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  1. Article ; Online: Dynamic kernel matching for non-conforming data

    Jared Ostmeyer / Lindsay Cowell / Scott Christley

    PLoS ONE, Vol 18, Iss

    A case study of T cell receptor datasets

    2023  Volume 3

    Abstract: Most statistical classifiers are designed to find patterns in data where numbers fit into rows and columns, like in a spreadsheet, but many kinds of data do not conform to this structure. To uncover patterns in non-conforming data, we describe an ... ...

    Abstract Most statistical classifiers are designed to find patterns in data where numbers fit into rows and columns, like in a spreadsheet, but many kinds of data do not conform to this structure. To uncover patterns in non-conforming data, we describe an approach for modifying established statistical classifiers to handle non-conforming data, which we call dynamic kernel matching (DKM). As examples of non-conforming data, we consider (i) a dataset of T-cell receptor (TCR) sequences labelled by disease antigen and (ii) a dataset of sequenced TCR repertoires labelled by patient cytomegalovirus (CMV) serostatus, anticipating that both datasets contain signatures for diagnosing disease. We successfully fit statistical classifiers augmented with DKM to both datasets and report the performance on holdout data using standard metrics and metrics allowing for indeterminant diagnoses. Finally, we identify the patterns used by our statistical classifiers to generate predictions and show that these patterns agree with observations from experimental studies.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Dynamic kernel matching for non-conforming data

    Jared Ostmeyer / Lindsay Cowell / Scott Christley

    PLoS ONE, Vol 18, Iss 3, p e

    A case study of T cell receptor datasets.

    2023  Volume 0265313

    Abstract: Most statistical classifiers are designed to find patterns in data where numbers fit into rows and columns, like in a spreadsheet, but many kinds of data do not conform to this structure. To uncover patterns in non-conforming data, we describe an ... ...

    Abstract Most statistical classifiers are designed to find patterns in data where numbers fit into rows and columns, like in a spreadsheet, but many kinds of data do not conform to this structure. To uncover patterns in non-conforming data, we describe an approach for modifying established statistical classifiers to handle non-conforming data, which we call dynamic kernel matching (DKM). As examples of non-conforming data, we consider (i) a dataset of T-cell receptor (TCR) sequences labelled by disease antigen and (ii) a dataset of sequenced TCR repertoires labelled by patient cytomegalovirus (CMV) serostatus, anticipating that both datasets contain signatures for diagnosing disease. We successfully fit statistical classifiers augmented with DKM to both datasets and report the performance on holdout data using standard metrics and metrics allowing for indeterminant diagnoses. Finally, we identify the patterns used by our statistical classifiers to generate predictions and show that these patterns agree with observations from experimental studies.
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article ; Online: The antibody genetics of multiple sclerosis

    NancyMonson / EricEastman / LindsayCowell

    Frontiers in Neurology, Vol

    comparing next-generation sequencing to Sanger sequencing

    2014  Volume 5

    Abstract: We previously identified a distinct mutation pattern in the antibody genes of B cells isolated from cerebrospinal fluid (CSF) that can identify patients who have relapsing-remitting multiple sclerosis (RRMS) and patients with clinically isolated ... ...

    Abstract We previously identified a distinct mutation pattern in the antibody genes of B cells isolated from cerebrospinal fluid (CSF) that can identify patients who have relapsing-remitting multiple sclerosis (RRMS) and patients with clinically isolated syndromes (CIS) that will convert to RRMS. This antibody gene signature (AGS) was developed using Sanger sequencing of single B cells. While potentially helpful to patients, Sanger sequencing is not an assay that can be practically deployed into clinic settings. In order to provide AGS evaluations to patients as part of their diagnostic workup, we developed protocols to generate AGS scores using next-generation sequencing (NGS) on CSF-derived cell pellets without the need to isolate single cells. This approach has the potential to increase the coverage of the B cell population being analyzed, reduce the time needed to generate AGS scores, and may improve the overall performance of the AGS approach as a diagnostic test in the future. However, no investigations have focused on whether NGS-based repertoires will properly reflect antibody gene frequencies and somatic hypermutation patterns defined by Sanger sequencing. To address this issue, we were able to isolate paired CSF samples from 8 patients who either had MS or were at risk to develop MS. Here we present data that antibody gene frequencies and somatic hypermutation patterns are similar in Sanger and NGS-based antibody repertoires from these paired CSF samples. In addition, AGS scores derived from the NGS database correctly identified the patients who initially had or subsequently converted to RRMS, with similar precision as the Sanger sequencing approach. Further investigation of the utility of the AGS in predicting conversion to MS using NGS-derived antibody repertoires in a larger cohort of patients is warranted.
    Keywords Multiple Sclerosis ; B cell ; Next-generation sequencing ; antibody ; Roche 454 ; Neurology. Diseases of the nervous system ; RC346-429 ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 ; Internal medicine ; RC31-1245 ; Medicine ; R
    Subject code 610 ; 616
    Language English
    Publishing date 2014-09-01T00:00:00Z
    Publisher Frontiers
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  4. Article ; Online: Cell type discovery and representation in the era of high-content single cell phenotyping

    Trygve Bakken / Lindsay Cowell / Brian D. Aevermann / Mark Novotny / Rebecca Hodge / Jeremy A. Miller / Alexandra Lee / Ivan Chang / Jamison McCorrison / Bali Pulendran / Yu Qian / Nicholas J. Schork / Roger S. Lasken / Ed S. Lein / Richard H. Scheuermann

    BMC Bioinformatics, Vol 18, Iss S17, Pp 7-

    2017  Volume 16

    Abstract: Abstract Background A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different ... ...

    Abstract Abstract Background A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. Results In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including “context annotations” in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. Conclusion The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and ...
    Keywords Cell ontology ; Single cell transcriptomics ; Cell phenotype ; Peripheral blood mononuclear cells ; Neuron ; Next generation sequencing ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
    Subject code 571
    Language English
    Publishing date 2017-12-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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