A High-Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data

<table>Illustration of the probe window scoring method. Panel (a) is a hypothetical aCGH experiment (samples A–F, probes 1–20). Horizontal bars are regions of copy number gain. Panel (b) is the same information in classification table format. Panel (c) shows scoring of the classification table for the first three of the 3-probe windows. Panels (d) and (e) summarise the scoring, with the graph plotting for each probe window (<svg height="7.1624999" id="M2" style="vertical-align:-0.11285pt" version="1.1" viewbox="0 0 8.7250004 7.1624999" width="8.7250004" xmlns="http://www.w3.org/2000/svg">
<g transform="matrix(1.25,0,0,-1.25,0,7.1625)">
<g transform="translate(72,-66.27)">
<text transform="matrix(1,0,0,-1,-71.95,66.44)">
<tspan style="font-size: 12.50px; " x="0" y="0">𝑥</tspan>
</text>
</g>
</g>
</svg>  axis) the percentage of samples showing the same aCGH classification (i.e., probe window score). This identifies two CRIs (probes 2–10 and 12–19). The MRIs within each region are shaded in panel (d).</table>

Advances in Bioinformatics

fig1

Figure 1

Figure 1: A High-Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data 

Figure 1 | A High-Throughput Computational Framework for Identifying Significant Copy Number Aberrations from Array Comparative Genomic Hybridisation Data 