Interactive Data Visualization: Omics
Omics data is complex — including many more features then other data which all have extensive biological annotation. The challenge of analysis has to do with biological intuition, statistical analysis and machine learning that can pick up on complex patterns and associations.
Often, biologists rely on external help to perform the data wrangling and statistical/algorithmic heavy-lifting while focusing on interpretation. Many biologists and bioinformaticians struggle with such dichotomy because the methods, algorithms and biological interpretation are deeply interlinked.
One straightfowrd way to overcome this challenge is to bring data and biology closer together. While this might not seem possible in a full and comprehensive way, interactive dashboards for visual exploration of complex data can definitely help.
Dashboards for Data Analysis and Biological Interpretation
Having worked with both bioinformaticians and biologists, our team at Pine Biotech was trying to find existing solutions that can help us simplify analysis. After exploring R shiny, D3.js and other solutions, we decided to utilize the open source Plotly solution to add dashboards to our platform.
Already helping thousands of users analyze their data, we wanted the final stages to be more interactive and intuitive to use. Here is a general overview of the platform we are co-developing with the Tauber Bioinformatics Research Center at University of Haifa in Israel:
Here are a few specific examples
Most of these dashboards are already available to use by our research users, are embedded on the OmicsLogic portal for students and can be previewed without account access on this link.
Transcriptomics: Differential Gene Expression
Most common way to study differential gene expression remains a study where you compare 2 conditions: i.e. disease vs. control. The traditional way to analyze results is to look for genes that are statistically significant and annotate them by group — for example if a gene has low intra-group variability and high between-group variability, we might want to know what is it’s function and how can it be related to the condition we are studying:
Metagenomics: QIIME2/DADA2
While 16s rRNA sequencing has become more accessible than ever, microbiome studies try to capture a variety of phenotypic, intra and inter-group variation that have variable insights depending on the selected taxonomic level of annotation. That is why this metagenomic analysis dashboard combines proportionate bar-lots, alpha and beta diversity measure analysis and interactive annotation that the user can select from all available options.
Single Cell RNA-Seq
Recently, scRNA-Seq has become an important component of analyses that many wish to perform. Easy to deploy packages like Seurat have also gained popularity due to standardized outputs that can lead to meaningful data interpretation. To help explore such results in an intuitive way on the cloud, our dashboard allows summary review, analysis of varible genes and geneses, as well as analysis of clusters based on custom or automated annotation.
Other possibilities and future directions:
Seeing how useful these dash apps can be in the hands of biologists, studnets and researchers, we will continue to build out additional dashboard-based analysis extensions to our platform. The vision is to help combine data from different pipelines and to have more flexibility to help customize and enrich these dashboards moving forward. If you have suggestions, comments or questions, I can be reached here: elia <at> P ine.bio
Elia Brodsky is the co-founder and CEO of Pine Biotech, a big data analysis solutions company focused on bioinformatics. We offer services, solutions and products used by thousands of users from all over the world. Learn more: https://pine-biotech.com/
