Data & Analytics for Claude Code

Read, write, and create single-cell data objects using Seurat (R) and Scanpy (Python). Use for loading 10X Genomics data, importing/exporting h5ad and RDS files, creating Seurat objects and AnnData objects, and converting between formats. Use when loading, saving, or converting single-cell data formats.

Detect and remove doublets (multiple cells captured in one droplet) from single-cell RNA-seq data. Uses Scrublet (Python), DoubletFinder (R), and scDblFinder (R). Essential QC step before clustering to avoid artificial cell populations. Use when identifying and removing doublets from scRNA-seq data.

Reconstruct cell lineage trees from CRISPR barcode tracing or mitochondrial mutations. Use when studying clonal dynamics, cell fate decisions, or developmental trajectories.

Find marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for differential expression between clusters, identifying cluster-specific markers, scoring gene sets, and assigning cell type labels. Use when finding marker genes and annotating clusters.

Analyze metabolite-mediated cell-cell communication using MeboCost for metabolic signaling inference between cell types. Predict metabolite secretion and sensing patterns from scRNA-seq data. Use when studying metabolic crosstalk between cell populations or metabolite-receptor interactions.

Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial). Use when working with data that measures multiple modalities per cell like RNA + protein or RNA + ATAC. Use when analyzing CITE-seq, Multiome, or other multi-modal single-cell data.

Analyze Perturb-seq and CROP-seq CRISPR screening data integrated with scRNA-seq. Use when identifying gene function through pooled genetic perturbations in single cells.

Quality control, filtering, and normalization for single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for calculating QC metrics, filtering cells and genes, normalizing counts, identifying highly variable genes, and scaling data. Use when filtering, normalizing, and selecting features in single-cell data.

Single-cell ATAC-seq analysis with Signac (R/Seurat) and ArchR. Process 10X Genomics scATAC data, perform QC, dimensionality reduction, clustering, peak calling, and motif activity scoring with chromVAR. Use when analyzing single-cell ATAC-seq data.

Analyzes alternative splicing at single-cell resolution using BRIE2 for probabilistic PSI estimation or leafcutter2 for cluster-based analysis with NMD detection. Identifies cell-type-specific splicing patterns. Use when analyzing isoform usage in scRNA-seq or finding splicing differences between cell populations.

Infer developmental trajectories and pseudotime from single-cell RNA-seq data using Monocle3, Slingshot, and scVelo for RNA velocity analysis. Use when inferring developmental trajectories or pseudotime.

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Process and analyze tissue images from spatial transcriptomics data using Squidpy. Extract image features, segment cells/nuclei, and compute morphological features from H&E or IF images. Use when processing tissue images for spatial transcriptomics.

Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy. Infer intercellular signaling, identify communication pathways, and visualize interaction networks. Use when analyzing cell-cell communication in spatial context.

Load spatial transcriptomics data from Visium, Xenium, MERFISH, Slide-seq, and other platforms using Squidpy and SpatialData. Read Space Ranger outputs, convert formats, and access spatial coordinates. Use when loading Visium, Xenium, MERFISH, or other spatial data.

Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy. Cluster spots considering both expression and spatial context to define anatomical regions. Use when identifying tissue domains or spatial regions.

Analyze high-resolution spatial platforms like Slide-seq, Stereo-seq, and Visium HD. Use when working with subcellular resolution or high-density spatial data.

Build spatial neighbor graphs for spatial transcriptomics data using Squidpy. Compute k-nearest neighbors, Delaunay triangulation, and radius-based connectivity for downstream spatial analyses. Use when building spatial neighborhood graphs.

Quality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.

Analyzes spatial proteomics data from CODEX, IMC, and MIBI platforms including cell segmentation and protein colocalization. Use when working with multiplexed imaging data, analyzing protein spatial patterns, or integrating spatial proteomics with transcriptomics.

Compute spatial statistics for spatial transcriptomics data using Squidpy. Calculate Moran's I, Geary's C, spatial autocorrelation, co-occurrence analysis, and neighborhood enrichment. Use when computing spatial autocorrelation or co-occurrence statistics.

Visualize spatial transcriptomics data using Squidpy and Scanpy. Create tissue plots with gene expression, clusters, and annotations overlaid on histology images. Use when visualizing spatial expression patterns.

Assesses RNA-seq data quality for splicing analysis including junction saturation curves, splice site strength scoring, and junction coverage metrics using RSeQC. Use when evaluating data suitability for splicing analysis or troubleshooting low event detection.

Quantifies alternative splicing events (PSI/percent spliced in) from RNA-seq using SUPPA2 from transcript TPM or rMATS-turbo from BAM files. Calculates inclusion levels for skipped exons, alternative splice sites, mutually exclusive exons, and retained introns. Use when measuring splice site usage or isoform ratios from RNA-seq data.

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Access and analyze AlphaFold protein structure predictions. Use when predicted structures are needed for proteins without experimental structures, or for confidence scores (pLDDT).

Searches molecular libraries for substructure matches using SMARTS patterns with RDKit. Filters compounds by pharmacophore features, functional groups, or scaffold matches with atom mapping. Use when finding compounds containing specific chemical moieties or filtering libraries by structural features.

Create publication-quality visualizations of immune repertoire data including circos plots, clone tracking, diversity plots, and network graphs. Use when generating figures for repertoire comparisons, clonal dynamics, or V(D)J gene usage.

Analyze single-cell TCR and BCR data integrated with gene expression using scirpy. Use when working with 10x Genomics VDJ data alongside scRNA-seq or when integrating immune receptor information with cell state analysis.

Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller.

Normalize indel representation and split multiallelic variants using bcftools norm. Use when comparing variants from different callers or preparing VCF for downstream analysis.

View, query, and understand VCF/BCF variant files using bcftools and cyvcf2. Use when inspecting variants, extracting specific fields, or understanding VCF format structure.

Merge, concatenate, sort, intersect, and subset VCF files using bcftools. Use when combining variant files, comparing call sets, or restructuring VCF data.

Generate variant statistics, sample concordance, and quality metrics using bcftools stats and gtcheck. Use when evaluating variant quality, comparing samples, or summarizing VCF contents.

<!-- COPYRIGHT NOTICE This file is part of the "Universal Biomedical Skills" project. Copyright c 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu All Rights Reserved. This code is proprietary and confidential. Unauthorized copying of this file, via any medium is strictly prohibited. Provenance:

<!-- COPYRIGHT NOTICE This file is part of the "Universal Biomedical Skills" project. Copyright c 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu All Rights Reserved. This code is proprietary and confidential. Unauthorized copying of this file, via any medium is strictly prohibited. Provenance:

<!-- COPYRIGHT NOTICE This file is part of the "Universal Biomedical Skills" project. Copyright c 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu All Rights Reserved. This code is proprietary and confidential. Unauthorized copying of this file, via any medium is strictly prohibited. Provenance:

<!-- COPYRIGHT NOTICE This file is part of the "Universal Biomedical Skills" project. Copyright c 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu All Rights Reserved. This code is proprietary and confidential. Unauthorized copying of this file, via any medium is strictly prohibited. Provenance:

<!-- COPYRIGHT NOTICE This file is part of the "Universal Biomedical Skills" project. Copyright c 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu All Rights Reserved. This code is proprietary and confidential. Unauthorized copying of this file, via any medium is strictly prohibited. Provenance: