A statistical framework for the design of microarray experiments and effective detection of differential gene expression

Motivation: Microarray experiments generate a high data volume. However, often due to financial or experimental considerations, e.g. lack of sample, there is little or no replication of the experiments or hybridizations. These factors combined with the intrinsic variability associated with the measurement of gene expression can result in an unsatisfactory detection rate of differential gene expression (DGE). Our motivation was to provide an easy to use measure of the success rate of DGE detection that could find routine use in the design of microarray experiments or in post-experiment assessment.

Effect of pooling samples on the efficiency of comparative studies using microarrays

Motivation: Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample pooling, and some approximate formulas were provided for the power and sample size calculations. It is desirable to have exact formulas for these calculations and have the approximate results checked against the exact ones. We show that the difference between the approximate and the exact results can be large.

Assessment Method for a Power Analysis to Identify Differentially Expressed Pathways

Gene expression data can provide a very rich source of information for elucidating the biological function on the pathway level if the experimental design considers the needs of the statistical analysis methods. The purpose of this paper is to provide a comparative analysis of statistical methods for detecting the differentially expression of pathways (DEP). In contrast to many other studies conducted so far, we use three novel simulation types, producing a more realistic correlation structure than previous simulation methods. This includes also the generation of surrogate data from two large-scale microarray experiments from prostate cancer and ALL. As a result from our comprehensive analysis of 41,004 parameter configurations, we find that each method should only be applied if certain conditions of the data from a pathway are met. Further, we provide method-specific estimates for the optimal sample size for microarray experiments aiming to identify DEP in order to avoid an underpowered design. Our study highlights the sensitivity of the studied methods on the parameters of the system. © 2012 Tripahti and Emmert-Streib.

Ultra-fast processing of gigapixel Tissue MicroArray images using high performance computing.

BACKGROUND:
tissue MicroArrays (TMAs) are a valuable platform for tissue based translational research and the discovery of tissue biomarkers. The digitised TMA slides or TMA Virtual Slides, are ultra-large digital images, and can contain several hundred samples. The processing of such slides is time-consuming, bottlenecking a potentially high throughput platform.
METHODS:
a High Performance Computing (HPC) platform for the rapid analysis of TMA virtual slides is presented in this study. Using an HP high performance cluster and a centralised dynamic load balancing approach, the simultaneous analysis of multiple tissue-cores were established. This was evaluated on Non-Small Cell Lung Cancer TMAs for complex analysis of tissue pattern and immunohistochemical positivity.
RESULTS:
the automated processing of a single TMA virtual slide containing 230 patient samples can be significantly speeded up by a factor of circa 22, bringing the analysis time to one minute. Over 90 TMAs could also be analysed simultaneously, speeding up multiplex biomarker experiments enormously.
CONCLUSIONS:
the methodologies developed in this paper provide for the first time a genuine high throughput analysis platform for TMA biomarker discovery that will significantly enhance the reliability and speed for biomarker research. This will have widespread implications in translational tissue based research.

Tailored Microarray Platform for the Detection of Marine Toxins

Currently, there are no fast in vitro broad spectrum screening bioassays for the detection of marine toxins. The aim of this study was to develop such an assay. In gene expression profiling experiments 17 marker genes were provisionally selected that were differentially regulated in human intestinal Caco-2 cells upon exposure to the lipophilic shellfish poisons azaspiracid-1 (AZA1) or dinophysis toxin-1 (DTX1). These 17 genes together with two control genes were the basis for the design of a tailored microarray platform for the detection of these marine toxins and potentially others. Five out of the 17 selected marker genes on this dedicated DNA microarray gave dear signals, whereby the resulting fingerprints could be used to detect these toxins. CEACAM1, DDIT4, and TUBB3 were up-regulated by both AZA1 and DTX1, TRIB3 was up-regulated by AZA1 only, and OSR2 by DTX1 only. Analysis by singleplex qRT-PCR revealed the up- and down-regulation of the selected RGS16 and NPPB marker genes by DTX1, that were not envisioned by the new developed dedicated array. The qRT-PCR targeting the DDIT4, RSG16 and NPPB genes thus already resulted in a specific pattern for AZA1 and DTX1 indicating that for this specific case qRT-PCR might a be more suitable approach than a dedicated array.

