Leveraging R Inside A LIMS Environment

by Si Vu

Every day the modern laboratory is evolving. In industries where the “first to market” is synonymous with success, industry leaders now more than ever are pushing the envelope of signal discovery and cutting-edge science. This has led to a rift where scientists are blazing a path into new and exciting territories, but where information systems have been struggling to keep pace with the shifting environment. Innovative scientific assays can no longer be properly catered by unsophisticated off-the-shelf systems. This has led to an increasing struggle for scientists to find validated systems that can address their complex needs.

Use Case

Just last year, I remember speaking to a scientist that was onboarding a new assay from their R&D department into the Quality Control lab. The R&D team developed a new assay that measured the potency of their product. The final part of the assay required the use of advanced logarithmic regression analysis to accurately calculate the potency of their product from the raw data coming from the instrument. Naturally, the R&D team developed this advanced analysis without issue using their native stack of statistical/programming tools. The computation might be standard in research and academia, but not so much that there was an exact commercial solution that satisfies their needs. But then the question was, how would the QA scientist then onboard this new assay into her validated environment?

R in Analytics

This growing need for advanced analytics in research and manufacturing can be seen in the widespread adoption of R and Python in the data science revolution. It is no coincidence that the two languages lauded for their ease of use and low barrier to entry have been at the center of this paradigm shift. Scientists now need to leverage new sophisticated statistics and data analysis techniques in their everyday work. Knowing that scientists are now flocking to these advanced statistical and data science tools, it is inevitable that these tools be made accessible in the technology stack critical to all labs: LIMS.

Progress So Far

This is not to say we are in completely uncharted territory. IT consultants and data scientists have been working towards integrating advanced analytics into LIMS for years in a variety of projects and industries. For instance, R packages in the bioinformatics industry have widespread adoption and many projects have brought those complex calculations into the LIMS processing workflow. Unfortunately there has only been some effort to consolidate the knowledge from these incredible projects. Now the breakthrough moment has been the recognition of the growing need of advanced analytics across multiple industries and that it is here to stay. Formalizing the integration of an analytics platform into LIMS is an outward declaration that LIMS can truly be the single solution for all the laboratory needs.

A truly exciting opportunity is the ability to introduce more advanced data visualizations, dashboards, and reporting. These projects have always been critical to the success of informatics tools and clients are often pushed to on-board yet another technology if they cannot achieve the exact results they need in LIMS, like SAS and Tableau. With the introduction of R into the LIMS environment, customers should be able to make use of all the data packages and tools they are already familiar with within their LIMS. As an example, there has already been some success generating specifically complex plots using R packages instead of native LIMS tools. There is also growing excitement around using industry recognized tools like R Shiny for interactive data visualizations to fuel insights.

Conclusion

The informatics industry is changing and LIMS must be ready to adapt. The data analytics needs in this space have been growing and only a concerted effort will be able to properly address these challenges now and in the future. Solution T0080, the “Data Science Engine”, represents LabWare’s first formal debut in enabling advanced analytics within the LIMS platform. In the coming months, we will be hoping to transform this solution into the data science platform for all LabWare customers. As always we think feedback and partnership with LabWare's extensive community will be critical to our success.

About Us

In early 2022, LabWare acquired CompassRed in the pursuit to integrate advanced analytics and ML/AI methods into the LabWare LIMS environment. The LabWare Analytics team was formed to leverage this expertise and bring industry-standard solutions to the laboratory. The technologies being developed for LIMS will provide a comprehensive suite of capabilities that would incorporate AI into laboratory operation/maintenance, support machine learning models on LIMS datasets, enable advanced reporting/visualizations, and empower other complex computations necessary for today’s labs.