Repertoire dynamics
# S3 method for immunr_dynamics vis(.data, .plot = c("smooth", "area", "line"), .order = NA, .log = F, ...)
.data | Output from the trackClonotypes function. |
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.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. |
if (FALSE) { # 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) }