Implementation of a gene expression index calculation method based on the PDNN model

SUMMARY: Gene expression index calculations from Affymetrix GeneChips have been dominated by the Affymetrix MAS, dChip, and RMA methods. A new method to estimate the gene expression value utilizing the probe sequence information named position-dependent nearest-neighbor (PDNN) has been suggested by Zhang et al. (2003). Here we describe an open source implementation of the PDNN method for the statistical language R. AVAILABILITY: The package can be downloaded from http://www.bioconductor.org/repository/devel/package/html/affypdnn.html CONTACT: hbjorn@cbs.dtu.dk.     

Empirical validation of the S-Score algorithm in the analysis of gene expression data

Background

Current methods of analyzing Affymetrix GeneChip^® microarray data require the estimation of probe set expression summaries, followed by application of statistical tests to determine which genes are differentially expressed. The S-Score algorithm described by Zhang and colleagues is an alternative method that allows tests of hypotheses directly from probe level data. It is based on an error model in which the detected signal is proportional to the probe pair signal for highly expressed genes, but approaches a background level (rather than 0) for genes with low levels of expression. This model is used to calculate relative change in probe pair intensities that converts probe signals into multiple measurements with equalized errors, which are summed over a probe set to form the S-Score. Assuming no expression differences between chips, the S-Score follows a standard normal distribution, allowing direct tests of hypotheses to be made. Using spike-in and dilution datasets, we validated the S-Score method against comparisons of gene expression utilizing the more recently developed methods RMA, dChip, and MAS5.

Results

The S-score showed excellent sensitivity and specificity in detecting low-level gene expression changes. Rank ordering of S-Score values more accurately reflected known fold-change values compared to other algorithms.

Conclusion

The S-score method, utilizing probe level data directly, offers significant advantages over comparisons using only probe set expression summaries.     

Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays

MOTIVATION: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a 'best' algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests. RESULTS: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.     

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.     

A high performance test of differential gene expression for oligonucleotide arrays

Logit-t employs a logit-transformation for normalization followed by statistical testing at the probe-level. Using four publicly-available datasets, together providing 2,710 known positive incidences of differential expression and 2,913,813 known negative incidences, performance of statistical tests were: Logit-t provided 75% positive-predictive value, compared with 5% for Affymetrix Microarray Suite 5, 6% for dChip perfect match (PM)-only, and 9% for Robust Multi-array Analysis at the p < 0.01 threshold. Logit-t provided 70% sensitivity, Microarray Suite 5 provided 46%, dChip provided 53% and Robust Multi-array Analysis provided 63%.     

The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies

Background  

Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers.     

Match-Only Integral Distribution (MOID) Algorithm for high-density oligonucleotide array analysis

Background

High-density oligonucleotide arrays have become a valuable tool for high-throughput gene expression profiling. Increasing the array information density and improving the analysis algorithms are two important computational research topics.

Results

A new algorithm, Match-Only Integral Distribution (MOID), was developed to analyze high-density oligonucleotide arrays. Using known data from both spiking experiments and no-change experiments performed with Affymetrix GeneChip^® arrays, MOID and the Affymetrix algorithm implemented in Microarray Suite 4.0 (MAS4) were compared. While MOID gave similar performance to MAS4 in the spiking experiments, better performance was observed in the no-change experiments. MOID also provides a set of alternative statistical analysis tools to MAS4. There are two main features that distinguish MOID from MAS4. First, MOID uses continuous P values for the likelihood of gene presence, while MAS4 resorts to discrete absolute calls. Secondly, MOID uses heuristic confidence intervals for both gene expression levels and fold change values, while MAS4 categorizes the significance of gene expression level changes into discrete fold change calls.

Conclusions

The results show that by using MOID, Affymetrix GeneChip^® arrays may need as little as ten probes per gene without compromising analysis accuracy.     

Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks

MOTIVATION: An increasingly common application of gene expression profile data is the reverse engineering of cellular networks. However, common procedures to normalize expression profiles generated using the Affymetrix GeneChips technology were originally developed for a rather different purpose, namely the accurate measure of differential gene expression between two or more phenotypes. As a result, current evaluation strategies lack comprehensive metrics to assess the suitability of available normalization procedures for reverse engineering and, in general, for measuring correlation between the expression profiles of a gene pair. RESULTS: We benchmark four commonly used normalization procedures (MAS5, RMA, GCRMA and Li-Wong) in the context of established algorithms for the reverse engineering of protein-protein and protein-DNA interactions. Replicate sample, randomized and human B-cell data sets are used as an input. Surprisingly, our study suggests that MAS5 provides the most faithful cellular network reconstruction. Furthermore, we identify a crucial step in GCRMA responsible for introducing severe artifacts in the data leading to a systematic overestimate of pairwise correlation. This has key implications not only for reverse engineering but also for other methods, such as hierarchical clustering, relying on accurate measurements of pairwise expression profile correlation. We propose an alternative implementation to eliminate such side effect.     

