In MD-Anderson-Bioinformatics/NGCHM-R: Next Generation Clustered Heat Maps

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
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Linkouts allow for a deeper exploration of the NG-CHM data by providing direct links to NCBI Databases, GeneCards, etc. This vignette demonstrates how to add linkouts to an NG-CHM.

These examples build on the setup from Getting Started.

Set Axis Type for Linkouts

To add linkouts, specify the axis type with chmAddAxisType(). For the demo data in this example, row labels correspond to genes, and are specified as HUGO gene symbols. The column labels are full 28-character TCGA aliquot barcodes.

hm <- chmNew("tcga-brca", matrix_data)
hm <- chmAddAxisType(hm, "row", "bio.gene.hugo")
hm <- chmAddAxisType(hm, "column", "bio.tcga.barcode.sample.vial.portion.analyte.aliquot")

Available Axis Types

The available axis types are:

bio.gene.entrezid: NCBI Entrez Gene Identifier
bio.gene.hugo: HUGO gene symbol
bio.geo.acc: GEO accession id
bio.go: Gene Ontology accession identifier
bio.mdacc.pathwayid: PathwaysWeb pathway identifier
bio.mirna: miRNA identifier
bio.pathway.msigdb.name: msig DB pathway name
bio.protein.uniprotid: Uniprot protein identifier
bio.pubmed: PubMed identifier
bio.tcga.barcode.sample: First 15 characters of TCGA aliquot barcode
bio.tcga.barcode.sample.vial: First 16 characters of TCGA aliquot barcode
bio.tcga.barcode.sample.vial.portion: First 19 characters of TCGA aliquot barcode
bio.tcga.barcode.sample.vial.portion.analyte: First 20 characters of a TCGA aliquot barcode
bio.tcga.barcode.sample.vial.portion.analyte.aliquot: Full 28 character TCGA aliquot barcode
bio.transcript.ensembl: Ensembl transcript identifier
bio.transcriptid: Entrez transcription identifier
scholar: Google Scholar search term
search: Generic search term

Resulting NG-CHM

Below is the resulting NG-CHM. To access the linkouts, click on the row or column labels to select them, then right click on the selection. This will open the label menu, displaying the linkout options.

library(NGCHMDemoData)
library(NGCHMSupportFiles)
library(NGCHM)
matrix_data_file <- system.file("extdata", "TCGA.BRCA.Expression.csv", package = "NGCHMDemoData")
matrix_data <- as.matrix(read.csv(matrix_data_file, header = TRUE, row.names = 1, check.names = FALSE, stringsAsFactors = FALSE))
covariate_data_file <- system.file("extdata", "TCGA.BRCA.TP53Mutation.csv", package = "NGCHMDemoData")
covariate_data <- as.matrix(read.csv(covariate_data_file, row.names = 1, check.names = FALSE, stringsAsFactors = FALSE))
covariate_vector <- as.vector(covariate_data) # create vector of mutation data
names(covariate_vector) <- rownames(covariate_data) # set the names
hm <- chmNew("TCGA BRCA Expression", matrix_data)
hm <- chmAddAxisType(hm, "row", "bio.gene.hugo")
hm <- chmAddAxisType(hm, "column", "bio.tcga.barcode.sample.vial.portion.analyte.aliquot")
invisible(chmExportToHTML(hm, "linkouts.html", overwrite = TRUE))
htmltools::tags$iframe(src = "linkouts.html", height = 800, width = "100%")