proteomicscopylot 0.1.2

Notebook-facing plotting and analysis helpers for ProteomicsCoPYlot.

Proteomics CoPYlot (React + FastAPI Version)

Proteomics CoPYlot is a tool for proteomics data analysis and visualization with a modern frontend/backend architecture. It provides the full analysis workflow and a separate read-only viewer workflow.

Features

• Upload and analyze proteomics tables from typical workflows (for example Spectronaut, DIA-NN, MaxQuant-derived exports)
• Pipeline-based UI with translated modules from the original project
• QC, statistical analysis, comparison, phospho-specific analysis, peptide-level modules, and summary/report generation
• Interactive and static plot outputs (Plotly + Matplotlib/Seaborn) with report integration
• Dedicated viewer mode for preloaded data without upload/annotation editing
• Password-gated viewer portal mode (/viewer, /viewer-ai) with project selection by password

Modes

• Analysis tool: full workflow with upload, annotation, analysis, and report generation
• Viewer: read-only mode for provided datasets via viewer_config.json
• Local viewer override: place local-only settings under local/, for example local/viewer_config.local.json.
• Viewer portal (optional): password login that maps each user session to a project folder in viewer_data/<ProjectName>/.

Pipelines

• Data
• Completeness
• QC Pipeline
• Statistical Analysis
• Peptide Level (when peptide data is available)
• Phospho-specific (when phospho data is available)
• Comparison
• Summary
• External Tool (analysis mode only)

Installation

Use the ordered install/run/build guide in readme.txt, organized as:

• Global notes and AI prerequisites
• Windows: install, run, production run, onefile EXE build
• macOS: install, run, production run, onefile app build
• Linux: install, run, production run, onefile app build
• Remote Ollama hosting over Tailscale

Notebook Package

The notebook-facing Python API is packaged as proteomicscopylot.

Local editable install:

python -m pip install -e .

Build distributions:

python -m pip install build twine
python build_pip_package.py

Upload a new version to PyPI:

python -m twine upload dist/*

Every PyPI upload needs a new version in pyproject.toml and proteomicscopylot/_version.py; existing versions cannot be overwritten.

Usage

Windows:

• Analysis: run_app.bat
• Viewer: run_viewer.bat
• Viewer (specific project): run_viewer.bat --project GRK_project or run_viewer.bat --GRK_project
• Analysis (AI): run_app_ai.bat [--model-name]
• Viewer (AI): run_viewer_ai.bat [--model-name] [--project GRK_project]

macOS:

• Analysis: ./run_app_macos.sh
• Viewer: ./run_viewer_macos.sh
• Viewer (specific project): ./run_viewer_macos.sh --project GRK_project or ./run_viewer_macos.sh --GRK_project
• Analysis (AI): ./run_app_ai_macos.sh [--model-name]
• Viewer (AI): ./run_viewer_ai_macos.sh [--model-name] [--project GRK_project]

Linux:

• Analysis: ./run_app_linux.sh
• Viewer: ./run_viewer_linux.sh
• Viewer (specific project): ./run_viewer_linux.sh --project GRK_project or ./run_viewer_linux.sh --GRK_project
• Analysis (AI): ./run_app_ai_linux.sh [--model-name]
• Viewer (AI): ./run_viewer_ai_linux.sh [--model-name] [--project GRK_project]

Viewer Portal (Password -> Project)

For hosted viewer portals (for example behind nginx on /viewer and /viewer-ai), enable:

• COPYLOT_VIEWER_MODE=1
• COPYLOT_VIEWER_PORTAL_ENABLED=1
• COPYLOT_VIEWER_ACCESS_FILE=/app/local/viewer_access.local.json

local/viewer_access.local.json format:

{
  "credentials": [
    { "password": "strong-pass-1", "project": "GRK_project", "label": "GRK Project" },
    { "password": "strong-pass-2", "project": "TKI_project", "label": "TKI Project" }
  ]
}

Project folders:

• viewer_data/GRK_project/viewer_config.json
• viewer_data/TKI_project/viewer_config.json

Each browser session is isolated (COPYLOT_SESSION_ISOLATION=1), and viewer project data is loaded into that specific session after successful login.

AI Prerequisite

AI mode requires a local Ollama installation (system dependency, not a Python pip package).

Examples:

• run_app_ai.bat --deepseek-r1:1.5b
• run_viewer_ai.bat --deepseek-r1:1.5b
• run_app_ai.bat --dev
• run_app_ai.bat --dev0
• run_app_ai.bat --dev2
• run_app_ai.bat --dev20
• run_viewer_ai.bat --dev
• run_viewer_ai.bat --dev0
• run_viewer_ai.bat --dev2
• run_viewer_ai.bat --dev20

Windows Onefile EXE Builds

PowerShell (inside project root):

• Tool: .\build_exe_windows.ps1 -Target Tool
• Viewer: .\build_exe_windows.ps1 -Target Viewer
• Viewer (single project bundle): .\build_exe_windows.ps1 -Target Viewer -ViewerProject GRK_project

CMD (inside project root):

• Tool: powershell -ExecutionPolicy Bypass -File ".\build_exe_windows.ps1" -Target Tool
• Viewer: powershell -ExecutionPolicy Bypass -File ".\build_exe_windows.ps1" -Target Viewer

Outputs:

• dist/ProteomicsCoPYlot.exe
• dist/DataViewer.exe

Windows AI Onefile EXE Builds

Build note:

• AI-enabled builds require frontend AI flags at build time.
• Runtime AI still requires COPYLOT_AI_MODE=1 when launching the EXE.

