As biology enters the post-genomics era, non-sequence biological data such as gene chip data almost expand exponentially. These biological data are often high-dimensional, heterogeneous, and networked, so the traditional analysis methods are not competent. CD Mitochondria has developed excellent algorithms to analyze these massive biological data and established unique decision support technologies such as basic data mining.
- Differentially expressed genes (DEGs) filtering: one of the applications of mtDNA microarray to detect the level of mitochondrial gene expression is the comparative experiment, which aims to compare the differential expression of genes under two conditions and identify the specific genes or significantly differentially expressed genes related to the conditions.
- Differential expression gene analysis: gene chips can be used to monitor differences in gene expression in different tissue samples, such as in normal cells and tumor cells.
(When comparing two different biological samples, screening according to the ratio value, it is generally believed that there is no significant difference in gene expression in the range of 0.5-2.0, but outside this range, it is considered that there is a significant difference in expression.)
- Cluster analysis: an unsupervised analysis method for statistical analysis of chip data. Analyze the relationship between mitochondrial genes or samples, and statistically compare the data by calculating the similar distance.
- Rich experience in data analysis and excellent analysis team
- Provide unique integrated services for mitochondrial gene chip data analysis
- Reliable data and results
- The number of chips required is small, simple and intuitive
CD Mitochondria has the most professional team to serve customers around the world. If you have any questions about the content of this service, please feel free to contact us. We look forward to your contact.
For Research Use Only. Not For Clinical Use.