There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
<p class="first" id="d4064355e53">The vocabulary of theoretical statistics can be
difficult to embrace from the viewpoint
of computational proteomics research, even though the notions it conveys are essential
to publication guidelines. For example, "adjusted p-values", "q-values", and "false
discovery rates" are essentially similar concepts, whereas "false discovery rate"
and "false discovery proportion" must not be confused, even though "rate" and "proportion"
are related in everyday language. In the interdisciplinary context of proteomics,
such subtleties may cause misunderstandings. This article aims to provide an easy-to-understand
explanation of these four notions (and a few other related ones). Their statistical
foundations are dealt with from a perspective that largely relies on intuition, addressing
mainly protein quantification but also, to some extent, peptide identification. In
addition, a clear distinction is made between concepts that define an individual property
(i.e., related to a peptide or a protein) and those that define a set property (i.e.,
related to a list of peptides or proteins).
</p>