NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #37
Submission information
Submission Number: 37
Submission ID: 145197
Submission UUID: b004aead-ce38-4a83-84b0-2b54f7a67f32
Submission URI: /nci/ods-data-jamboree/abstractsubmissions
Submission Update: /nci/ods-data-jamboree/abstractsubmissions?token=Hu3123SEbbZ0NhTjvTrg0Vj_OLjjtxifPMU7TxrSsc8
Created: Mon, 06/23/2025 - 20:13
Completed: Mon, 06/23/2025 - 20:13
Changed: Mon, 06/23/2025 - 20:13
Remote IP address: 10.208.24.253
Submitted by: Anonymous
Language: English
Is draft: No
Presenter Information
Brian
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Capaldo
Ph.D
Biomedical informatics specialist
National Cancer Institute
Rockville
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Abstract Information
Employment of statistical methods or existing computational, mathematical, or informatics tools
linear modeling, xenograft
Mixed effect modeling of human and mouse data in xenograft studies corrects for missed contamination or transcriptomic spillover
Xenograft models present a unique opportunity to study patient samples in an in vivo model, however, contamination of mouse reads can result in spurious interpretations of the data. Using linear mixed effect models, we can effectively remove influences of the mouse transcriptome on the human data.