High-accuracy ML model for leukemic stem cell (LSC) identification from single-cell multi-omics data (TEA‑seq, CITE‑seq, scRNA‑seq).
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Updated
Jan 11, 2026 - Jupyter Notebook
High-accuracy ML model for leukemic stem cell (LSC) identification from single-cell multi-omics data (TEA‑seq, CITE‑seq, scRNA‑seq).
Reproducible benchmark of four scRNA-seq cell type annotation methods (SingleR, CellTypist, ACTINN, scANVI) on PBMC3K
End-to-end single-cell RNA-seq pipeline with scVI, Scanpy, and CellTypist for QC, clustering, annotation, and differential expression.
Single-Cell Atlas Builder is a modular platform for processing, integrating, and visualizing single-cell RNA-seq datasets. It combines FastAPI, Scanpy, and CellTypist for efficient analysis, with optional LLM-powered summaries for cluster and pathway interpretation.
LangGraph-orchestrated agentic pipeline for scRNA-seq analysis with LLM-guided QC, clustering, and cell type annotation
A hands-on single-cell RNA-seq bone marrow project focused on learning the full reasoning chain: how raw expression matrices become QC-filtered cells, clusters, immune-cell annotations, marker evidence, pathway scores, and biologically interpretable results.
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