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1.
Figure 5.

Figure 5. From: MSB: A mean-shift-based approach for the analysis of structural variation in the genome.

Application of MSB and other eight methods to an array-CGH profile of chromosome 13 in a glioblastoma multiforme sample (GBM31). The profile has a partial loss with subtle amplitude change. MSB, CGHseg, GLAD, HMM, and CBS clearly identified this region.

Lu-yong Wang, et al. Genome Res. 2009 January;19(1):106-117.
2.
Figure 6.

Figure 6. From: MSB: A mean-shift-based approach for the analysis of structural variation in the genome.

Output of MSB and eight other methods to an array-CGH data set of chromosome 2 in a NSCLC adenocarcinoma case. This profile contains a wide amplification region and a short deletion segment. MSB and CGHseg clearly identified these regions. A straightforward post-processing refined the MSB results.

Lu-yong Wang, et al. Genome Res. 2009 January;19(1):106-117.
3.
Figure 4.

Figure 4. From: MSB: A mean-shift-based approach for the analysis of structural variation in the genome.

Application of MSB and eight other methods to an array-CGH profile of the three amplifications around EGFR in the GBM29 sample. MSB, CGHseg, GLAD, wavelet, and quantreg clearly detected all three amplifications correctly. CBS detected three amplifications with the wrong amplitude. lowess only detected the first two amplifications as one larger region. CLAC took these three amplifications as one region. HMM performed the worst, with no detection.

Lu-yong Wang, et al. Genome Res. 2009 January;19(1):106-117.
4.
Figure 7.

Figure 7. From: MSB: A mean-shift-based approach for the analysis of structural variation in the genome.

Proof-of-concept application of MSB to Illumina CD signal on a part of chromosome 21. The data is from position 46,162,500–46,164,711 on the x-axis. The y-axis shows the half representation of frequencies (actual frequencies are multiplying the numbers by 2). Light color shows the experimental results. MSB identified several regions of changed copy number, shown in black lines.

Lu-yong Wang, et al. Genome Res. 2009 January;19(1):106-117.
5.
Figure 2.

Figure 2. From: MSB: A mean-shift-based approach for the analysis of structural variation in the genome.

An example of simulated array-CGH data by composed aberrations with increasing width (2, 5, 10, 20, and 40 probes). This signal profile consists of five aberrations of width in increasing order. The amplitude of aberration is 1. Gaussian noise with σ = 0.25 was imposed onto the signal profile in the simulated data. MSB, CGHseg, and HMM clearly detected all five aberrations.

Lu-yong Wang, et al. Genome Res. 2009 January;19(1):106-117.
6.
Figure 1.

Figure 1. From: MSB: A mean-shift-based approach for the analysis of structural variation in the genome.

Mean-shift mode finding: A simple example of an array-CGH data segment from glioblastoma sample. (A) Mean-shift process: The successive set of triangles shows the yj, more particularly (, ), in the mean-shift iterations, while their connecting dashed lines show the mean-shift vector. (B) Mean-shift smoothing in the intensity domain: The successive set of triangles shows (, ), where refers to the spatial location, and refers to the intensity domain. The value zi = (, ) after convergence is the filtered data point. Here, we visualize only 59 points for the purpose of illustration. The data represent a small segment of chromosome 7 of the GBM29 sample. The data consist of 67 probes among the nucleotide positions ranging from 54,908,778–64,080,642 on chromosome 7 of GBM29. The points represent the actual measurements of the CGH experiments along the chromosome segment. The straight lines show the results of MSB. The sets of successive locations shown by triangles converge to the local modes of the intensity domain. The last one of these successive locations is the point of convergence for each set. The eight points on the left side are attracted by the mode at the amplitude of 4.75 in the intensity domain, while the 51 points on the right are attracted by the mode at the amplitude of 0.2 in the intensity domain. Clearly, the eighth and ninth points (shown with stars) are attracted by different modes separately.

Lu-yong Wang, et al. Genome Res. 2009 January;19(1):106-117.
7.
Figure 3.

Figure 3. From: MSB: A mean-shift-based approach for the analysis of structural variation in the genome.

Receiver operating characteristic (ROC) analysis for array-CGH algorithms on simulation data sets. These data sets were simulated at different aberration widths and signal-to-noise ratios (SNRs). Each row represents three different SNR levels (4, 2, and 1, from top to bottom, respectively), and the columns represent aberration widths of 40, 20, 10, and 5 probes from left to right, respectively. The x-axis is 1 − specificity (the false positive rate) and the y-axis is the sensitivity (true positive rate). The curves were generated by measuring the sensitivity and specificity on simulated data at different threshold levels. The green curve refers to MSB. Its 90% confidence intervals are shown by the green bars for different levels. The blue curve is for CGHseg (Picard et al. 2005); the cyan point curve for GLAD (Hupe et al. 2004); the yellow dot curve for wavelet (Hsu et al. 2005); the solid yellow curve is for CBS (Olshen and Venkatraman 2002, 2004; Hsu et al. 2005); the magenta dot curve is for lowess (Beheshti et al. 2003); the blue dot curve is for quantreg (Eilers and de Menezes 2005); the red dot curve is for ChARM (Myers et al. 2004; Lai et al. 2005), the solid black curve is for HMM (Fridlyand et al. 2004); and the solid purple curve is for CLAC (Wang et al. 2005). The bottom right of each figure at SNR level 4 and 2 shows the zoom-in at the low false positive rate region.

Lu-yong Wang, et al. Genome Res. 2009 January;19(1):106-117.

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