3. Data, Science & AI

Automates differential gene expression analysis and pathway enrichment for single-cell RNA-seq clusters, generating publication-ready visualizations.

Enables fine-grained re-clustering on arbitrary cell subsets using full clustering workflows (PCA, UMAP, etc.) beyond standard subclustering methods.

Prepares Seurat objects for metabolic landscape analysis, routing to downstream metabolic processes without direct configuration.

Calculates metabolic pathway activity scores across cell groups and visualizes results via heatmaps and violin plots for comparative metabolic state analysis.

Infers ligand-receptor interactions and cell-cell communication networks from single-cell RNA-seq data using the LIANA+ framework.

TESSA integrates TCR sequence and transcriptome data via Bayesian modeling to map functional TCR repertoire landscapes.

Enables targeted differential expression analysis between custom cell groups in single-cell RNA-seq data with automatic pathway enrichment and visualization.

Calculates module scores within the immunopipe bioinformatics pipeline for immune repertoire data analysis.

Analyzes and visualizes top-expressed genes in T/B cell clusters, followed by pathway enrichment for biological characterization.

Generates immunopipe configuration files for single-cell immunology data analysis (scRNA-seq, scTCR/BCR-seq) based on data type and requirements.

Imputes missing values in scRNA-seq data using ALRA, scImpute, or MAGIC to enhance metabolic pathway analysis accuracy.

Processes sample metadata to perform statistical analysis and generate visual reports for data insights.

Core preprocessing for single-cell RNA-seq data using Seurat, including QC, normalization, and multi-sample integration for downstream analysis.

Loads pre-processed single-cell RNA-seq data from Seurat R objects into immunopipe for analysis, bypassing raw data processing steps.

Ensures AI responses are evidence-based and free from hallucinations, particularly when analyzing code or providing recommendations.

Enables local document search with RAG, semantic, and hybrid (BM25/vector) capabilities for AI-powered answers with citations from user knowledge bases.

Fine-tunes large language model weights using DSPy's BootstrapFinetune optimizer for improved task-specific performance.

Engineers XML-based context structures for Claude AI agents to optimize attention efficiency and prompt performance.

Facilitates peer-to-peer communication between Claude Code AI instances using agent-to-agent (a2a) protocol for collaborative agent coordination.

Enables DSPy's AI model optimization techniques to enhance existing Haystack question-answering pipelines for improved accuracy and efficiency.

Automatically creates high-quality few-shot examples for language models using teacher models within the DSPy framework.

Optimizes agentic AI systems through LLM reflection on execution trajectories, improving autonomous agent performance and adaptability.

Comprehensive evaluation metrics and testing framework for validating DSPy AI program performance and behavior.

Builds and optimizes RAG pipelines with ColBERTv2 retrieval in DSPy for enhanced AI application performance.