Lolliplot-class: Class Lolliplot

In GenVisR: Genomic Visualizations in R

Description

An S4 class for the lolliplot object, under development!!!

Usage

Lolliplot(
  input,
  transcript = NULL,
  species = "hsapiens",
  host = "www.ensembl.org",
  txdb = NULL,
  BSgenome = NULL,
  emphasize = NULL,
  DomainPalette = NULL,
  MutationPalette = NULL,
  labelAA = TRUE,
  plotALayers = NULL,
  plotBLayers = NULL,
  sectionHeights = NULL,
  verbose = FALSE
)

Arguments

input	Object of class MutationAnnotationFormat, GMS, VEP, or a data.table with appropriate columns
transcript	Character string specifying the ensembl transcript for which to plot, should be a transcript which corresponds to the gene parameter.
species	Character string specifying a species when using biomaRt queries
host	Character string specifying a host to connect to when using biomaRt queries
txdb	A bioconoductor txdb object to annotate amino acid positions, required only if amino acid changes are missing (see details).
BSgenome	A bioconductor BSgenome object to annotate amino acid positions, required only if amino acid changes are missing (see details).
emphasize	Character vector specifying a list of mutations to emphasize.
DomainPalette	Character vector specifying the colors used for encoding protein domains
MutationPalette	Character vector specifying the colors used for encoding mutations
labelAA	Boolean specifying if labels should be added to emphasized mutations
plotALayers	list of ggplot2 layers to be passed to the density plot.
plotBLayers	list of ggplot2 layers to be passed to the lolliplot.
sectionHeights	Numeric vector specifying relative heights of each plot section, should sum to one. Expects a value for each section.
verbose	Boolean specifying if status messages should be reported.

Slots

PlotA

gtable object for the top sub-plot

PlotB

gtable object for the bottom sub-plot

Grob

gtable object storing the arranged plot

primaryData

data.table object storing the primary data

geneData

data.table object storing gene and domain coordinates

Examples

# Load a pre-existing data set
dataset <- PIK3CA

# mode 1, amino acid changes are not present

library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(BSgenome.Hsapiens.UCSC.hg38)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
BSgenome <- BSgenome.Hsapiens.UCSC.hg38

keep <- c("Chromosome", "Start_Position", "End_Position", "Reference_Allele",
          "Tumor_Seq_Allele2", "Tumor_Sample_Barcode", "Gene", "Variant_Classification")
dataset.mode1 <- dataset[,keep]
colnames(dataset.mode1) <- c("chromosome", "start", "stop", "reference", "variant",
                             "sample", "gene", "consequence")


# mode 2, amino acid changes are present

keep <- c("Chromosome", "Start_Position", "End_Position", "Reference_Allele",
          "Tumor_Seq_Allele2", "Tumor_Sample_Barcode", "Gene", "Variant_Classification",
          "Transcript_ID", "HGVSp")
dataset.mode2 <- dataset[,keep]
colnames(dataset.mode2) <- c("chromosome", "start", "stop", "reference", "variant",
                             "sample", "gene", "consequence", "transcript", "proteinCoord")

# run Lolliplot

object <- Lolliplot(dataset.mode1, transcript="ENST00000263967",
                    species="hsapiens", txdb=txdb, BSgenome=BSgenome)
object <- Lolliplot(dataset.mode2, transcript="ENST00000263967",
                    species="hsapiens")

GenVisR documentation built on Dec. 28, 2020, 2 a.m.