The prnSig and pepSig utilities in proteoQ perform significance analysis for protein and peptide data, respectively. In what follows the step of p-value assessment, multiple test corrections using the stats::p.

I have shown previously some unique aspects in exporting PSMs from Mascot, for uses with proteoQ. In this post, I will do the same for more search engines, e.

Redundancy handling When exporting Mascot PSMs, one choice is to apply upfrontly the principle of parsimony by excluding both same-set and sub-set proteins (2011) whose presence may not be unambiguously determined.

The LFQ workflows in proteoQ take the same metadata files as those with TMT procedures; namely, expt_smry.xlsx and frac_smry.xlsx by default. No prefractionation For experiments without peptide prefractionation, we only need to prepare the expt_smry.

\[\newcommand{\mx}[1]{\mathbf{#1}} \def\pone{\mathbf{\mathbf{\phi}}_{1}} \def\ponet{\mathbf{\mathbf{\phi}}_{1}^{T}} \def\phat{\hat{\phi}_{1}} \def\X{\mathbf{X}} \def\Xt{\mathbf{X}^{T}} \def\y{\mathbf{y}} \def\A{\mathbf{A}} \def\B{\mathbf{B}} \def\W{\mathbf{W}} \def\argmax{\mathrm{arg\, max}} \def\argmaxphii{\underset{\left \| \pone \right \|=1}{\argmax}}\] Linear discriminant analysis (LDA) is popular for both the classification and the dimension reduction of data at two or more categories.