scMultiModalMap

Version 1

single-cell multimodal data map

scMultiModalMap is an innovative database designed to equip researchers with tools for exploring, analyzing, and visualizing single-cell multimodal datasets. By leveraging high-resolution data from advanced technologies like CITE-seq and 10x Genomics Multiome, scMultiModalMap connects complex biological data with actionable insights. The platform integrates these multimodal datasets using deep learning models, facilitating effective harmonization and cross-modal analyses to reveal hidden patterns in cellular heterogeneity and regulatory mechanisms.

174

Datasets

3,227,920

Cells

UMAP Visualization
UMAP Visualization

Use the UMAP visualization to interactively explore cell populations across different samples, conditions, clusters and cell types, as well as to examine modality features (e.g., genes, proteins, or chromatin accessibility peaks) at the single-cell level.

Feature Level Comparison
Feature Level Comparison

Use the feature level comparison to interactively explore the distribution of modality features (e.g., genes, proteins, or chromatin accessibility peaks) across cell types.

Cross-Modal Feature Correlation Analysis
Cross-Modal Feature Correlation Analysis

Use the cross-modal feature correlation analysis to explore the pairwise relationships between features (e.g., genes, proteins, or chromatin accessibility peaks) across different modalities, which may help identify significant correlations, revealing potential functional associations, co-regulation mechanisms, or biomarker candidates for further investigation.

Cross-Modal Feature Regression Analysis
Cross-Modal Feature Regression Analysis

Use the cross-modal feature regression analysis to model how the expression of a target feature (e.g., a surface protein, or a marker gene) is influenced by other features (e.g., genes, or chromatin accessibility peaks) across cells, which enables the identification of predictive regulators, supports inference of directional interactions, and facilitates hypothesis generation about gene regulation mechanisms within complex cellular systems.

Cross-Modal Gene Regulatory Network Inference
Cross-Modal Gene Regulatory Network Inference

Use the cross-modal gene regulatory network inference to infer transcription factor-regulatory element (TF-RE) binding scores, cis-regulatory scores and trans-regulatory scores of the cell population and each cell type.

Cell Type Compositional Analysis
Cell Type Compositional Analysis

Use the cell type compositional analysis to determine which cell types showed significant changes in proportion under different conditions.

Cell-cell Communication Inference
Cell-cell Communication Inference

Use the cell-cell communication inference to infer the biologically significant cell-cell communication networks and signaling pathways.

NEWS
Citation
  • Version 1 scMultiModalMap will be published later!

If you have any question or comment, please feel free to contact us by scmmm@cncb.ac.cn.