Statistical strategies to reveal potential vibrational markers for in vivo analysis by confocal Raman spectroscopy

Abstract. The analysis of biological systems by spectroscopic techniques involves the evaluation of hundreds to thousands of variables. Hence, different statistical approaches are used to elucidate regions that discriminate classes of samples and to propose new vibrational markers for explaining various phenomena like disease monitoring, mechanisms of action of drugs, food, and so on. However, the technical statistics are not always widely discussed in applied sciences. In this context, this work presents a detailed discussion including the various steps necessary for proper statistical analysis. It includes univariate parametric and nonparametric tests, as well as multivariate unsupervised and supervised approaches. The main objective of this study is to promote proper understanding of the application of various statistical tools in these spectroscopic methods used for the analysis of biological samples. The discussion of these methods is performed on a set of in vivo confocal Raman spectra of human skin analysis that aims to identify skin aging markers. In the Appendix, a complete routine of data analysis is executed in a free software that can be used by the scientific community involved in these studies.

[1]  Hugh Barr,et al.  Near‐infrared Raman spectroscopy for the classification of epithelial pre‐cancers and cancers , 2002 .

[2]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[3]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[4]  Tahir Mehmood,et al.  A review of variable selection methods in Partial Least Squares Regression , 2012 .

[5]  A. Martin,et al.  RM1 semi empirical and DFT: B3LYP/3-21G theoretical insights on the confocal Raman experimental observations in qualitative water content of the skin dermis of healthy young, healthy elderly and diabetic elderly women's. , 2015, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[6]  Lutz Franzen,et al.  Applications of Raman spectroscopy in skin research--From skin physiology and diagnosis up to risk assessment and dermal drug delivery. , 2015, Advanced drug delivery reviews.

[7]  Monica Casale,et al.  A new algorithm for seriation and its use in similarity dendrograms , 2007 .

[8]  R. Das,et al.  Raman spectroscopy: Recent advancements, techniques and applications , 2011 .

[9]  Tom Fearn,et al.  Orthogonal Signal Correction , 1999 .

[10]  L. Buydens,et al.  Use of the bootstrap and permutation methods for a more robust variable importance in the projection metric for partial least squares regression. , 2013, Analytica chimica acta.

[11]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[12]  Jürgen Popp,et al.  How to pre-process Raman spectra for reliable and stable models? , 2011, Analytica chimica acta.

[13]  M Muratore,et al.  Raman spectroscopy and partial least squares analysis in discrimination of peripheral cells affected by Huntington's disease. , 2013, Analytica chimica acta.

[14]  Ganesh D. Sockalingum,et al.  Micro-Raman spectroscopy of mixed cancer cell populations , 2005 .

[15]  Claudia Beleites,et al.  Validation of soft classification models using partial class memberships: An extended concept of sensitivity & co. applied to grading of astrocytoma tissues , 2013, 1301.0264.

[16]  Multivariate figures of merit (FOM) investigation on the effect of instrument parameters on a Fourier transform-near infrared spectroscopy (FT-NIRS) based content uniformity method on core tablets. , 2015, Journal of pharmaceutical and biomedical analysis.

[17]  G. Jiang,et al.  Applications of Raman-based techniques to on-site and in-vivo analysis , 2011 .

[18]  M. Barker,et al.  Partial least squares for discrimination , 2003 .

[19]  Sebastian Wachsmann-Hogiu,et al.  Chemical analysis in vivo and in vitro by Raman spectroscopy--from single cells to humans. , 2009, Current opinion in biotechnology.

[20]  B. Schrader,et al.  Investigation of skin and skin lesions by NIR-FT-Raman spectroscopy , 1998 .

[21]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[22]  R. Poppi,et al.  Classification of Amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation. , 2013, Talanta.

[23]  K. Tsai,et al.  Diagnostic opportunities based on skin biomarkers. , 2013, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[24]  S. Wold,et al.  Orthogonal projections to latent structures (O‐PLS) , 2002 .

[25]  Miguel de la Guardia,et al.  Vibrational spectroscopy provides a green tool for multi-component analysis , 2010 .

[26]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[27]  Christoph Krafft,et al.  Near infrared Raman spectroscopic mapping of native brain tissue and intracranial tumors. , 2005, The Analyst.

[28]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[29]  M. Rantalainen,et al.  OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification , 2006 .

[30]  J. Winefordner,et al.  Raman spectroscopy in bioanalysis. , 2000, Talanta.

[31]  J. Kirkland,et al.  Aging and adipose tissue: potential interventions for diabetes and regenerative medicine , 2016, Experimental Gerontology.

[32]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[33]  Peter Lasch,et al.  Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging , 2012 .

[34]  H. Lilliefors On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[35]  Bernhard Lendl,et al.  Raman spectroscopy in chemical bioanalysis. , 2004, Current opinion in chemical biology.

[36]  Philip S. Yu,et al.  Outlier detection for high dimensional data , 2001, SIGMOD '01.

[37]  Brian C Wilson,et al.  Diagnostic potential of near-infrared Raman spectroscopy in the colon: differentiating adenomatous from hyperplastic polyps. , 2003, Gastrointestinal endoscopy.

[38]  Airton Abrahão Martin,et al.  FT-Raman spectroscopy study for skin cancer diagnosis , 2003 .

[39]  N Stone,et al.  Analysis of human tear fluid by Raman spectroscopy. , 2008, Analytica chimica acta.

[40]  S. Wold,et al.  Orthogonal signal correction of near-infrared spectra , 1998 .

[41]  Igor K Lednev,et al.  Raman spectroscopy offers great potential for the nondestructive confirmatory identification of body fluids. , 2008, Forensic science international.

[42]  James P Freyer,et al.  Raman spectroscopy detects biochemical changes due to proliferation in mammalian cell cultures. , 2005, Biophysical journal.

[43]  N Stone,et al.  The use of Raman spectroscopy to identify and grade prostatic adenocarcinoma in vitro , 2003, British Journal of Cancer.

[44]  Hugh Barr,et al.  Raman spectroscopy, a potential tool for the objective identification and classification of neoplasia in Barrett's oesophagus , 2003, The Journal of pathology.

[45]  Rekha Gautam,et al.  Review of multidimensional data processing approaches for Raman and infrared spectroscopy , 2015, EPJ Techniques and Instrumentation.