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Calculate Startrac-based Diversity Indices

Source: R/startracDiversity.R
StartracDiversity.Rd

This function utilizes the STARTRAC approach to calculate T cell diversity metrics based on the work of Zhang et al. (2018, Nature) PMID: 30479382. It can compute three distinct indices: clonal expansion (expa), cross-tissue migration (migr), and state transition (tran).

Usage

StartracDiversity(
  sc.data,
  clone.call = NULL,
  chain = "both",
  index = c("expa", "migr", "tran"),
  type = NULL,
  group.by = NULL,
  pairwise = NULL,
  export.table = NULL,
  palette = "inferno",
  cloneCall = NULL,
  exportTable = NULL,
  ...
)

Arguments

sc.data

The single-cell object after combineExpression(). For SCE objects, the cluster variable must be in the meta data under "cluster".

clone.call

Defines the clonal sequence grouping. Accepted values are: gene (VDJC genes), nt (CDR3 nucleotide sequence), aa (CDR3 amino acid sequence), or strict (VDJC + nt). A custom column header can also be used.

chain

The TCR/BCR chain to use. Use both to include both chains (e.g., TRA/TRB). Accepted values: TRA, TRB, TRG, TRD, IGH, IGL, IGK, Light (for both light chains), or both (for TRA/B and Heavy/Light).

index

A character vector specifying which indices to calculate. Options: "expa", "migr", "tran". Default is all three.

type

The metadata variable that specifies tissue type for migration analysis.

group.by

A column header in the metadata or lists to group the analysis by (e.g., "sample", "treatment"). If NULL, data will be analyzed as by list element or active identity in the case of single-cell objects.

pairwise

The metadata column to be used for pairwise comparisons. Set to the type variable for pairwise migration or "cluster" for pairwise transition.

export.table

If TRUE, returns a data frame or matrix of the results instead of a plot.

palette

Colors to use in visualization - input any hcl.pals.

cloneCall

[Deprecated] Use clone.call instead.

exportTable

[Deprecated] Use export.table instead.

...

Additional arguments passed to the ggplot theme

Value

A ggplot object visualizing STARTRAC diversity metrics or data.frame if exportTable = TRUE.

Details

The function requires a type variable in the metadata, which specifies the tissue origin or any other categorical variable for migration analysis.

Indices:

  • expa (Clonal Expansion): Measures the extent of clonal proliferation within a T cell cluster. It is calculated as 1 - normalized Shannon entropy. A higher value indicates greater expansion of a few clones.

  • migr (Cross-Tissue Migration): Quantifies the movement of clonal T cells across different tissues (as defined by the type parameter). It is based on the entropy of a clonotype's distribution across tissues.

  • tran (State Transition): Measures the developmental transition of clonal T cells between different functional clusters. It is based on the entropy of a clonotype's distribution across clusters.

Pairwise Analysis: The pairwise parameter enables the calculation of migration or transition between specific pairs of tissues or clusters, respectively.

  • For migration (index = "migr"), set pairwise to the type column (e.g., pairwise = "Type").

  • For transition (index = "tran"), set pairwise to "cluster".

Author

Liangtao Zheng

Examples

# Getting the combined contigs
combined <- combineTCR(contig_list, 
                        samples = c("P17B", "P17L", "P18B", "P18L", 
                                    "P19B","P19L", "P20B", "P20L"))

# Getting a sample of a Seurat object
scRep_example  <- get(data("scRep_example"))
scRep_example  <- combineExpression(combined, scRep_example)
scRep_example$Patient <- substring(scRep_example$orig.ident,1,3)
scRep_example$Type <- substring(scRep_example$orig.ident,4,4) 

# Calculate a single index (expansion)
StartracDiversity(scRep_example, 
                  type = "Type", 
                  group.by = "Patient",
                  index = "expa")

                  
# Calculate pairwise transition 
StartracDiversity(scRep_example, 
                  type = "Type", 
                  group.by = "Patient",
                  index = "tran",
                  pairwise = "cluster")