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Probe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements.

ABSTRACT: BACKGROUND: Affymetrix GeneChip microarrays are popular platforms for expression profiling in two types of studies: detection of differential expression computed by p-values of t-test and estimation of fold change between analyzed groups. There are many different preprocessing algorithms for summarizing Affymetrix data. The main goal of these methods is to remove effects of non-specific hybridization, and to optimally combine information from multiple probes annotated to the same transcript. The methods are benchmarked by comparison with reference methods, such as quantitative reverse-transcription PCR (qRT-PCR). RESULTS: We present a comprehensive analysis of agreement between Affymetrix GeneChip and qRT-PCR results. We analyzed the influence of filtering by fraction Present calls introduced by J.N. McClintick and H.J. Edenberg (2006) and 2 mapping procedures: updated probe sets definitions proposed by Dai et al. (2005) and our "naive mapping" method. Because of evolution of genome sequence annotations since the time when microarrays were designed, we also studied the effect of the annotation release date. These comparisons were prepared for 6 popular preprocessing algorithms (MAS5, PLIER, RMA, GC-RMA, MBEI, and MBEImm) in the 2 above-mentioned types of studies. We used data sets from 6 independent biological experiments. As a measure of reproducibility of microarray and qRT-PCR values, we used linear and rank correlation coefficients. CONCLUSIONS: We show that filtering by fraction Present calls increased correlations for all 6 preprocessing algorithms. We observed the difference in performance of PM-MM and PM-only methods: using MM probes increased correlations in fold change studies, but PM-only methods proved to perform better in detection of differential expression. We recommend using GC-RMA for detection of differential expression and PLIER for estimation of fold change. The use of the more recent annotation improves the results in both types of studies, encouraging re-analysis of old data.

SUBMITTER: Mieczkowski J

PROVIDER: S-EPMC2841208 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Probe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements.

Mieczkowski Jakub J Tyburczy Magdalena E ME Dabrowski Michal M Pokarowski Piotr P

BMC bioinformatics 20100224

<h4>Background</h4>Affymetrix GeneChip microarrays are popular platforms for expression profiling in two types of studies: detection of differential expression computed by p-values of t-test and estimation of fold change between analyzed groups. There are many different preprocessing algorithms for summarizing Affymetrix data. The main goal of these methods is to remove effects of non-specific hybridization, and to optimally combine information from multiple probes annotated to the same transcri ...[more]

PMID: 20181266

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Project description:BackgroundSeveral high throughput technologies have been employed to identify differentially regulated genes that may be molecular targets for drug discovery. Here we compared the sets of differentially regulated genes discovered using two experimental approaches: a subtracted suppressive hybridization (SSH) cDNA library methodology and Affymetrix GeneChip technology. In this "case study" we explored the transcriptional pattern changes during the in vitro differentiation of human monocytes to myeloid dendritic cells (DC), and evaluated the potential for novel gene discovery using the SSH methodology.ResultsThe same RNA samples isolated from peripheral blood monocyte precursors and immature DC (iDC) were used for GeneChip microarray probing and SSH cDNA library construction. 10,000 clones from each of the two-way SSH libraries (iDC-monocytes and monocytes-iDC) were picked for sequencing. About 2000 transcripts were identified for each library from 8000 successful sequences. Only 70% to 75% of these transcripts were represented on the U95 series GeneChip microarrays, implying that 25% to 30% of these transcripts might not have been identified in a study based only on GeneChip microarrays. In addition, about 10% of these transcripts appeared to be "novel", although these have not yet been closely examined. Among the transcripts that are also represented on the chips, about a third were concordantly discovered as differentially regulated between iDC and monocytes by GeneChip microarray transcript profiling. The remaining two thirds were either not inferred as differentially regulated from GeneChip microarray data, or were called differentially regulated but in the opposite direction. This underscores the importance both of generating reciprocal pairs of SSH libraries, and of real-time RT-PCR confirmation of the results.ConclusionsThis study suggests that SSH could be used as an alternative and complementary transcript profiling tool to GeneChip microarrays, especially in identifying novel genes and transcripts of low abundance.

Project description:BackgroundMany of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.ResultsWe assess performance based on multiple sets of samples processed over HG U133A Affymetrix GeneChip arrays. We show that the refRMA workflow, when used in conjunction with a large, biologically diverse training set, results in the same general characteristics as that of RMA in its classic form when comparing overall data structure, sample-to-sample correlation, and variation. Further, we demonstrate that the refRMA workflow and reference set can be robustly applied to naïve organ types and to benchmark data where its performance indicates respectable results.ConclusionOur results indicate that a biologically diverse reference database can be used to train a model for estimating probe set intensities of exclusive test sets, while retaining the overall characteristics of the base algorithm. Although the results we present are specific for RMA, similar versions of other multi-array normalization and summarization schemes can be developed.

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Probe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements.

Publications

Probe set filtering increases correlation between Affymetrix GeneChip and qRT-PCR expression measurements.

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