M-CGH: Analysing microarray-based CGH experiments

Abstract

Background: Microarray-based comparative genomic hybridisation (array CGH) is a technique by which variation in relative copy numbers between two genomes can be analysed by competitive hybridisation to DNA microarrays. This technology has most commonly been used to detect chromosomal amplifications and deletions in cancer. Dedicated tools are needed to analyse the results of such experiments, which include appropriate visualisation, and to take into consideration the physical relation in the genome between the probes on the array.

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

“…Lastly, the implementation was primarily aimed at comparative analysis between the frequentist and Bayesian approach, as opposed to overall speed. To address these issues and make Bayesian CNV inference feasible even on a laptop, we propose the combination of HMMs with another popular signal processing technology: Haar wavelets have previously been used in CNV detection [63], mostly as a preprocessing tool for statistical downstream applications [24][25][26][27][28] or simply as a visual aid in GUI applications [17,64]. A combination of smoothing and segmentation has been suggested as likely to improve results [65], and here we show that this is indeed the case.…”

“…The core idea for our method expands on our prior work [62] and affirms a conjecture by Lai et al [65] that a combination of smoothing and segmentation will likely improve results. One ingredient of our method are Haar wavelets, which have previously been used for pre-processing and visualization [17,64]. In a sense, they quantify and identify regions of high variation and allow to summarize the data at various levels of resolution, somewhat similar to how an expert would perform a visual analysis.…”

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

“…The core idea for our method expands on our prior work [ 62 ] and affirms a conjecture by Lai et al [ 65 ] that a combination of smoothing and segmentation will likely improve results. One ingredient of our method are Haar wavelets, which have previously been used for pre-processing and visualization [ 21 , 64 ]. In a sense, they quantify and identify regions of high variation, and allow to summarize the data at various levels of resolution, somewhat similar to how an expert would perform a visual analysis.…”

“…To address these issues and make Bayesian CNV inference feasible even on a laptop, we propose the combination of HMMs with another popular signal processing technology: Haar wavelets have previously been used in CNV detection [ 63 ], mostly as a preprocessing tool for statistical downstream applications [ 28 – 32 ] or simply as a visual aid in GUI applications [ 21 , 64 ]. A combination of smoothing and segmentation has been suggested as likely to improve results [ 65 ], and here we show that this is indeed the case.…”

OH-2, a hyperdiploid myeloma cell line without an IGH translocation, has a complex translocation juxtaposing MYC near MAFB and the IGK locus

“…The genomic microarray was done using 500 ng genomic DNA sample from OH-2 cell line and primary cells after methods as described [16,17].…”