LnCeVar 2.0 help document.
LnCeVar 2.0 is an updated resource and web tools for genomic variations disrupting ceRNA networks from single-sell/spatial transcriptomics data.
We construct LnCeVar 2.0, which is an updated resource and web tools for genomic variations disrupting ceRNA networks from single-cell/spatial transcriptomics data. We would like to submit it for publication in the Database Issue of 
. LnCeVar 2.0 is freely available at http://bio-bigdata.hrbmu.edu.cn/LnCeVar or http://www.bio-bigdata.net/LnCeVar.
LnCeVar is a comprehensive database of genomic variations that disturb ceRNA network regulation. The first version was released in 2020 and published in . To date, the LnCeVar database has received 64 citations
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and more than 200,000 visits from 117 countries. The LnCeVar database is continuously updated and serves as an important resource for investigating the functions and mechanisms of personalized genomic variations in human diseases.
In recent years, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (stRNA-seq) have greatly expanded our understanding of complex microbial ecosystems and variant cell states, providing single-cell and spatial resolution in individual tissue sections. Within the tumour microenvironment, cells exhibit distinct behaviours that are influenced by the precise regulation of genome variations and gene expression specific to individual cells. Therefore, identifying these variations and analysing how they affect intracellular gene regulatory networks will improve our understanding of disease pathology at the cellular level and further advance precision medicine.
Fig 1-1. LnCeVar 2.0's Background.
We are pleased to introduce LnCeVar 2.0, an updated database with significantly expanded data and improved features, including:
- (i) New collection of 812 scRNA-seq and stRNA-seq datasets across 102 diseases/phenotypes with different clinical treatments (such as chemotherapy, immunotherapy and targeted therapy) and normal tissues;
- (ii) Newly identified 5,218,062 cell-specific and spatial spot-specific SNV-ceRNA events and functional networks for 2,673,603 single cells/spatial spots;
- (iii) Manually curated more than 20,000 experimentally supported gene biomarkers and SNV-ceRNA events associated with cancer cell metastasis, recurrence, prognosis, circulation, drug resistance, immune response, etc.
- (iv) Newly developed functional tools, including 5 comprehensive and 12 mini analysis tools, for performing multi-level cross-talk analysis and 3D visualisation.
- (v) Novel inference of SNV effects on cell type, states and functions at single-cell/spatial level. Expanded curation of lncRNA/mRNA/pseudogene expression profiles and follow-up clinical information of thousands of cancer patients.
LnCeVar 2.0 provides a user-friendly search and browse interface. In addition, as an important addition to the database, we have created a panel of flexible tools (including five comprehensive analysis tools and 12 mini analysis tools) to facilitate data retrieval and analysis. For example, the CeVarCluster and CeVarTraject tools investigate the patterns of gene expression and SNV mutations in SNV-ceRNA events, and examine the distribution of cell clusters/types/states/trajectories. The CeVarCellState tool illustrates how SNV-ceRNA events affect cell states along developmental trajectories to reveal the dynamic interactions that drive cell fate decisions. The CeVarSC3D and CeVarST3D tools conduct multi-level crosstalk analysis of SNVs, ceRNA networks and cell states contributing to individual disease pathology at the single-cell and spatial level, presented in an interactive 3D view. Each of the 12 mini tools provides fast and easy-to-use analysis, including functional, hallmark and cell state annotations, cell clustering, survival and correlation analyses, and network construction. Overall, we anticipate that this updated database will facilitate the investigation of fine-tuned SNV-ceRNA networks at single-cell and spatial resolution, helping us to understand the regulatory mechanisms behind complex microbial ecosystems.
LnCeVar 2.0: a resource and tools for genomic variations disrupting ceRNA networks from single-sell/spatial transcriptomics.
1) Navigation Bar
Navigate through core modules: Home, Browse, Comprehensive Tools, Mini Tools, Statistics, Download, and Help. The layout is minimal yet efficient for both search and analysis.
