ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data.

Medo, Matúš; Aebersold, Daniel M.; Medova, Michaela (2019). ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data. BMC bioinformatics, 20(1), p. 563. BioMed Central 10.1186/s12859-019-3144-3

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Radio-Onkologie
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DBMR Forschung Mu35 > Forschungsgruppe Radio-Onkologie

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology

UniBE Contributor:

Medo, Matúš; Aebersold, Daniel and Medova, Michaela

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1471-2105

Publisher:

BioMed Central

Language:

English

Submitter:

Beatrice Scheidegger

Date Deposited:

26 Nov 2019 15:07

Last Modified:

08 Dec 2019 02:45

Publisher DOI:

10.1186/s12859-019-3144-3

PubMed ID:

31706265

Uncontrolled Keywords:

Differential expression analysis Imputation Proteomics Significance

BORIS DOI:

10.7892/boris.135035

URI:

https://boris.unibe.ch/id/eprint/135035

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