alluvialClonotypes: Exploring interaction of clonotypes by seurat or SCE dynamics

Description Usage Arguments Value Examples

View source: R/seuratFunctions.R

Description

View the proportional contribution of clonotypes by seurat or SCE object meta data after combineExpression(). The visualization is based on the ggalluvial package, which requires the aesthetics to be part of the axes that are visualized. Therefore, alpha, facet, and color should be part of the the axes you wish to view or will add an additional stratum/column to the end of the graph.

Usage

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alluvialClonotypes(
  sc,
  cloneCall = c("gene", "nt", "aa", "gene+nt"),
  y.axes = NULL,
  color = NULL,
  alpha = NULL,
  facet = NULL
)

Arguments

sc

The seurat or SCE object to visualize after combineExpression(). For SCE objects, the cluster variable must be in the meta data under "cluster".

cloneCall

How to call the clonotype - CDR3 gene (gene), CDR3 nucleotide (nt) or CDR3 amino acid (aa), or CDR3 gene+nucleotide (gene+nt).

y.axes

The columns that will seperate the proportional visualizations.

color

The column header or clonotype(s) to be highlighted.

alpha

The column header to have gradieted opacity.

facet

The column label to seperate.

Value

Alluvial ggplot comparing clonotype distribution across selected parameters.

Examples

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#Getting the combined contigs
combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), 
rep(c("P", "T"), 3), cells ="T-AB")

#Getting a sample of a Seurat object
screp_example <- get(data("screp_example"))
sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example))
sce <- Seurat::as.SingleCellExperiment(sce)

#Using combineExpresion()
sce <- combineExpression(combined, sce)

#Using alluvialClonotypes()
alluvialClonotypes(sce, cloneCall = "gene", 
y.axes = c("Patient", "cluster"), color = "cluster")

scRepertoire documentation built on Nov. 8, 2020, 7 p.m.