Leveraging two-way probe-level block design for identifying differential gene expression with high-density oligonucleotide arrays

Background  

To identify differentially expressed genes across experimental conditions in oligonucleotide microarray experiments, existing statistical methods commonly use a summary of probe-level expression data for each probe set and compare replicates of these values across conditions using a form of the t-test or rank sum test. Here we propose the use of a statistical method that takes advantage of the built-in redundancy architecture of high-density oligonucleotide arrays.     

A microarray analysis for differential gene expression in the soybean genome using Bioconductor and R

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip(R) soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css.ncifcrf.gov/services/download/MicroarraySoybean.zip.     

Probe-level measurement error improves accuracy in detecting differential gene expression

MOTIVATION: Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level measurement error provides useful information which can help in the identification of differentially expressed genes. RESULTS: We propose a Bayesian method to include probe-level measurement error into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for efficient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational efficiency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in dataset and a mouse time-course dataset. Results show that the inclusion of probe-level measurement error improves accuracy in detecting differential gene expression. AVAILABILITY: The MAP approximation and variational inference described in this paper have been implemented in an R package pplr. The MCMC method is implemented in Matlab. Both software are available from http://umber.sbs.man.ac.uk/resources/puma.     

Integrating probe-level expression changes across generations of Affymetrix arrays

There is an urgent need for bioinformatic methods that allow integrative analysis of multiple microarray data sets. While previous studies have mainly concentrated on reproducibility of gene expression levels within or between different platforms, we propose a novel meta-analytic method that takes into account the vast amount of available probe-level information to combine the expression changes across different studies. We first show that the comparability of relative expression changes and the consistency of differentially expressed genes between different Affymetrix array generations can be considerably improved by determining the expression changes at the probe-level and by considering the latest information on probe-level sequence matching instead of the probe annotations provided by the manufacturer. With the improved probe-level expression change estimates, data from different generations of Affymetrix arrays can be combined more effectively. This will allow for the full exploitation of existing results when designing and analyzing new experiments.     

Molecular mechanisms underlying Grateloupia imbricata (Rhodophyta) carposporogenesis induced by methyl jasmonate

When applied in vitro, methyl jasmonate is sensed by the red seaweed Grateloupia imbricate, substantially and visually affecting its carposporogenesis. However, although there is some understanding of the morphological changes induced by methyl jasmonate in vitro, little is known about the genes that are involved in red seaweed carposporogenesis and how their protein products act. For the work reported herein, the expression of genes in red seaweed that encode enzymes involved in the synthesis of methyl jasmonate (jasmonic acid carboxyl methyl transferase and a putative methyl transferase) was monitored. Additionally the genes involved in oxidation (cytochrome P450 and WD40), jasmonate synthesis, signal transduction, and regulation of reactive oxygen species (MYB), and reproduction (ornithine decarboxylase) were monitored. To determine when or if the aforementioned genes were expressed during cystocarp development, fertilized and fertile thalli were exposed to methyl jasmonate and gene expression was measured after 24 and 48 h. The results showed that methyl jasmonate promoted differential gene expression in fertilized thalli by 24 h and upregulated expression of the ornithine decarboxylase gene only by 48 h in fertile thalli (0.75 ± 003 copies · μL^?1 at 24 h vs. 1.11 ± 0.04 copies · μL^?1 at 48 h). We conclude that Ornithine decarboxylase expression involves methyl jasmonate signaling as well as development and maturation of cystocarps.     

Rank of Correlation Coefficient as a Comparable Measure for Biological Significance of Gene Coexpression

Information regarding gene coexpression is useful to predict gene function. Several databases have been constructed for gene coexpression in model organisms based on a large amount of publicly available gene expression data measured by GeneChip platforms. In these databases, Pearson''s correlation coefficients (PCCs) of gene expression patterns are widely used as a measure of gene coexpression. Although the coexpression measure or GeneChip summarization method affects the performance of the gene coexpression database, previous studies for these calculation procedures were tested with only a small number of samples and a particular species. To evaluate the effectiveness of coexpression measures, assessments with large-scale microarray data are required. We first examined characteristics of PCC and found that the optimal PCC threshold to retrieve functionally related genes was affected by the method of gene expression database construction and the target gene function. In addition, we found that this problem could be overcome when we used correlation ranks instead of correlation values. This observation was evaluated by large-scale gene expression data for four species: Arabidopsis, human, mouse and rat.     

Exploration,normalization, and summaries of high density oligonucleotide array probe level data

In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.     

Roles of protein kinase Cα isozyme in the regulation of oxidative stress and neuropeptide Y gene expression in phenylpropanolamine-mediated appetite suppression

Hypothalamic neuropeptide Y (NPY) is an appetite stimulant in the brain. Although regulation of NPY expression has been reported to contribute to the appetite-suppressing effect of phenylpropanolamine (PPA), it is still unknown if protein kinase C (PKC) is involved in this effect. Rats were daily treated with PPA for 4 days. Changes in food intake, hypothalamic NPY, PKC, and proopiomelanocortin (POMC) mRNA levels were assessed and compared. Results showed that the NPY gene was down-regulated following PPA treatment, which was parallel with the decrease of feeding. Moreover, several isotypes of PKC mRNA level (α, βI, βII, γ, δ, η, λ, ε, and ζ) were changed. Among these, α, δ, and λ PKC were up-regulated along with POMC gene expression which coincided with down-regulation of the NPY gene. To further determine if PKCα was involved, infusions of antisense oligonucleotide into the cerebroventricle were performed at 1 h before daily PPA treatment in free-moving rats. Results showed that PKCα knock-down could modify both anorexia induced by PPA and the NPY mRNA levels. Moreover, PKCα knock-down could also modify superoxide dismutase (SOD) gene expression. It is suggested that PKCα participates in the regulation of PPA-mediated appetite suppression via the modulation of NPY and SOD gene expression.     

Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability

Ratios of nitrogen (N) isotopes in leaves could elucidate underlying patterns of N cycling across ecological gradients. To better understand global-scale patterns of N cycling, we compiled data on foliar N isotope ratios (δ¹⁵N), foliar N concentrations, mycorrhizal type and climate for over 11 000 plants worldwide. Arbuscular mycorrhizal, ectomycorrhizal, and ericoid mycorrhizal plants were depleted in foliar δ¹⁵N by 2‰, 3.2‰, 5.9‰, respectively, relative to nonmycorrhizal plants. Foliar δ¹⁵N increased with decreasing mean annual precipitation and with increasing mean annual temperature (MAT) across sites with MAT ≥ −0.5°C, but was invariant with MAT across sites with MAT < −0.5°C. In independent landscape-level to regional-level studies, foliar δ¹⁵N increased with increasing N availability; at the global scale, foliar δ¹⁵N increased with increasing foliar N concentrations and decreasing foliar phosphorus (P) concentrations. Together, these results suggest that warm, dry ecosystems have the highest N availability, while plants with high N concentrations, on average, occupy sites with higher N availability than plants with low N concentrations. Global-scale comparisons of other components of the N cycle are still required for better mechanistic understanding of the determinants of variation in foliar δ¹⁵N and ultimately global patterns in N cycling.     

Nonparametric testing for DNA copy number induced differential mRNA gene expression

Summary .  The central dogma of molecular biology relates DNA with mRNA. Array CGH measures DNA copy number and gene expression microarrays measure the amount of mRNA. Methods that integrate data from these two platforms may uncover meaningful biological relationships that further our understanding of cancer. We develop nonparametric tests for the detection of copy number induced differential gene expression. The tests incorporate the uncertainty of the calling of genomic aberrations. The test is preceded by a "tuning algorithm" that discards certain genes to improve the overall power of the false discovery rate selection procedure. Moreover, the test statistics are "shrunken" to borrow information across neighboring genes that share the same array CGH signature. For each gene we also estimate its effect, its amount of differential expression due to copy number changes, and calculate the coefficient of determination. The method is illustrated on breast cancer data, in which it confirms previously reported findings, now with a more profound statistical underpinning.     

A systematic statistical linear modeling approach to oligonucleotide array experiments

We outline and describe steps for a statistically rigorous approach to analyzing probe-level Affymetrix GeneChip data. The approach employs classical linear mixed models and operates on a gene-by-gene basis. Forgoing any attempts at gene presence or absence calls, the method simultaneously considers the data across all chips in an experiment. Primary output includes precise estimates of fold change (some as low as 1.1), their statistical significance, and measures of array and probe variability. The method can accommodate complex experiments involving many kinds of treatments and can test for their effects at the probe level. Furthermore, mismatch probe data can be incorporated in different ways or ignored altogether. Data from an ionizing radiation experiment on human cell lines illustrate the key concepts.     

Novel role for the δ-opioid receptor in hypoxic preconditioning in rat retinas

δ-Opioid receptor (DOR) is an oxygen-sensitive protein whose function in the rat retina is unknown. We examined whether DOR is involved in hypoxic preconditioning (HPC)-mediated retinoprotection following intraocular pressure (IOP) elevation. Rats were exposed to intermittent hypoxia (10% oxygen) to induce HPC. Unilateral retinal ischemia/reperfusion injury was induced by elevating IOP to 100 mmHg for 1 h. HPC attenuated the loss of neuronal marker expression and increased pro-apoptotic caspase 3 activity in the IOP retina. Excess superoxide production and 8-iso-prostaglandin F2α accumulation caused by enhanced oxidant protein expression and reduced antioxidant enzyme level after IOP elevation were largely abrogated by HPC. HPC markedly increased the expression of hypoxia-inducible factor-1α (HIF-1α) and DOR, but intravitreal administration of HIF-1α-specific small interfering RNA abrogated the up-regulation of DOR. This suggested that DOR functions downstream of HIF-1α. However, the endogenous content of leucine enkephalin in retinas was not affected by HPC or IOP. Treatment of retinas with the DOR antagonist naltrindole attenuated the HPC-induced protection and activation of extracellular signal-regulated kinase. These results suggest a novel mechanism of HPC-mediated retinoprotection whereby HIF-1α induces the expression of DOR, and DOR-mediated activation of extracellular signal-regulated kinase triggers cellular events that correct the redox imbalance in the post-ischemic retina.     

Systematic order-dependent effect in expression values, variance, detection calls and differential expression in Affymetrix GeneChips

MOTIVATION: Affymetrix GeneChips are common 3' profiling platforms for quantifying gene expression. Using publicly available datasets of expression profiles from human and mouse experiments, we sought to characterize features of GeneChip data to better compare and evaluate analyses for differential expression, regulation and clustering. We uncovered an unexpected order dependence in expression data that holds across a variety of chips in both human and mouse data. RESULTS: Order dependence among GeneChips affected relative expression measures pre-processed and normalized with the Affymetrix MAS5.0 algorithm and the robust multi-array average summarization method. The effect strongly influenced detection calls and tests for differential expression and can potentially significantly bias experimental results based on GeneChip profiling.