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title DE-LIMP Proteomics
emoji 🧬
colorFrom blue
colorTo green
sdk docker
sdk_version 4.5.0
app_file app.R
pinned true
license mit
tags
proteomics
shiny
bioinformatics
differential-expression
mass-spectrometry
limma
dia-nn

DE-LIMP: Differential Expression & Limpa Proteomics 🧬

An interactive R Shiny application for differential expression analysis of DIA-NN proteomics data. Built on Limpa (a Bioconductor package for DIA proteomics normalization and quantification) and Limma (a widely-used statistical framework for differential expression), with Google Gemini AI integration.

✨ What's New in v3.7.0

  • 🧪 Contaminant Analysis: Summary cards, per-sample bar chart, keratin flagging, contaminant heatmap. Signal Distribution and Expression Grid also highlight contaminants.
  • 🔬 Data Explorer: Quartile-based abundance profiles and sample-sample scatter plots -- explore data without requiring DE analysis
  • 🌐 NCBI Proteome Download: Search NCBI Datasets by organism, download RefSeq FASTA with automatic gene symbol mapping (local/HPC only)
  • 📂 SSH File Browser: Visual directory browser for remote HPC navigation with clickable breadcrumbs and color-coded entries (local/HPC only)
  • 🔄 No-Replicates Mode: Quantification completes normally with n=1 per group; DE is skipped gracefully
  • 🏷️ Environment Badge: Colored badge showing deployment mode (Docker/HPC/Local/HF)

Previous highlights (v3.5): Run Comparator (cross-tool DE comparison), Search & Analysis History, Chromatography QC, smart HPC partitions

Earlier (v3.1): UI overhaul (dark navbar, accordion sidebar, DE Dashboard sub-tabs). (v3.0): Multi-Omics MOFA2 | Phosphoproteomics (site-level DE, KSEA, motifs) | GSEA 4-database | AI Summary | XIC Viewer (local/HPC only)

🚀 Features

📊 Interactive Analysis

  • Volcano Plots - Fully interactive (Plotly). Click or box-select to highlight
  • Heatmaps - Auto-scaled Z-score heatmaps of significant proteins
  • Contaminant Analysis - Summary cards, bar charts, keratin flagging, and contaminant heatmap
  • Data Explorer - Quartile abundance profiles and sample-sample scatter plots
  • QC Trends - Monitor run quality with group averages
  • Multi-Protein Violin Plots - Compare expression distributions

🤖 AI-Powered Analysis (Google Gemini)

API Key Required: You must provide your own free Gemini API key. Get one at Google AI Studio and paste it into the sidebar. AI Summary sends only summary statistics (protein names, logFC, adj.P.Val). Data Chat sends per-sample expression data for top DE proteins to enable interactive Q&A.

  • AI Summary - Analyzes all contrasts at once: top DE proteins, cross-comparison biomarkers, and CV stability metrics. Export as standalone HTML report
  • Interactive Data Chat - Conversational AI with full dataset context (QC stats, top DE proteins, phospho sites when available). Auto-Analyze button for one-click reports
  • Interactive AI + Plot Connection - Select proteins in volcano/table to set AI context; AI responses highlight proteins in your plots automatically
  • Export for Claude - Download your complete analysis as a .zip for deep analysis with Claude, ChatGPT, or other AI assistants (includes DE results, expression matrix, QC metrics, GSEA, methods text, and more)
  • Save Chat History - Download conversations as plain text

💾 Session Management

  • Save/Load Sessions - Preserve analysis state (.rds files)
  • Reproducibility Logging - Export complete R code
  • Example Data - One-click demo dataset (Affinisep vs Evosep)

🎓 Education & Resources

  • Embedded proteomics training materials
  • UC Davis Proteomics video tutorials
  • Methodology citations (limpa, limma, DIA-NN)

📖 Quick Start

  1. Load Data: Upload DIA-NN .parquet file or use "Load Example Data"
  2. Assign Groups: Use auto-guess or manual assignment
  3. Run Pipeline: Click "▶ Run Pipeline" -- the app normalizes your data, quantifies proteins, and runs statistical tests to identify which proteins differ significantly between your groups
  4. Explore Results: Interactive plots, tables, GSEA, AI chat

🔬 Methodology

  • Normalization: Data Point Correspondence (DPC-CN)
  • Quantification: DPC-Quant (Detection Probability Curve Quantification)
  • Statistics: limma empirical Bayes moderation
  • FDR Correction: Benjamini-Hochberg

📚 Resources

⚠️ Hosted Version Limitations

This Hugging Face deployment has some limitations compared to a local installation:

  • No DIA-NN search or XIC viewer -- these require local file access and are only available on local/HPC installations
  • Upload size limits -- very large files may fail to upload or cause timeouts
  • Sessions don't persist -- your analysis is lost when you close the browser tab; use "Save Session" to download an .rds file you can reload later
  • Shared resources -- large datasets or complex analyses may be slower than on a dedicated machine

For the full experience, install DE-LIMP locally.

🛠 System Requirements (Local Installation Only)

The following requirements apply only if you are installing DE-LIMP on your own machine. The hosted version above requires nothing but a web browser.

  • R 4.5+
  • Bioconductor 3.22+
  • All dependencies auto-install on first run

👨‍🔬 Developer

Brett Phinney - UC Davis Proteomics Core Facility

Built with ❤️ for the proteomics community