Our consultants are well versed in statistical genetics and GWAS data meta-analysis. It's only natural that our software development team has encountered this type of visualization in multiplie different projects.

The GWAS statistics in this example were gathered from public resources. This build uses Vue.js on the front end, Django on the backend, with a simple NoSQL database at the backend.

Our clients are often interested in picking the bast target. Transcriptomics is a rich resource of information on the tissue- and phenotype-dependent level of expression of each gene, with thousands of publicly available datasets.

Here we have gathered a large collection of data from GEO, and visualized it all in one spot. The data was downloaded and processed using R Bioconductor packages. The app was built using Plotly Dash.

Often, a multitude of datasets is available for a given modality. In biomedical data, this is especially true for the transcriptomics data. Unfortunately, combining the findings from these datasets is non-trivial, due to batch effects, and other uncaptured covariates. In these cases, the best practice is to carry out a meta-analysis of the datasets.

In this example, we have carried out a meta-analysis of numerous datasets from GEO (fixed effects, or random effects, as appropriate) using the `metafor` library. All summary data across various diseases, tissues, and genes was stored in a Postgres database. The visualization displayed in the panel was built using R Shiny.

As yet another level of insight into transcriptomics data, it's quite common for our clients to be interested in a visualization of differential expression from every gene in a differential expression analysis.

Here we show an app that displays a volcano plot, and highlights a selsction of genes. Visualization coded in R Shiny, with Plotly.js powering the interactive visualization.

It is very common for biotech companies to generate large volumes of data. We have developed a number of solutions which helped our clents capture their data in the best format / data representation possible, store it in a queryable (if needed, relational) database, and empowered them to understand the data better by making it searchable. Client data, once it's captured properly, can be then presented to the scientists as apps (much like the ones showed above). We have also provided more powerful ways to access the data once it has been clensed - for example via universal, programmatically accessible REST APIs.