ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data.
Publisher DOI
PubMed ID
31706265
Abstract
BACKGROUND
Data from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as "imputation", avoids apparent infinite fold-change values. However, the procedure comes at a cost: Imputing a large number of missing values has the potential to significantly impact the results of the subsequent differential expression analysis.
RESULTS
We propose a method that identifies differentially expressed proteins by ranking their observed changes with respect to the changes observed for other proteins. Missing values are taken into account by this method directly, without the need to impute them. We illustrate the performance of the new method on two distinct datasets and show that it is robust to missing values and, at the same time, provides results that are otherwise similar to those obtained with edgeR which is a state-of-art differential expression analysis method.
CONCLUSIONS
The new method for the differential expression analysis of proteomic data is available as an easy to use Python package.
Data from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as "imputation", avoids apparent infinite fold-change values. However, the procedure comes at a cost: Imputing a large number of missing values has the potential to significantly impact the results of the subsequent differential expression analysis.
RESULTS
We propose a method that identifies differentially expressed proteins by ranking their observed changes with respect to the changes observed for other proteins. Missing values are taken into account by this method directly, without the need to impute them. We illustrate the performance of the new method on two distinct datasets and show that it is robust to missing values and, at the same time, provides results that are otherwise similar to those obtained with edgeR which is a state-of-art differential expression analysis method.
CONCLUSIONS
The new method for the differential expression analysis of proteomic data is available as an easy to use Python package.
Date Issued
2019-11-09
Publication Type
Article
Subject(s)
Subjects
Differential expression analysis Imputation Proteomics Significance
Language(s)
en
Author(s)
Journal
BMC bioinformatics
Publisher
BioMed Central
ISSN
1471-2105
Access(Rights)
open.access