vis.immunr_dynamics: Repertoire dynamics

In charlotterich/immunarch_code: Painless Bioinformatics Analysis of T-Cell and B-Cell Immune Repertoires in R

Description

Repertoire dynamics

Usage

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

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.

Examples

## Not run: 
# Load an example data that comes with immunarch
data(immdata)

# 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)

## End(Not run)

charlotterich/immunarch_code documentation built on Jan. 24, 2020, 12:08 a.m.