Phil Lorenzi
Welcome back to Article 8 in the 42 Factors Series of educational articles aimed at helping researchers improve data quality in omic experiments. In the previous article about choosing the Number of Replicates to use in metabolomic experiments, we noted that although biological variability is often large, the variability associated with time is often larger. Therefore, adding time points rather than statistical replicates usually yields a better return on investment
Phil Lorenzi
Welcome back to Article 6 in the 42 Factors Series. First and foremost, congratulations on making it this far. You have serious tenacity, and the time you’re investing in reading these articles will improve the quality of your work with omic and other data…or at least stimulate your brain. Either way, kudos to you.Today I’ll shift gears slightly and discuss a factor that doesn’t directly affect quantitative rigor or reproducibility,
Phil Lorenzi
What is the best allocation of a fixed experimental budget…replicates or additional experimental design points? Series article 7. #metabolomics #lipidomics #massspectrometry #cancer #cancerresearch #analyticalchemistry Looking for ways to clean up your metabolomic and other omic data? Check out these tips. 42 Factors Series – #7 Welcome back to Article 7 in the 42 Factors Series. In the wake of recent, high-profile articles (Wasserstein et al.; Ruberg et al.) addressing the
Phil Lorenzi
Series article 5. #metabolomics #lipidomics #massspectrometry #cancer #research #analyticalchemistry Welcome back to Article 5 in a series dedicated to increasing awareness of factors that can affect metabolomic (and other omic) data quality. Today I’ll continue the 42 Factors Series with a discussion of pre-normalization and how it can increase rigor and reproducibility in omic data (with my usual emphasis on metabolomics). Factor – Pre-Normalization It wouldn’t be fair for my
Phil Lorenzi
Welcome back to Article 4 in a series dedicated to increasing awareness of factors that can affect metabolomic (and other omic) data quality. Following the previous article about how the choice of adherent cell detachment method can affect metabolomic and other omic data, today I’ll continue the 42 Factors Series with a discussion of matrix effects. We’re not talking about simulated reality and the ability to dodge bullets (sorry, I
Phil Lorenzi
Looking for ways to clean up your metabolomic and other omic data? Check out these tips.Thanks for coming back for article 3 in a series dedicated to increasing awareness of factors that can affect metabolomic (and other omic) data quality. At this point I hope you’re glad that you didn’t have to spend 15 years conducting these experiments yourself! Following the previous article on Sample Storage Time, today I’ll continue
Phil Lorenzi
Looking for ways to clean up your metabolomic and other omic data? Check out these tips. No way. You’re back?! You’re obviously either really dedicated or exceptionally bored. The 42 Factors Series may seem daunting, but I’m glad you’re here…we have a lot more to discuss. The previous article was the first in a series dedicated to increasing awareness of factors that can affect metabolomic (and other omic) data quality.
Phil Lorenzi
Looking for ways to clean up your Metabolomic and other Omic data? Check out these tips. Many researchers are aware that metabolomics is a finicky science with a multitude of factors that can affect data quality, but how many such factors are there? We’ve identified and optimized several per year over the past 15 years, and we keep finding new ones. When the number reached 42—“the answer to life, the