Browsing by Author "Zhang, Xiang"

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    A New Method of Peak Detection for Analysis of Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry Data
    (Duke University Press, 2014) Kim, Seongho; Ouyang, Ming; Jeong, Jaesik; Shen, Changyu; Zhang, Xiang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    We develop a novel peak detection algorithm for the analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm first performs baseline correction and denoising simultaneously using the NEB model, which also defines peak regions. Peaks are then picked using a mixture of probability distribution to deal with the co-eluting peaks. Peak merging is further carried out based on the mass spectral similarities among the peaks within the same peak group. The algorithm is evaluated using experimental data to study the effect of different cut-offs of the conditional Bayes factors and the effect of different mixture models including Poisson, truncated Gaussian, Gaussian, Gamma, and exponentially modified Gaussian (EMG) distributions, and the optimal version is introduced using a trial-and-error approach. We then compare the new algorithm with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used EMG mixture models.
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    An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS
    (Springer Nature, 2013-04-10) Jeong, Jaesik; Zhang, Xiang; Shi, Xue; Kim, Seongho; Shen, Changyu; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Background: Since peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment. Results: The proposed post-hoc method was validated with three datasets such as a mixture of compound standards, metabolite extract from mouse liver, and metabolite extract from wheat. Compared to the existing methods, the proposed approach improved peak alignment in terms of various performance measures. Also, post-hoc approach was verified to improve peak alignment by manual inspection. Conclusions: The proposed approach, which combines the information of metabolite identification and alignment, clearly improves the accuracy of peak alignment in terms of several performance measures. R package and examples using a dataset are available at http://mrr.sourceforge.net/download.html.
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    A large, consistent plasma proteomics data set from prospectively collected breast cancer patient and healthy volunteer samples
    (BMC, 2011-05-27) Riley, Catherine P; Zhang, Xiang; Nakshatri, Harikrishna; Schneider, Bryan; Regnier, Fred E; Adamec, Jiri; Buck, Charles
    Background Variability of plasma sample collection and of proteomics technology platforms has been detrimental to generation of large proteomic profile datasets from human biospecimens. Methods We carried out a clinical trial-like protocol to standardize collection of plasma from 204 healthy and 216 breast cancer patient volunteers. The breast cancer patients provided follow up samples at 3 month intervals. We generated proteomics profiles from these samples with a stable and reproducible platform for differential proteomics that employs a highly consistent nanofabricated ChipCube™ chromatography system for peptide detection and quantification with fast, single dimension mass spectrometry (LC-MS). Protein identification is achieved with subsequent LC-MS/MS analysis employing the same ChipCube™ chromatography system. Results With this consistent platform, over 800 LC-MS plasma proteomic profiles from prospectively collected samples of 420 individuals were obtained. Using a web-based data analysis pipeline for LC-MS profiling data, analyses of all peptide peaks from these plasma LC-MS profiles reveals an average coefficient of variability of less than 15%. Protein identification of peptide peaks of interest has been achieved with subsequent LC-MS/MS analyses and by referring to a spectral library created from about 150 discrete LC-MS/MS runs. Verification of peptide quantity and identity is demonstrated with several Multiple Reaction Monitoring analyses. These plasma proteomic profiles are publicly available through ProteomeCommons. Conclusion From a large prospective cohort of healthy and breast cancer patient volunteers and using a nano-fabricated chromatography system, a consistent LC-MS proteomics dataset has been generated that includes more than 800 discrete human plasma profiles. This large proteomics dataset provides an important resource in support of breast cancer biomarker discovery and validation efforts.
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    Risk of hematologic malignancies after breast ductal carcinoma in situ treatment with ionizing radiation
    (Springer Nature, 2021-03-02) Wang, Kang; Li, Zhuyue; Chen, Xingxing; Zhang, Jianjun; Xiong, Yongfu; Zhong, Guochao; Shi, Yang; Li, Qing; Zhang, Xiang; Li, Hongyuan; Xiang, Tingxiu; Foukakis, Theodoros; Radivoyevitch, Tomas; Ren, Guosheng; Epidemiology, School of Public Health
    The increased incidence of secondary hematologic malignancies (SHM) is a well-known, potentially fatal, complication after cancer treatment. It is unknown if patients with ductal carcinoma in situ (DCIS) of the breast treated with external beam radiotherapy (RT) and who survive long-term have increased risks of secondary hematologic malignancies (SHM), especially for low/intermediate-risk subsets with limited benefits from RT. DCIS patients in Surveillance, Epidemiology, and End Results (SEER) registries (1975–2016) were identified. Relative risks (RR), hazard ratio (HR), and standardized incidence ratios (SIR) were calculated to assess the SHM risk and subsequent survival times. SHM development, defined as a nonsynchronous SHM occurring ≥1 year after DCIS diagnosis, was our primary endpoint. Of 184,363 eligible patients with DCIS, 77,927 (42.3%) in the RT group, and 106,436 (57.7%) in the non-RT group, 1289 developed SHMs a median of 6.4 years (interquartile range, 3.5 to 10.3 years) after their DCIS diagnosis. Compared with DCIS patients in the non-RT group, RT was associated with increased early risk of developing acute lymphoblastic leukemia (ALL; hazard ratio, 3.15; 95% CI, 1.21 to 8.17; P = 0.02), and a delayed risk of non-Hodgkin lymphoma (NHL; hazard ratio, 1.33; 95% CI, 1.09 to 1.62; P < 0.001). This increased risk of ALL and NHL after RT was also observed in subgroup analyses restricted to low/intermediate-risk DCIS. In summary, our data suggest that RT after breast conserving surgery for DCIS patients should be cautiously tailored, especially for low and intermediate-risk patients. Long-term SHM surveillance after DCIS diagnosis is warranted.