A class of models for analyzing GeneChip gene expression analysis array data

Comparison of Two Methods for Detecting Alternative Splice Variants Using GeneChip(®) Exon Arrays

The Affymetrix GeneChip Exon Array can be used to detect alternative splice variants. Microarray Detection of Alternative Splicing (MIDAS) and Partek(®) Genomics Suite (Partek(®) GS) are among the most popular analytical methods used to analyze exon array data. While both methods utilize statistical significance for testing, MIDAS and Partek(®) GS could produce somewhat different results due to different underlying assumptions. Comparing MIDAS and Partek(®) GS is quite difficult due to their substantially different mathematical formulations and assumptions regarding alternative splice variants. For meaningful comparison, we have used the previously published generalized probe model (GPM) which encompasses both MIDAS and Partek(®) GS under different assumptions. We analyzed a colon cancer exon array data set using MIDAS, Partek(®) GS and GPM. MIDAS and Partek(®) GS produced quite different sets of genes that are considered to have alternative splice variants. Further, we found that GPM produced results similar to MIDAS as well as to Partek(®) GS under their respective assumptions. Within the GPM, we show how discoveries relating to alternative variants can be quite different due to different assumptions. MIDAS focuses on relative changes in expression values across different exons within genes and tends to be robust but less efficient. Partek(®) GS, however, uses absolute expression values of individual exons within genes and tends to be more efficient but more sensitive to the presence of outliers. From our observations, we conclude that MIDAS and Partek(®) GS produce complementary results, and discoveries from both analyses should be considered.

Three methods for optimization of cross-laboratory and cross-platform microarray expression data

Microarray gene expression data becomes more valuable as our confidence in the results grows. Guaranteeing data quality becomes increasingly important as microarrays are being used to diagnose and treat patients (1–4). The MAQC Quality Control Consortium, the FDA's Critical Path Initiative, NCI's caBIG and others are implementing procedures that will broadly enhance data quality. As GEO continues to grow, its usefulness is constrained by the level of correlation across experiments and general applicability. Although RNA preparation and array platform play important roles in data accuracy, pre-processing is a user-selected factor that has an enormous effect. Normalization of expression data is necessary, but the methods have specific and pronounced effects on precision, accuracy and historical correlation. As a case study, we present a microarray calibration process using normalization as the adjustable parameter. We examine the impact of eight normalizations across both Agilent and Affymetrix expression platforms on three expression readouts: (1) sensitivity and power, (2) functional/biological interpretation and (3) feature selection and classification error. The reader is encouraged to measure their own discordant data, whether cross-laboratory, cross-platform or across any other variance source, and to use their results to tune the adjustable parameters of their laboratory to ensure increased correlation.

Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

Cross-species analysis of gene expression in non-model mammals: reproducibility of hybridization on high density oligonucleotide microarrays

BACKGROUND

Gene expression profiles of non-model mammals may provide valuable data for biomedical and evolutionary studies. However, due to lack of sequence information of other species, DNA microarrays are currently restricted to humans and a few model species. This limitation may be overcome by using arrays developed for a given species to analyse gene expression in a related one, an approach known as "cross-species analysis". In spite of its potential usefulness, the accuracy and reproducibility of the gene expression measures obtained in this way are still open to doubt. The present study examines whether or not hybridization values from cross-species analyses are as reproducible as those from same-species analyses when using Affymetrix oligonucleotide microarrays.

RESULTS

The reproducibility of the probe data obtained hybridizing deer, Old-World primates, and human RNA samples to Affymetrix human GeneChip U133 Plus 2.0 was compared. The results show that cross-species hybridization affected neither the distribution of the hybridization reproducibility among different categories, nor the reproducibility values of the individual probes. Our analyses also show that a 0.5% of the probes analysed in the U133 plus 2.0 GeneChip are significantly associated to un-reproducible hybridizations. Such probes-called in the text un-reproducible probe sequences- do not increase in number in cross-species analyses.

CONCLUSION

Our study demonstrates that cross-species analyses do not significantly affect hybridization reproducibility of GeneChips, at least within the range of the mammal species analysed here. The differences in reproducibility between same-species and cross-species analyses observed in previous studies were probably caused by the analytical methods used to calculate the gene expression measures. Together with previous observations on the accuracy of GeneChips for cross-species analysis, our analyses demonstrate that cross-species hybridizations may provide useful gene expression data. However, the reproducibility and accuracy of these measures largely depends on the use of appropriated algorithms to derive the gene expression data from the probe data. Also, the identification of probes associated to un-reproducible hybridizations-useless for gene expression analyses- in the studied GeneChip, stress the need of a re-evaluation of the probes' performance.

