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Model-based Analysis of 2-Color Arrays (MA2C)

ABSTRACT: High-density oligonucleotide tiling-microarrays are currently providing a powerful tool for genome-wide in vivo DNA footprinting assays, yielding unprecedented insights into tissuespecific protein-DNA interactions and chromatin structure. Despite the impressive advances, however, the technology still suffers from numerous complications caused by background noise and probespecific effects. A few computational methods modeling sequencerelated probe effects are now available for Affymetrix tiling arrays, but no counterpart is yet available for two-color arrays. A novel normalization method based on the GC content of probes is developed for two-color tiling-arrays. The proposed method, together with robust estimates of the model parameters, is shown to perform superbly on published data sets. Accompanying the normalization method, a robust algorithm for detecting peak regions is formulated and also shown to perform well compared to other approaches. The tools presented herein have been implemented for NimbleGen tiling arrays as a stand-alone Java program, which can also display various plots of statistical analysis for quality control of experiments. Upon changing the file format, the program also works on Agilent data. Keywords: ChIP-chip

ORGANISM(S): Homo sapiens

PROVIDER: GSE7523 | GEO | 2007/06/26

SECONDARY ACCESSION(S): PRJNA99387

REPOSITORIES: GEO

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Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling To assay technical reproducibility, 3 technical replicates from brain and heart RNA (Stratagene) were hybridized on Agilent human miRNA microarrays. To determine sensitivity and specificity, heart and brain RNA were mixed in the following ratios: 50% heart 50% brain, 25% heart 75% brain and 5% heart 95% brain. Each of the dilutions was hybridized in a technical duplicate on Agilent human miRNA arrays. To assay the effect of p53 expression on miRNA levels in the human SCC13 cell line, RNA from 3 biological replicates of p53-expressing or control cells were hybridized in technical duplicates on the microarrays, resulting in a total of 12 hybridizations.

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Model-based Analysis of 2-Color Arrays (MA2C)

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