Cargando…

The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging

Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in it...

Descripción completa

Detalles Bibliográficos
Autores principales: Liberda, Danuta, Pięta, Ewa, Pogoda, Katarzyna, Piergies, Natalia, Roman, Maciej, Koziol, Paulina, Wrobel, Tomasz P., Paluszkiewicz, Czeslawa, Kwiatek, Wojciech M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073124/
https://www.ncbi.nlm.nih.gov/pubmed/33924045
http://dx.doi.org/10.3390/cells10040953
_version_ 1783684061625057280
author Liberda, Danuta
Pięta, Ewa
Pogoda, Katarzyna
Piergies, Natalia
Roman, Maciej
Koziol, Paulina
Wrobel, Tomasz P.
Paluszkiewicz, Czeslawa
Kwiatek, Wojciech M.
author_facet Liberda, Danuta
Pięta, Ewa
Pogoda, Katarzyna
Piergies, Natalia
Roman, Maciej
Koziol, Paulina
Wrobel, Tomasz P.
Paluszkiewicz, Czeslawa
Kwiatek, Wojciech M.
author_sort Liberda, Danuta
collection PubMed
description Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.
format Online
Article
Text
id pubmed-8073124
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-80731242021-04-27 The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging Liberda, Danuta Pięta, Ewa Pogoda, Katarzyna Piergies, Natalia Roman, Maciej Koziol, Paulina Wrobel, Tomasz P. Paluszkiewicz, Czeslawa Kwiatek, Wojciech M. Cells Article Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler’s smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing. MDPI 2021-04-20 /pmc/articles/PMC8073124/ /pubmed/33924045 http://dx.doi.org/10.3390/cells10040953 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liberda, Danuta
Pięta, Ewa
Pogoda, Katarzyna
Piergies, Natalia
Roman, Maciej
Koziol, Paulina
Wrobel, Tomasz P.
Paluszkiewicz, Czeslawa
Kwiatek, Wojciech M.
The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging
title The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging
title_full The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging
title_fullStr The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging
title_full_unstemmed The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging
title_short The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging
title_sort impact of preprocessing methods for a successful prostate cell lines discrimination using partial least squares regression and discriminant analysis based on fourier transform infrared imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073124/
https://www.ncbi.nlm.nih.gov/pubmed/33924045
http://dx.doi.org/10.3390/cells10040953
work_keys_str_mv AT liberdadanuta theimpactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT pietaewa theimpactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT pogodakatarzyna theimpactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT piergiesnatalia theimpactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT romanmaciej theimpactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT koziolpaulina theimpactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT wrobeltomaszp theimpactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT paluszkiewiczczeslawa theimpactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT kwiatekwojciechm theimpactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT liberdadanuta impactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT pietaewa impactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT pogodakatarzyna impactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT piergiesnatalia impactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT romanmaciej impactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT koziolpaulina impactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT wrobeltomaszp impactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT paluszkiewiczczeslawa impactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging
AT kwiatekwojciechm impactofpreprocessingmethodsforasuccessfulprostatecelllinesdiscriminationusingpartialleastsquaresregressionanddiscriminantanalysisbasedonfouriertransforminfraredimaging