Modern single-cell and single-nucleus RNA sequencing allow us to profile every cell within a brain tumor, uncovering the diverse lineages and cell signals that drive growth and therapy resistance. However, each experiment can produce terabytes of raw reads and millions of barcodes, demanding significant CPU, GPU, and memory resources – far beyond the limits of a laptop. This talk will show how high-performance computing (HPC) systems transform that data deluge into biological insight.
I will walk through an end-to-end analysis pipeline that pairs the Cell Ranger aligner with an nf-core workflow for efficient, reproducible processing on CPU and GPU nodes. Interactive exploration then transitions to Seurat, where large-memory nodes accelerate dimensionality reduction, clustering, and differential expression analysis and integration of hundreds of thousands of cells. HPC infrastructure also enables RNA velocity calculations, and trajectory analysis that would be impractical on local workstations.
Benchmarks will illustrate how parallel job arrays and optimized space management can cut runtimes from days to hours while lowering costs. My goal is to provide researchers and students with a clear roadmap for harnessing supercomputers to advance neuro-oncology and other data-intensive areas of life science.
