R: Visualise clonotype dynamics

vis.immunr_dynamics {immunarch} | R Documentation

## Visualise clonotype dynamics

### Description

Visualise clonotype dynamics

### Usage

    ## S3 method for class 'immunr_dynamics'
    vis(.data, .plot = c("smooth", "area", "line"), .order = NA, .log = FALSE, ...)

### Arguments

`.data` | Output from the trackClonotypes function.
`.plot` | Character. Either "smooth", "area" or "line". Each specifies a type of plot for visualisation of clonotype dynamics.
`.order` | Numeric or character vector. Specifies the order to samples, e.g., it used for ordering samples by timepoints. Either See "Examples" below for more details.
`.log` | Logical. If TRUE then use log-scale for the frequency axis.
`...` | Not used here.

### Value

A ggplot2 object.

### Examples

    # Load an example data that comes with immunarch
    data(immdata)

    # Make the data smaller in order to speed up the examples
    immdata$data <- immdata$data[c(1, 2, 3, 7, 8, 9)]
    immdata$meta <- immdata$meta[c(1, 2, 3, 7, 8, 9), ]

    # Option 1
    # Choose the first 10 amino acid clonotype sequences
    # from the first repertoire to track
    tc <- trackClonotypes(immdata$data, list(1, 10), .col = "aa")
    # Choose the first 20 nucleotide clonotype sequences
    # and their V genes from the "MS1" repertoire to track
    tc <- trackClonotypes(immdata$data, list("MS1", 20), .col = "nt+v")

    # Option 2
    # Choose clonotypes with amino acid sequences "CASRGLITDTQYF" or "CSASRGSPNEQYF"
    tc <- trackClonotypes(immdata$data, c("CASRGLITDTQYF", "CSASRGSPNEQYF"), .col = "aa")

    # Option 3
    # Choose the first 10 clonotypes from the first repertoire
    # with amino acid sequences and V segments
    target <- immdata$data[[1]] %>%
      select(CDR3.aa, V.name) %>%
      head(10)
    tc <- trackClonotypes(immdata$data, target)

    # Visualise the output regardless of the chosen option
    # Therea are three way to visualise it, regulated by the .plot argument
    vis(tc, .plot = "smooth")
    vis(tc, .plot = "area")
    vis(tc, .plot = "line")

    # Visualising timepoints
    # First, we create an additional column in the metadata with randomly choosen timepoints:
    immdata$meta$Timepoint <- sample(1:length(immdata$data))
    immdata$meta
    # Next, we create a vector with samples in the right order,
    # according to the "Timepoint" column (from smallest to greatest):
    sample_order <- order(immdata$meta$Timepoint)
    # Sanity check: timepoints are following the right order:
    immdata$meta$Timepoint[sample_order]
    # Samples, sorted by the timepoints:
    immdata$meta$Sample[sample_order]
    # And finally, we visualise the data:
    vis(tc, .order = sample_order)

[Package immunarch version 0.6.7 Index]