STEM Analysis Service

The gene regulatory network is a continuous and complex dynamic system. When the organism changes in a certain order or is stimulated by the external environment (such as induced by different concentrations of chemical drugs), the changes in gene expression will also show trend characteristics. Trend analysis is to discover the trend characteristics of gene expression, concentrate the genes with the same change characteristics in a change trend, so as to find the most representative gene group in the experimental change process. STEM (Short Time-series Expression Miner) is mainly used to analyze short-term experimental data, and can also be used for multiple sets of small sample data. Recommend 3 to 8 sets of data. Generally applicable research directions are: time series data at multiple time points, such as multiple developmental periods, and multiple time points sampling after processing.

Figure 1. STEM analysis service.

Algorithm

STEM uses a new clustering algorithm to analyze time series gene expression trends. The clustering algorithm first selects a set of different and representative temporal expression profiles as model profiles. The model is selected independently of the data, and theoretically ensures that the selected model profile is representative. Then, according to each normalized gene expression pattern, it is assigned to the time expression pattern with the highest correlation coefficient in the model. Since the selection of the model is independent of the data, the algorithm can determine which time expression patterns are statistically significant to enrich genes through permutation tests. After allocating the time expression pattern to each gene, the clustering algorithm uses standard hypothesis testing to determine which time expression pattern is assigned in the true order of time points compared to the average number of model profiles assigned in the time series.

Overall solutions

CD ComputaBio uses an analysis method that uses a large number of genes and a small number of time points to identify statistically significant temporal expression profiles and genes related to these profiles.
CD ComputaBio also supports gene ontology (GO) enrichment analysis of gene sets with the same temporal expression pattern, thereby providing a means for statistically accurate biological interpretation of important temporal expression patterns.
The integration of STEM and GO is two-way. STEM can easily identify and visualize the behavior of genes belonging to a specific GO category, thereby determining which expression profiles are enriched by genes in that category.
Finally, STEM also supports comparing the time response of genes across experimental conditions.

Services items

Project name	STEM Analysis Service
Our features	Data requirements Expression profile chip or sequencing data (preprocessed) Downstream analysis The analysis after obtaining the significantly enriched time expression pattern includes: 1. Functional enrichment of genes in temporal expression patterns 2. Correlation between gene expression and traits in temporal expression patterns Key information of the mining module: 1. Find the core gene in the temporal expression pattern 2. Use the relationship to predict the time expression mode function
Cycle	Depends on the time you need to simulate and the time required for the system to reach equilibrium.
Product delivery mode	The simulation results provide you with the raw data and analysis results of molecular dynamics.
Price	Inquiry

CD ComputaBio provides corresponding professional STEM analysis service. Our STEM analysis service has proven to be very useful for understanding the biochemical basis of physiological events at different stages of drug development (even in different fields such as materials science). The CD ComputaBio team has worked in this field for more than a decade and published his findings in top scientific journals. If you need network analysis services, please feel free to contact us.