PXD005486

PXD005486 is an original dataset announced via ProteomeXchange.

Title	Statistical models for the analysis of isobaric Tags multiplexed quantitative proteomics
Description	ABSTRACT: Mass spectrometry is being used to identify protein biomarkers that can facilitate development of drug treatment. Mass spectrometry based proteomics results in complex proteomic data that is hierarchical in nature often with small sample size studies. Generalized linear models (GLM) is the most popular approach in proteomics to compare protein abundances between groups. However, GLM does not address all the complexities of proteomics data such as repeated measures and variance heterogeneity. Linear Models for Microarray Data (LIMMA) and mixed models are two approaches that can address some of these data complexities to provide better statistical estimates. We compared these three statistical models to demonstrate when each approach is the best. We evaluated these methods using a dataset of known protein abundances, Systemic Lupus Erythematosus (SLE) dataset, and simulated dataset. We found in general the mixed model findings to be a subset of GLM findings which were a subset of LIMMA findings. Regardless of peptides/PSM/Fold-change restrictions or FDR, less findings were removed from the mixed model than LIMMA since the mixed model is more likely to identify proteins with a larger fold change. Although the peptides/PSM restrictions led to less findings (but higher percentage of findings), with combined FDR the findings were the same or had a large overlap with no restriction and FDR findings. As the percentage of findings were higher with the restrictions this indicated these may be the more reliable proteins. The conclusion is that the mixed model was the most protective of the type I error with the smaller MSE while LIMMA had the better overall statistical properties.
HostingRepository	PRIDE
AnnounceDate	2017-07-28
AnnouncementXML	Submission_2017-07-28_13:03:27.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Raghothama Chaerkady
SpeciesList	scientific name: Escherichia coli; NCBI TaxID: 562; scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	iodoacetamide derivatized residue
Instrument	Q Exactive; Orbitrap Fusion

Revision	Datetime	Status	ChangeLog Entry
0	2016-12-02 01:18:57	ID requested
⏵ 1	2017-07-28 13:03:28	announced

D'Angelo G, Chaerkady R, Yu W, Hizal DB, Hess S, Zhao W, Lekstrom K, Guo X, White WI, Roskos L, Bowen MA, Yang H, Statistical Models for the Analysis of Isobaric Tags Multiplexed Quantitative Proteomics. J Proteome Res, 16(9):3124-3136(2017) [pubmed]

curator keyword: Technical

submitter keyword: : Proteomics, Mixed models, Statistical Models, Biomarkers, TMT

Raghothama Chaerkady
contact affiliation	Raghothama Chaerkady, Ph.D. Scientist II One MedImmune Way Gaithersburg, MD 20878
contact email	chaerkadyr@medimmune.com
lab head
Raghothama Chaerkady
contact affiliation	MedImmune
contact email	chaerkadyr@medimmune.com
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD005486
     1. Label: PRIDE project
     2. Name: Statistical models for the analysis of isobaric Tags multiplexed quantitative proteomics