Trelliscope: A system for detailed visualization in the deep analysis of large complex data

Hafen, Ryan; Gosink, Luke; McDermott, Jason; Rodland, Karin D.; Dam, Kerstin Kleese van; Cleveland, William S.

doi:10.1109/ldav.2013.6675164

Cited by 18 publications

(26 citation statements)

References 12 publications

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“…For this trend analysis, data was further filtered to include only protein groups that were identified in at least 2 individuals. The expression patterns/trends of those 1747 proteins were uploaded online and can be visualized using Trelliscope (https://ascm.shinyapps.io/Zhang_Control_Trend_Analysis/), and a brief introduction for accessing this database was provided in the Supplementary Material [35]. Overall, more than 50% of plasma proteins identified in this study had age-dependent expression trends, which demonstrates the importance of longitudinal profiling study to identify the potential biomarkers specific to childhood diseases, and the requirement of strictly age-matched clinical samples in a cross-sectional study in pediatric population.…”

Section: Resultsmentioning

confidence: 99%

Temporal profiles of plasma proteome during childhood development

Bramer

Webb-Robertson

et al. 2017

Journal of Proteomics

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Human blood plasma proteome reflects physiological changes associated with a child’s development as well as development of disease states. While age-specific normative values are available for proteins routinely measured in clinical practice, there is paucity of comprehensive longitudinal data regarding changes in human plasma proteome during childhood. We applied TMT-10plex isobaric labeling-based quantitative proteomics to longitudinally profile the plasma proteome in 10 healthy children during their development, each with 9 serial time points from 9 months to 15 years of age. In total, 1828 protein groups were identified at peptide and protein level false discovery rate of 1% and with at least two razor and unique peptides. The longitudinal expression profiles of 1747 protein groups were statistically modeled and their temporal changes were categorized into 7 different patterns. The patterns and relative abundance of proteins obtained by LC-MS were also verified with ELISA. To our knowledge, this study represents the most comprehensive longitudinal profiling of human plasma proteome to date. The temporal profiles of plasma proteome obtained in this study provide a comprehensive resource and reference for biomarker studies in childhood diseases.

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Section: Resultsmentioning

confidence: 99%

Temporal profiles of plasma proteome during childhood development

Bramer

Webb-Robertson

et al. 2017

Journal of Proteomics

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“…In this study, our proteomic imaging platform was capable of mapping >2000 proteins with 100-µm spatial resolution, thereby capturing the unique protein expression patterns of the LE, S, and GE cell types. Visualization of this large dataset was made possible through the development of a custom implementation of the powerful, open-source platform Trelliscope 36 , which also serves as an interactive, web-based interface for facile data dissemination. The application of this innovative platform to proteome mapping in a mouse uterus model system clearly demonstrates the exciting potential of proteome imaging to advance biomedical research.…”

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confidence: 99%

Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution

et al. 2020

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Biological tissues exhibit complex spatial heterogeneity that directs the functions of multicellular organisms. Quantifying protein expression is essential for elucidating processes within complex biological assemblies. Imaging mass spectrometry (IMS) is a powerful emerging tool for mapping the spatial distribution of metabolites and lipids across tissue surfaces, but technical challenges have limited the application of IMS to the analysis of proteomes. Methods for probing the spatial distribution of the proteome have generally relied on the use of labels and/or antibodies, which limits multiplexing and requires a priori knowledge of protein targets. Past efforts to make spatially resolved proteome measurements across tissues have had limited spatial resolution and proteome coverage and have relied on manual workflows. Here, we demonstrate an automated approach to imaging that utilizes label-free nanoproteomics to analyze tissue voxels, generating quantitative cell-type-specific images for >2000 proteins with 100-µm spatial resolution across mouse uterine tissue sections preparing for blastocyst implantation.

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“…When a dataset is too large for direct visualisation, which is becoming increasingly common, cognostics are sometimes used as a divide-and-conquer approach [19] . Visualisation using cognostics is facilitated with the Trelliscope tool [19] .…”

Section: From Data To Informationmentioning

confidence: 99%

Environmental Informatics

Cressie¹,

Burden²,

Shumack³

et al. 2017

Wiley StatsRef: Statistics Reference Online

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Environmental Informatics uses a panoply of tools from the Statistics, Mathematics, Computing, and Visualisation disciplines. It uses these tools to reveal, quantify, and validate scientific hypotheses in the environmental sciences, with the quantification of uncertainty central to its approach. There is now a strong recognition that scientific models need to incorporate stochastic components throughout: While it has always been recognised that data have a component of measurement error, attention is now being given to the quantification of model error, and it is becoming accepted by environmental scientists that probability models for the latter allows for a coherent way to make scientific inference. In Environmental Informatics, uncertainty may be assigned not only to datasets of measurements, but also to computer-generated climate-model output. Methodological advances, in the form of hierarchical statistical models and the accompanying computational developments, have expanded the scope of statistical analyses into very large spatial domains. This has led to studies of the dynamical evolution of entire spatial fields of geophysical variables, where results are given in terms of predictive distributions. Environmental Informatics is not only involved in characterising the environment, it can also be used to make decisions about mitigation and adaptation strategies. The steps taken by environmental scientists, from data to information, from information to knowledge, and from knowledge to decisions, are all taken in the presence of uncertainty. Environmental Informatics encompasses all these aspects. Abstract Environmental Informatics uses a panoply of tools from the Statistics, Mathematics, Computing, and Visualisation disciplines. It uses these tools to reveal, quantify, and validate scientific hypotheses in the environmental sciences, with the quantification of uncertainty central to its approach. There is now a strong recognition that scientific models need to incorporate stochastic components throughout: While it has always been recognised that data have a component of measurement error, attention is now being given to the quantification of model error, and it is becoming accepted by environmental scientists that probability models for the latter allows for a coherent way to make scientific inference. In Environmental Informatics, uncertainty may be assigned not only to datasets of measurements, but also to computer-generated climate-model output. Methodological advances, in the form of hierarchical statistical models and the accompanying computational developments, have expanded the scope of statistical analyses into very large spatial domains. This has led to studies of the dynamical evolution of entire spatial fields of geophysical variables, where results are given in terms of predictive distributions. Environmental Informatics is not only involved in characterising the environment, it can also be used to make decisions about mitigation and adaptation strategies. The steps taken by environmental...

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Trelliscope: A system for detailed visualization in the deep analysis of large complex data

Cited by 18 publications

References 12 publications

Temporal profiles of plasma proteome during childhood development

Temporal profiles of plasma proteome during childhood development

Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution

Environmental Informatics

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