| Autor: |
Dipen M. Maru, Scott Kopetz, Jennifer S. Davis, Ignacio I. Wistuba, Riham Katkhuda, David Menter, Michael J. Overman, Van K. Morris, Mark J. Routbort, Omkara Veeranki, Alicia Mejia, Hector A. Alvarez, Bradley M. Broom, Manyam Ganiraju, Wei Wei, Baili Zhang, Zhimin Tong, Huiqin Chen, Justin Windham, Neelima G. Reddy, Wonyul Lee, Dzifa Y. Duose, Yusha Liu, Rajyalakshmi Luthra, Jeffrey S. Morris |
| Rok vydání: |
2023 |
| DOI: |
10.1158/1078-0432.22478931 |
| Popis: |
Nanostring CMS Classifier Performance: Performance of Nanostring 100 gene CMS classifier applied to FFPE samples (6A and 6C) and FF samples (6B and 63D). The top two figures plot 4-class accuracy vs. classification confidence, with the dots marking individual samples either correctly (1.0) or incorrectly (0.0), with color indicat-ing correct CMS. The line contains a generalized additive model (GAM) fit to these data with 95% pointwise confidence bands, and demonstrates that samples classified with greater confidence were more likely to be cor-rectly classified. Figure 6C and 6D plot 4-class accuracy vs. RNA quality, defined as 0nt (FFPE) or RIN (FF). Note that there is little if any association of CMS accuracy with RNA quality, suggesting that the perfor-mance of classifier is robust to RNA quality in this study. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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