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Before running this pipeline you should do next steps:
immdata
variable (it must be a list with mitcr data frames).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 make an output .html file with it's results.load('../data/twb.rda') immdata <- twb library(tcR)
N <- 10000 immdata <- lapply(immdata, head, N)
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))
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))
crs1 <- repOverlap(immdata, .vgene = T, .norm=F, .verbose=F) crs2 <- repOverlap(immdata, .vgene = T, .norm=T, .verbose=F) do.call(grid.arrange, list(vis.heatmap(crs1, .title = 'Number of shared clones + V', .legend = 'Shared clones'), vis.heatmap(crs2, .title = 'Number of shared clones + V'), nrow = 1))
crs1 <- repOverlap(immdata, .seq = "aa", .vgene = T, .norm=F, .verbose=F) crs2 <- repOverlap(immdata, .seq = "aa", .vgene = T, .norm=T, .verbose=F) do.call(grid.arrange, list(vis.heatmap(crs1, .title = 'Number of shared clonotypes + V'), vis.heatmap(crs2, .title = 'Number of shared clonotypes + V'), nrow = 1))
vis.gene.usage(immdata, HUMAN_TRBV, .ncol = 2, .coord.flip = F)
# Change the groups variable for plotting V-usage boxplot for groups. groups <- list(Group.A = names(immdata)[1:(length(immdata) / 2)], Group.B = names(immdata)[(length(immdata) / 2 + 1) : length(immdata)]) vis.group.boxplot(geneUsage(immdata, HUMAN_TRBV, .norm = T), groups, .rotate.x = T)
vis.gene.usage(immdata, HUMAN_TRBJ, .coord.flip=F, .ncol = 2)
res <- js.div.seg(immdata, HUMAN_TRBV, .frame='all', .verbose = F) vis.heatmap(round(res, 5))
res <- js.div.seg(immdata, HUMAN_TRBV, .frame='all', .verbose = F) vis.radarlike(res, .ncol = 2)
pca.segments(immdata)
pca.segments.2D(immdata, .genes = list(HUMAN_TRBV, HUMAN_TRBJ))
top.cross.plot(top.cross(permutedf(immdata), seq(500, min(sapply(immdata, nrow)), 500), .verbose = F))
imm.sh <- shared.repertoire(immdata, 'av', .verbose = F) shared.clones.count(imm.sh) shared.representation(imm.sh)
clmn <- 'Read.count' if (!is.na(immdata[[1]]$Umi.count[1])) { clmn <- 'Umi.count' } vis.rarefaction(rarefaction(immdata, .col = clmn, .verbose = F), list(A = c("Subj.A", "Subj.B"), B = c("Subj.C", "Subj.D")), .log = T)
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