Quantifying Clonal Bias

Compiled: May 04, 2024

Source: vignettes/articles/Clonal_Bias.Rmd

StartracDiversity

From the excellent work by Lei Zhang, et al., the authors introduce new methods for looking at clones by cellular origins and cluster identification. Their STARTRAC software has been adapted to work with scRepertoire and please read and cite their excellent work.

In order to use the StartracDiversity() function, you will need to include the product of the combinedExpression() function. The second requirement is a column header in the meta data of the Seurat object that has tissue of origin. In the example data, type corresponds to the column “Type”, which includes the “P” and “T” classifiers. The indices can be subsetted for a specific patient or examined overall using the by variable. Importantly, the function uses only the strict definition of a clone of the VDJC genes and the CDR3 nucleotide sequence.

The indices output includes:

  • expa - Clonal Expansion
  • migr - Cross-tissue Migration
  • tran - State Transition
StartracDiversity(scRep_example, 
                  type = "Type", 
                  group.by = "Patient")

clonalBias

A new metric proposed by Massimo et al, clonalBias(), like STARTRAC is a clonal metric that seeks to quantify how individual clones are skewed towards a specific cellular compartment or cluster.

split.by

  • Variable used to calculate the baseline frequencies

group.by

  • The compartment/variable used for the purpose of the comparison

min.expand

  • Cut point for frequency (default = 10) 
clonalBias(scRep_example, 
           cloneCall = "aa", 
           split.by = "Patient", 
           group.by = "seurat_clusters",
           n.boots = 10, 
           min.expand =5)