Browsing by Author "Parker, Sarah J."

Now showing 1 - 6 of 6
  • Automating context dependent gaze metrics for evaluation of laparoscopic surgery manual skills
    Deng, Shiyu; Kulkarni, Chaitanya; Parker, Sarah J.; Barnes, Laura E.; Wang, Tianzi; Hartman-Kenzler, Jacob; Safford, Shawn; Lau, Nathan (2022-03)
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    Bioinformatic Analysis of Coronary Disease Associated SNPs and Genes to Identify Proteins Potentially Involved in the Pathogenesis of Atherosclerosis
    Mao, Chunhong; Howard, Timothy D.; Sullivan, Dan; Fu, Zongming; Yu, Guoqiang; Parker, Sarah J.; Will, Rebecca; Vander Heide, Richard S.; Wang, Yue; Hixson, James; Van Eyk, Jennifer; Herrington, David M. (Open Access Pub, 2017-03-04)
    Factors that contribute to the onset of atherosclerosis may be elucidated by bioinformatic techniques applied to multiple sources of genomic and proteomic data. The results of genome wide association studies, such as the CardioGramPlusC4D study, expression data, such as that available from expression quantitative trait loci (eQTL) databases, along with protein interaction and pathway data available in Ingenuity Pathway Analysis (IPA), constitute a substantial set of data amenable to bioinformatics analysis. This study used bioinformatic analyses of recent genome wide association data to identify a seed set of genes likely associated with atherosclerosis. The set was expanded to include protein interaction candidates to create a network of proteins possibly influencing the onset and progression of atherosclerosis. Local average connectivity (LAC), eigenvector centrality, and betweenness metrics were calculated for the interaction network to identify top gene and protein candidates for a better understanding of the atherosclerotic disease process. The top ranking genes included some known to be involved with cardiovascular disease (APOA1, APOA5, APOB, APOC1, APOC2, APOE, CDKN1A, CXCL12, SCARB1, SMARCA4 and TERT), and others that are less obvious and require further investigation (TP53, MYC, PPARG, YWHAQ, RB1, AR, ESR1, EGFR, UBC and YWHAZ). Collectively these data help define a more focused set of genes that likely play a pivotal role in the pathogenesis of atherosclerosis and are therefore natural targets for novel therapeutic interventions.
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    Comparative assessment and novel strategy on methods for imputing proteomics data
    Shen, Minjie; Chang, Yi-Tan; Wu, Chiung-Ting; Parker, Sarah J.; Saylor, Georgia; Wang, Yizhi; Yu, Guoqiang; Van Eyk, Jennifer E.; Clarke, Robert; Herrington, David M.; Wang, Yue (2022-01-20)
    Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factorization, a low-rank global matrix factorization framework, capable of integrating local similarity derived from additional data types. We also explore a biologically-inspired latent variable modeling strategy—convex analysis of mixtures—for missing value imputation and present preliminary experimental results. While some winners emerged from our comparative assessment, the evaluation is intrinsically imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Nevertheless, we show that our fused regularization matrix factorization provides a novel incorporation of external and local information, and the exploratory implementation of convex analysis of mixtures presents a biologically plausible new approach.
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    Cosbin: cosine score-based iterative normalization of biologically diverse samples
    Wu, Chiung-Ting; Shen, Minjie; Du, Dongping; Cheng, Zuolin; Parker, Sarah J.; Lu, Yingzhou; Van Eyk, Jennifer E.; Yu, Guoqiang; Clarke, Robert; Herrington, David M.; Wang, Yue (Oxford University Press, 2022)
    Motivation: Data normalization is essential to ensure accurate inference and comparability of gene expression measures across samples or conditions. Ideally, gene expression data should be rescaled based on consistently expressed reference genes. However, to normalize biologically diverse samples, the most commonly used reference genes exhibit striking expression variability and size-factor or distribution-based normalization methods can be problematic when the amount of asymmetry in differential expression is significant. Results: We report an efficient and accurate data-driven method-Cosine score-based iterative normalization (Cosbin)-to normalize biologically diverse samples. Based on the Cosine scores of cross-condition expression patterns, the Cosbin pipeline iteratively eliminates asymmetric differentially expressed genes, identifies consistently expressed genes, and calculates sample-wise normalization factors. We demonstrate the superior performance and enhanced utility of Cosbin compared with six representative peer methods using both simulation and real multi-omics expression datasets. Implemented in open-source R scripts and specifically designed to address normalization bias due to significant asymmetry in differential expression across multiple conditions, the Cosbin tool complements rather than replaces the existing methods and will allow biologists to more accurately detect true molecular signals among diverse phenotypic groups. Availability and implementation: The R scripts of Cosbin pipeline are freely available at https://github.com/MinjieSh/Cosbin. Supplementary information: Supplementary data are available at Bioinformatics Advances online.
