MassIVE MSV000080742

Imported Reanalysis Dataset Public PXD001426

EBprot: Bayesian Analysis of Labelling-based Quantitative Proteomics Data

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Description

Labelling-based proteomics is a powerful method for detection of differentially expressed proteins (DEPs) between biological samples. The current data analysis platform relies on protein-level ratios, where peptide-level ratios are averaged to yield a single summary ratio for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is incorporated into the differential expression (DE) analysis. Here we propose a novel probabilistic framework EBprot that directly models the peptide-to-protein hierarchy and rewards the proteins with reproducible quantification over multiple peptides. To evaluate its performance with known DE states, we first verified that the peptide-level analysis of EBprot provides more accurate estimation of the false discovery rates and better receiver-operating characteristic than other protein ratio analyses using simulation datasets, and confirmed the superior classification performance in a UPS1 mixture spike-in dataset. To illustrate the performance of EBprot in realistic applications, we applied EBprot to a SILAC dataset for lung cancer subtype analysis and an iTRAQ dataset for time course phosphoproteome analysis of EGF-stimulated HeLa cells, each featuring a different experimental design. Through these various examples, we show that the peptide-level analysis of EBprot provides a competitive advantage over alternative methods for the DE analysis of labelling-based quantitative datasets. [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Stable isotope labelling ; Quantitative analysis ; Differential expression ; Hierarchical mixture model

Contact

Principal Investigators:
(in alphabetical order) 		Hyung Won Choi, NUS, N/A
Submitting User: ccms

Publications

Koh HW, Swa HL, Fermin D, Ler SG, Gunaratne J, Choi H.
EBprot: Statistical analysis of labeling-based quantitative proteomics data.
Proteomics. 2015 Aug;15(15):2580-91. Epub 2015 May 28.

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Identification Results
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Quantification Results
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Number of distinct conditions across all analyses (original submission and reanalyses) associated with this dataset.

Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

"N/A" means no results of this type were submitted.
Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

Distinct replicate labels are counted across all files submitted in the "Metadata" category having a "BioReplicate" or "Replicate" column in this dataset.

"N/A" means no results of this type were submitted.
Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

The technical replicate count is defined as the maximum number of times any one distinct combination of condition and biological replicate was analyzed across all files submitted in the "Metadata" category. In the case of fractionated experiments, only the first fraction is considered.

"N/A" means no results of this type were submitted.
Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

"N/A" means no results of this type were submitted.
Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

"N/A" means no results of this type were submitted.
Number of distinct unmodified peptide sequences reported across all analyses (original submission and reanalyses) associated with this dataset.

"N/A" means no results of this type were submitted.
Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

"N/A" means no results of this type were submitted.
Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

"N/A" means no results of this type were submitted.
Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
This dataset may not contain all raw spectra data as originally deposited in PRIDE. It has been imported to MassIVE for reanalysis purposes, so its spectra data here may consist solely of processed peak lists suitable for reanalysis with most software.