Dataset Information

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

ABSTRACT: We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of each protein is based on the standardized Z-statistic based on the posterior distribution of the log fold change parameter, guided by the false discovery rate estimated by a well-known Empirical Bayes method. We evaluated the classification performance of QPROT using the quantification calibration data from the clinical proteomic technology assessment for cancer (CPTAC) study and a recently published Escherichia coli benchmark dataset, with evaluation of FDR accuracy in the latter.QPROT is a statistical framework with computational software tool for comparative quantitative proteomics analysis. It features various extensions of QSPEC method originally built for spectral count data analysis, including probabilistic treatment of missing values in protein intensity data. With the increasing popularity of label-free quantitative proteomics data, the proposed method and accompanying software suite will be immediately useful for many proteomics laboratories. This article is part of a Special Issue entitled: Computational Proteomics.

SUBMITTER: Choi H

PROVIDER: S-EPMC4630079 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

Choi Hyungwon H Kim Sinae S Fermin Damian D Tsou Chih-Chiang CC Nesvizhskii Alexey I AI

Journal of proteomics 20150804

We introduce QPROT, a statistical framework and computational tool for differential protein expression analysis using protein intensity data. QPROT is an extension of the QSPEC suite, originally developed for spectral count data, adapted for the analysis using continuously measured protein-level intensity data. QPROT offers a new intensity normalization procedure and model-based differential expression analysis, both of which account for missing data. Determination of differential expression of ...[more]

PMID: 26254008

Similar Datasets

Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Project description:BackgroundMany studies have provided algorithms or methods to assess a statistical significance in quantitative proteomics when multiple replicates for a protein sample and a LC/MS analysis are available. But, confidence is still lacking in using datasets for a biological interpretation without protein sample replicates. Although a fold-change is a conventional threshold that can be used when there are no sample replicates, it does not provide an assessment of statistical significance such as a false discovery rate (FDR) which is an important indicator of the reliability to identify differentially expressed proteins. In this work, we investigate whether differentially expressed proteins can be detected with a statistical significance from a pair of unlabeled protein samples without replicates and with only duplicate LC/MS injections per sample. A FDR is used to gauge the statistical significance of the differentially expressed proteins.ResultsWe have experimented to operate on several parameters to control a FDR, including a fold-change, a statistical test, and a minimum number of permuted significant pairings. Although none of these parameters alone gives a satisfactory control of a FDR, we find that a combination of these parameters provides a very effective means to control a FDR without compromising the sensitivity. The results suggest that it is possible to perform a significance analysis without protein sample replicates. Only duplicate LC/MS injections per sample are needed. We illustrate that differentially expressed proteins can be detected with a FDR between 0 and 15% at a positive rate of 4-16%. The method is evaluated for its sensitivity and specificity by a ROC analysis, and is further validated with a [15N]-labeled internal-standard protein sample and additional unlabeled protein sample replicates.ConclusionWe demonstrate that a statistical significance can be inferred without protein sample replicates in label-free quantitative proteomics. The approach described in this study would be useful in many exploratory experiments where a sample amount or instrument time is limited. Naturally, this method is also suitable for proteomics experiments where multiple sample replicates are available. It is simple, and is complementary to other more sophisticated algorithms that are not designed for dealing with a small number of sample replicates.

Project description:Identification of the proteins that are associated with estrogen receptor (ER) status is a first step towards better understanding of the hormone-dependent nature of breast carcinogenesis. Although a number of gene expression analyses have been conducted, protein complement has not been systematically investigated to date. Because proteins are primary targets of therapeutic drugs, in this study, we have attempted to identify proteomic signatures that demarcate ER-positive and -negative breast cancers. Using highly enriched breast tumor cells, replicate analyses from 3 ERα+ and 3 ERα- human breast tumors resulted in the identification of 2,995 unique proteins with ≥2 peptides. Among these, a number of receptor tyrosine kinases and intracellular kinases that are abundantly expressed in ERα+ and ERα- breast cancer tissues were identified. Further, label-free quantitative proteome analysis revealed that 236 proteins were differentially expressed in ERα+ and ERα- breast tumors. Among these, 141 proteins were selectively up-regulated in ERα+, and 95 proteins were selectively up-regulated in ERα- breast tumors. Comparison of differentially expressed proteins with a breast cancer database revealed 98 among these have been previously reported to be involved in breast cancer. By Gene Ontology molecular function, dehydrogenase, reductase, cytoskeletal proteins, extracellular matrix, hydrolase, and lyase categories were significantly enriched in ERα+, whereas selected calcium-binding protein, membrane traffic protein, and cytoskeletal protein were enriched in ERα- breast tumors. Biological process and pathway analysis revealed that up-regulated proteins of ERα+ were overrepresented by proteins involved in amino acid metabolism, proteasome, and fatty acid metabolism, while up-regulated proteins of ERα- were overrepresented by proteins involved in glycolysis pathway. The presence and relative abundance of 4 selected differentially abundant proteins (liprin-α1, fascin, DAP5, and β-arrestin-1) were quantified and validated by immunohistochemistry. In conclusion, unlike in vitro cell culture models, the in vivo signaling proteins and pathways that we have identified directly from human breast cancer tissues may serve as relevant therapeutic targets for the pharmacological intervention of breast cancer.

Dataset Information

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

Publications

QPROT: Statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets