all_times <- list() # store the time for each chunk knitr::knit_hooks$set(time_it = local({ now <- NULL function(before, options) { if (before) { now <<- Sys.time() } else { res <- difftime(Sys.time(), now, units = "secs") all_times[[options$label]] <<- res } } })) knitr::opts_chunk$set( tidy = TRUE, tidy.opts = list(width.cutoff = 95), message = FALSE, warning = FALSE, time_it = TRUE ) suppressMessages(library(scRepertoire)) data("contig_list") combined.TCR <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L"))
Depending on the pipeline used to generate the single-cell object, there may be inherent mismatches in the barcodes in the single-cell object and the output of combineBCR()
or combineTCR()
. In particular, by default, Seurat will amend the suffix of the barcodes with _X, so the barcodes change like:
original: ACGTACGTACGTACGT-1 seurat-modified: ACGTACGTACGTACGT-1_1
scRepertoire uses the samples in combineTCR()
or combineBCR()
to add a prefix to the barcodes (using the samples and/or ID parameters):
original: ACGTACGTACGTACGT-1 scRepertoire-modified: Sample1_ACGTACGTACGTACGT-1
The easiest way to make these compatible is to rename the cell barcodes in the Seurat object by using the RenameCells()
from the SeuratObject package.
cell.barcodes <- rownames(seuratObj[[]]) #removing the _1 at the end of the barcodes) cell.barcodes <- stringr::str_split(cell.barcodes, "_", simplify = TRUE)[,1] #adding the prefix of the orig.ident to the barcodes, assuming that is the sample ids cell.barcodes <- paste0(seuratObj$orig.ident, "_", cell.barcodes) seuratObj <- RenameCells(seuratObj, new.names = cell.barcodes)
For all visualizations in scRepertoire, there are 2 ways to adjust the color scheme:
hcl.pals()
. clonalQuant(combined.TCR, cloneCall="strict", chain = "both", scale = TRUE, palette = "Zissou 1") clonalQuant(combined.TCR, cloneCall="strict", chain = "both", scale = TRUE) + scale_fill_manual(values = hcl.colors(8,"geyser"))
Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use exportTable = TRUE. It will return the data frame used to make the graph instead of the visual output.
clonalQuant_output <- clonalQuant(combined.TCR, cloneCall="strict", scale = TRUE, exportTable = TRUE) clonalQuant_output
We are working on submitting the scRepertoire as a peer review article,
Submit a GitHub issue - if possible please include a reproducible example. Alternatively, an example with the internal scRep_example and contig_list would be extremely helpful.
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