Data & Analytics

Interactive Mermaid visualization showing faq question pattern analysis workflow

Interactive Mermaid visualization showing mkdocs build process workflow

PathML provides comprehensive support for loading whole-slide images (WSI) from 160+ proprietary medical imaging formats. The framework abstracts vendor-specific complexities through unified slide classes and interfaces, enabling seamless access to image pyramids, metadata, and regions of interest across different file formats.

PathML provides tools for constructing spatial graphs from tissue images to represent cellular and tissue-level relationships. Graph-based representations enable sophisticated spatial analysis, including neighborhood analysis, cell-cell interaction studies, and graph neural network applications. These graphs capture both morphological features and spatial topology for downstream computational analysis.

Aeon provides forecasting algorithms for predicting future time series values.

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This reference provides patterns and frameworks for designing experiments across scientific domains. Use these patterns to develop rigorous tests for generated hypotheses.

Complete reference for molecular descriptors available in RDKit's `Descriptors` module.

This document covers the fundamental concepts and building blocks of LaminDB: Artifacts, Records, Runs, Transforms, Features, and data lineage tracking.

Combine multiple AnnData objects along either observations or variables axis.

Gtars provides a comprehensive CLI for genomic interval analysis directly from the terminal.

DrugBank provides extensive chemical property data including molecular structures, physicochemical properties, and calculated descriptors. This information enables structure-based analysis, similarity searches, and QSAR modeling.

The Bio module provides unified functions for processing and analyzing multiple physiological signals simultaneously. It acts as a wrapper that coordinates signal-specific processing functions and enables integrated multi-modal analysis.

Arboreto requires gene expression data in one of two formats:

全面的AB测试分析工具，支持实验设计、统计检验、用户分群分析和可视化报告生成。用于分析产品改版、营销活动、功能优化等AB测试结果，提供统计显著性检验和深度洞察。

Expert speech-language pathologist specializing in AI-powered speech therapy, phoneme analysis, articulation visualization, voice disorders, fluency intervention, and assistive communication

--> --- name: bio-spatial-transcriptomics-spatial-visualization description: 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. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- Create visualizations f...

--> --- name: bio-spatial-transcriptomics-spatial-statistics description: 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. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_...

--> --- name: bio-spatial-transcriptomics-spatial-deconvolution description: Estimate cell type composition in spatial transcriptomics spots using reference-based deconvolution. Use cell2location, RCTD, SPOTlight, or Tangram to infer cell type proportions from scRNA-seq references. Use when estimating cell type composition in spatial spots. tool_type: python primary_tool: cell2location measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: ...

--> --- name: bio-spatial-transcriptomics-image-analysis description: 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. tool_type: python primary_tool: squidpy measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_comma...

--> --- name: spatial-transcriptomics-analysis description: Automated analysis pipeline for Spatial Transcriptomics (Visium, Xenium) integrating histology and gene expression. keywords: - spatial-transcriptomics - visium - xenium - scanpy - squidpy measurable_outcome: Process a Visium dataset, identify spatially variable genes, and generate spatial feature plots within 30 minutes. license: MIT metadata: author: MD BABU MIA, PhD version: "1.0.0" compatibility: - system: python 3.9+ allowed-too...

Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.

The TF-differential-binding pipeline performs differential transcription factor (TF) binding analysis from ChIP-seq datasets (TF peaks) using the DiffBind package in R. It identifies genomic regions where TF binding intensity significantly differs between experimental conditions (e.g., treatment vs. control, mutant vs. wild-type). Use the TF-differential-binding pipeline when you need to analyze the different function of the same TF across two or more biological conditions, cell types, or tre...

The differential-region-analysis pipeline identifies genomic regions exhibiting significant differences in signal intensity between experimental conditions using a count-based framework and DESeq2. It supports detection of both differentially accessible regions (DARs) from open-chromatin assays (e.g., ATAC-seq, DNase-seq) and differential transcription factor (TF) binding regions from TF-centric assays (e.g., ChIP-seq, CUT&RUN, CUT&Tag). The pipeline can start from aligned BAM files or a prec...