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Frequently Asked Questions

Compiled: April 03, 2026

Source: vignettes/articles/FAQ.Rmd
FAQ.Rmd

Barcode Mismatch

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().

Common Barcode Modification Examples

Seurat-modified barcodes: By default, Seurat will append _X to the suffix of the barcodes.

  • original: ACGTACGTACGTACGT-1
  • seurat-modified: ACGTACGTACGTACGT-1_1

scRepertoire-modified barcodes: scRepertoire uses the samples and/or ID parameters in combineTCR() or combineBCR() to add a prefix to the barcodes.

  • original: ACGTACGTACGTACGT-1
  • scRepertoire-modified: Sample1_ACGTACGTACGTACGT-1

Solution: Renaming Cell Barcodes in Seurat

The easiest way to make these compatible is to rename the cell barcodes in the Seurat object using RenameCells() from the SeuratObject package

# Assuming 'seuratObj' is your Seurat object
cell.barcodes <- rownames(seuratObj[[]])
# removing the _1 at the end of the barcodes (adjust regex if your suffix differs)
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)

Adjusting Color Palettes

For all visualizations in scRepertoire, you have two primary ways to adjust the color scheme.

Methods for Color Adjustment

  • Internal Palette Selection: Change the palette parameter within scRepertoire functions to the desired color scheme. This approach uses the built-in palettes of grDevices, and you can access the list of available color schemes using hcl.pals().
  • Adding a ggplot Layer: Extend the scRepertoire plot (which is a ggplot object) by adding a ggplot2 layer with a new color scheme using scale_fill_manual() or similar functions.

Using Internal Palette Selection 

# Internal Palette Selection
clonalQuant(combined.TCR, 
            cloneCall="strict", 
            chain = "both", 
            scale = TRUE, 
            palette = "Zissou 1")

Using ggplot2 System: 

# Using gg System
clonalQuant(combined.TCR, 
            cloneCall="strict", 
            chain = "both", 
            scale = TRUE) + 
  scale_fill_manual(values = hcl.colors(8,"geyser"))

Adjusting Plot Theme

Since scRepertoire functions return ggplot objects, modifying the general appearance or theme of the plot is straightforward, similar to adjusting color palettes—by adding a ggplot2 theme layer.

# Original clonalQuant plot
clonalQuant(combined.TCR, 
            cloneCall="strict", 
            chain = "both", 
            scale = TRUE)

# Modifying the theme of the clonalQuant plot
clonalQuant(combined.TCR, 
            cloneCall="strict", 
            chain = "both", 
            scale = TRUE) + 
  theme_classic()

Adjusting Order of Plotting

The order of grouping/group.by variables in scRepertoire plots (whether along an axis or in color legends) can be precisely controlled using the order.by parameter.

Key Parameter for Plot Order

  • order.by: A character vector defining the desired order of elements for the group.by variable. It’s crucial that the strings in this vector exactly match the group.by strings. Alternatively, setting order.by = "alphanumeric" will automatically sort groups alphanumerically.
clonalQuant(combined.TCR, 
            cloneCall="strict", 
            chain = "both", 
            scale = TRUE, 
            order.by = c("P17B","P18B","P19B","P20B","P17L","P18L","P19L","P20L"))

Getting Data Used in Plots

Within each of the general analysis functions in scRepertoire, there’s an option to export the underlying data frame used to create the visualization.

Key Parameter for Data Export

  • exportTable: Set this parameter to TRUE to return the data frame used to generate the graph instead of the visual output.
clonalQuant_output <- clonalQuant(combined.TCR, 
                                  cloneCall="strict", 
                                  scale = TRUE, 
                                  exportTable = TRUE)
clonalQuant_output
##   contigs values total   scaled
## 1     745   P17B  2805 26.55971
## 2    2117   P17L  2893 73.17663
## 3    1254   P18B  1328 94.42771
## 4    1202   P18L  1278 94.05321
## 5    5544   P19B  6942 79.86171
## 6    1619   P19L  2747 58.93702
## 7    6087   P20B  8991 67.70103
## 8     192   P20L   201 95.52239

Citing scRepertoire

When using scRepertoire in your research, please cite the appropriate version of the package.

Citation Details

  • Version 2: Yang, Q, & Safina, K., Nguyen, K., Tuong, Z.K., & Borcherding, N. (2025). “scRepertoire 2: Enhanced and efficient toolkit for single-cell immune profiling.” PLoS Computational Biology https://doi.org/10.1371/journal.pcbi.1012760
  • Version 1: Borcherding, Nicholas, Nicholas L. Bormann, and Gloria Kraus. “scRepertoire: An R-based toolkit for single-cell immune receptor analysis.” F1000Research https://doi.org/10.12688/f1000research.22139.2

Bug Reports/New Features

Your feedback is valuable for improving scRepertoire! If you encounter a bug or have a suggestion for a new feature, please report it.

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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