2) Quick Access
- Quick Search: Search for SNV, ceRNA, gene, disease, or dataset instantly.
- Analysis Tools: Redirect to all major tools in one click.
- LnCeVar 1.0: Access the previous version for comparative insights.
3) Comprehensive Analysis Tools
High-level tools for large-scale ceRNA disruption analysis:
- CeVarCluster: Investigate the patterns of gene expression and SNV mutations in SNV-ceRNA events, and examine the distribution of cell clusters/types.
- CeVarTraject: Investigate the patterns of gene expression and SNV mutations in SNV-ceRNA events, and examine the dynamics of cell trajectories.
- CeVarCellState: Investigate how SNV-ceRNA events affect cell states along developmental trajectories to reveal the dynamic interactions that drive cell fate decisions.
- CeVarSC3D: Conduct multi-level crosstalk analysis of SNVs, ceRNA networks and cell states contributing to individual disease pathology at the single-cell level, presented in an interactive 3D view.
- CeVarST3D: Conduct multi-level crosstalk analysis of SNVs, ceRNA networks and cell states contributing to individual disease pathology at the spatial level, presented in an interactive 3D view.
4) Mini Analysis Tools
Focused tools for fine-grained insights:
5) New Features
- 812 curated scRNA-seq & stRNA-seq samples
- 1.77M+ single cells, 0.90M+ spatial spots
- 102 diseases represented
- 5.21M+ SNV-ceRNA regulatory events
- 16,937 validated gene biomarkers
6) Related Works
- LnCeCell 2.0: Spatial/single-cell ceRNA atlas across 1.37M+ cells
- CellTracer: SNV-ceRNA driven fate mapping in development
- CellMarker 2.0: Curated marker genes for human/mouse tissues
- LncACTdb 3.0: Experimentally validated ceRNA axes for precision oncology
- Lnc2Cancer 3.0: Annotated cancer-associated lncRNA/circRNA database
- LincSNP 3.0: Functional SNPs in lncRNA/circRNA regions
7) Database Update & Comparison
LnCeVar 2.0 expands dramatically over v1.0:
- +114% growth in validated SNV-ceRNA events
- +230% increase in gene biomarkers
- +183% expansion in analytical tools
- +72% more bulk RNA-seq event types
- ...
- Version comparison table available with visual metrics
Fig 2-1. LnCeVar 2.0's Home.
Investigate the patterns of gene expression and SNV mutations in SNV-ceRNA events, and examine the distribution of cell clusters/types.
1) Search Panel
Select the dataset, sample, and gene of interest to query SNV-ceRNA events. This panel enables precise filtering of candidate events and focuses analysis on relevant cell populations or SNVs.
2) ceRNA-SNV Event Table
Displays SNV-ceRNA event pairs, including SNV ID, genomic position, and ceRNA partner gene. Each row is clickable and links to downstream visualizations for detailed exploration.
3) Basic Information Panel
Provides dataset metadata such as sample ID, disease, cell type, SNV annotation, platform, ceRNA biotype, and publication reference. This panel serves as a contextual reference for the selected ceRNA-SNV event.
4) Advanced Control
Customize visualization parameters including cell type grouping, embedding coordinates (e.g., UMAP), point size, and axis display. Users can refine how the data is visualized based on analysis focus.
5) CellCluster by Class
Displays unsupervised clustering results using transcriptomic features. Cells are grouped by similarity, aiding in identifying biologically distinct clusters.
6) CellCluster by Cell Type
Cells are annotated and colored by known cell types (e.g., myeloblasts, erythroblasts), allowing researchers to evaluate biological identity within clusters.
7) ceRNA1 Expression
Shows the ceRNA1 expression pattern of gene across cells, reflecting its activation state and potential roles in specific cell groups.
8) SNV Occurrence
Maps the location and frequency of the selected SNV across the cell embedding, indicating mutation burden within specific clusters.
