Skip to contents

Analyze K-mer Motif Composition

Source: R/percentKmer.R
percentKmer.Rd

This function calculates and visualizes the frequency of k-mer motifs for either nucleotide (nt) or amino acid (aa) sequences. It produces a heatmap showing the relative composition of the most variable motifs across samples or groups.

Usage

percentKmer(
  input.data,
  chain = "TRB",
  clone.call = NULL,
  group.by = NULL,
  order.by = NULL,
  motif.length = 3,
  min.depth = 3,
  top.motifs = 30,
  export.table = NULL,
  palette = "inferno",
  cloneCall = NULL,
  exportTable = NULL,
  ...
)

Arguments

input.data

The product of combineTCR(), combineBCR(), or combineExpression()

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).

clone.call

Defines the clonal sequence grouping. Accepted values are: nt (CDR3 nucleotide sequence) or aa (CDR3 amino acid sequence).

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.

order.by

A character vector defining the desired order of elements of the group.by variable. Alternatively, use alphanumeric to sort groups automatically.

motif.length

The length of the kmer to analyze

min.depth

Minimum count a motif must reach to be retained in the output (>= 1). Default: 3.

top.motifs

Return the n most variable motifs as a function of median absolute deviation

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 displaying a heatmap of motif percentages. If exportTable = TRUE, a matrix of the raw data is returned.

Details

The function first calculates k-mer frequencies for each sample/group. By default, it then identifies the 30 most variable motifs based on the Median Absolute Deviation (MAD) across all samples and displays their frequencies in a heatmap.

Examples

# Making combined contig data
combined <- combineTCR(contig_list, 
                        samples = c("P17B", "P17L", "P18B", "P18L", 
                                    "P19B","P19L", "P20B", "P20L"))

# Using percentKmer()
percentKmer(combined, 
            chain = "TRB", 
            motif.length = 3)