Before analysis

Before running this pipeline you should do next steps:

  1. Save your parsed MiTCR data to the immdata variable (it must be a list with mitcr data frames).

    immdata <- parse.folder('/home/username/mitcrdata/')
  2. Save the immdata variable to the some folder as the .rda file.

    save(immdata, file = '/home/username/immdata.rda')
  3. In the code block below change the path string to the path to yours immdata.rda file. After that click the Knit HTML button to start analysis and make an output .html file with it’s results.

load('../data/twb.rda')
immdata <- twb
library(tcR)
  1. Friendly advice: run the pipeline on first N top sequences first and then set up the size of figures.
N <- 10000
immdata <- lapply(immdata, head, N)

Number of shared clones and clonotypes

Number of shared clones (CDR3 nucleotide sequences)

– without and with normalisation

crs1 <- repOverlap(immdata, .norm=F, .verbose=F)
crs2 <- repOverlap(immdata, .norm=T, .verbose=F)
do.call(grid.arrange, list(vis.heatmap(crs1, .title = 'Number of shared clones', .legend = 'Shared clones'), vis.heatmap(crs2, .title = 'Number of shared clones', .legend = 'Shared clones'), nrow = 1))

Number of shared clonotypes (CDR3 amino acid sequences)

– without and with normalisation

crs1 <- repOverlap(immdata, .seq = "aa", .norm=F, .verbose=F)
crs2 <- repOverlap(immdata, .seq = "aa", .norm=T, .verbose=F)
do.call(grid.arrange, list(vis.heatmap(crs1), vis.heatmap(crs2), nrow = 1))