Before running this pipeline you should do next steps:
immdata
variable (it could be a mitcr data frame or a list).immdata <- parse.folder('/home/username/mitcrdata/')
immdata
variable to the some folder as the .rda
file.save(immdata, file = '/home/username/immdata.rda')
immdata.rda
file. After that click the Knit HTML button to start analysis and the script will make a html report.load('../data/twb.rda') immdata <- twb[1:2] library(tcR)
N <- 50000 immdata <- head(immdata, N)
cloneset.stats(immdata) repseq.stats(immdata)
if (has.class(immdata, 'list')) { for (i in 1:length(immdata)) { plot(vis.gene.usage(immdata[[i]], HUMAN_TRBV, .main = paste0(names(immdata)[i], ' ', 'V-usage'))) } } else { vis.gene.usage(immdata, HUMAN_TRBV, .coord.flip=F) }
if (has.class(immdata, 'list')) { for (i in 1:length(immdata)) { plot(vis.gene.usage(immdata[[i]], HUMAN_TRBJ, .main = paste0(names(immdata)[i], ' ', 'J-usage'))) } } else { vis.gene.usage(immdata, HUMAN_TRBJ, .coord.flip=F) }
if (has.class(immdata, 'list')) { for (i in 1:length(immdata)) { plot(vis.count.len(immdata[[i]], .name = paste0(names(immdata)[i], ' ', 'CDR3 length'))) } } else { vis.count.len(immdata) }
if (has.class(immdata, 'list')) { for (i in 1:length(immdata)) { plot(vis.number.count(immdata[[i]], .name = paste0(names(immdata)[i], ' ', 'read histogram'))) } } else { vis.number.count(immdata) }
vis.top.proportions(immdata)
kms <- get.kmers(immdata, .verbose = F) vis.kmer.histogram(kms, .position = 'fill')
clmn <- 'Read.count' if (has.class(immdata, 'list')) { if (!is.na(immdata[[1]]$Umi.count[1])) { clmn <- 'Umi.count' } } else { if (!is.na(immdata$Umi.count[1])) { clmn <- 'Umi.count' } } vis.rarefaction(rarefaction(immdata, .col = clmn, .verbose = F), .log = T)
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