How Progenesis QI resolves the problem of missing values

I’ve been at Nonlinear Dynamics for ten years now. In that time, we’ve seen the Progenesis range develop beyond just proteomics and, in 2013, we were acquired by Waters, although Progenesis QI will work on label-free data from any major MS vendor. I was originally brought into Nonlinear Dynamics to generate leads, so after 2 days of training, I started calling people to tell them about this unique technology. I loved my product and was really keen! However, sometimes people were so busy doing their research and subsequent data analysis that they were too busy to fully understand why Progenesis QI was so different. They had no time to save themselves some time! This can still be the case, even though independent reviews of Progenesis QI say things like this:

“Gold standard for label-free LCMS data analysis across all instrument platforms.”

Mark McComb, Boston University, US

So how can we get people to quickly understand why Progenesis QI is different? In order to do that, researchers need to understand the major problem in Omics data analysis: the holes in experimental data – known as missing values – that can be introduced by inefficient software. So, to help us get our point across in an easily-digestible, quick-to-read format, we produced this infographic to help you understand what switching to Progenesis QI means for your research. Please do have a look. If this piques your interest, at the end there is a 16 minute video in which Dr Paul Goulding describes in detail the scale of the missing values problem and how Progenesis QI uniquely resolves this.

Visual guide to the missing values problem and the unique Progenesis QI solution

Interested in learning more? Whatever instrument you use, why not download Progenesis QI or Progenesis QI for proteomics and analyse ALL of your data?

Post a Comment

Your email is never shared. Required fields are marked *

*
*