CD Genomics is able to use univariate analysis to study biological problems, providing customers with appropriate statistical methods for their experimental data to get the right analysis results.
Introduction of Univariate Analysis
The univariate analysis mainly includes mean and median analysis, which compares whether there is a significant difference between the means or medians of multiple variables in two subsample groups. Commonly used methods of univariate analysis when conducting analysis of differences between two sample groups include fold change analysis, t-test, and volcano plot analysis. Univariate analysis can follow the following idea: first, determine the type of information in the sample, then determine whether the data are univariate or multifactorial, then determine whether it is a single sample, two samples, or multiple samples, and finally, determine whether the data meet the prerequisites required for the test method.
Figure 1. Associations of HDL-C, LDL-C, triglycerides, Apolipoprotein A1, and Apolipoprotein B with AF in univariable Mendelian randomization analysis. (Yang S, et al., 2021)
Explore Our Univariate Analysis
Deliverables
The main purpose of univariate data plotting is to show the pattern of data distribution in a single feature, most commonly variance, mean, histogram, etc. Based on the univariate analysis we provide the following types of analysis results.
- Bar Chart
- Pie Charts
- Histogram
- Box line chart
Biostatistical Univariate Analysis
Statistical analysis consists of two parts: statistical description and statistical inference. Quantitative variables calculate the concentrated trend indicators and discrete trend indicators, while qualitative variables calculate the frequency and probability of individual attributes. The commonly used univariate statistical analysis methods are as follows.
| One-sample t-test |
Paired-sample t-test |
Independent samples t-test |
| Chi-square test |
Independent chi-square test |
One-way ANOVA |
| Two-way ANOVA |
Wilcoxon rank sum test |
Friedman test |
| Mann-Whitney U test |
Kruskal-Wallis test |
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We are able to use univariate analysis to help our customers achieve high throughput data analysis in multi-omics. For questions about analysis content, project cycle, and pricing, please click online inquiry.
How It Works
CD Genomics is a professional bioinformatics service provider with years of experience in NGS and long-read sequencing (PacBio SMRT and Oxford Nanopore platforms) data, proteomics and metabolomics data analysis, integrated analysis services, database construction, and other bioinformatics solutions.
CD Genomics has professional bioinformatics experts who have successfully provided univariate analysis to researchers in many different fields. 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.
References
- Peng Y, et al. Identification of a prognostic and therapeutic immune signature associated with hepatocellular carcinoma. Cancer Cell Int. 2021 Feb 10; 21(1): 98.
- Yang S, et al. The Relationship between Blood Lipids and Risk of Atrial Fibrillation: Univariable and Multivariable Mendelian Randomization Analysis. Nutrients. 2021 Dec 31; 14(1): 181.
