Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data

Algorithms for Analysis of Array CGH Data

Supplementary Website for

Lai W, Johnson MJ, Kucherlapati R, Park PJ. Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data, Bioinformatics, 2005 Aug 4; [Epub ahead of print]

Reference in Entrez PubMed: Click here for the abstract

In addition to some additional information related to the above paper, we also hope to have here other information to help the researchers in this field. Your contributions are welcomed. If you're looking for help with your analysis, see the FAQ section below.

Tables and Figures

Table 1. List of Algorithms Tested in This Paper with links to their websites

Name	Reference	Method	Software	Type
CGHseg	Picard et al. (2005)	CGH Segmentation	CGHseg, Nov. 2004 (MATLAB)	E
Quantreg	Eilers and de Menezes (2005)	Quantile Smoothing	quantreg, v3.76 (R)*	S
CLAC	Wang et al. (2005)	Clustering Along Chromosomes	CLAC, v01-1 (R)	S, E
GLAD	Hupe et al. (2004)	Adaptive Weights Smoothing	GLAD, v1.0.2 (R)	S, E
CBS	Olshen et al. (2004)	Circular Binary Segmentation	DNAcopy, v1.1.1 (R)	E
HMM	Fridlyand et al. (2004)	Hidden Markov Model	aCGH, v1.1.4 (R)	E
Wavelet	Hsu et al. (2005)	Maximal Overlap Discrete Wavelet Transform	waveslim, v1.4 (R)*	S
Lowess		Locally Weighted Regression	stats, v2.0.1 (R)*	S
ChARM	Myers et al. (2004)	Chromosomal Aberration Region Miner	ChARM, v1.6 (JAVA)	S, E
GA	Jong et al. (2003)	Genetic Local Search	aCGHSmooth, Nov. 2004 (exec)	E
ACE	Lingjaerde et al. (2005)	Analysis of Copy Errors	CGH-Explorer, v2.3 (JAVA)	S, E

For the last column, 'S' and 'E' indicate that the algorithm has a step for smoothing and estimation, respectively. Three methods (Quantreg, Wavelet, and Lowess) are for smoothing only. Some methods or packages did not have specific names; others had names that are too generic. We have created short abbreviations in such cases (e.g., we have called the method in Picard et al. (2005) based on the name of their downloadable file). These names are used in the subsequent figures. * indicates those using existing R packages: Quantreg and Wavelet methods were implemented by us based on the descriptions given in the papers; Lowess is our implementatino using the existing R function. CGHseg was ported to R from MATLAB by us.

Higher resolution version in PDF:

Figure 1, Figure 2, Figure 3, Figure 4

Data Used in the Analysis

Profiles of artificial chromosomes in the Simulated Data: Lai_et_al_art_chrom_profiles.xls (3MB)
The true and false positive rates used in plotting Figure 2: Lai_et_al_TPR_FPR_profiles.xls (620KB)
Normalized glioblastoma profiles from the data set of Bredel et al. (2005)

The raw data can be found at the Stanford Microarray Database: Click Here to be Directed to the Raw Data
Chromosome 13 in GBM31 (used in Figure 3): Chrom_13_GBM31.xls (120KB)
Excerpt of chromosome 7 in GBM29 from 40 to 65 Mb (used in Figure 4): Chrom_7_from40_to65Mb_GBM29.xls (40KB)
All of the GBM profiles: GBM_normalized_using_Bredel_et_al_dataset.xls (24MB)

Extra Profiles

(Under Construction)

Frequently Asked Questions

Where can I download the implementations for the algorithms?

Unfortunately, we do not have a simple R function that will automatically run all the algorithms. We understand that this would be convenient for many people who are interested in comparing their new algorithm to the existing ones, but writing such a function involves too much work, both in dealing with many different interfaces required by different algorithms and in keeping up with the changes to the packages. Please check the links provided in Table 1 to learn about the details of each algorithm.

What are the settings used for each algorithm?

When possible, we used the default settings given in the algorithms. A more detailed list of settings used can be downloaded here: Lai_et_al_algorithm_settings.txt

How are true positive rates (TPRs) and false positive rates (FPRs) calculated in the simulated data?

TPR = (the number of probes within the aberration width that is above a threshold) / (the total number of probes within the aberration width)
FPR = (the number of probes outside the aberration width that is above a threshold) / (the total number of probes outside the aberration width)

For algorithms that do not automatically call a region a gain/loss or an amplification/deletion, how does one do so?

Some researchers define thresholds for gains and losses based on the amplitudes of known regions of aberrations from other array data. Others scale their thresholds based on the noise level (e.g., standard deviation) of their array data.

We have some array data we would like to analyze. Can you help?

We are open to collaborations, both in methodological research and in their applications. We do not, however, have time to provide (free) analysis for your data. If you are generating or plan to generate a substantial amount of data for an interesting biological problem, we are open to discussion, especially if you're in the Boston Longwood Medical Area.

Whom may I contact for more information?

Correspondences should be addressed to Peter Park: peter_park@harvard.edu

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