Single-cell RNA sequencing (scRNA-seq) captures transcriptomic profiles of individual cells, revealing cellular heterogeneity hidden in bulk analyses. HPCBio can perform analyses that will

1. Transform your raw FASTQ files to cell-specific gene counts, quality-filtered to remove empty droplets, dying cells (high mitochondrial %), and doublets,
2. Normalize and visualize data to assess the global structure of cell populations,
3. Cluster cells with similar transcriptional profiles into putative cell types or states, and
4. Perform cell type annotation using known marker genes, automated reference-based tools, or differential expression between clusters.

Follow-Up Analyses:

• Trajectory / Pseudotime Analysis – Infer developmental or differentiation paths (Monocle 3, scVelo for RNA velocity)
• Cell–Cell Communication – Infer ligand-receptor interactions between cell types (CellChat, NicheNet, LIANA)
• Gene Regulatory Networks – Reconstruct transcription factor regulons (SCENIC/pySCENIC)
• Multi-omics Integration – Joint analysis with ATAC-seq, proteomics, or spatial data (Seurat v5, MOFA+, WNN)
• Spatial Transcriptomics – Map gene expression in tissue context (Visium, Xenium, MERFISH + Squidpy/Giotto)
• Compositional Analysis – Test changes in cell type proportions across conditions (scCODA, propeller)
• Gene Set / Pathway Scoring – Score cells for pathway activity (AUCell, decoupleR, GSVA)
• Perturbation Analysis – Model transcriptional responses to genetic or drug perturbations (scGen, CellOracle)

We have experience with tools such as:

• The 10x Genomics pipeline (Cell Ranger, Loupe Browser)
• Curio Bioscience data workflow
• Parse Bioscience data workflow
• alevin-fry / STARsolo / Kallisto|bustools – alignment and quantification
• Seurat –scRNA-seq analysis
• Scanpy– Python equivalent of Seurat
• scDblFinder / DoubletFinder– Doublet detection and removal
• Harmony / scVI / BBKNN– Batch correction and data integration across samples
• CellTypist / SingleR– Automated reference-based cell type annotation
• edgeR – Differential gene expression testing