9) ceRNA2 Expression
Displays ceRNA2 expression at single-cell resolution. This helps interpret whether SNV presence correlates with activation of NEAT1.
10) ceRNA1 Mean Expression in Clusters
Boxplots show average ceRNA1 expression levels across clusters, revealing cell groups with higher or lower gene activity.
11) SNV Occurrence Frequency in Clusters
Histogram showing the distribution of SNV presence across clusters, helping to evaluate the mutational enrichment in specific cell types.
12) ceRNA2 Mean Expression in Clusters
Compares ceRNA2 expression across clusters using boxplots. This enables investigation of SNV-ceRNA regulatory effects at a population level.
Fig 3-1. CeVarCluster analysis interface.
Investigate the patterns of gene expression and SNV mutations in SNV-ceRNA events, and examine the dynamics of cell trajectories.
1) Search Panel
Select the dataset, sample, and target gene to retrieve SNV-ceRNA events of interest. This allows for precise identification of trajectory-relevant events and their downstream effects.
2) ceRNA-SNV Event Table
The table displays SNV-ceRNA pairs with event metadata including SNV ID, coordinates, and ceRNA gene symbol. Clicking on any row navigates to event-specific visual analysis.
3) Basic Information Panel
Provides detailed metadata about the selected sample and event, including ceRNA and SNV annotation, platform, tissue, disease, ceRNA type, and external literature references.
4) Advanced Control
Allows customization of visual parameters such as pseudotime version, point size, cell group, and axis components. Useful for tailoring the trajectory layout and highlighting specific biological features.
5) CellCluster by Class
Visualizes cells clustered based on gene expression features. The layout reflects developmental progression and enables identification of discrete cell groups along the trajectory.
6) CellCluster by Pseudotime
Colors each cell according to pseudotime values, providing an intuitive view of developmental dynamics. Helpful to correlate SNV or gene expression with time-dependent biological states.
7) ceRNA1 Expression
Shows expression levels of the upstream ceRNA1 across the trajectory. Peaks and valleys along pseudotime suggest its functional timing and role in lineage development.
8) SNV Occurrence
Highlights the distribution of the selected SNV along the pseudotime trajectory. This allows identification of mutation hotspots and their potential correlation with developmental windows.
9) ceRNA2 Expression
Depicts ceRNA2 expression patterns across pseudotime. This enables comparison with SNV impact and upstream regulator (e.g., CD93) for assessing the entire SNV-ceRNA cascade.
10) ceRNA1 Mean Expression in Clusters
Boxplots displaying ceRNA1 expression across pseudotime-defined clusters. Useful for quantifying ceRNA activity differences among cell stages or branches.
11) SNV Occurrence Frequency in Clusters
Summarizes SNV presence as percentage or count per cluster. This facilitates quantification of mutation load and helps identify SNV-enriched cell states along the trajectory.
12) ceRNA2 Mean Expression in Clusters
Boxplots showing ceRNA2 expression across clusters. This enables comparative analysis between SNV-affected and unaffected cell states during development.
Fig 4-1. CeVarTraject analysis interface.
Investigate how SNV-ceRNA events affect cell states along developmental trajectories to reveal the dynamic interactions that drive cell fate decisions.
1) Search Panel
Select the dataset, sample, and ceRNA of interest to identify SNV-ceRNA events. This panel enables targeted exploration of how genetic variation may affect cell state transitions.
2) ceRNA-SNV Event Table
This table displays pairs of ceRNA1, SNV, and ceRNA2 for analysis. Users can click on any event to trigger full downstream visualization of its impact on cell states and expression.
3) Basic Information Panel
Provides sample-level metadata such as tissue origin, disease, SNV locus, platform, ceRNA biotypes, and species. Useful for contextualizing the biological relevance of the selected SNV-ceRNA event.
