MassIVE MSV000096112

Complete Public PXD056868

Designing a Comparative Proteomics Experiment: Retention-time Alignment and Imputation Algorithms Affect Statistical Comparisons Between Samples

Subscribe Comment Convert Spectra Reanalyze Spectra Compare Results Add Reanalysis

Description

Comparative proteomics experiments reveal biomarkers by using statistical tests to determine proteins expressed with higher abundance in one sample versus another. However, comparative experiments can be complicated by variability from all aspects of proteomics workflows. To account for variability, software for database searching contains retention-time alignment and imputation algorithms to correct for retention-time shifts and assign abundances to missing proteins. While these algorithms improve quantification and reduce processing time, we hypothesize that they alter statistical comparisons between samples when samples are searched together. Herein, we search different cleanup methods or single proteins separately versus together in Progenesis Qi for proteomics database searching software. Our results show that searching samples together increases the number of identifications by each sample, enhances protein similarity between samples, and leads to false transfers. Further, we demonstrate that searching samples together affects protein abundance, differentially expressed proteins, and confidence scores due to retention-time alignment and imputation algorithms. Ultimately, we highlight that careful consideration of the search design is necessary to determine biomarkers in comparative proteomics experiments. [doi:10.25345/C5251FX4N] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Database Searching ; Imputation ; Missing Proteins ; Proteomics ; Retention-time Alignment ; Stochasticity ; DatasetType:Proteomics

Contact

Principal Investigators:
(in alphabetical order) 		Elyssia Gallagher, Baylor University, United States of America
Submitting User: jess_conforti1
Number of Files:
Total Size:
Spectra:
Subscribers:
 
Owner Reanalyses
Experimental Design
    Conditions:
    Biological Replicates:
    Technical Replicates:
 
Identification Results
    Proteins (Human, Remapped):
    Proteins (Reported):
    Peptides:
    Variant Peptides:
    PSMs:
 
Quantification Results
    Differential Proteins:
    Quantified Proteins:
 
Browse Dataset Files Browse Results
 
FTP Download Link (click to copy):

Species

Instrument

Modifications

- Dataset Reanalyses

+ Dataset History

Click here to queue conversion of this dataset's submitted spectrum files to open formats (e.g. mzML). This process may take some time.

When complete, the converted files will be available in the "ccms_peak" subdirectory of the dataset's FTP space (accessible via the "FTP Download" link to the right).
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.