Analysing multi-marker data often requires advanced biostatistical methods to translate raw output data into credible and substantiated interpretation and biological knowledge.
BioXpedia offer statistical analysis of your data, for example gene expression or protein screening data. We can provide a standard statistical service, or you can choose a customized biostatistical analysis for more complex project designs and analyses. The biostatistical analyses are performed by bioinformatic professionals experienced in handling data output from the technologies that BioXpedia offers. Our biostatistical analysis and consultancy ensure that you acquire the maximal value and information output from the bioanalytical data of your project.
Download description of our biostatistical service (pdf)
Standard Biostatistical Services
The standard statistical service includes an investigation of which biomarkers are significantly different between groups of interest, for example between a disease and control group of patients. A fast and reliable way to get the most out of your experiment!
The standard biostatistical service includes:
- Test for normal distribution of data to investigate assumptions for statistical tests.
- Hierarchical cluster analysis or Principal Component Analysis (PCA) to investigate how the profile of all samples group together.
- Identification of significant differential markers between groups, either by parametric or non-parametric tests. Correction for multiple testing is performed using false discovery rate.
- Visual presentation of all significant markers and fold change in a Volcano plot for each group comparison.
- Standard report including detailed description of statistical analysis, boxplots for all biological molecules and a table listing significance, fold change, median and interquartile range (IQR) of all markers.
Tailored Biostatistical Service
If you have a project with a more complex experimental design, for example time-series data, or need additional statistical analyses and bioinformatics, we can tailor our biostatistical service targeted specifically to your project.
We also offer analyses to support decision on project and experimental design, for example power analysis and sample size calculation.
Some examples of the tailored biostatistical analysis we can offer are presented below.
Please contact us to hear more, or if you are interested in a data analysis that is not represented here. Contact Us
Examples of Our Tailored Biostatistical Analyses
Linear Mixed Effects Analysis
This data analysis focuses on using linear mixed effects models to determine the effects of one or more variables of interest for time series data.
The data analysis includes the following components:
- Detailed PDF report
- Data handling.
- Visualization of the time series data.
- Employment of linear mixed effects models.
- Adjusting for confounding factors.
Clustering Analysis
This data analysis focuses on using clustering methods to gain new knowledge about e.g. molecular subgroups or patterns of protein or gene expression.
The data analysis includes the following components:
- Detailed PDF report.
- Data handling.
- Employment of state-of-the-art clustering methods.
- Visualization of the clustering results.
Predictive Models
This data analysis focuses on developing a biomarker signature for e.g. diagnostics or prognostics.
The data analysis includes the following components:
- Detailed PDF report.
- Data handling.
- Development of biological signature.
- Employment of state-of-the-art machine learning methods
- Evaluation and visualization of performance using ROC plots.
Annotation and Enrichment Analysis
This data analysis focuses on using gene set enrichment analysis (GSEA) to determine if a class (e.g. pathways) of genes or proteins are over-represented in a large set of genes or proteins.
The data analysis includes the following components:
- Detailed PDF report.
- Data handling.
- Employment of GSEA.
- Visualization of enriched genes or proteins.
Network Analysis
The network analysis data analysis for visualization of the correlation or association among markers in a complex biological system.
The data analysis includes the following components:
- Detailed PDF report.
- Data handling.
- Employment of correlation analysis.
- Visualization of the biological network.
Survival Analysis
This data analysis focuses on using survival analysis to determine if two different groups of patients have different survival rates.
The data analysis includes the following components:
- Detailed PDF report.
- Data handling.
- Employment of cox regression models.
- Visualization of survival using Kaplan-Meier curves and logrank tests.