Skill UI

Automated guidance for data normalization tool tasks in ML training, offering best practices, production-ready code/configuration generation, and output validation.

Histolab streamlines whole-slide microscopy by automating tissue detection, tile extraction, and stain normalization for H&E scans, making it ideal for lightweight dataset prep and quick tile-based analysis.

Scanpy is a scalable Python toolkit for single-cell RNA-seq analyses, covering QC, normalization, dimensionality reduction, clustering, marker discovery, annotation, and publication-ready visualization across h5ad/10X/CSV datasets.

deepTools is a comprehensive command-line toolkit designed for processing and analyzing high-throughput sequencing data. It handles core genomics tasks including BAM to bigWig conversion, quality control assessment (PCA, correlation), normalization, and generating publication-quality visualizations (heatmaps, profiles) for ChIP-seq, RNA-seq, and ATAC-seq data. Essential for reproducible genomic research workflows.

Histolab is a specialized Python library for digital pathology designed to process Whole Slide Images (WSI). It automates critical steps including sophisticated tissue detection, customizable tile extraction (random, grid, score-based), and stain normalization. It is essential for preparing high-quality, structured datasets for deep learning model training and comprehensive quantitative whole slide analysis.

A comprehensive Python toolkit built on AnnData for analyzing single-cell RNA-seq data. It supports the complete workflow, including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker identification, and cell type annotation. Users benefit from a suite of ready-to-run CLI scripts that guide the entire process from raw data loading to publication-quality visualization.

A comprehensive set of guidelines for generating maps, figures, and tables for academic journals. This guide emphasizes cartographic standards, statistical rigor, and maximizing the visual argument, ensuring that all exhibits are reproducible, legible in print, and adhere to strict word count limitations. It covers everything from projection choice and data normalization to model plot interpretation and handling spatial uncertainty.

A comprehensive toolkit for validating and converting genomic interval coordinates across diverse file formats (e.g., BED, VCF, GFF/GTF, SAM/BAM) and assembly versions (e.g., GRCh37, GRCh38). It solves complex issues like 0-based vs 1-based conventions, half-open vs inclusive ranges, and variant representation normalization, ensuring data integrity in bioinformatics pipelines.

Scanpy is a powerful, scalable Python toolkit built on AnnData for comprehensive single-cell RNA-seq data analysis. It supports the complete biological workflow, including quality control, normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker gene identification, and sophisticated cell type annotation, enabling the generation of publication-quality figures.