Univariate analysis

Univariate analysis

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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.

Associations of HDL-C, LDL-C, triglycerides, Apolipoprotein A1, and Apolipoprotein B with AF in univariable Mendelian randomization analysis.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


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

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.

BioInsight Data Analysis

Uncover the intricacies of your biological data with CD Genomics' Bioinformatics Analysis. Our suite of services goes beyond analysis – it's a precision-driven exploration of your data landscape.

KEGG Enrichment Analysis Illuminate pathways, identifying enriched biological processes. OPLS-DA Service Enhance data interpretation through orthogonal projections.
Biological Network Analysis Decipher complex interactions within intricate biological networks. PLS-DA Service Achieve optimal discrimination and classification in multivariate data.
Time Series Analysis Gain insights into dynamic processes by unlocking temporal patterns. Volcano Plot Service Effectively visualize and enhance interpretation of significant changes.
GO Annotation Analysis Service Enhance functional understanding by annotating genes with GO terms. SAM Service (Significance Analysis of Microarrays) Precision-driven analysis identifying significant changes in microarray data.
GO Enrichment Analysis Identify enriched biological processes through Gene Ontology terms. Correlation Analysis Reveal intricate patterns and relationships within your data.
Fold Change Analysis Crucial identification of significant changes in gene expression. Heatmap Service Display the correlation between environmental variables and selected species.
Directed Acyclic Graph (DAG) Analysis Simplify interpretation by visualizing relationships in complex biological data. Enrichment Analysis Generate a bar graph for enrichment analysis.
Gene Set Enrichment Analysis Reveal functional associations by identifying enriched gene sets. Evolutionary Analysis Explore the sequence relationships in the evolutionary process from a molecular evolution perspective.
Domain Enrichment Service Understand domain distribution, enriching biological insights. Principal Component Analysis (PCA) Simplify the analysis of data sets.
Domain Annotation Explore protein domains, revealing structural and functional significance. Principal Co-ordinates Analysis (PCoA) Visualize the similarity or difference of data.
Functional Annotation Service Comprehensively annotate biological data, unlocking functional insights.

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.

How It Works

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.


  1. 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.
  2. 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.

1: What types of data are suitable for Univariate Analysis?

Univariate analysis is versatile and can be applied to various types of data, including gene expression, protein abundance, and other molecular biology data. It helps in identifying patterns, trends, and outliers in a single variable.

2: What are the typical steps involved in Bioinformatic Data Analysis?

The analysis typically involves data preprocessing, quality control, alignment, feature extraction, statistical analysis, and interpretation of results. The specific steps may vary depending on the type of data and research goals.

3: What statistical tests are included in your Bioinformatics Analysis Service?

A4: Our Univariate Analysis Service includes a range of statistical tests depending on the nature of your data. Common tests may include t-tests, ANOVA, Mann-Whitney U test, and others, tailored to meet the requirements of your study.

4: Can your service handle large-scale datasets?

Yes, our Data Analysis Service is designed to handle both small-scale and large-scale datasets. We utilize efficient algorithms and computational resources to ensure accurate and timely analysis, regardless of dataset size.

5: Can I integrate the results of Univariate Analysis with other bioinformatic analyses?

Absolutely. The results from univariate analysis can seamlessly integrate with other analyses, forming a holistic view of your biological data. Bioinformatic data analysis covers a wide range of biological data types, including DNA sequencing data, RNA expression data, protein structure data, and more. The service is tailored to suit the specific needs of your research project.

6: How do I get started with the Bioinformatics Analysis Service?

To get started, simply contact our support team or use our online platform to submit your data. Our team will guide you through the process, including data preparation and analysis options.

* For Research Use Only. Not for use in diagnostic procedures.
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