Tool (AI):

cd C:\PythonProject\ProteomicsCoPYlot\frontend
cmd /c "set VITE_APP_MODE=analysis&& set VITE_AI_ENABLED=1&& npm run build"
Set-Content -Path .\dist\.copylot_mode.txt -Value analysis -NoNewline
cd ..
powershell -ExecutionPolicy Bypass -File ".\build_exe_windows.ps1" -Target Tool -SkipFrontendBuild

Tool (AI dev20 default endpoint):

cd C:\PythonProject\ProteomicsCoPYlot\frontend
$DEV20_ENDPOINT = (Get-Content ..\local\dev_ai.local.env | Where-Object { $_ -like "COPYLOT_DEV2_OLLAMA_ENDPOINT=*" } | Select-Object -First 1).Split("=", 2)[1]
cmd /c "set VITE_APP_MODE=analysis&& set VITE_AI_ENABLED=1&& set VITE_AI_DEFAULT_OLLAMA_ENDPOINT=$DEV20_ENDPOINT&& set VITE_AI_DEFAULT_OLLAMA_MODEL=llama3.1:8b&& npm run build"
Set-Content -Path .\dist\.copylot_mode.txt -Value analysis -NoNewline
cd ..
powershell -ExecutionPolicy Bypass -File ".\build_exe_windows.ps1" -Target Tool -SkipFrontendBuild

Viewer (AI):

cd C:\PythonProject\ProteomicsCoPYlot\frontend
cmd /c "set VITE_APP_MODE=viewer&& set VITE_AI_ENABLED=1&& npm run build"
Set-Content -Path .\dist\.copylot_mode.txt -Value viewer -NoNewline
cd ..
powershell -ExecutionPolicy Bypass -File ".\build_exe_windows.ps1" -Target Viewer -SkipFrontendBuild

Viewer (AI dev20 default endpoint):

cd C:\PythonProject\ProteomicsCoPYlot\frontend
$DEV20_ENDPOINT = (Get-Content ..\local\dev_ai.local.env | Where-Object { $_ -like "COPYLOT_DEV2_OLLAMA_ENDPOINT=*" } | Select-Object -First 1).Split("=", 2)[1]
cmd /c "set VITE_APP_MODE=viewer&& set VITE_AI_ENABLED=1&& set VITE_AI_DEFAULT_OLLAMA_ENDPOINT=$DEV20_ENDPOINT&& set VITE_AI_DEFAULT_OLLAMA_MODEL=llama3.1:8b&& npm run build"
Set-Content -Path .\dist\.copylot_mode.txt -Value viewer -NoNewline
cd ..
powershell -ExecutionPolicy Bypass -File ".\build_exe_windows.ps1" -Target Viewer -SkipFrontendBuild

Run AI EXEs:

set COPYLOT_AI_MODE=1
.\dist\ProteomicsCoPYlot.exe
.\dist\DataViewer.exe

Project Structure

• frontend/: React + TypeScript UI
• backend/: FastAPI API + services
• viewer_data/: data folder for viewer inputs
• viewer_data/<ProjectName>/: project-specific viewer folders (each with its own viewer_config.json)
• viewer_data/local/<ProjectName>/: ignored local/private viewer projects
• viewer_config.json: viewer dataset/config mapping
• viewer_access.example.json: template for password-to-project mapping in viewer portal mode
• launch.py: shared runtime launcher
• launch_tool.py: analysis launcher entrypoint
• launch_viewer.py: viewer launcher entrypoint

Docker Compose (Viewer Portals)

Use the dedicated compose file for hosted viewer portals:

docker compose -f docker-compose.viewer.yml up -d --build

Containers:

• proteomics-viewer on port 18081 (non-AI viewer)
• proteomics-viewer-ai on port 18082 (AI-enabled viewer)

Both expect a local local/viewer_access.local.json file and shared viewer_data/ folder.

Nginx Routing Example

An example nginx config is included at:

• deploy/nginx/proteomics-data.de.conf.example

It routes:

• / -> analysis app
• /ai -> analysis AI app
• /viewer -> viewer portal (password-based project selection)
• /viewer-ai -> viewer AI portal

Deploy on Render (Docker)

This repository now includes:

• Dockerfile for combined frontend+backend deployment
• .dockerignore for smaller and faster Docker builds
• render.yaml Blueprint for one-click Render setup

Steps:

1. Push this repository to GitHub.
2. In Render, create a Blueprint and point it to this repo (it will detect render.yaml).
3. If proteomics-copylot is already taken in your Render workspace, rename the service in render.yaml and push again.
4. Wait for the first deploy to finish, then open the generated *.onrender.com URL.
5. Confirm health endpoint works at /api/health.

Notes:

• Free plan instances have important limits and can sleep when idle.
• Service filesystems are ephemeral; runtime-written local files are not durable across redeploys.
• AI mode is disabled by default in render.yaml (COPYLOT_AI_MODE=0).

License

Licensed under the MIT License. See LICENSE.