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A stepwise framework for the normalization of array CGH data - PubMed

  • ️Sat Jan 01 2005

A stepwise framework for the normalization of array CGH data

Mehrnoush Khojasteh et al. BMC Bioinformatics. 2005.

Abstract

Background: In two-channel competitive genomic hybridization microarray experiments, the ratio of the two fluorescent signal intensities at each spot on the microarray is commonly used to infer the relative amounts of the test and reference sample DNA levels. This ratio may be influenced by systematic measurement effects from non-biological sources that can introduce biases in the estimated ratios. These biases should be removed before drawing conclusions about the relative levels of DNA. The performance of existing gene expression microarray normalization strategies has not been evaluated for removing systematic biases encountered in array-based comparative genomic hybridization (CGH), which aims to detect single copy gains and losses typically in samples with heterogeneous cell populations resulting in only slight shifts in signal ratios. The purpose of this work is to establish a framework for correcting the systematic sources of variation in high density CGH array images, while maintaining the true biological variations.

Results: After an investigation of the systematic variations in the data from two array CGH platforms, SMRT (Sub Mega base Resolution Tiling) BAC arrays and cDNA arrays of Pollack et al., we have developed a stepwise normalization framework integrating novel and existing normalization methods in order to reduce intensity, spatial, plate and background biases. We used stringent measures to quantify the performance of this stepwise normalization using data derived from 5 sets of experiments representing self-self hybridizations, replicated experiments, detection of single copy changes, array CGH experiments which mimic cell population heterogeneity, and array CGH experiments simulating different levels of gene amplifications and deletions. Our results demonstrate that the three-step normalization procedure provides significant improvement in the sensitivity of detection of single copy changes compared to conventional single step normalization approaches in both SMRT BAC array and cDNA array platforms.

Conclusion: The proposed stepwise normalization framework preserves the minute copy number changes while removing the observed systematic biases.

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Figures

Figure 1
Figure 1

A smoothed M-XY plot illustrating spatial bias. The plot displays representation of log2 ratios based on the corresponding spot location on the microarray, the plot is smoothed with a moving median filter.

Figure 2
Figure 2

Normalization of self-self hybridization data. Relative standard deviation (s.d.) of log2 ratios averaged across arrays MM-1 through MM-4 using all data points are shown in blue. The repeated analysis of relative s.d. after removal of the weakest 10% of spots is shown in red. The numbers on the horizontal axis refer to the methods used for normalization listed on Table 2.

Figure 3
Figure 3

Normalization of hybridization data from replicate experiments. 8 replicate array CGH experiments were done comparing sample DNA from H526 cell line and the reference normal male genomic DNA. A. Graph shows the average of the standard deviations of log2 ratios for the same spot across 8 replicate arrays. B. shows the ICC and Average correlation coefficient of replicate arrays. Horizontal axis represents the method number listed in Table 2.

Figure 4
Figure 4

Normalization of hybridization data from male and female DNA. For each of arrays MF-1 and MF-2, a T-test was performed on the two groups of log ratios, i.e. log ratios for the autosomal clones and those for the X chromosome clones. Values of T-statistic after each normalization method are shown. Horizontal axis represents the method number listed in Table 2.

Figure 5
Figure 5

Normalization of hybridization data from samples mimicking heterogeneous cell populations and single copy alterations. Array CGH data were generated for samples mimicking single copy loss (deletion) or single copy gain (amplification) with contamination of increasing proportion of reference DNA, indicated as percentage on the horizontal axis. The experimental procedure for the array CGH experiments was previously described [5]. Global median normalization (method 1), stepwise normalization (method 12), global median normalization with background subtraction (method 13), and 3 step normalization with background subtraction (method 19) were applied. T-statistic values computed before and after normalization for arrays T1-T10 are summarized.

Figure 6
Figure 6

Normalization of hybridization data from cDNA arrays. Array CGH data were generated for samples simulating varying levels of gene amplification and deletion for X-chromosomal genes on the array. Global median normalization (method 1), stepwise normalization (method 12), global median normalization with background subtraction (method 13), and 3 step normalization with background subtraction (method 19) were applied. T-statistic values computed before and after normalization for arrays X1-X5 are summarized.

Figure 7
Figure 7

Chromosome plots before and after normalization. Plot of log2 signal ratios for clones (from chromosome 1 in A and chromosome 2 in B) versus their location across the chromosome. The profiles from left to right are: H526-1 data with global median normalization (method 1), H526-1 data with stepwise normalization (method 12), H526-5 data with global median normalization (method 13), H526-5 data with stepwise normalization (method 19). Each dot on the SeeGH plot represents a BAC clone. A shift in signal ratio to the left of center line indicates a copy number reduction, while a shift to the right indicates a gain. Blue arrow points to a high level segmental amplification. The arrow in part B points to the micro-amplification.

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