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Single-Cell RNA-seq Analysis
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Single-Cell RNA-seq Analysis

Cell Population & State Discovery

We build complete single-cell workflows that reveal cell populations, disease-specific cell states, treatment response programs, and cell-type-specific gene expression changes. From raw count matrices to annotated atlases and trajectory models, every step is documented in reproducible notebooks and presented with publication-ready figures.

Key Features

  • QC, Normalisation & Batch Correction

    Cell and gene filtering, doublet detection, normalisation, and batch correction ensure a clean, comparable foundation across samples and conditions.

  • Dimensionality Reduction & Clustering

    UMAP/t-SNE embeddings, graph-based clustering, and marker discovery partition the transcriptional landscape into interpretable cell populations.

  • Cell-Type Annotation & Reference Mapping

    Canonical marker panels and reference-atlas mapping assign confident identities to each cluster, cross-validated against published cell-type signatures.

  • Trajectory, Ligand–Receptor & Pathway Activity

    Pseudotime and trajectory inference, differential abundance, cell-type-specific DE, ligand–receptor signalling, and pathway activity scoring connect cell states to biology.