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    Condensing Raman spectrum for single-cell phenotype analysis

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    1471-2105-16-S18-S15.pdf
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    Type
    Article
    Authors
    Sun, Shiwei
    Wang, Xuetao
    Gao, Xin cc
    Ren, Lihui
    Su, Xiaoquan
    Bu, Dongbo
    Ning, Kang
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2015-12-10
    Online Publication Date
    2015-12-10
    Print Publication Date
    2015
    Permanent link to this record
    http://hdl.handle.net/10754/593294
    
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    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.
    Citation
    Condensing Raman spectrum for single-cell phenotype analysis 2015, 16 (Suppl 18):S15 BMC Bioinformatics
    Publisher
    Springer Nature
    Journal
    BMC Bioinformatics
    DOI
    10.1186/1471-2105-16-S18-S15
    PubMed ID
    26681607
    Additional Links
    http://www.biomedcentral.com/1471-2105/16/S18/S15
    ae974a485f413a2113503eed53cd6c53
    10.1186/1471-2105-16-S18-S15
    Scopus Count
    Collections
    Articles; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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