Dataset Information

Experiment design beyond gut feeling: statistical tests and power to detect differential metabolites in mass spectrometry data

ABSTRACT: We use metabolite profiles of the model plant Arabidopsis thaliana measured on an UPLC-ESI/QqTOF-MS as a pilot study to estimate variance levels which then can be used to obtain several statistical parameters (e.g. minimum detectable effect, number of required replicates and error probabilities) which influence the design of a subsequent study.

INSTRUMENT(S): micrOTOF-Q II ESI-Qq-TOF (Bruker)

PROVIDER: MTBLS74 | MetaboLights | 2014-11-06

REPOSITORIES: MetaboLights

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	WTA01-11S_1-D,2_01_27748.mzData	Other
	WTA01-12S_1-D,2_01_27819.mzData	Other
	WTA01-21S_2-D,2_01_27784.mzData	Other
	WTA01-22S_2-D,2_01_27855.mzData	Other
	WTA02-11S_1-B,2_01_27731.mzData	Other

Items per page:

1 - 5 of 220

Similar Datasets

Project description:Practical experiments drive important scientific discoveries in biology, but theory-based research studies also contribute novel-sometimes paradigm-changing-findings. Here, we appraise the roles of theory-based approaches focusing on the experiment-dominated wet-biology research areas of microbial growth and survival, cell physiology, host-pathogen interactions, and competitive or symbiotic interactions. Additional examples relate to analyses of genome-sequence data, climate change and planetary health, habitability, and astrobiology. We assess the importance of thought at each step of the research process; the roles of natural philosophy, and inconsistencies in logic and language, as drivers of scientific progress; the value of thought experiments; the use and limitations of artificial intelligence technologies, including their potential for interdisciplinary and transdisciplinary research; and other instances when theory is the most-direct and most-scientifically robust route to scientific novelty including the development of techniques for practical experimentation or fieldwork. We highlight the intrinsic need for human engagement in scientific innovation, an issue pertinent to the ongoing controversy over papers authored using/authored by artificial intelligence (such as the large language model/chatbot ChatGPT). Other issues discussed are the way in which aspects of language can bias thinking towards the spatial rather than the temporal (and how this biased thinking can lead to skewed scientific terminology); receptivity to research that is non-mainstream; and the importance of theory-based science in education and epistemology. Whereas we briefly highlight classic works (those by Oakes Ames, Francis H.C. Crick and James D. Watson, Charles R. Darwin, Albert Einstein, James E. Lovelock, Lynn Margulis, Gilbert Ryle, Erwin R.J.A. Schrödinger, Alan M. Turing, and others), the focus is on microbiology studies that are more-recent, discussing these in the context of the scientific process and the types of scientific novelty that they represent. These include several studies carried out during the 2020 to 2022 lockdowns of the COVID-19 pandemic when access to research laboratories was disallowed (or limited). We interviewed the authors of some of the featured microbiology-related papers and-although we ourselves are involved in laboratory experiments and practical fieldwork-also drew from our own research experiences showing that such studies can not only produce new scientific findings but can also transcend barriers between disciplines, act counter to scientific reductionism, integrate biological data across different timescales and levels of complexity, and circumvent constraints imposed by practical techniques. In relation to urgent research needs, we believe that climate change and other global challenges may require approaches beyond the experiment.

Dataset Information

Experiment design beyond gut feeling: statistical tests and power to detect differential metabolites in mass spectrometry data

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets