Function reference • scRepertoire

Loading and Processing Contigs Functions that load, combine, and process the single-cell contig information.
`addVariable()`	Adding variables after combineTCR() or combineBCR()
`combineBCR()`	Combining the list of B cell receptor contigs into clones
`combineTCR()`	Combining the list of T cell receptor contigs into clones
`createHTOContigList()`	Generate a contig list from a multiplexed experiment
`exportClones()`	Exporting clones
`loadContigs()`	Loading the contigs derived from single-cell sequencing
`subsetClones()`	Subset the product of combineTCR() or combineBCR()
Visualizing Clones Functions plotting clonal data.
`clonalAbundance()`	Demonstrate the relative abundance of clones by group or sample
`clonalCluster()`	Clustering adaptive receptor sequences by edit distance
`clonalCompare()`	Demonstrate the difference in clonal proportion between clones
`clonalDiversity()`	Calculate the clonal diversity for samples or groupings
`clonalHomeostasis()`	Examining the clonal homeostasis of the repertoire
`clonalLength()`	Demonstrate the distribution of clonal length
`clonalOverlap()`	Examining the clonal overlap between groups or samples
`clonalProportion()`	Examining the clonal space occupied by specific clones
`clonalQuant()`	Quantify the unique clones by group or sample
`clonalRarefaction()`	Calculate rarefaction based on the abundance of clones
`clonalScatter()`	Scatter plot comparing the clonal expansion of two samples
`clonalSizeDistribution()`	Hierarchical clustering of clones using Gamma-GPD spliced threshold model
`vizGenes()`	Visualizing the distribution of gene usage
Summarizing Repertoire Functions to summarize clonal sequences across the repertoire.
`percentAA()`	Examining the relative amino acid composition by position
`positionalEntropy()`	Examining the diversity of amino acids by position
`positionalProperty()`	Examining the mean property of amino acids by position
`percentGenes()`	Examining the VDJ gene usage across clones
`percentKmer()`	Examining the relative composition of kmer motifs in clones.
`percentVJ()`	Quantifying the V and J gene usage across clones
Single-Cell Object Processing and Visualizations Functions to add or visualize clonal information along a single-cell object.
`alluvialClones()`	Alluvial plotting for single-cell object meta data
`clonalBias()`	Examine skew of clones towards a cluster or compartment
`clonalNetwork()`	Visualize clonal network along reduced dimensions
`clonalOccupy()`	Visualize the number of single cells with cloneSizes by cluster
`clonalOverlay()`	Visualize distribution of clonal frequency overlaid on dimensional reduction plots
`combineExpression()`	Adding clone information to a single-cell object
`getCirclize()`	Generate data frame to be used with circlize R package to visualize clones as a chord diagram.
`highlightClones()`	Highlighting specific clones in Seurat
`StartracDiversity()`	Startrac-based diversity indices for single-cell RNA-seq
Data Reference data for package functions.
`contig_list`	A list of 8 single-cell T cell receptor sequences runs.
`mini_contig_list`	Processed subset of `contig_list`
`scRep_example`	A Seurat object of 500 single T cells,

Reference

Loading and Processing Contigs

Visualizing Clones

Summarizing Repertoire

Single-Cell Object Processing and Visualizations

Data