Data analysis on omics

An introduction


At Biogenity, we offer a vast number of different types of data analyses. We have you covered, whether you are looking for, e.g.,

  • Quality control of your data
  • Tailored bioinformatic annotation approaches
  • Statistics
  • Biological system bioinformatics
  • Biomolecular modeling
  • Diagnostic and prognostic modeling

Our mission is not only to deliver excellent data science, the communication of the science in the data is also vital for us. We are constantly developing and adding new analytical modules to provide new data expressions, bringing forward all biological messages within.

If you’d like to learn more, we are happy to discuss your project and help you get the most out of your omics data.


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Most common types of data analysis provided

Statistical analysis

Data analysis

At Biogenity, we are experienced with the different omics data. We perform the required filtering and cleaning of the data before the statistical data analysis, so you get reliable data and test the distribution of the data allowing for the correct statistical methods to be applied. We are also looking for outliers, allowing these to be removed before or the statistical analysis to be performed with and without.


The best statistical insight

At Biogenity, the statistical test is not just a student t-test or ANOVA. We are testing the data for normal distribution, etc., and taking into account if it is a large or low sample size. We choose the statistical tools that provide you with the most power for your statistical insight into your omics data. Our report includes all relevant information like fold change, coefficient of variation, p-values, and adjusted p-values, giving you different layers of information to evaluate your findings.


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Bioinformatical analysis of expression profile

Data analysis

Bioinformatics is becoming increasingly vital for getting the most value from omics-based studies. At Biogenity, bioinformatics is not only a table of which biological processes could be found using an enrichment analysis.

We are looking for enriched pathways, disease pathways, biological processes, molecular functions, and cellular components. The data is presented in a table and mapped in network plots to highlight which molecules have been annotated and how different, e.g., pathways are molecularly connected. We also offer interactive plots, where you can discover the biological networks, bring forward the molecules of interest, and see how there are affecting the pathways of interest.

Biogenity's data analysis extensively provides you with different figures and tools to discover the information in your data, allowing you to succussed in uncovering the relevant biology for your research aim.


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Data analysis powered by Artificial Intelligence

Data analysis

Machine- and Deep Learning are becoming increasingly important tools to reach goals such as personalized medicine. At Biogenity, we are experts in biomolecular, diagnostic, and prognostic modeling. We provide dedicated data scientists to match biological data with the correct Machine- and Deep Learning methods to develop the best models for your research questions. We can work with all types of biological and clinical data by having expertise with a vast range of different approaches. Examples include discriminant analysis, gradient boosting, explainable boosting machines, deep neural networks, and more.


Explainable AI to reveal how a model works

Good models are essential, but knowing why and how a model works is as vital as the actual prediction. This is why we have a focus on explainable AI. An example of why it is important could be; why have a panel of 150 biomarkers if only 15 are needed, and which questions about our patients are essential? These types of information can be hidden in Deep Learning models, so opening up the black box of these models is necessary. This is why Biogenity also offers this insight into the AI models, so you know all about your models.

Peptide and protein annotation

Data analysis

At Biogenity, we can also process raw files from most proteomics and peptidomics studies. Processing mass spectrometry-derived data can be complex. Different approaches deliver diverse insight, e.g., proteins can be missing in proteome analysis, yet if we look at the peptide level, we may discover peptides that are fragments of the proteins. These may be relevant, and therefore the fragmentation spectra of the peptides need to be validated.

Whether you want us to confirm the finding, search the data using new databases, perform denovo sequencing or validate MS/MS spectra, we got you covered.

Delivery and consultation

Data analysis


Your data analysis delivered directly to your inbox

When your data analysis is ready, we deliver it directly to your inbox. Your data and reports are easy to access and secured with encryption and password protection. You will receive an easy-to-follow report describing the data analysis on your omics and results hereof, step-by-step, together with a folder containing all tables and figures. To ensure that you have the material needed, we always provide a vast number of figures and tables.


Consultation to answer your analysis questions

It is important to us that you get the most out of your data analysis, which is why we always offer a consultation after delivery where you can ask all your questions about the results. Together, we ensure that you have the best prerequisite for your further research. The consultation can also be used to explore the next step, whether you are looking to expand the project with additional omics studies or validate the results.

What you can expect:

  • Explanation of the different steps in the sample analysis
  • Discussion of the data
  • Explanation of the different steps in the data analysis
  • Q&A to the reports

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Application and purposes for omics data analysis

Data analysis can be widely used for omics research. Here is a list of examples of what data analysis can include and what methods can be applied.

What a data analysis can include:

  • Transcription
  • Preprocessing
  • Outlier investigation
  • Missing value strategies
  • Differential expression analysis
  • Time series analysis
  • Predictive modeling
  • Predictive model exploration
  • Pathways analysis
  • Biological process analysis
  • Molecular function analysis
  • Cellular component analysis
  • Data integration

Methods that can be used:

  • Standard deviation
  • Shapiro–Wilk test
  • Principal component analysis
  • K-medoids
  • K-nearest neighbors
  • Partition Around Medoids clustering
  • Ward's minimum variance
  • Analysis of variance
  • Linear Models for MicroArray
  • Wilcoxon signed-rank test
  • Benjamini-Hochberg Procedure
  • Bonferroni correction
  • Gene set enrichment analysis
  • K-fold cross-validation
  • K-fold cross-validation
  • Random forests
  • Gradient descent
  • Gradient Boosting Machine
  • Explainable Boosting Machine
  • Neural network
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