The software for interactive evaluation of mass spectra stability and reproducibility
Author(s) -
Evgeny Zhvansky,
Anatoly Sorokin,
Denis S. Bormotov,
Konstantin V. Bocharov,
Denis S. Zavorotnyuk,
Daniil G. Ivanov,
Е. Н. Николаев,
Igor Popov
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa1072
Subject(s) - outlier , computer science , software , reproducibility , source code , data mining , mass spectrum , similarity (geometry) , pattern recognition (psychology) , stability (learning theory) , artificial intelligence , mass spectrometry , machine learning , statistics , mathematics , chemistry , image (mathematics) , chromatography , programming language , operating system
Mass spectrometry (MS) methods are widely used for the analysis of biological and medical samples. Recently developed methods, such as DESI, REIMS and NESI allow fast analyses without sample preparation at the cost of higher variability of spectra. In biology and medicine, MS profiles are often used with machine learning (classification, regression, etc.) algorithms and statistical analysis, which are sensitive to outliers and intraclass variability. Here, we present spectra similarity matrix (SSM) Display software, a tool for fast visual outlier detection and variance estimation in mass spectrometric profiles. The tool speeds up the process of manual spectra inspection, improves accuracy and explainability of outlier detection, and decreases the requirements to the operator experience. It was shown that the batch effect could be revealed through SSM analysis and that the SSM calculation can also be used for tuning novel ion sources concerning the quality of obtained mass spectra.
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