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    COT: an efficient and accurate method for detecting marker genes among many subtypes
    Lu, Yingzhou; Wu, Chiung-Ting; Parker, Sarah J.; Cheng, Zuolin; Saylor, Georgia; Van Eyk, Jennifer E.; Yu, Guoqiang; Clarke, Robert; Herrington, David M.; Wang, Yue (Oxford University Press, 2022)
    Motivation: Ideally, a molecularly distinct subtype would be composed of molecular features that are expressed uniquely in the subtype of interest but in no others-so-called marker genes (MGs). MG plays a critical role in the characterization, classification or deconvolution of tissue or cell subtypes. We and others have recognized that the test statistics used by most methods do not exactly satisfy the MG definition and often identify inaccurate MG. Results: We report an efficient and accurate data-driven method, formulated as a Cosine-based One-sample Test (COT) in scatter space, to detect MG among many subtypes using subtype expression profiles. Fundamentally different from existing approaches, the test statistic in COT precisely matches the mathematical definition of an ideal MG. We demonstrate the performance and utility of COT on both simulated and real gene expression and proteomics data. The open source Python/R tool will allow biologists to efficiently detect MG and perform a more comprehensive and unbiased molecular characterization of tissue or cell subtypes in many biomedical contexts. Nevertheless, COT complements not replaces existing methods. Availability and implementation: The Python COT software with a detailed user's manual and a vignette are freely available at https://github.com/MintaYLu/COT. Supplementary information: Supplementary data are available at Bioinformatics Advances online.
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    Proteomic analysis of descending thoracic aorta identifies unique and universal signatures of aneurysm and dissection
    Saddic, Louis; Orosco, Amanda; Guo, Dongchuan; Milewicz, Dianna M.; Troxlair, Dana; Vander Heide, Richard; Herrington, David M.; Wang, Yue; Azizzadeh, Ali; Parker, Sarah J. (Elsevier, 2022)
    Objective: Very few clinical predictors of descending thoracic aorta dissection have been determined. Although aneurysms can dissect in a size-dependent process, most descending dissections will occur without prior enlargement. We compared the proteomic profiles of normal, dissected, aneurysm, and both aneurysm and dissected descending thoracic aortas to identify novel biomarkers and further understand the molecular pathways that lead to tissue at risk of dissection. Methods: We performed proteomic profiling of descending thoracic aortas with four phenotypes: normal (n = 46), aneurysm (n = 22), dissected (n = 12), and combined aneurysm and dissection (n = 8). Pairwise differential protein expression analyses using a Bayesian approach were then performed to identify common proteins that were dysregulated between each diseased tissue type and control aorta and to uncover unique proteins between aneurysmal and dissected aortas. Network and Markov cluster algorithms of differentially expressed proteins were used to find enriched ontology processes. A convex analysis of mixtures was also performed to identify the molecular subtypes within the different tissue types. Results: The diseased aortas had 71 common differentially expressed proteins compared with the control, including higher amounts of the protein thrombospondin 1. We found 42 differentially expressed proteins between the aneurysm and dissected tissue, with an abundance of apolipoproteins in the former and higher quantities of extracellular matrix proteins in the latter. The convex analysis of mixtures showed enhancement of a molecular subtype enriched in contractile proteins within the control tissue compared with the diseased tissue, in addition to increased proportions of molecular subtypes enriched in inflammation and red blood cell expression in the aneurysmal compared with the dissected tissue. Conclusions: We found some overlapping differentially expressed proteins in aneurysmal and nonaneurysmal descending thoracic aortas at risk of dissection compared with normal aortas. However, we also found uniquely altered molecular pathways that might uncover mechanisms for dissection.