44 non-methylated immune-specific CpG sites, ",

"0-1value")), "

", " ", list(list("IHC"), list("as estimated by image analysis of haematoxylin and eosin stain slides produced by the Nationwide ", "Childrens Hospital Biospecimen Core Resource, 0-1 value")), " ", " ", list(list("CPE"), list("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")), " ", " ... ")

Usage

Tumor.purity

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

Value

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

Examples

dataPrep_raw <- UseRaw_afterFilter(dataPrep, dataFilt)

TCGA batch information from Biospecimen Metadata Browser

Description

TCGA batch information from Biospecimen Metadata Browser

Format

A data frame with 11382 rows and 3 variables

TCGA CHOL MAF

Description

TCGA CHOL MAF

Format

A tibble: 3,555 x 34

Calculate pvalues

Description

Calculate pvalues using wilcoxon test

Usage

calculate.pvalues(data, groupCol = NULL, group1 = NULL,
  group2 = NULL, paired = FALSE, method = "BH", exact = TRUE,
  cores = 1, save = FALSE)

Arguments

Details

Verify if the data is significant between two groups. For the methylation we search for probes that have a difference in the mean methylation and also a significant value. Input: A SummarizedExperiment object that will be used to compared two groups with wilcoxon test, a boolean value to do a paired or non-paired test Output: p-values (non-adj/adj) histograms, p-values (non-adj/adj)

Value

Data frame with cols p values/p values adjusted

Data frame with two cols p-values/p-values adjusted

Examples

nrows <- 200; ncols <- 20
counts <- matrix(runif(nrows * ncols, 1, 1e4), 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"), 10),
row.names=LETTERS[1:20],
group=rep(c("group1","group2"),c(10,10)))
data <- SummarizedExperiment::SummarizedExperiment(
assays=S4Vectors::SimpleList(counts=counts),
rowRanges=rowRanges,
colData=colData)
data <- calculate.pvalues(data,"group")

TCGA CHOL MAF transformed to maftools obejct

Description

TCGA CHOL MAF transformed to maftools obejct

Format

An object of class MAF

Clinical data TCGA BRCA

Description

Clinical data TCGA BRCA

Format

A data frame with 1061 rows and 109 variables

A list of data frames with clinical data parsed from XML (code in vignettes)

Description

A list of data frames with clinical data parsed from XML (code in vignettes)

Format

A list with 7 elements

Create samples information matrix for GDC samples

Description

Create samples information matrix for GDC samples add subtype information

Usage

colDataPrepare(barcode)

Arguments

Examples

query.met <- GDCquery(project = c("TCGA-GBM","TCGA-LGG"),
legacy = TRUE,
data.category = "DNA methylation",
platform = c("Illumina Human Methylation 450",
"Illumina Human Methylation 27"))
colDataPrepare(getResults(query.met)$cases)

TCGA data matrix BRCA

Description

TCGA data matrix BRCA

Format

A data frame with 20531 rows (genes) and 50 variables (samples)

TCGA data matrix BRCA DEGs

Description

TCGA data matrix BRCA DEGs

Format

A data frame with 3649 rows and 6 variables

TCGA data SummarizedExperiment READ

Description

TCGA data SummarizedExperiment READ

Format

A SummarizedExperiment of READ with 2 samples

TCGA data matrix READ

Description

TCGA data matrix READ

Format

A data frame with 20531 rows (genes) and 2 variables (samples)

Calculate diffmean methylation between two groups

Description

Calculate diffmean methylation of probes between two groups removing lines that has NA values.

Usage

diffmean(data, groupCol = NULL, group1 = NULL, group2 = NULL,
  save = FALSE)

Arguments

Value

Saves in the rowRages(data) the columns: mean.group1, mean.group2 diffmean.group1.group2; Where group1 and group2 are the names of the groups.