Re-analysis of microarray data reveals insights into altered transcriptional activity of TH17 and TReg signalling in psoriasis

Identifying differential expression of genes in psoriatic and healthy skin by microarray data analysis is a key approach to understand the pathogenesis of psoriasis. Analysis of more than one dataset to identify genes commonly upregulated reduces the likelihood of false positives and narrows down the possible signature genes. Genes controlling the critical balance between T helper 17 and regulatory T cells are of special interest in psoriasis. Our objectives were to identify genes that are consistently upregulated in lesional skin from three published microarray datasets. We carried out a reanalysis of gene expression data extracted from three experiments on samples from psoriatic and nonlesional skin using the same stringency threshold and software and further compared the expression levels of 92 genes related to the T helper 17 and regulatory T cell signaling pathways. We found 73 probe sets representing 57 genes commonly upregulated in lesional skin from all datasets. These included 26 probe sets representing 20 genes that have no previous link to the etiopathogenesis of psoriasis. These genes may represent novel therapeutic targets and surely need more rigorous experimental testing to be validated. Our analysis also identified 12 of 92 genes known to be related to the T helper 17 and regulatory T cell signaling pathways, and these were found to be differentially expressed in the lesional skin samples.

RMA with quantile normalization mixes biological signals between different sample groups in microarray data analysis

Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples.

Generation of palladium clusters on Au(111) electrodes: Experiments and simulations

The properties of palladium clusters, generated with the electrochemical scanning tunneling microscope, have been investigated both by experiments and by computer simulations. The clusters are found to be larger and more stable if the tip is moved further towards the electrode surface in the generation process. The simulations suggest that the larger clusters consist of a palladium - gold mixture, which is more stable than pure palladium. Dissolution of the clusters occurs from the edges rather than layer by layer

Identification of 5-Fluorouracil-inducible Target Genes Using cDNA Microarray Profiling

The fluoropyrimidine 5-Fluorouracil (5-FU) is widely used in the treatment of cancer. To identify novel downstream mediators of tumor cell response to 5-FU, we used DNA microarray technology to identify genes that are transcriptionally activated by 5-FU treatment in the MCF-7 breast cancer cell line. Of 2400 genes analyzed, 619 were up-regulated by >3-fold. Highly up-regulated genes (>6-fold) with signal intensities of >3000 were analyzed by Northern blot. Genes that were consistently found to be up-regulated were spermine/spermidine acetyl transferase (SSAT), annexin II, thymosin-beta-10, chaperonin-10, and MAT-8. Treatment of MCF-7 cells with the antifolate tomudex and DNA-damaging agent oxaliplatin also resulted in up-regulation of each of these targets. The 5-FU-induced activation of MAT-8, thymosin-beta-10, and chaperonin-10 was abrogated by inactivation of p53 in MCF-7 cells, whereas induction of SSAT and annexin II was significantly reduced in the absence of p53. Moreover, each of these genes contained more than one potential p53-binding site, suggesting that p53 may play an important regulatory role in 5-FU-induced expression of these genes. In addition, we found that basal expression levels of SSAT, annexin II, thymosin beta-10, and chaperonin-10 were increased (by approximately 2-3-fold), and MAT-8 expression dramatically increased (by approximately 10-fold) in a 5-FU-resistant colorectal cancer cell line (H630-R10) compared with the parental H630 cell line, suggesting these genes may be useful biomarkers of resistance. These results demonstrate the potential of DNA microarrays to identify novel genes involved in mediating the response of tumor cells to chemotherapy.

Microcontroller based double beam modulation system for atomic scattering experiments

Double beam modulation is widely used in atomic collision experiments in the case where the noise arising froth each of the beams exceeds the measured signal. A method for minimizing the statistical uncertainty in a measured signal in a given time period is discussed, and a flexible modulation and counting system based on a low cost PIC microcontroller is described. This device is capable of modifying the acquisition parameters in real time during the course of an experimental run. It is shown that typical savings in data acquisition time of approximately 30% can be achieved using this optimized modulation scheme.