Correlation Analysis

Correlation Analysis

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CD Genomics, as a professional bioinformatics service provider, can help customers analyze trends among different variables in their data using a variety of correlation analysis methods.

Introduction of Correlation Analysis

Correlation analysis is a statistical analysis method to analyze the correlation between two or more random variables. It can be classified as perfectly correlated, uncorrelated, and incompletely correlated according to the degree of correlation. In data analysis, correlation is an uncertain relationship and regression relationship is a definite relationship. Generally, we analyze the correlation relationship first, and then further analyze the regression relationship after the correlation relationship is cleared. Currently, tables, covariances, and correlation coefficients are usually used to analyze the correlation between two variables. Correlation analysis is of great significance in data statistics and can help researchers quickly discover patterns between data and promote research progress.

General pipeline of poly(A) site canonical correlation analysis.Figure 1. General pipeline of poly(A) site canonical correlation analysis. (Ye W, et al., 2019)

Application Field

Correlation analysis can be used for but not limited to the following research:

  • For analysis of differences in drug sensitivity of different subpopulations of cells.
  • For gene expression correlation analysis.
  • Used to analyze the correlation between protein function and disease.
  • Used to analyze the correlation between disease prognosis and gene expression.
  • Used for analysis of microbial interactions.


  • Correlation scattergram.
  • Correlation heat map.
  • Correlation volcano diagram.
  • Correlation histogram.
  • Correlation coefficients.

CD Genomics Correlation Analysis Pipeline

CD Genomics Correlation Analysis Pipeline

Bioinformatics Analysis Content

  • Charts to show correlation. We are able to use scatter plots to help customers determine the correlation between variables by looking at the distribution of data points.
  • Covariance and covariance matrix. The covariance can be used to measure the degree of interdependence and direction of changes in two random variables.
  • Correlation coefficient. We are able to provide different correlation coefficient analysis depending on the type of data distribution provided by the customers.
  • Conclusion analysis. We can help our customers analyze whether the data are correlated and whether there is a positive or negative correlation between the two variables.
  • We are able to provide correlation analysis for continuous, dichotomous, ordered categorical, and unordered categorical variables.
  • We are able to use a variety of software to perform correlation analysis and visualize the results through correlation heatmaps, netmaps, etc.

For correlation analysis, we can help you choose the right analysis method based on the data you provide. For questions about analysis content, project cycle, and pricing, please click online inquiry.

How It Works

CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.

How It Works

CD Genomics has professional bioinformatics experts who have successfully helped researchers in many different fields with their correlation analysis of biological data. Our professional skills and enthusiasm will provide you with high-quality analysis services. If you are interested in our services, please contact us for more details.


  1. Ye W, et al. Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis. BMC Genomics. 2019 Jan 22; 20(1): 75.
* For Research Use Only. Not for use in diagnostic procedures.
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