CapMux: a Snakemake pipeline for early demultiplexing of split-pool scRNA-seq data into sample-resolved outputs

Single-cell RNA sequencing methods based on split-pool combinatorial barcoding enable high-throughput profiling, yet sample identity is often encoded during early barcoding steps rather than through the library index. Consequently, reads from multiple biological samples remain pooled, complicating per-sample analysis and selective extraction of samples of interest. Here, I present CapMux, a Snakemake-based pipeline for processing split-pool scRNA-seq data from raw sequencing files to sample-resolved outputs. CapMux supports workflows starting from either BCL files or FASTQ files and reconstructs sample identity by integrating sub-library index information with the experiment-specific barcoding plate layout. The pipeline was developed for the CapSeq method but is configurable for related scRNA-seq combinatorial barcoding designs through specification of barcode positions and experimental layout. CapMux resolves pooled data into outputs for each sample, enabling independent quality control summaries, mapping statistics, count matrices, and downstream visualizations. Runtime benchmarking indicated that secondary demultiplexing step added only a modest computational overhead. Together, these results show that CapMux provides a practical and adaptable framework for recovering sample-level resolution from split-pool scRNA-seq data.