PeptideMind - applying machine learning algorithms to assess replicate quality in shotgun proteomic data

D. C. L. Handler, P. A. Haynes*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

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    Abstract

    Assessment of replicate quality is an important process for any shotgun proteomics experiment. One fundamental question in proteomics data analysis is whether any specific replicates in a set of analyses are biasing the downstream comparative quantitation. In this paper, we present an experimental method to address such a concern. PeptideMind uses a series of clustering Machine Learning algorithms to assess outliers when comparing proteomics data from two states with six replicates each. The program is a JVM native application written in the Kotlin language with Python sub-process calls to scikit-learn. By permuting the six data replicates provided into four hundred triplet non redundant pairwise comparisons, PeptideMind determines if any one replicate is biasing the downstream quantitation of the states. In addition, PeptideMind generates useful visual representations of the spread of the significance measures, allowing researchers a rapid, effective way to monitor the quality of those identified proteins found to be differentially expressed between sample states.

    Original languageEnglish
    Article number100644
    Pages (from-to)1-6
    Number of pages6
    JournalSoftwareX
    Volume13
    DOIs
    Publication statusPublished - Jan 2021

    Bibliographical note

    Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • Classification
    • Data quality
    • Data validation
    • False discovery
    • Kotlin
    • Label-free shotgun proteomics
    • Machine learning
    • Protein quantitation
    • Spectral counting
    • Statistics

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