Bioinformatic methods for GRC scRNAseq poster v1

Background:Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of complex tissues and has advanced our understanding of cellular heterogeneity. Many platforms are available, each with trade-offs in sensitivity, accuracy, and throughput.Methods:We evaluated five scRNA-seq platforms—10X Genomics GEM-X, 10X Genomics Flex, 10X Genomics OCM, Parse Biosciences, and Illumina Single Cell—using manufacturer protocols and a uniform bioinformatics pipeline. Unlike typical benchmarking with peripheral blood mononuclear cells, we used patient-derived acute myeloid leukemia (AML) apheresis samples to evaluate performance on clinically relevant, heterogeneous material. While basic QC metrics were evaluated, we focus here on the value of increased sequencing depth compared to vendor recommendations.Results: As expected, differences in basic metrics were observed among the methods, although all platforms recovered diverse cell populations. While differences in cluster resolution were observed, most clusters were represented across all methods. Unique clusters, comprised of cells with low gene detection, were associated with a single platform, requiring further investigation. Surprisingly, although number of genes detected increased significantly, higher sequencing depth did not affect clustering, independent of platform. Conclusions:Platform performance differed by metric, underscoring that no single technology is universally optimal. Overall, we found limited value with increased sequencing depth, although further analyses remain to be done. Cell typing and differential expression analysis may benefit from more sequencing although we identified nearly identical clusters independent of sequencing depth. Ultimately, selection of an optimal scRNA-seq platform depends on budget, biological system, and specific experimental goals.