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the web analysis platform for omicverse and omicclaw

omicverse-web 최신버젼 다운로드

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# OmicVerse Web Tutorial

OmicVerse Web is a browser-based platform for single-cell and multi-omics analysis. It wraps the full analytical power of OmicVerse into an interactive visual interface — no coding required to go from raw data to publication-ready results.

> 📚 For the Chinese version, please check [使用指南 (中文版)](README_CN.md)

---

## Table of Contents

1. [Installation](#1-installation)
2. [Starting the Server](#2-starting-the-server)
3. [Interface Overview](#3-interface-overview)
4. [Uploading Data](#4-uploading-data)
5. [Preprocessing](#5-preprocessing)
6. [Dimensionality Reduction & Visualization](#6-dimensionality-reduction--visualization)
7. [Clustering](#7-clustering)
8. [Cell Annotation](#8-cell-annotation)
9. [Differential Gene Expression](#9-differential-gene-expression)
10. [Differential Cell Type Composition](#10-differential-cell-type-composition)
11. [Trajectory Analysis](#11-trajectory-analysis)
12. [Code Executor](#12-code-executor)
13. [AI Agent](#13-ai-agent)
14. [File Manager & Terminal](#14-file-manager--terminal)
15. [Remote Server Deployment](#15-remote-server-deployment)

---

## 1. Installation

OmicVerse Web offers two installation methods.

### Option 1: Install from PyPI (Recommended)

```bash
pip install omicverseweb
```

### Option 2: Install from the GitHub Repository

Use this option if you want the latest development version or plan to contribute:

```bash
git clone https://github.com/Starlitnightly/omicverse-web.git
cd omicverse-web
pip install -e .
```

> **Recommended**: Install inside a dedicated conda environment to avoid dependency conflicts.
>
> ```bash
> conda create -n omicverse python=3.10
> conda activate omicverse
> pip install omicverseweb
> ```

---

## 2. Starting the Server

After installation, run the following command in your terminal:

```bash
omicverse-web
```

The server starts at `http://localhost:5050` by default (automatically increments to 5051, 5052 … if the port is occupied). A successful start looks like:

```
* OmicVerse Web running on http://localhost:5050
```

Open that URL in your browser to access the platform.

### Optional Arguments

```bash
omicverse-web --port 8080          # Specify a port
omicverse-web --no-debug           # Disable debug mode (recommended for production)
omicverse-web --remote             # Remote mode (use with SSH tunnel)
```

---

## 3. Interface Overview

After opening `http://localhost:5050`, click **Launch Analysis** on the landing page to enter the main analysis interface.

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318031649002.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/omicverse-web/main/)

The main interface is divided into three areas:

**Left Sidebar**
- 📁 File Browser — manage local files with a right-click context menu
- 🧬 Variable Viewer — inspect variables in the live kernel in real time
- 💻 Terminal Panel — a full shell session in the browser
- 📊 Memory Monitor — live process and system memory usage

**Top Tab Bar**
Arranged in analysis order: Preprocessing → Visualization → Clustering → Annotation → DEG → DCT → Trajectory

**Main Panel (right)**
The parameter panel and result display area for the currently active tab

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318031850308.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318031901273.png#gh-dark-mode-only)

---

## 4. Uploading Data

Click the **Upload** button in the top toolbar (or drag a file into the file browser) and select a local `.h5ad` file.

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032100222.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032042315.png#gh-dark-mode-only)

Once uploaded, the status bar displays basic data information:

```
✓ Data loaded
  Cells: 8,542 | Genes: 33,538
```

> **Supported format**: Standard AnnData `.h5ad` files.
> To convert from other formats, use the Code Executor to load the file with `scanpy` and save it as `.h5ad`.

---

## 5. Preprocessing

Switch to the **Preprocessing** tab and execute each step in order.

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032240638.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032255306.png#gh-dark-mode-only)

### 5.1 Filter Cells and Genes

Click the **Filter Cells / Filter Genes** tool card and set the thresholds:

| Parameter | Description | Recommended |
|-----------|-------------|-------------|
| Min genes per cell | Minimum number of genes detected per cell | 200 |
| Max genes per cell | Upper limit (to remove potential doublets) | 5000 |
| Min cells per gene | Minimum number of cells a gene must be expressed in | 3 |
| Max mito % | Maximum allowed mitochondrial gene fraction | 0.2 |

Click **Run**. The right panel shows a before/after comparison of cell and gene counts.