Comparing Aurora A and Aurora B as molecular targets for growth inhibition of pancreatic cancer cells

To address the increased need to understand the similarities and differences in targeting Aurora A or Aurora B for the treatment of cancer, we systematically evaluated the relative importance of Aurora A and/or Aurora B as molecular targets using antisense oligonucleotides. It was found that perturbations in Aurora A and Aurora B signaling result in growth arrest and apoptosis preferentially in cancer cells. The biological fingerprints of Aurora A and Aurora B inhibition were compared and contrasted in efforts to identify the superior therapeutic target. Due to the different biological responses, we conclude that each Aurora kinase should be treated as autonomous drug targets, which can be targeted independently or in combination. We observed no advantages to targeting both kinases simultaneously and feel that an Aurora A-targeted therapy may have some beneficial consequences over an Aurora B-targeted therapy, such as mitotic arrest and the rapid induction of apoptosis.

Evaluation of Two Outlier-Detection-Based Methods for Detecting Tissue-Selective Genes from Microarray Data

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

A statistical method for predicting splice variants between two groups of samples using GeneChip expression array data

BACKGROUND

Alternative splicing of pre-messenger RNA results in RNA variants with combinations of selected exons. It is one of the essential biological functions and regulatory components in higher eukaryotic cells. Some of these variants are detectable with the Affymetrix GeneChip that uses multiple oligonucleotide probes (i.e. probe set), since the target sequences for the multiple probes are adjacent within each gene. Hybridization intensity from a probe correlates with abundance of the corresponding transcript. Although the multiple-probe feature in the current GeneChip was designed to assess expression values of individual genes, it also measures transcriptional abundance for a sub-region of a gene sequence. This additional capacity motivated us to develop a method to predict alternative splicing, taking advance of extensive repositories of GeneChip gene expression array data.

RESULTS

We developed a two-step approach to predict alternative splicing from GeneChip data. First, we clustered the probes from a probe set into pseudo-exons based on similarity of probe intensities and physical adjacency. A pseudo-exon is defined as a sequence in the gene within which multiple probes have comparable probe intensity values. Second, for each pseudo-exon, we assessed the statistical significance of the difference in probe intensity between two groups of samples. Differentially expressed pseudo-exons are predicted to be alternatively spliced. We applied our method to empirical data generated from GeneChip Hu6800 arrays, which include 7129 probe sets and twenty probes per probe set. The dataset consists of sixty-nine medulloblastoma (27 metastatic and 42 non-metastatic) samples and four cerebellum samples as normal controls. We predicted that 577 genes would be alternatively spliced when we compared normal cerebellum samples to medulloblastomas, and predicted that thirteen genes would be alternatively spliced when we compared metastatic medulloblastomas to non-metastatic ones. We checked the consistency of some of our findings with information in UCSC Human Genome Browser.

CONCLUSION

The two-step approach described in this paper is capable of predicting some alternative splicing from multiple oligonucleotide-based gene expression array data with GeneChip technology. Our method employs the extensive repositories of gene expression array data available and generates alternative splicing hypotheses, which can be further validated by experimental studies.

Evaluation of methods for oligonucleotide array data via quantitative real-time PCR

BACKGROUND

There are currently many different methods for processing and summarizing probe-level data from Affymetrix oligonucleotide arrays. It is of great interest to validate these methods and identify those that are most effective. There is no single best way to do this validation, and a variety of approaches is needed. Moreover, gene expression data are collected to answer a variety of scientific questions, and the same method may not be best for all questions. Only a handful of validation studies have been done so far, most of which rely on spike-in datasets and focus on the question of detecting differential expression. Here we seek methods that excel at estimating relative expression. We evaluate methods by identifying those that give the strongest linear association between expression measurements by array and the "gold-standard" assay. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) is generally considered the "gold-standard" assay for measuring gene expression by biologists and is often used to confirm findings from microarray data. Here we use qRT-PCR measurements to validate methods for the components of processing oligo array data: background adjustment, normalization, mismatch adjustment, and probeset summary. An advantage of our approach over spike-in studies is that methods are validated on a real dataset that was collected to address a scientific question.

RESULTS

We initially identify three of six popular methods that consistently produced the best agreement between oligo array and RT-PCR data for medium- and high-intensity genes. The three methods are generally known as MAS5, gcRMA, and the dChip mismatch mode. For medium- and high-intensity genes, we identified use of data from mismatch probes (as in MAS5 and dChip mismatch) and a sequence-based method of background adjustment (as in gcRMA) as the most important factors in methods' performances. However, we found poor reliability for methods using mismatch probes for low-intensity genes, which is in agreement with previous studies.

CONCLUSION

We advocate use of sequence-based background adjustment in lieu of mismatch adjustment to achieve the best results across the intensity spectrum. No method of normalization or probeset summary showed any consistent advantages.