CD Genomics is a data analysis service company for biological big data analysis. We are able to provide our customers with a comprehensive range of high-quality multivariate analysis services. We are committed to being a gas pedal for our customers' research projects by selecting the appropriate type of analysis based on their data and research context.
Introduction of Multivariate Analysis
The connections between things are often multifaceted, and changes in one thing may be influenced by more than one factor. Multivariate analysis is a statistical method of data that can help us analyze the data one-inch relationship between variables and identify the multifaceted nature of risk factors and the relative magnitude of their corresponding variables. In practical applications, there are several multivariate analysis methods available depending on different data types and contextual problems.
Figure 1. Improved SERS multivariate analysis by ERS calibration for subtype classification of living breast normal and cancer cells. (Nam W, et al., 2021)
Application Field
Multivariate analysis can be used for but not limited to the following research:
- Aids in the diagnosis of disease.
- For epidemiological risk factor analysis.
- For clinical trial data analysis.
- For analysis of dose-response of drugs or toxicants.
Deliverables
- Two-dimensional principal component analysis scattergram.
- Corresponding analysis scattergram.
- Cluster analysis dendrogram.
CD Genomics Multivariate Analysis Pipeline
Bioinformatics Analysis Content
CD Genomics is able to provide a wide range of multivariate analysis services to help clients obtain critical information from massive amounts of data as quickly as possible.
- Multiple linear regression analysis. Regression analysis is the basic method for understanding multivariate analysis. In practical applications, we use statistical software for statistical analysis to obtain directly the estimates of the biased regression coefficients and the test results of the independent variables, as well as the degree of fit of the model to the actual data.
- Principal component analysis. Principal component analysis is one of the most widely used data dimensionality reduction algorithms, which can minimize the loss of information contained in the original indicators while reducing the number of indicators to be analyzed, and achieve the purpose of comprehensive analysis of the collected data.
- Discriminant analysis. Discriminant analysis is a statistical discriminant and grouping technique. Discriminant analysis can be divided into distance discriminant, Bayes discriminant, and Fisher discriminant, etc.
- Correspondence analysis. The purpose of the correspondence analysis is to downscale and visualize. Correspondence analysis can reveal the differences between categories of the same variable and the correspondence between the categories of different variables.
- Logistics regression analysis. Logistic regression analysis can be used to study influence relationships and is often used to analyze risk factors for a particular disease.
- Cluster analysis. The goal of cluster analysis is to collect data to classify on the basis of similarity and is a way to simplify data through data modeling.
For multivariate analysis, we will help you choose the right type of multivariate analysis services 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.
CD Genomics has professional bioinformatics experts who have successfully helped researchers in many different fields with their multivariate 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.
Reference
- Nam W, et al. Plasmonically Calibrated Label-Free Surface-Enhanced Raman Spectroscopy for Improved Multivariate Analysis of Living Cells in Cancer Subtyping and Drug Testing. Anal Chem. 2021 Mar 16; 93(10): 4601-4610.