### 5.2 Normalization

Click **Normalize** to scale each cell's total UMI count to a common target (default: 10,000).

### 5.3 Log Transformation

Click **Log1p** to apply a log(1 + x) transformation, compressing the data distribution.

### 5.4 Highly Variable Gene Selection

Click **HVG** to select the most informative genes for downstream analysis:

| Parameter | Recommended |
|-----------|-------------|
| Top genes | 2000 |
| Flavor | seurat_v3 |

### 5.5 Scaling

Click **Scale** to apply per-gene z-score normalization (`max_value` defaults to 10).

---

## 6. Dimensionality Reduction & Visualization

Switch to the **Visualization** tab.

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032422655.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032433156.png#gh-dark-mode-only)

### 6.1 PCA

Click **PCA**, set the number of principal components (default: 50), and click Run.

### 6.2 Build Neighbor Graph

Click **Neighbors**:

| Parameter | Recommended | Description |
|-----------|-------------|-------------|
| n_neighbors | 15 | Number of neighbors — balances local vs. global UMAP structure |
| n_pcs | 40 | Number of PCs to use |

### 6.3 UMAP

Click **UMAP**. The embedding appears in the canvas on the right once complete.

### 6.4 Adjusting Visualization Parameters

Use the controls above the plot to:
- Switch **Color by** — color by gene expression, cell metadata, or cluster labels
- Adjust **point size** and **opacity**
- Switch the rendering backend (Standard / Rasterized / **GPU**)

> **GPU rendering mode**: Recommended for datasets with more than 100,000 cells. Powered by WebGL for smooth real-time pan and zoom.

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032536570.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032542091.png#gh-dark-mode-only)

---

## 7. Clustering

Switch to the **Clustering** tab.

### Leiden Clustering (Recommended)

| Parameter | Description |
|-----------|-------------|
| Resolution | Controls granularity — higher values produce more clusters (recommended: 0.3–1.0) |

Click **Run**. Results are written to `adata.obs['leiden']` and the UMAP plot updates automatically.

---

## 8. Cell Annotation

Switch to the **Annotation** tab, which offers three annotation methods.

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032634072.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032642506.png#gh-dark-mode-only)

### 8.1 CellTypist (Recommended — pretrained model)

Select a model that matches your sample type:

| Model | Best for |
|-------|----------|
| Immune_All_Low.pkl | Fine-grained immune cell classification |
| Immune_All_High.pkl | Coarse-grained immune cell classification |
| Human_Lung_Atlas.pkl | Human lung cells |

Enable **Majority Voting** (neighborhood-based smoothing for consistency) and click **Run**.

### 8.2 SCSA (Database Matching)

Select the species (Human / Mouse), specify the cluster column (`leiden`), and click **Run**.

### 8.3 AI-Assisted Annotation (GPT-4)

Enter your OpenAI API Key and set the number of top marker genes per cluster (default: 10). Click **Run** — the AI infers cell types from marker genes and provides a written explanation.

---

## 9. Differential Gene Expression

Switch to the **DEG** tab.

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032808241.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032813952.png#gh-dark-mode-only)

### 9.1 Configure the Comparison

1. **Group by** — select the grouping column (e.g., `cell_type` or `leiden`)
2. **Group 1** — select the experimental group
3. **Group 2** — select the reference group (or choose `rest` for all other cells)
4. **Method** — choose a statistical test: wilcoxon / t-test / mannwhitney

Click **Analyze**.

### 9.2 Viewing Results

After analysis, the following are displayed automatically:

- **Volcano plot** — Log2FC on the x-axis, −log10(p-value) on the y-axis; click a point to highlight that gene

- **Results table** — all differentially expressed genes with statistics, sortable by FDR or Log2FC

- **Violin plot** — click any gene in the table to view its expression distribution across the two groups

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032922771.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318032929037.png#gh-dark-mode-only)

---

## 10. Differential Cell Type Composition

Switch to the **DCT** tab to analyze changes in cell type proportions across sample conditions.

