Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals

Tran, Phuong Thi Minh

doi:10.1093/nar/gnf053

Cited by 105 publications

(64 citation statements)

References 21 publications

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“…different background correction methods might avoid or mitigate the problems in the first place. Avoiding background correction altogether is recommended by Yang et al (2001a) and Tran et al (2002). Qin et al (2004) examined the effect of background correction on bias and the ability to detect spike-in ratio controls.…”

Section: Introductionmentioning

confidence: 99%

A comparison of background correction methods for two-colour microarrays

et al. 2007

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Motivation: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. Results: Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexpþoffset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data. Availability: The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from

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Section: Introductionmentioning

confidence: 99%

A comparison of background correction methods for two-colour microarrays

et al. 2007

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“…In the case of background correction, some have proposed to refrain from any background correction (Yang et al 2001, Tran et al 2002 while others report complex strategies (Ritchie et al 2007, Silver et al 2009). In any case, it can be argued that background correction does not greatly influence endpoint data when the microarray hybridization resulted in low nonspecific binding.…”

Section: Data Processing and Analysismentioning

confidence: 99%

Microarray analysis of gene expression during early development: a cautionary overview

Robert

2010

Reproduction

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The rise of the 'omics' technologies started nearly a decade ago and, among them, transcriptomics has been used successfully to contrast gene expression in mammalian oocytes and early embryos. The scarcity of biological material that early developmental stages provide is the prime reason why the field of transcriptomics is becoming more and more popular with reproductive biologists. The potential to amplify scarce mRNA samples and generate the necessary amounts of starting material enables the relative measurement of RNA abundance of thousands of candidates simultaneously. So far, microarrays have been the most commonly used high-throughput method in this field. Microarray platforms can be found in a wide variety of formats, from cDNA collections to long or short oligo probe sets. These platforms generate large amounts of data that require the integration of comparative RNA abundance values in the physiological context of early development for their full benefit to be appreciated. Unfortunately, significant discrepancies between datasets suggest that direct comparison between studies is difficult and often not possible. We have investigated the sample-handling steps leading to the generation of microarray data produced from prehatching embryo samples and have identified key steps that significantly impact the downstream results. This review provides a discussion on the best methods for the preparation of samples from early embryos for microarray analysis and focuses on the challenges that impede dataset comparisons from different platforms and the reasons why methodological benchmarking performed using somatic cells may not apply to the atypical nature of prehatching development.

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“…0.5 μg of mRNA was reverse-transcribed using oligo(dT) [12][13][14][15][16][17][18] (Invitrogen) and Superscript II RNaseH -Reverse Transcriptase (Invitrogen) following manufacturer instructions. Quantification of cDNA was performed using Oligreen ssDNA Quantitation Reagent (Invitrogen).…”

Section: Real-time Pcrmentioning

confidence: 99%

“…The data was filtered to eliminate dust derived data points (spots with size less than 75 pixels or with a mean to median correlation of less than 80% [15], saturated data points (spots with a proportion of saturated pixels greater than 20%), and low signal data points (spots with signal to background ratio below 1.2).…”

Section: Data Filtration and Normalizationmentioning

confidence: 99%

Distal Tumors Elicit Distinctive Gene Expression Changes in Mouse Brain, Different from Those Induced by Arthritis

Alvarez¹,

Salibe²,

Stolovitzky³

et al. 2009

TONEURJ

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Abstract:Background: Tumor progression is characterized by high mutation rates, each mutation potentially generating an "alarm" signal. The brain is the main integrator of signals arising in the periphery from changes in homeostasis. We hypothesized that tumors growing at a distant site might be a stimulus strong enough to be molecularly sensed and integrated by the brain.Results: Transcriptome analysis of the mouse hypothalamus, midbrain, and pre-fontal cortex at different time points following administration at a distant site of mammary, lung and colon cancer cells evidenced cancer-type and brain-region specific changes in gene expression. On the contrary, no significant gene expression changes were detected in the liver. The hypothalamus was the region with the largest number of differentially expressed genes. On the array and off the array analysis of hypothalamic samples using real time PCR confirmed changes in genes associated with synaptic activity and sickness response, respectively. Gene clustering allowed the discrimination between each cancer model and between the cancer models and arthritis. Conclusions:The present data provides evidence of changes in gene expression in the brain during progression of distal tumors and arthritis highlighting a potential link between distal pathological processes and the brain.

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Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals

Cited by 105 publications

References 21 publications

A comparison of background correction methods for two-colour microarrays

A comparison of background correction methods for two-colour microarrays

Microarray analysis of gene expression during early development: a cautionary overview

Distal Tumors Elicit Distinctive Gene Expression Changes in Mouse Brain, Different from Those Induced by Arthritis

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