KC-SMARTR: An R package for detection of statistically significant aberrations in multi-experiment aCGH data
Jorma J. de Ronde; Christiaan Klijn; Arno Velds; Henne Holstege; Marcel Reinders; Jos Jonkers; Lodewyk F.A. Wessels · 2010 · BMC Research Notes
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Most approaches used to find recurrent or differential DNA Copy Number Alterations (CNA) in array Comparative Genomic Hybridization (aCGH) data from groups of tumour samples depend on the discretization of the aCGH data to gain, loss or no-change states. This causes loss of valuable biological information in tumour samples, which are frequently heterogeneous. We have previously developed an algorithm, KC-SMART, that bases its estimate of the magnitude of the CNA at a given genomic location on kernel convolution (Klijn et al., 2008). This accounts for the intensity of the probe sign
Abstract by Jorma J. de Ronde; Christiaan Klijn; Arno Velds; Henne Holstege; Marcel Reinders; Jos Jonkers; Lodewyk F.A. Wessels, BMC Research Notes (2010) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/1756-0500-3-298