Tipping-Point Prediction Version 5.0 (Jan. 17, 2021) Chinese version
Contact us Email: scliurui@scut.edu.cn
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     What is TPD?

TPD: Tipping-Point Prediction.

The time evolution or dynamic change of many biological systems, such as disease progression and cell differentiation process, is not always smooth but occasionally abrupt, that is, there is a tipping-point or critical state during such a process at which the system state shifts irreversibly from one state (e.g.normal state) to another state (e.g. disease state), just before the critical transition. It is challenging and also important to predict such a critical state with the measured omics data, which is a key to achieve the predictive or preventive medicine. In this summary, we introduce a web service, the Tipping-Point Prediction (TPD), developed to effectively and rapidly identify the tipping point during the dynamical process of biological systems, and further its leading molecules or network. The web service is based on our computational method called sample-based local network entropy (SNE), which is a model-free data-driven approach with solid theoretical background, i.e., the dynamic network biomarker (DNB). Specifically, TPD is user-friendly and capable to explore the criticality of the dynamics from high-dimensional omics-data in terms of network entropy, thereby capturing not only the early-warning signals of the impending transition but also its leading network (DNB), which are intuitively a group of strong correlated and fluctuated molecules.

The details of the web service are as follows: The input of TPD should be the time-series or stage-course data (expression matrix) in CSV, XLS, TXT files. In the web tool, there is a flexible option for users to assign samples to certain time points. The web tool outputs multifarious visualized results including SNE scores and curves with suggested critical point, the identified key genes (or leading genes) and their information, DNB (the leading network that may drive the critical transition), the dynamical process of the leading network, the survival analysis based on SNE score that may help to identify dark genes (non-differential in terms of expression but differential in terms of network entropy, which may play important roles during the dynamic evolution). Thus, the web tool offers significantly enhanced display of the data and results.

Browser compatibility: TPD web tool is user-friendly for common browsers in operation systems including Linux, MacOS, Windows.

User terms: TPD is publicly accessible and non-commercial, and only provided for academic use. TPD does not reserve any uploaded data. It cleans the data and analysis results immediately the user closes the webpage. There is no user data retention time.

     TPD Release Notes

Current release: Release 5.0, Jan. 17, 2021

(a) Add 'Cell Fate Commitment' section.

(b) Real-time crawling the latest data and visualizing the results of TPD in COVID-19 Outbreak prediction.

(c) Add new demos and support one click to analysis and data download.

  1. Home
  2. Analysis

Tipping-Point Detection

Bulk data analysis

Detecting tipping point (deterioration) based on bulk data

Please input data on the card below




    

you have chosen 0 genes.

The uploaded dataset contains 0 columns. Please indicate the information of samples and time points.


There are 1 time points/ stages.

Examples (Bulk data demos):
Acute lung injury (GSE2565 & Mus musculus)
Lung adenocarcinoma (TCGA_LUAD & Homo musculus)
Thyroid carcinoma (TCGA_COAD & Homo sapiens)
Lung Squamous Cell Carcinoma (TCGA_LUSC & Homo musculus)
Thyroid cancer (TCGA_THCA & Homo musculus)
Examples(Multi-sample):
Stomach adenocarcinoma (TCGA_STAD & Homo musculus)
Uterine Corpus Endometrial Carcinoma (TCGA_UCEC & Homo musculus)

Single-cell data analysis

Detecting tipping point (cell fate commitment) based on single-cell data

Please input data on the card below



    

you have chosen 0 genes.

The uploaded dataset contains 0 columns. Please indicate the information of samples and time points.


There are 1 time points/ stages.

Examples (Single-cell data demos):
mouse embryonic fibroblasts to neurons (MEF-to-Neurons & Mus musculus)
neural progenitor cells to neurons (NPCs-to-Neurons & Homo sapiens)
human embryonic stem cells to definitive endoderm cells (hESCs-to-DECs & Homo sapiens)
mouse hepatoblasts cells to hepatocytes and cholangiocytes cells (MHCs-to-HCCs & Mus musculus)

Output

Mean value result

The significant increase (pvalue<0.05) indicate the tipping point during the progression of complex diseases.

Landscape display

Landscape Surface

The dynamical change of local scores demonstrates the landscape of the network entropy in a global landscape view.

Evolution of dynamic networks

Click on the title for larger image

With the dynamic evolution of network graph, critical points are monitored in real time. The color of each node corresponds to its value (blue for low value, red for high value); the thickness of each edge corresponds to its correlation!
Top Highest Entropy Genes

Download gene list && DAVID Bioinformatics Resources David Analysis Download

ID GeneID Value Description Location Family Drugs
Top Lowest Entropy Genes

Download gene list && DAVID Bioinformatics Resources David Analysis Download

ID GeneID Value Description Location Family Drugs

Survival analysis

Different patient samples in survival analysis by high CI and low CI

With survival analysis, it clearly shows that survival lines of patient with high CI in signal gene is significantly different from survival line of patient with low CI.

Clustering of cells

Figure1: Clustering of cells.

The clustering analysis based on the gene-specific local SGE values can distinguish the state of cell population.

Visualization of marker genes

Figure 2-4: Visualization of marker genes. Umap clustering and hierarchical clustering.

Discovering the marker genes: they can be used for determining cell type according to the reports of related literatures.

Correlation of the categories

Figure5: Correlation of the categories.

Infer relationships among various cell types , i.e., cell type‐to‐cell type communication according to the correlation of cell type.

TPD is a data analysis website to identify the tipping point during disease progression.

Guide

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Link

  • KEGG pathway
  • DAVID 6.8
  • Gene Ontology

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