Dataset Information

Improved intensity-based label-free quantification via proximity-based intensity normalization (PIN).

ABSTRACT: Researchers are increasingly turning to label-free MS1 intensity-based quantification strategies within HPLC-ESI-MS/MS workflows to reveal biological variation at the molecule level. Unfortunately, HPLC-ESI-MS/MS workflows using these strategies produce results with poor repeatability and reproducibility, primarily due to systematic bias and complex variability. While current global normalization strategies can mitigate systematic bias, they fail when faced with complex variability stemming from transient stochastic events during HPLC-ESI-MS/MS analysis. To address these problems, we developed a novel local normalization method, proximity-based intensity normalization (PIN), based on the analysis of compositional data. We evaluated PIN against common normalization strategies. PIN outperforms them in dramatically reducing variance and in identifying 20% more proteins with statistically significant abundance differences that other strategies missed. Our results show the PIN enables the discovery of statistically significant biological variation that otherwise is falsely reported or missed.

SUBMITTER: Van Riper SK

PROVIDER: S-EPMC3993879 | biostudies-other | 2014 Mar

REPOSITORIES: biostudies-other

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Improved intensity-based label-free quantification via proximity-based intensity normalization (PIN).

Van Riper Susan K SK de Jong Ebbing P EP Higgins LeeAnn L Carlis John V JV Griffin Timothy J TJ

Journal of proteome research 20140226 3

Researchers are increasingly turning to label-free MS1 intensity-based quantification strategies within HPLC-ESI-MS/MS workflows to reveal biological variation at the molecule level. Unfortunately, HPLC-ESI-MS/MS workflows using these strategies produce results with poor repeatability and reproducibility, primarily due to systematic bias and complex variability. While current global normalization strategies can mitigate systematic bias, they fail when faced with complex variability stemming from ...[more]

PMID: 24571364

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Project description:Normalization is an important step in the analysis of quantitative proteomics data. If this step is ignored, systematic biases can lead to incorrect assumptions about regulation. Most statistical procedures for normalizing proteomics data have been borrowed from genomics where their development has focused on the removal of so-called 'batch effects.' In general, a typical normalization step in proteomics works under the assumption that most peptides/proteins do not change; scaling is then used to give a median log-ratio of 0. The focus of this work was to identify other factors, derived from knowledge of the variables in proteomics, which might be used to improve normalization. Here we have examined the multi-laboratory data sets from Phase I of the NCI's CPTAC program. Surprisingly, the most important bias variables affecting peptide intensities within labs were retention time and charge state. The magnitude of these observations was exaggerated in samples of unequal concentrations or "spike-in" levels, presumably because the average precursor charge for peptides with higher charge state potentials is lower at higher relative sample concentrations. These effects are consistent with reduced protonation during electrospray and demonstrate that the physical properties of the peptides themselves can serve as good reporters of systematic biases. Between labs, retention time, precursor m/z, and peptide length were most commonly the top-ranked bias variables, over the standardly used average intensity (A). A larger set of variables was then used to develop a stepwise normalization procedure. This statistical model was found to perform as well or better on the CPTAC mock biomarker data than other commonly used methods. Furthermore, the method described here does not require a priori knowledge of the systematic biases in a given data set. These improvements can be attributed to the inclusion of variables other than average intensity during normalization.

Dataset Information

Improved intensity-based label-free quantification via proximity-based intensity normalization (PIN).

Publications

Improved intensity-based label-free quantification via proximity-based intensity normalization (PIN).

Similar Datasets

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