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

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
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First Name: Brian









Middle Initial: {Empty}









Last Name: Capaldo









Degree(s): Ph.D









Position/Title/Career Status: Biomedical informatics specialist









Organization: National Cancer Institute









Organization Address:
Rockville










Email: brian.capaldo@nih.gov









Other (Please Specify): {Empty}













Abstract Information
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Abstract Category: Employment of statistical methods or existing computational, mathematical, or informatics tools









Abstract Keywords: linear modeling, xenograft









Abstract Title: Mixed effect modeling of human and mouse data in xenograft studies corrects for missed contamination or transcriptomic spillover









Abstract Summary:
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. 










Upload Abstract: https://events.cancer.gov/sites/default/files/webform/nci_office_of_data_sharing_abstr/145197/Xenograft%20models%20are%20widely%20used%20as%20a%20surrogate%20for%20better%20molecular%20characterization%20of%20patient%20disease.docx