4) Advanced Control
Adjust key parameters for visualization: cell state category, correlation method (e.g., Pearson), point size, and display settings such as tracklines or axis toggling. Click "Apply Changes" to update all plots.
5) Cell State Score Plot
Visualizes the distribution of selected cell state scores across cells in the pseudotime space, helping reveal when and where specific cell states are active.
6) Cell State vs. Pseudotime Correlation
Plots the relationship between cell state score and pseudotime progression, quantifying how dynamic a given state is during developmental transitions.
7) Cell State Boxplot
Shows the distribution of cell state scores across different discrete pseudotime-defined states, allowing comparison among states or conditions.
8) ceRNA1 Expression
Displays the expression profile of ceRNA1 across cells, aligned with the pseudotime projection. Highlights when this upstream regulator is transcriptionally active.
9) SNV Occurrence
Shows the spatial distribution of SNV-bearing cells along pseudotime, useful for identifying mutation-rich cell states or transitions.
10) ceRNA2 Expression
Depicts the downstream ceRNA2 expression along the developmental trajectory, providing insight into possible regulation by ceRNA1 and/or SNVs.
11) ceRNA1 - Cell State Interplay
Scatterplot illustrating the correlation between ceRNA1 expression and cell state scores, highlighting their regulatory interaction across pseudotime.
12) SNV Impact on Cell State
Boxplot comparing the selected cell state scores between cells with different SNV alleles (e.g., REF vs. ALT), assessing the direct effect of mutations on functional cell behavior.
13) ceRNA2 - Cell State Interplay
Correlates ceRNA2 expression with the selected cell state score across pseudotime. This figure supports hypothesis generation on ceRNA2’s functional involvement in fate determination.
Fig 5-1. CeVarCellState analysis interface.
Conduct multi-level crosstalk analysis of SNVs, ceRNA networks and cell states contributing to individual disease pathology at the single-cell level, presented in an interactive 3D view.
1) Search Panel
Select a dataset and gene of interest to retrieve candidate SNV-ceRNA events. The selected ceRNA1 and ceRNA2 pair will be analyzed in an interactive 3D visualization platform using scRNA-seq data.
2) ceRNA-SNV Event Table
Displays curated SNV-ceRNA event pairs including SNV loci, ceRNA1, and ceRNA2 names. Users can sort or search for target events, then click "View" to initiate 3D cell mapping for the selected event.
3) Basic Information Panel
Provides metadata for the current dataset: sample name, tissue type, platform, SNV and ceRNA annotations, and ceRNA categories (e.g., protein-coding, lncRNA). This metadata guides biological interpretation of results.
4) CeVarSC3D Platform
The interactive 3D platform enables real-time manipulation and visualization of single-cell data in three dimensions. Users can configure UMAP axes, assign visual encodings (e.g., cell type, SNV status), and rotate or zoom to explore cellular spatial structure.
The right panel provides key event-specific information, including expression profiles of ceRNA1 and ceRNA2, SNV genotype distributions, and summary statistics of SNV+ vs. SNV - cells. The platform highlights how SNV disruptions may drive distinct spatial organization of transcriptomic states.
Fig 6-1. CeVarSC3D interactive 3D single-cell analysis interface.
Conduct multi-level crosstalk analysis of SNVs, ceRNA networks and cell states contributing to individual disease pathology at the spatial level, presented in an interactive 3D view.
1) Search Panel
Select a spatial transcriptomics dataset, sample, and gene of interest. The tool will extract relevant SNV-ceRNA pairs for visualization. This panel is the entry point for exploring how SNV mutations impact spatial ceRNA regulation.
2) ceRNA-SNV Event Table
Lists all candidate SNV-ceRNA events from the selected sample. Each entry includes ceRNA1, SNV, and ceRNA2 information. Users can click "View" to launch the 3D spatial visualization for the corresponding event.
