Data & Analytics

Cards are the primary container component in modern bslib dashboards. They group related content with borders and padding, helping users digest, engage with, and navigate through information.

All economic indicators return US data and follow the same response structure: ```json { "name": "Real Gross Domestic Product", "interval": "annual", "unit": "billions of chained 2012 dollars", "data": [{"date": "2023-01-01", "value": "22067.1"}, ...] } ```

Quick reference for Isabelle/HOL syntax when modeling functional programs.

Data reconciliation is the process of ensuring data consistency and integrity across systems during and after migrations. This document provides comprehensive strategies, tools, and implementation patterns for detecting, measuring, and correcting data discrepancies in migration scenarios.

You are an AI pricing scientist that designs, executes, and analyzes pricing experiments using rigorous statistical methods.

You are an AI specialist focused on identifying and analyzing user experience friction points through behavioral signals, enabling proactive intervention and UX optimization.

You are an AI unit economics analyst that calculates and forecasts lifetime value by customer cohort to optimize acquisition, retention, and monetization strategies.

Complete time-course expression analysis workflow from expression matrix to temporal patterns and pathway enrichment. Covers temporal differential expression (limma splines or DESeq2 LRT), Mfuzz soft clustering of expression profiles, optional circadian rhythm detection with MetaCycle or CosinorPy, GAM trajectory fitting with mgcv, and per-cluster pathway enrichment with clusterProfiler. Supports both R and Python alternatives at each step.

Polishing improves assembly accuracy by using additional sequencing data to correct errors. Essential for long-read assemblies which have higher raw error rates.

Complete gene regulatory network inference workflow from processed single-cell data to regulon discovery and perturbation simulation. Supports RNA-only analysis with pySCENIC (GRNBoost2 + RcisTarget + AUCell) and multiome analysis with SCENIC+ for enhancer-driven GRNs. Includes CellOracle for in silico perturbation simulation.

Structural variants (SVs) are genomic alterations typically >50bp that include deletions, insertions, inversions, duplications, and translocations. Short-read SV calling uses paired-end and split-read information to detect these events.

This workflow processes single-cell RNA-seq data from 10X Genomics Cell Ranger output to annotated cell types. It supports both Seurat (R) and Scanpy (Python) implementations.

This guide covers creating reproducible Jupyter notebooks with parameterization for automated analysis pipelines.

End-to-end workflow for label-free proteomics analysis from MaxQuant/DIA-NN output to differential protein abundance.

End-to-end workflow for biomarker discovery combining feature selection, model training with nested cross-validation, interpretation, and validation. Produces a validated biomarker panel with an accompanying classifier.

Unique Molecular Identifiers (UMIs) are random sequences added to molecules before PCR amplification. They enable distinguishing PCR duplicates from biological duplicates, crucial for accurate quantification in RNA-seq, targeted sequencing, and single-cell applications.

Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations. Use PROACTIVELY for business intelligence or strategic analysis.

Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to phenotypes using WGCNA and CEMiTool. Detects hub genes and module-trait relationships from bulk or single-cell expression data. Use when finding co-expression modules, identifying hub genes, or relating gene networks to clinical or experimental variables.

- Working on business analyst tasks or workflows - Needing guidance, best practices, or checklists for business analyst

Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.

End-to-end gene regulatory network inference pipeline from processed single-cell data to regulon discovery and perturbation simulation. Supports RNA-only (pySCENIC) and multiome (SCENIC+) paths. Use when building gene regulatory networks from single-cell transcriptomic or multiome data.

This reference covers file formats used in microscopy, medical imaging, remote sensing, and scientific image analysis.

PyOpenMS provides specialized tools for untargeted metabolomics analysis including feature detection optimized for small molecules, adduct grouping, compound identification, and integration with metabolomics databases.

Interactive Mermaid visualization showing chapter organization workflow