Comparison of different microarray data analysis programs and description of a database for microarray data management

Data analysis and management represent a major challenge for gene expression studies using microarrays. Here, we compare different methods of analysis and demonstrate the utility of a personal microarray database. Gene expression during HIV infection of cell lines was studied using Affymetrix U-133 A and B chips. The data were analyzed using Affymetrix Microarray Suite and Data Mining Tool, Silicon Genetics GeneSpring, and dChip from Harvard School of Public Health. A small-scale database was established with FileMaker Pro Developer to manage and analyze the data. There was great variability among the programs in the lists of significantly changed genes constructed from the same data. Similarly choices of different parameters for normalization, comparison, and standardization greatly affected the outcome. As many probe sets on the U133 chip target the same Unigene clusters, the Unigene information can be used as an internal control to confirm and interpret the probe set results. Algorithms used for the determination of changes in gene expression require further refinement and standardization. The use of a personal database powered with Unigene information can enhance the analysis of gene expression data.     

AMarge

AMarge is a web tool for the automatic quality assessment of Affymetrix GeneChip data. It is essential to have a trustworthy set of chips in order to derive gene expression data for phenotypic analysis, and AMarge provides a complete and rigorous web-accessible tool to fulfill this need. The quality assessment steps include image plots of weights derived from a robust linear model fit of the data, a 3'/5' RNA digestion plot, and Affymetrix Microarray Suite version 5.0 (MAS 5.0) quality standard procedures. Furthermore, robust multi-array average expression values are generated in order to have a start-up expression set for the subsequent analysis. The results of the complete analysis are summarised and returned as an HTML report. AVAILABILITY: The AMarge web interface is accessible at http://nin.crg.es/cgi-binf/AMargeWeb.cgi. A mirror server is also available at http://bioinformatics.istge.it/AMarge-bin/AMargeWeb.cgi. The software implementing all these methods is part of the Bioconductor project (http://www.bioconductor.org).     

Sequence biases in large scale gene expression profiling data

We present the results of a simple, statistical assay that measures the G+C content sensitivity bias of gene expression experiments without the requirement of a duplicate experiment. We analyse five gene expression profiling methods: Affymetrix GeneChip, Long Serial Analysis of Gene Expression (LongSAGE), LongSAGELite, ‘Classic’ Massively Parallel Signature Sequencing (MPSS) and ‘Signature’ MPSS. We demonstrate the methods have systematic and random errors leading to a different G+C content sensitivity. The relationship between this experimental error and the G+C content of the probe set or tag that identifies each gene influences whether the gene is detected and, if detected, the level of gene expression measured. LongSAGE has the least bias, while Signature MPSS shows a strong bias to G+C rich tags and Affymetrix data show different bias depending on the data processing method (MAS 5.0, RMA or GC-RMA). The bias in the Affymetrix data primarily impacts genes expressed at lower levels. Despite the larger sampling of the MPSS library, SAGE identifies significantly more genes (60% more RefSeq genes in a single comparison).     

Effects of filtering by Present call on analysis of microarray experiments

Background

Affymetrix GeneChips^® are widely used for expression profiling of tens of thousands of genes. The large number of comparisons can lead to false positives. Various methods have been used to reduce false positives, but they have rarely been compared or quantitatively evaluated. Here we describe and evaluate a simple method that uses the detection (Present/Absent) call generated by the Affymetrix microarray suite version 5 software (MAS5) to remove data that is not reliably detected before further analysis, and compare this with filtering by expression level. We explore the effects of various thresholds for removing data in experiments of different size (from 3 to 10 arrays per treatment), as well as their relative power to detect significant differences in expression.

Results

Our approach sets a threshold for the fraction of arrays called Present in at least one treatment group. This method removes a large percentage of probe sets called Absent before carrying out the comparisons, while retaining most of the probe sets called Present. It preferentially retains the more significant probe sets (p ≤ 0.001) and those probe sets that are turned on or off, and improves the false discovery rate. Permutations to estimate false positives indicate that probe sets removed by the filter contribute a disproportionate number of false positives. Filtering by fraction Present is effective when applied to data generated either by the MAS5 algorithm or by other probe-level algorithms, for example RMA (robust multichip average). Experiment size greatly affects the ability to reproducibly detect significant differences, and also impacts the effect of filtering; smaller experiments (3–5 samples per treatment group) benefit from more restrictive filtering (≥50% Present).

Conclusion

Use of a threshold fraction of Present detection calls (derived by MAS5) provided a simple method that effectively eliminated from analysis probe sets that are unlikely to be reliable while preserving the most significant probe sets and those turned on or off; it thereby increased the ratio of true positives to false positives.     

Increased power of microarray analysis by use of an algorithm based on a multivariate procedure

MOTIVATION: The power of microarray analyses to detect differential gene expression strongly depends on the statistical and bioinformatical approaches used for data analysis. Moreover, the simultaneous testing of tens of thousands of genes for differential expression raises the 'multiple testing problem', increasing the probability of obtaining false positive test results. To achieve more reliable results, it is, therefore, necessary to apply adjustment procedures to restrict the family-wise type I error rate (FWE) or the false discovery rate. However, for the biologist the statistical power of such procedures often remains abstract, unless validated by an alternative experimental approach. RESULTS: In the present study, we discuss a multiplicity adjustment procedure applied to classical univariate as well as to recently proposed multivariate gene-expression scores. All procedures strictly control the FWE. We demonstrate that the use of multivariate scores leads to a more efficient identification of differentially expressed genes than the widely used MAS5 approach provided by the Affymetrix software tools (Affymetrix Microarray Suite 5 or GeneChip Operating Software). The practical importance of this finding is successfully validated using real time quantitative PCR and data from spike-in experiments. AVAILABILITY: The R-code of the statistical routines can be obtained from the corresponding author. CONTACT: Schuster@imise.uni-leipzig.de