Examples

nrows <- 200; ncols <- 20
counts <- matrix(runif(nrows * ncols, 1, 1e4), 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"), 10),
row.names=LETTERS[1:20],
group=rep(c("group1","group2"),c(10,10)))
data <- SummarizedExperiment::SummarizedExperiment(
assays=S4Vectors::SimpleList(counts=counts),
rowRanges=rowRanges,
colData=colData)
diff.mean <- TCGAbiolinks:::diffmean(data,groupCol = "group")

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

Description

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

Usage

gaiaCNVplot(calls, threshold = 0.01)

Arguments

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)

A RangedSummarizedExperiment two samples with gene expression data from vignette aligned against hg38

Description

A RangedSummarizedExperiment two samples with gene expression data from vignette aligned against hg38

Format

A RangedSummarizedExperiment: 56963 genes, 2 samples

A RangedSummarizedExperiment two samples with gene expression data from vignette aligned against hg19

Description

A RangedSummarizedExperiment two samples with gene expression data from vignette aligned against hg19

Format

A RangedSummarizedExperiment: 21022 genes, 2 samples

geneInfo for normalization of RNAseq data

Description

geneInfo for normalization of RNAseq data

Format

A data frame with 20531 rows and 2 variables

geneInfoHT for normalization of HTseq data

Description

geneInfoHT for normalization of HTseq data

Format

A data frame with 23486 rows and 2 variables

Get a matrix of interactions of genes from biogrid

Description

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

Usage

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

Arguments

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)
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="  ", stringsAsFactors=FALSE)
names.genes.de <- c("PLCB1","MCL1","PRDX4","TTF2","TACC3", "PARP4","LSM1")
net.biogrid.de <- getAdjacencyBiogrid(tmp.biogrid, names.genes.de)

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, legacy = FALSE)

Arguments

Value

A data frame

Examples

summary <- getDataCategorySummary("TCGA-ACC", legacy = TRUE)

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()

Get hg19 or hg38 information from biomaRt

Description

Get hg19 or hg38 information from biomaRt

Usage

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

Arguments

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

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 = F)

Arguments

Examples

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

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

Value

Table with query results

Examples

query <- GDCquery(project = "TCGA-GBM",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "HTSeq - 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, legacy = FALSE, files.access = NA)

Arguments

Value

A data frame with the maf file information

Examples

summary <- getSampleFilesSummary("TCGA-LUAD")
summary <- getSampleFilesSummary(c("TCGA-OV","TCGA_ACC"))

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 = "hg38", TSS = list(upstream = NULL, downstream = NULL))

Arguments

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)
getTSS <- getTSS()
getTSS <- getTSS(genome.build = "hg38", TSS=list(upstream=1000, downstream=1000))

Extract information from TCGA barcodes.

Description

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

Usage

get_IDs(data)

Arguments

Value

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

Biplot for Principal Components using ggplot2

Description

Biplot for Principal Components using ggplot2

Usage

ggbiplot(pcobj, choices = 1:2, scale = 1, pc.biplot = TRUE,
  obs.scale = 1 - scale, var.scale = scale, groups = NULL,
  ellipse = FALSE, ellipse.prob = 0.68, labels = NULL,
  labels.size = 3, alpha = 1, var.axes = TRUE, circle = FALSE,
  circle.prob = 0.69, varname.size = 3, varname.adjust = 1.5,
  varname.abbrev = FALSE)

Arguments

Value

a ggplot2 plot

Author

Vincent Q. Vu.

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 samples with both DNA methylation (HM450K) and Gene expression data from GDC databse

Description

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

Usage

matchedMetExp(project, legacy = FALSE, n = NULL)

Arguments

Value

A vector of barcodes

Examples

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

A DNA methylation RangedSummarizedExperiment for 8 samples (only first 20 probes) aligned against hg19

Description

A DNA methylation RangedSummarizedExperiment for 8 samples (only first 20 probes) aligned against hg19

Format

A RangedSummarizedExperiment: 20 probes, 8 samples

MSI data for two samples

Description

MSI data for two samples

Format

A data frame: 2 rows, 4 columns

A data frame with all TCGA molecular subtypes

Description

A data frame with all TCGA molecular subtypes

Format

A data frame with 7,734 lines and 10 columns

tabSurvKMcompleteDEGs

Description

tabSurvKMcompleteDEGs

Format

A data frame with 200 rows and 7 variables