SMIntegration : Spatial Multi-omics Integration Platform

Introduction

SMIntegration is an innovative open-source platform for integrated analysis of spatial transcriptomics and metabolomics data. It integrates spatial pattern recognition, differential comparison, network construction, and functional annotation into a unified workflow. Designed to address key challenges in spatial multi-omics correlation analysis, SMIntegration enables researchers to explore gene-metabolite co-regulation mechanisms through an intuitive web interface, revealing spatial heterogeneity in tissue development and disease progression.

Key Features

🔍 Spatial Pattern Discovery

• Automated identification of spatial expression patterns for genes/metabolites
• Moran's I correlation analysis between transcriptomic/metabolomic modules

🧩 Pixel-level Clustering & Cell Annotation

• 4 clustering algorithms: Seurat-LV, Seurat-LM, Seurat-SLM, UMAP-kmeans
• SingleR-based automatic cell type annotation with reference datasets
• Custom cell type annotation support

⚖️ Flexible Differential Analysis

• ROI selection via:
  • Interactive tissue imaging
  • Clustering results
  • Cell annotation mapping
• Differentially expressed genes (DEGs) and metabolites (DAMs) detection
• Group-specific gene-metabolite correlation networks

🧬 Functional Analysis

• KEGG pathway co-annotation analysis
• KEGG pathway overlap analysis

📊 Interactive Visualization

• Gene-metabolite co-localization analysis
• RGB overlay imaging for multi-feature visualization

Getting Started

🌐 Online Access

Access the live platform without installation: 🔗 [[https://metax.genomics.cn/app/SMIntegration]

💻 Local Deployment (Docker)

🔗 [[Docker Installation Guide]

# Pull the latest Docker image
docker pull mzlabresearch/smintegration:v-1.0

# Run with ShinyProxy
docker run -d -p 8787:3838  mzlabresearch/smintegration:v-1.0

Please use a web browser to access: http://localhost:8787

📥 Input Data Format

SMIntegration requires two feature matrices as input files:

Key Requirements:

1、Pre-aligned datasets: Spatial metabolomics and transcriptomics must share matched spatial resolution and identical pixel coordinates. Registration can be performed using either the built-in alignment tool in the Upload tab or externally with SpatialData.

2、Supported Formats: SMIntegration requires two feature matrices in TXT or RDS format:

TXT Format:

• Columns 1-4: Feature name (metabolite or gene), pixel coordinates (x, y), feature abundance (Intensity or MIDCount)
• Each row represents one spatial pixel

RDS Format (Seurat object):

• Spatial location information stored in meta.data (x, y)
• Feature matrix stored in data@assays$Spatial (Format: Features as rows, spatial pixels as columns, values represent feature abundance)

📖 Detailed formatting guide available in-app (Tutorial Panel → Data Preparation Requirements)

Usage Workflow

1. Upload Data: Import feature matrix in Overall Distribution Analysis Panel
2. Core Analysis:

• Pattern Recognition: Identify spatial expression modules
• Pixel Clustering: Group pixels using 4 algorithms
• Cell Annotation: Automated (SingleR) or manual mapping
• Differential Analysis: Compare regions (manual/automatic ROI selection)
• Network Construction: Build condition-specific gene-metabolite networks
• Functional Analysis: Pathway mapping and co-annotation analysis
• Visualization：Dynamic exploration of spatial distributions and co-localization patterns

Example Data

Test dataset: Mouse brain coronal adjacent sections

1. Spatial Transcriptomics Data:

• Acquisition & Processing: Stereo-seq, 0.05μm resolution, aggregated to 50μm resolution
• Content: 14,530 pixels × 500 genes

2. Spatial Metabolomics Data:

• Acquisition & Processing: AFADESI (+) mode, 50μm resolution, spatially registered to identical pixel coordinates
• Content: 14,530 pixels × 500 metabolites

Access: Built-in dataset in SMIntegration (Tutorial Panel → Demo Datasets)

Raw data: NGDC OMIX Repository ID: OMIX011674

Community & Support

Developed by Haoke Deng (denghaoke@genomics.cn)
Last update: 2026-02-09