Generating analytical data can be a complex process. It can involve navigating internal and/or external approval processes, hours of experiment planning, and the set-up and use of complex equipment. But even with all of these complexities, the most difficult part of the process often ends up being the analysis of the data in a way that generates meaningful, actionable insights.
Experiment data can get siloed, making it difficult to access, and it often takes different shapes that don’t inherently integrate. Scientists need to be able to join and view data from multiple sources in order to understand how measurements from one analysis may correlate, or be impacted by, measurements from another analysis.
Connecting Assay Data in LabKey Biologics
LabKey Biologics provides powerful querying capabilities that allow teams to join data from multiple experiments and view this data as a single grid or report. This data can then be searched, filtered, and shared just like any other grid of data within LabKey Biologics.
This power is essential when observing the relationship between measures. For example, a team may want to investigate whether or not a correlation exists between characteristics like pH and temperature and observed cell growth in a bioreactor. These measures are collected by different instruments, but they need to be viewed side-by-side in order to conduct this analysis.
Building a Query
Using the powerful SQL Schema Browser within LabKey Server, teams can build and execute queries that combine data from different datasets. One common way to compare assay measures is to join datasets by common Sample IDs. This type of join allows the user to see the measurements from multiple assays side-by-side for each Sample ID. Teams can design and execute queries of varying complexity, comparing two assay measures or many assay measures.
Leveraging Sample Lineage
Users can also query the lineage of each sample and present the parent sample (Bioreactor Run) to which a sample is tied as well as what Expression System entity was used in its creation. Sample lineage provides context for the joined assay data, allowing scientists to easily ask questions and conduct comparisons with other sequences that are seen in different experiments.
Manipulating the Joined Data Within Biologics
Once queries have been created, they operate much like any other grid of data in LabKey Biologics. The results of queries are automatically updated every time new data is added into the system and lineage details like Sample IDs, Bioreactor Runs, and Expression Systems are automatically rendered as links, allowing for easier navigation to details about the entities. Query data grids can be manipulated with all of the standard operations available in LabKey Biologics allowing users to search, sort or filter integrated data. Query data can also be easily exported to allow for analysis in downstream systems.