Compound Identification Using Partial and Semipartial Correlations for Gas Chromatography–Mass Spectrometry Data

Compound identification is a key component of data analysis in the applications of gas chromatography–mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which the dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semipartial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standards and Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semipartial correlations always outperform both the dot product and its composite measure. The mixture similarity with semipartial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53,1.3) for fragment ion intensity and <i>m</i>/<i>z</i> value, respectively

Additional file 4: of Three endoplasmic reticulum-associated fatty acyl-coenzyme a reductases were involved in the production of primary alcohols in hexaploid wheat (Triticum aestivum L.)

Figure S4. Genetic transformation of TaFAR6, TaFAR7 and TaFAR8 in tomato cv MicroTom and rice cv Zhonghua 11. a, Schematic representation of constructs used in the transformation experiments. LB, T-DNA left border; 35S polyA, CaMV 35S polyA; Hygromycin, Hygromycin resistance gene; Tnos, NOS terminator; RB, T-DNA right border. b, Plant architecture of T1 transgenic lines at the flowering stage. c, PCR screening of transgenic T1 generation tomato plants by detecting the presence of TaFARs genes. d, Expression analysis of three TaFARs in different overexpression transgenic lines and CK by qRT-PCR. e, Plant architecture of T1 transgenic rice lines at the filling stage. f, PCR screening of transgenic T1 generation rice plants by detecting the presence of TaFARs genes. g, Expression analysis of three TaFARs in different overexpression transgenic rice lines and CK by qRT-PCR. (PDF 10567 kb

Additional file 1: of Three endoplasmic reticulum-associated fatty acyl-coenzyme a reductases were involved in the production of primary alcohols in hexaploid wheat (Triticum aestivum L.)

Table S1. Primers for vector construction and expression analysis. (XLSX 11 kb

Additional file 5: of Three endoplasmic reticulum-associated fatty acyl-coenzyme a reductases were involved in the production of primary alcohols in hexaploid wheat (Triticum aestivum L.)

Figure S5. Epicuticular wax crystal patterns on fruits and leaves of transgenic tomato detected by SEM. The epicuticular wax crystal patterns on the fruits surfaces (a–d). CK (a), TaFAR6 overexpression plants (b), TaFAR7 overexpression plants (c) and TaFAR8 overexpression plants (d), respectively. The epicuticular wax crystal patterns on the leaves of adaxial surfaces (e–h) and abaxial surfaces (i–l). CK (e, i), TaFAR6 overexpression plants (f, j), TaFAR7 overexpression plants (g, k), TaFAR8 overexpression plants (h, l). CK is the empty pCXSN vector control. Scale bars = 2 μm. (PDF 6000 kb

Additional file 6: of Three endoplasmic reticulum-associated fatty acyl-coenzyme a reductases were involved in the production of primary alcohols in hexaploid wheat (Triticum aestivum L.)

Figure S6. Epicuticular wax crystal patterns on flag leaves and sheath of transgenic rice detected by SEM. The epicuticular wax crystal patterns on the leaves of adaxial surfaces (a–d), the leaves of abaxial surfaces (e–h) and the sheath surfaces (i–l). CK plants (a, e, i), TaFAR6 overexpression plants (b, f, j), TaFAR7 overexpression plants (c, g, k), TaFAR8 overexpression plants (d, h, j). CK is the empty pCXSN vector control. Scale bars = 2 μm. (PDF 9005 kb

Additional file 2: of Three endoplasmic reticulum-associated fatty acyl-coenzyme a reductases were involved in the production of primary alcohols in hexaploid wheat (Triticum aestivum L.)

Figure S2. SDS-PAGE of TaFAR6, TaFAR7 and TaFAR8 in E. coli. Arrows indicate the His-TaFAR fusion proteins. The empty vector pET28a is as control. M, protein marker. (PDF 5508 kb

Data Preprocessing Method for Liquid Chromatography–Mass Spectrometry Based Metabolomics

A set of data preprocessing algorithms for peak detection and peak list alignment are reported for analysis of liquid chromatography–mass spectrometry (LC–MS)-based metabolomics data. For spectrum deconvolution, peak picking is achieved at the selected ion chromatogram (XIC) level. To estimate and remove the noise in XICs, each XIC is first segmented into several peak groups based on the continuity of scan number, and the noise level is estimated by all the XIC signals, except the regions potentially with presence of metabolite ion peaks. After removing noise, the peaks of molecular ions are detected using both the first and the second derivatives, followed by an efficient exponentially modified Gaussian-based peak deconvolution method for peak fitting. A two-stage alignment algorithm is also developed, where the retention times of all peaks are first transferred into the <i>z</i>-score domain and the peaks are aligned based on the measure of their mixture scores after retention time correction using a partial linear regression. Analysis of a set of spike-in LC–MS data from three groups of samples containing 16 metabolite standards mixed with metabolite extract from mouse livers demonstrates that the developed data preprocessing method performs better than two of the existing popular data analysis packages, MZmine2.6 and XCMS², for peak picking, peak list alignment, and quantification

Data Dependent Peak Model Based Spectrum Deconvolution for Analysis of High Resolution LC-MS Data

A data dependent peak model (DDPM) based spectrum deconvolution method was developed for analysis of high resolution LC-MS data. To construct the selected ion chromatogram (XIC), a clustering method, the density based spatial clustering of applications with noise (DBSCAN), is applied to all <i>m</i>/<i>z</i> values of an LC-MS data set to group the <i>m</i>/<i>z</i> values into each XIC. The DBSCAN constructs XICs without the need for a user defined <i>m</i>/<i>z</i> variation window. After the XIC construction, the peaks of molecular ions in each XIC are detected using both the first and the second derivative tests, followed by an optimized chromatographic peak model selection method for peak deconvolution. A total of six chromatographic peak models are considered, including Gaussian, log-normal, Poisson, gamma, exponentially modified Gaussian, and hybrid of exponential and Gaussian models. The abundant nonoverlapping peaks are chosen to find the optimal peak models that are both data- and retention-time-dependent. Analysis of 18 spiked-in LC-MS data demonstrates that the proposed DDPM spectrum deconvolution method outperforms the traditional method. On average, the DDPM approach not only detected 58 more chromatographic peaks from each of the testing LC-MS data but also improved the retention time and peak area 3% and 6%, respectively

Correlation between Selected Resting Hemodynamics and Selected PFT Parameters for PH Patients.

<p>The abbreviation definitions are same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121690#pone.0121690.t001" target="_blank">Table 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121690#pone.0121690.t002" target="_blank">Table 2</a>.</p><p>Correlation between Selected Resting Hemodynamics and Selected PFT Parameters for PH Patients.</p

Changes in CPET Parameters from Rest to End of Unloaded Cycling in PH Patients and Control.

<p>Values are expressed as mean ± SD.</p><p>Δdenotes the changes from rest to the end of unloaded cycling exercise.</p><p>#p < 0.05, vs control group;</p><p>*p < 0.05, vs no-shunt PPH group using unpaired t test.</p><p>The abbreviation definitions are same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121690#pone.0121690.t001" target="_blank">Table 1</a></p><p>Changes in CPET Parameters from Rest to End of Unloaded Cycling in PH Patients and Control.</p