Advancing Scientific Analysis with the MAP Store

Friday - April 26 2024, 10:51 UTC - 6 months ago

Data scientist Lisa Bramer and biostatistician Kelly Stratton created the MAP store to make data analysis more approachable and scalable for researchers. The store offers smaller, more specific apps for different types of data analysis and includes visualization and the ability for apps to work together. Early adopter Sneha Couvillion praises the store for its helpful tools, such as the PMart app which allows for quick and easy statistical analysis. The store has advanced scientific analysis and improved efficiency and accuracy for researchers.

Data analysis has become a vital part of scientific research, and advancements in technology have allowed for more in-depth and advanced analysis methods. However, with these advancements come challenges, such as making data analysis more approachable and scalable for researchers who may not have a background in statistics or access to biostatisticians.

To address this issue, data scientist Lisa Bramer and biostatistician Kelly Stratton, from the Pacific Northwest National Laboratory (PNNL) and the Environmental Molecular Sciences Laboratory, created the MAP store. This online platform offers a suite of different apps for various types of data analysis, making it easier for researchers to access and use data early and often.

Instead of adding capabilities to a single oversized app, the MAP store offers smaller, more bite-sized apps that can be incorporated into a unified platform. This approach is beneficial for researchers like Sneha Couvillion, a biomedical researcher at PNNL, who may not have a team of biostatisticians at their disposal. The MAP store allows researchers to dive into their data on their own and make informed decisions about their experiments, rather than having to wait for a biostatistician to go over the data with them.

The PMart app, one of the apps available in MAP, offers a quick and easy way to perform basic statistical analysis on data. This has been especially helpful for Couvillion and her smaller studies, where she wants a quick look at the data before making decisions about further research.

But data analysis isn't just about numbers and statistics - visualization also plays a crucial role in making sense of data. With traditional methods, researchers would have to rely on lists or rows of text, but with the inclusion of visualization in the PMart app, users can now see their data in a more visual and interactive format. This has been a game-changer for researchers who are more inclined towards visual data.

The MAP store's PMart app has continued to evolve over the years with the help of PNNL's Laboratory Directed Research and Development Program. This evolution has allowed the app to process a wider range of data types, such as general omics data, and includes quality control measures and visualizations for statistical comparisons. And for those who want to take their analyses a step further, the app provides the computational commands for an expert statistician to reproduce the results, making it a useful tool for the entire scientific community.

The apps in the MAP store also work together to enhance data analysis. For example, the data produced by the PMart app can be transferred to the MODE app for further analysis. MODE excels at finding trends and creating interactive visual displays, making it ideal for fields where research results may change rapidly. This functionality allows researchers to have a more comprehensive understanding of their data and make more informed decisions about their experiments.

In conclusion, the development of the MAP store by Lisa Bramer and Kelly Stratton has greatly advanced scientific data analysis. By creating a platform that offers various apps for different types of data analysis, the MAP store has made data analysis more approachable for non-statisticians and more scalable for researchers with limited access to biostatisticians. This, combined with the inclusion of visualization and the ability for the apps to work together, has improved the efficiency and accuracy of data analysis, ultimately benefiting the entire scientific community.