10.1021/acs.jproteome.8b00523.s006
Jakob Willforss
Jakob
Willforss
Aakash Chawade
Aakash
Chawade
Fredrik Levander
Fredrik
Levander
NormalyzerDE:
Online Tool for Improved Normalization
of Omics Expression Data and High-Sensitivity Differential Expression
Analysis
American Chemical Society
2018
spike-in data sets
omics data sets
expression analysis
Several normalization approaches
RT
Bayes Limma approach
normalization quality assessment
Omics Expression Data
Expression Analysis Technical biases
2018-10-02 00:00:00
Dataset
https://acs.figshare.com/articles/dataset/NormalyzerDE_Online_Tool_for_Improved_Normalization_of_Omics_Expression_Data_and_High-Sensitivity_Differential_Expression_Analysis/7207019
Technical biases
are introduced in omics data sets during data
generation and interfere with the ability to study biological mechanisms.
Several normalization approaches have been proposed to minimize the
effects of such biases, but fluctuations in the electrospray current
during liquid chromatography–mass spectrometry gradients cause
local and sample-specific bias not considered by most approaches.
Here we introduce a software named NormalyzerDE that includes a generic
retention time (RT)-segmented approach compatible with a wide range
of global normalization approaches to reduce the effects of time-resolved
bias. The software offers straightforward access to multiple normalization
methods, allows for data set evaluation and normalization quality
assessment as well as subsequent or independent differential expression
analysis using the empirical Bayes Limma approach. When evaluated
on two spike-in data sets the RT-segmented approaches outperformed
conventional approaches by detecting more peptides (8–36%)
without loss of precision. Furthermore, differential expression analysis
using the Limma approach consistently increased recall (2–35%)
compared to analysis of variance. The combination of RT-normalization
and Limma was in one case able to distinguish 108% (2597 vs 1249)
more spike-in peptides compared to traditional approaches. NormalyzerDE
provides widely usable tools for performing normalization and evaluating
the outcome and makes calculation of subsequent differential expression
statistics straightforward. The program is available as a web server
at http://quantitativeproteomics.org/normalyzerde.