posted on 2020-09-16, 09:43authored bySung-Huan Yu, Pelagia Kyriakidou, Jürgen Cox
Isobaric
labeling has the promise of combining high sample multiplexing
with precise quantification. However, normalization issues and the
missing value problem of complete n-plexes hamper
quantification across more than one n-plex. Here,
we introduce two novel algorithms implemented in MaxQuant that substantially
improve the data analysis with multiple n-plexes.
First, isobaric matching between runs makes use of the three-dimensional
MS1 features to transfer identifications from identified to unidentified
MS/MS spectra between liquid chromatography–mass spectrometry
runs in order to utilize reporter ion intensities in unidentified
spectra for quantification. On typical datasets, we observe a significant
gain in MS/MS spectra that can be used for quantification. Second,
we introduce a novel PSM-level normalization, applicable to data with
and without the common reference channel. It is a weighted median-based
method, in which the weights reflect the number of ions that were
used for fragmentation. On a typical dataset, we observe complete
removal of batch effects and dominance of the biological sample grouping
after normalization. Furthermore, we provide many novel processing
and normalization options in Perseus, the companion software for the
downstream analysis of quantitative proteomics results. All novel
tools and algorithms are available with the regular MaxQuant and Perseus
releases, which are downloadable at http://maxquant.org.