Journal: BioData Mining

Article Title: Label-free data standardization for clinical metabolomics

doi: 10.1186/s13040-017-0132-x

Figure Lengend Snippet: Typical mass spectra of human blood plasma metabolites standardized according to the SantaOmics algorithm. a The initial mass spectrum of human plasma metabolites. The mass spectrum was obtained after the direct infusion of a blood plasma sample into an electrospray ion source of a hybrid quadrupole time-of-flight mass spectrometer (maXis, Bruker Daltonics). b Detection of the normalization value for a particular mass ( m/z 225) in the mass spectrum. The substances from the selected range ( m/z 225 ± 50) of the mass spectrum are plotted according to their decreasing peak intensity. The place of maximum curvature of the curve (knee point), which approximates the range of intensities, corresponds to the normalization value ( depicted by the arrow ). c Maximum curvature detection by intensity derivative calculations. The derivative maximum ( depicted by the arrow ) corresponds to the knee point, which indicates the normalization value on the y-axis of plot B. d Normalization curve that was built by approximation of the normalization values calculated over the entire range of the mass spectrum. e Standardized mass spectrum that was obtained by dividing the peak intensities of the initial mass spectrum by the normalization curve

Article Snippet: The Matlab source code providing the SantaOmics algorithm (file ‘SantaOmics.m’) and the datasets supporting the conclusions of this article are available in the FigShare repository [ https://figshare.com/s/276e4292a9796e2114ee ; doi:10.6084/m9.figshare.3153982].

Techniques: Clinical Proteomics, Mass Spectrometry

Journal: BioData Mining

Article Title: Label-free data standardization for clinical metabolomics

doi: 10.1186/s13040-017-0132-x

Figure Lengend Snippet: The test results of the SantaOmics algorithm. The mass peaks were extensively distorted in different ways, and the SantaOmics algorithm was applied to standardize the distorted mass spectra. The initial and distorted by multiplication (10×) mass spectra before ( a ) and after ( b ) standardization. Initial and linearly distorted (right corner is suppressed, left corner is powered) mass spectra before ( c ) and after ( d ) standardization. Initial and nonlinearly distorted (right and left corner are suppressed, center of spectrum powered) mass spectra before ( e ) and after ( f ) standardization. R 2 , coefficient of determination for linear approximation of the data; the value equal to 1 confirmed that the SantaOmics algorithm is capable of correcting extensive distortions in the mass spectra

Article Snippet: The Matlab source code providing the SantaOmics algorithm (file ‘SantaOmics.m’) and the datasets supporting the conclusions of this article are available in the FigShare repository [ https://figshare.com/s/276e4292a9796e2114ee ; doi:10.6084/m9.figshare.3153982].

Techniques:

Journal: BioData Mining

Article Title: Label-free data standardization for clinical metabolomics

doi: 10.1186/s13040-017-0132-x

Figure Lengend Snippet: Mass spectra of the same blood plasma sample obtained at different ranges of mass detection before ( a ) and after ( b ) standardization, according to the SantaOmics algorithm. The overlapping area of the standardized mass spectra ( c ) demonstrated the similarity in peak intensities. R 2 , coefficient of determination for linear approximation of the data calculated for peak intensities; r, correlation coefficient

Article Snippet: The Matlab source code providing the SantaOmics algorithm (file ‘SantaOmics.m’) and the datasets supporting the conclusions of this article are available in the FigShare repository [ https://figshare.com/s/276e4292a9796e2114ee ; doi:10.6084/m9.figshare.3153982].

Techniques: Clinical Proteomics

Journal: BioData Mining

Article Title: Label-free data standardization for clinical metabolomics

doi: 10.1186/s13040-017-0132-x

Figure Lengend Snippet: Mass spectra standardization by the SantaOmics algorithm in the inter-instrumental experiment. a-d Overlapped mass spectra obtained by maXis and other mass spectrometers before standardization. e-h The same mass spectra after standardization

Article Snippet: The Matlab source code providing the SantaOmics algorithm (file ‘SantaOmics.m’) and the datasets supporting the conclusions of this article are available in the FigShare repository [ https://figshare.com/s/276e4292a9796e2114ee ; doi:10.6084/m9.figshare.3153982].

Techniques:

Journal: BioData Mining

Article Title: Label-free data standardization for clinical metabolomics

doi: 10.1186/s13040-017-0132-x

Figure Lengend Snippet: Averaged data for Passing Bablok analysis and Spearman correlation for mass spectra of the same biosamples after normalization according to the SantaOmics algorithm

Article Snippet: The Matlab source code providing the SantaOmics algorithm (file ‘SantaOmics.m’) and the datasets supporting the conclusions of this article are available in the FigShare repository [ https://figshare.com/s/276e4292a9796e2114ee ; doi:10.6084/m9.figshare.3153982].

Techniques: