Condensing Raman spectrum for single-cell phenotype analysis

Handle URI:
http://hdl.handle.net/10754/593294
Title:
Condensing Raman spectrum for single-cell phenotype analysis
Authors:
Sun, Shiwei; Wang, Xuetao; Gao, Xin ( 0000-0002-7108-3574 ) ; Ren, Lihui; Su, Xiaoquan; Bu, Dongbo; Ning, Kang
Abstract:
Background In recent years, high throughput and non-invasive Raman spectrometry technique has matured as an effective approach to identification of individual cells by species, even in complex, mixed populations. Raman profiling is an appealing optical microscopic method to achieve this. To fully utilize Raman proling for single-cell analysis, an extensive understanding of Raman spectra is necessary to answer questions such as which filtering methodologies are effective for pre-processing of Raman spectra, what strains can be distinguished by Raman spectra, and what features serve best as Raman-based biomarkers for single-cells, etc. Results In this work, we have proposed an approach called rDisc to discretize the original Raman spectrum into only a few (usually less than 20) representative peaks (Raman shifts). The approach has advantages in removing noises, and condensing the original spectrum. In particular, effective signal processing procedures were designed to eliminate noise, utilising wavelet transform denoising, baseline correction, and signal normalization. In the discretizing process, representative peaks were selected to signicantly decrease the Raman data size. More importantly, the selected peaks are chosen as suitable to serve as key biological markers to differentiate species and other cellular features. Additionally, the classication performance of discretized spectra was found to be comparable to full spectrum having more than 1000 Raman shifts. Overall, the discretized spectrum needs about 5storage space of a full spectrum and the processing speed is considerably faster. This makes rDisc clearly superior to other methods for single-cell classication.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Condensing Raman spectrum for single-cell phenotype analysis 2015, 16 (Suppl 18):S15 BMC Bioinformatics
Publisher:
Springer Nature
Journal:
BMC Bioinformatics
Issue Date:
9-Dec-2015
DOI:
10.1186/1471-2105-16-S18-S15
Type:
Article
ISSN:
1471-2105
Additional Links:
http://www.biomedcentral.com/1471-2105/16/S18/S15
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorSun, Shiweien
dc.contributor.authorWang, Xuetaoen
dc.contributor.authorGao, Xinen
dc.contributor.authorRen, Lihuien
dc.contributor.authorSu, Xiaoquanen
dc.contributor.authorBu, Dongboen
dc.contributor.authorNing, Kangen
dc.date.accessioned2016-01-11T13:28:26Zen
dc.date.available2016-01-11T13:28:26Zen
dc.date.issued2015-12-09en
dc.identifier.citationCondensing Raman spectrum for single-cell phenotype analysis 2015, 16 (Suppl 18):S15 BMC Bioinformaticsen
dc.identifier.issn1471-2105en
dc.identifier.doi10.1186/1471-2105-16-S18-S15en
dc.identifier.urihttp://hdl.handle.net/10754/593294en
dc.description.abstractBackground In recent years, high throughput and non-invasive Raman spectrometry technique has matured as an effective approach to identification of individual cells by species, even in complex, mixed populations. Raman profiling is an appealing optical microscopic method to achieve this. To fully utilize Raman proling for single-cell analysis, an extensive understanding of Raman spectra is necessary to answer questions such as which filtering methodologies are effective for pre-processing of Raman spectra, what strains can be distinguished by Raman spectra, and what features serve best as Raman-based biomarkers for single-cells, etc. Results In this work, we have proposed an approach called rDisc to discretize the original Raman spectrum into only a few (usually less than 20) representative peaks (Raman shifts). The approach has advantages in removing noises, and condensing the original spectrum. In particular, effective signal processing procedures were designed to eliminate noise, utilising wavelet transform denoising, baseline correction, and signal normalization. In the discretizing process, representative peaks were selected to signicantly decrease the Raman data size. More importantly, the selected peaks are chosen as suitable to serve as key biological markers to differentiate species and other cellular features. Additionally, the classication performance of discretized spectra was found to be comparable to full spectrum having more than 1000 Raman shifts. Overall, the discretized spectrum needs about 5storage space of a full spectrum and the processing speed is considerably faster. This makes rDisc clearly superior to other methods for single-cell classication.en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.urlhttp://www.biomedcentral.com/1471-2105/16/S18/S15en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.subjectRaman Spectrumen
dc.subjectLinear Discriminant Analysis (LDA)en
dc.subjectK Nearest Neighbor(k-NN)en
dc.subjectDiscretizationen
dc.titleCondensing Raman spectrum for single-cell phenotype analysisen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalBMC Bioinformaticsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionKey Lab of Intelligent Information Processing, Institute of Computing Technology of the Chinese Academy of Sciences, 100190 Beijing, Chinaen
dc.contributor.institutionCAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Bioinformatics Group of Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101 Shandong, P. R. Chinaen
dc.contributor.institutionCUDA Research Centre of Qingdao, Qingdao, 266101 Shandong, Chinaen
dc.contributor.institutionCollege of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074 Hubei, Chinaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorGao, Xinen
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