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033050768.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033056198.png#gh-dark-mode-only)

### Parameters

| Parameter | Description |
|-----------|-------------|
| Cell type column | Column name for cell types (e.g., `cell_type`) |
| Sample column | Column name for sample identifiers |
| Condition column | Column name for the condition (e.g., `disease` vs `control`) |
| Reference cell type | Reference cell type for sccoda (required) |
| Method | sccoda or Milo |

Click **Run** to generate:
- **Composition bar chart** — stacked bar plot of cell type proportions per sample

- **Effect size plot** — highlights cell types with significant compositional changes

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033130431.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033136364.png#gh-dark-mode-only)

---

## 12. Code Executor

For custom analyses, click the **Code** button in the top-right corner to open the built-in code editor.

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033354945.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033401965.png#gh-dark-mode-only)

The kernel is pre-populated with the following variables, ready to use:

```python
sc      # scanpy
pd      # pandas
np      # numpy
plt     # matplotlib.pyplot
odata   # the current AnnData object
```

Example usage:

```python
# Inspect the data
print(odata)

# Violin plot for a specific gene
sc.pl.violin(odata, keys='CD3D', groupby='leiden')
plt.show()

# Save the current results
odata.write_h5ad('result.h5ad')
```

Press **Shift+Enter** to execute. Output appears in real time below the editor.

---

## 13. AI Agent

The AI Agent understands natural language and automatically generates and executes analysis code on your behalf.

### 13.1 Configuration

Click the **Agent** icon in the sidebar to expand the configuration panel:

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033508098.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033513447.png#gh-dark-mode-only)

| Field | Description |
|-------|-------------|
| API Key | Your Claude or OpenAI API key |
| Model | e.g., `claude-opus-4-6`, `gpt-4o` |
| Endpoint | Custom API endpoint (optional, OpenAI-compatible) |

### 13.2 How to Use

Type your task in the chat box and press **Send**:

**Example prompts:**

```
Run Leiden clustering with resolution 0.5, then show the UMAP colored by cluster
```

```
Find the top 20 differentially expressed genes between CD4 T cells and CD8 T cells, and plot a volcano
```

```
My data has multiple batches in the 'batch' column. Run Harmony batch correction and recompute the UMAP
```

The Agent displays each step: reasoning → code generation → execution → result figure.

---

## 14. File Manager & Terminal

### 14.1 File Browser

Click the 📁 icon in the left sidebar to expand the file browser.

Supported actions (right-click context menu):

- Create folder / file
- Rename / delete / copy / paste
- Double-click to open `.h5ad`, `.ipynb`, text files, and images

### 14.2 Built-in Terminal

Click the 💻 icon in the left sidebar to create a shell session (bash / zsh) and run any command:

```bash
# Install additional packages
pip install harmonypy

# Check GPU status
nvidia-smi

# Run a custom script
python my_analysis.py
```

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033647397.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033653098.png#gh-dark-mode-only)

### 14.3 Package Manager

The **Environment** panel lets you search for and install Python packages without switching to the terminal:

![Light Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033800930.png#gh-light-mode-only)
![Dark Mode](https://raw.githubusercontent.com/Starlitnightly/ImageStore/main/omicverse_img/20260318033809084.png#gh-dark-mode-only)

---

## 15. Remote Server Deployment

To run OmicVerse Web on a remote server and access it from a local browser via an SSH tunnel:

### On the Server

```bash
# Install
pip install omicverseweb

# Start in remote mode (bind to loopback only)
omicverse-web --remote --no-debug
```

### On Your Local Machine — Create an SSH Tunnel

```bash
ssh -L 5050:127.0.0.1:5050 username@your-server.com -N
```

Then open `http://localhost:5050` in your local browser.

### Background (Persistent) Execution

```bash
nohup omicverse-web --remote --no-debug > omicverse_web.log 2>&1 &
```

---

## References

- [OmicVerse GitHub](https://github.com/Starlitnightly/omicverse)
- [OmicVerse Web GitHub](https://github.com/Starlitnightly/omicverse-web)
- [PyPI: omicverseweb](https://pypi.org/project/omicverseweb/)
- Paper: *OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing*, Nature Communications (2024), 15:5983

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