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. 2011 Aug 4:12:324.
doi: 10.1186/1471-2105-12-324.

rapmad: Robust analysis of peptide microarray data

Bernhard Y Renard  1 , Martin Löwer, Yvonne Kühne, Ulf Reimer, Andrée Rothermel, Ozlem Türeci, John C Castle, Ugur Sahin
Affiliations

rapmad: Robust analysis of peptide microarray data

Bernhard Y Renard et al. BMC Bioinformatics. .

Abstract

Background: Peptide microarrays offer an enormous potential as a screening tool for peptidomics experiments and have recently seen an increased field of application ranging from immunological studies to systems biology. By allowing the parallel analysis of thousands of peptides in a single run they are suitable for high-throughput settings. Since data characteristics of peptide microarrays differ from DNA oligonucleotide microarrays, computational methods need to be tailored to these specifications to allow a robust and automated data analysis. While follow-up experiments can ensure the specificity of results, sensitivity cannot be recovered in later steps. Providing sensitivity is thus a primary goal of data analysis procedures. To this end we created rapmad (Robust Alignment of Peptide MicroArray Data), a novel computational tool implemented in R.

Results: We evaluated rapmad in antibody reactivity experiments for several thousand peptide spots and compared it to two existing algorithms for the analysis of peptide microarrays. rapmad displays competitive and superior behavior to existing software solutions. Particularly, it shows substantially improved sensitivity for low intensity settings without sacrificing specificity. It thereby contributes to increasing the effectiveness of high throughput screening experiments.

Conclusions: rapmad allows the robust and sensitive, automated analysis of high-throughput peptide array data. The rapmad R-package as well as the data sets are available from http://www.tron-mz.de/compmed.

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Figures

Figure 1
Figure 1
Flowchart. Flowchart of the data analysis pipeline for extracting a list of signal-carrying peptides from the measured intensities of a peptide microarray scan.
Figure 2
Figure 2
Unreliable Spot Finding. Scatter plot of intensities from slides of two print batches with the same high antibody concentration on a binary logarithm scale. Due to experimental noise, we see departures from the diagonal line on which we would expect all data points. The quality control algorithm identifies approximately 2.5% of all data points in each print batch as unreliable across all subarrays; these peptides are removed accordingly (colored in magenta, cyan and orange), resulting in an increase of the coefficient of variation of approximately 3%. While not identifying all outlying observation, the removed spots primarly affect peptide spots which show large variation between the print batches.
Figure 3
Figure 3
Sensitivity, Specificity and Accuracy in Comparison. Sensitivity, specificity and accuracy for our approach without and with secondary antibody binding removal in comparison to the approaches of [2] and [17]. Both, the high (3 ng/ml) and the low (1 ng/ml) spike-in antibody concentration slides were evaluated for both print batches (left and right). 95% bootstrap confidence intervals were computed based on 1000 times resampled peptide intensities and are shown by dashed lines. For all approaches, the specificity remains rather constant when reducing the spike-in antibody concentration, while we see a general decline in sensitivity and accuracy. For the approaches of [2] and [17] the decline in accuracy is rather steep, our approach shows still good accuracy above 0.8 for the low antibody concentration.
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