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Separations
Special Issues
Chemometrics in Metabolomics and Proteomics
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Chemometrics in Metabolomics and Proteomics

A special issue of Separations (ISSN 2297-8739). This special issue belongs to the section "Chromatographic Separations".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 2735

Special Issue Editor

Dr. Aneta Sawikowska

E-Mail Website
Guest Editor
1. Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, 60-637 Poznan, Poland
2. Institute of Bioorganic Chemistry, Polish Academy of Sciences, 60-479 Poznan, Poland
Interests: chemometrics; mathematics; applied statistics; computer science; metabolomic data processing; high-throughput data integration; transcriptomic and epigenomic analyses; plant genetics; R programming; Genstat; chromatographic data analysis; metabolomic, proteomic and phenotypic data analysis; barley

Special Issue Information

Dear Colleagues,

The Special Issue “Chemometrics in metabolomics and proteomics” is currently being prepared by the journal Separations. Research papers targeting computational chromatographic peak separation and chemometrics are invited to this issue. Papers regarding chromatographic data processing and analysis, metabolomic data analysis, proteomic data analysis, simulations, high-throughput data integration, and estimation of the covariance matrix for chromatographic data are also welcome.

Modern chromatography largely uses the technique of liquid chromatography coupled with mass spectrometry (LC–MS) and gas chromatography coupled with mass spectrometry (GC–MS). Analyses of chromatographic data in various experimental systems and from various types of devices (e.g., LC-MS, GC-MS, UPLC-UV) are also within our scope.

Dr. Aneta Sawikowska
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Separations is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Chemometrics
  • Chromatography
  • Metabolomics
  • Proteomics
  • Chromatographic peak separation
  • Computational peak deconvolution
  • Simulations
  • Chromatographic data processing
  • High-throughput data integration
  • Chromatographic data analysis

Published Papers (1 paper)

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Research

13 pages, 17994 KiB  
Article
Identification of Block-Structured Covariance Matrix on an Example of Metabolomic Data
by Adam Mieldzioc, Monika Mokrzycka and Aneta Sawikowska
Separations 2021, 8(11), 205; https://doi.org/10.3390/separations8110205 - 4 Nov 2021
Cited by 4 | Viewed by 2262
Abstract
Modern investigation techniques (e.g., metabolomic, proteomic, lipidomic, genomic, transcriptomic, phenotypic), allow to collect high-dimensional data, where the number of observations is smaller than the number of features. In such cases, for statistical analyzing, standard methods cannot be applied or lead to ill-conditioned estimators [...] Read more.
Modern investigation techniques (e.g., metabolomic, proteomic, lipidomic, genomic, transcriptomic, phenotypic), allow to collect high-dimensional data, where the number of observations is smaller than the number of features. In such cases, for statistical analyzing, standard methods cannot be applied or lead to ill-conditioned estimators of the covariance matrix. To analyze the data, we need an estimator of the covariance matrix with good properties (e.g., positive definiteness), and therefore covariance matrix identification is crucial. The paper presents an approach to determine the block-structured estimator of the covariance matrix based on an example of metabolomic data on the drought resistance of barley. This method can be used in many fields of science, e.g., in agriculture, medicine, food and nutritional sciences, toxicology, functional genomics and nutrigenomics. Full article
(This article belongs to the Special Issue Chemometrics in Metabolomics and Proteomics)
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