# CellBarcode \[!\[check\](https://github.com/wenjie1991/CellBarcode/actions/workflows/r.yml/badge.svg)\](https://github.com/wenjie1991/CellBarcode/actions) \[!\[BioC status\](http://www.bioconductor.org/shields/build/release/bioc/CellBarcode.svg)\](https://bioconductor.org/checkResults/release/bioc-LATEST/CellBarcode) \*\*CellBarcode\*\* is an R package for dealing with \*\*Cellular DNA barcoding\*\* sequencing data. The R package was created by Wenjie SUN, Anne-Marie Lyne, and Leïla Perié at Institut Curie. ## Types of barcodes \*\*CellBarcode\*\* can handle all types of DNA barcodes, provided that: - The barcodes have a pattern that can be matched by a regular expression. - Each barcode is within a single sequencing read. ## What you can do with \*\*CellBarcode\*\* - Perform quality control for the DNA sequence results, and filter the sequences according to their quality metrics. - Identify barcode (and UMI) information in sequencing results. - Performs quality control and deal with the spurious sequences that come from potential PCR & sequence errors. - Provide toolkits to make it easier to manage samples and barcodes with metadata. ## Installing ### Install the development version from GitHub \`\`\`r if(!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes") remotes::install\_github("wenjie1991/CellBarcode") \`\`\` ## Getting Started Here is an example of a basic workflow: \`\`\`r library(CellBarcode) library(magrittr) # The example data is a mix of MEF lines with known barcodes # 2000 reads for each file have been sampled for this test dataset # Data can be accessed here: https://zenodo.org/records/10027002 example\_data <- system.file("extdata", "mef\_test\_data", package = "CellBarcode") fq\_files <- dir(example\_data, "gz", full=TRUE) # prepare metadata metadata <- stringr::str\_split\_fixed(basename(fq\_files), "\_", 10)\[, c(4, 6)\] metadata <- data.frame(metadata) sample\_name <- apply(metadata, 1, paste, collapse = "\_") colnames(metadata) = c("cell\_number", "replication") rownames(metadata) = sample\_name metadata # extract UMI barcode with regular expression bc\_obj <- bc\_extract( fq\_files, pattern = "(.{12})CTCGAGGTCATCGAAGTATCAAG(.+)TAGCAAGCTCGAGAGTAGACCTACT", pattern\_type = c("UMI" = 1, "barcode" = 2), sample\_name = sample\_name, metadata = metadata ) bc\_obj # sample subset operation, select 'mixa' bc\_sub <- bc\_subset(bc\_obj, sample=replication == "mixa") bc\_sub # filter the barcode, UMI barcode amplicon >= 2 & UMI counts >= 2 bc\_sub <- bc\_cure\_umi(bc\_sub, depth = 2) %>% bc\_cure\_depth(depth = 2) # select barcodes with a white list bc\_sub\[c("AAGTCCAGTACTATCGTACTA", "AAGTCCAGTACTGTAGCTACTA"), \] # export the barcode counts to data.frame head(bc\_2df(bc\_sub)) # export the barcode counts to matrix head(bc\_2matrix(bc\_sub)) \`\`\` ## License \[MIT\](https://choosealicense.com/licenses/mit/) ## Citation If you use \*\*CellBarcode\*\* in your research, please cite the following paper: \[Sun, W. et al. Extracting, filtering and simulating cellular barcodes using CellBarcode tools. Nat Comput Sci 1–16 (2024)\](https://www.nature.com/articles/s43588-024-00595-7)