3) Basic Information Panel
Displays detailed metadata for the current analysis: ceRNA types, SNV locus and alleles, disease type, organ source, sequencing platform, and total cell count. This supports biological interpretation and data reproducibility.
4) CeVarST3D Platform
The core component of this tool is a fully interactive 3D viewer that maps spatial coordinates from spatial transcriptomic slices. Users can set X/Y/Z axes from dimensional or spatial parameters, color cells by pseudotime, SNV status, or cell state scores, and navigate freely with zoom and rotation.
The platform integrates visual cues such as SNV cell clustering, spatial hotspots of ceRNA expression, and the dynamic distribution of disease-relevant cell states. The side panel summarizes ceRNA metadata, mutation statistics, and spatial distribution of cell types under different genotype or ceRNA expression levels.
Fig 7-1. CeVarST3D spatial transcriptomics 3D visualization interface.
Investigate the overall number of detected genes and mutations in different cell clusters/types.
Fig 8-1. CellCluster.
Investigate the overall number of detected genes and mutations along with dynamic trajectories.
Fig 9-1. CellTraject.
Investigate the dynamic gene expression and SNV mutation patterns of SNV-ceRNA events.
Fig 10-1. CellExp.
Interactive analysis of SNVs impacts on ceRNA expression pattern across different datasets.
Fig 11-1. CeSNV.
Interactive analysis between SNV genotype and cell states along the pseudo-time lineage.
Fig 12-1. CellState.
Analyse the changes in cell state induced by gene expression and SNV genotypes in each SNV-ceRNA event.
Fig 13-1. CeState.
Analysis of cancer hallmark alterations induced by SNV-mediated perturbations and networks.
Fig 14-1. Hallmark.
Analysis of dysregulated functions induced by SNV-mediated perturbations and networks.
Fig 15-1. Function.
Construct a dataset specific SNV-ceRNA network and visualisation for a candidate gene or SNV.
Fig 16-1. CeSNVNet.
Survival analysis of ceRNAs across thousands of malignant cancer samples.
Fig 17-1. Survival.
View the spatial map of corresponding clusters and cell types/states, as well as the overall number of detected genes and mutations in cells.
Fig 18-1. CellSpatialView.
View the spatial map of ceRNA expression and SNV mutations, and investigate their impact on disease characteristics by integrating spatial pathological section information.
Fig 19-1. CeSNVSpatialView.
How to download dataset.
LnCeVar 2.0 provides a curated and comprehensive repository of single-cell and spatial transcriptomics datasets focusing on genomic variations that disrupt ceRNA networks. This platform integrates over 812 scRNA-seq and stRNA-seq samples, spanning more than 2 million single cells and spatial spots, and emphasizes SNV-ceRNA interactions supported by experimental validation.
The Download Data section is designed with a user-friendly, interactive interface. It displays rich metadata across numerous dimensions - including dataset name, disease and disease type, organ source, sequencing platform, SNV counts, gene counts, and more - supporting both basic queries and advanced exploration. Each entry in the dataset table includes intuitive icons enabling users to download associated data in multiple formats such as CSV TXT JSON PRINT.
The dataset table provides structured insights into:
- Sample Characteristics: Disease classification (e.g., melanoma, leukemia), tissue/organ origin, preservation type, and cell counts.
- Molecular Profiling: Gene counts and SNV counts per dataset.
- Technical Metadata: Sequencing platform (e.g., 10x Genomics), species (Homo sapiens), and publication references (PMIDs).
- Data Access: One-click, color-coded icons enabling direct data export.
This feature-rich interface supports column toggling, live searching, and format-specific export, providing a highly customizable experience for genomic variation analysis. LnCeVar 2.0 empowers researchers to explore and interpret SNV-ceRNA regulatory disruption at unprecedented resolution in both single-cell and spatial contexts.
Fig 20-1. Download data table (scRNA-seq).
Fig 20-2. Download data table (stRNA-seq).
