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
| First Name | Brian |
|---|---|
| Middle Initial | |
| Last Name | Capaldo |
| Degree(s) | Ph.D |
| Position/Title/Career Status | Biomedical informatics specialist |
| Organization | National Cancer Institute |
| Organization Address | Rockville |
| brian.capaldo@nih.gov | |
| Other (Please Specify) | |
| 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. |
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