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Bioinformatics. 2010 October 1; 26(19): 2484–2485.
Published online 2010 August 11. doi:  10.1093/bioinformatics/btq457
PMCID: PMC2944205

NoiseMaker: simulated screens for statistical assessment

Abstract

Summary: High-throughput screening (HTS) is a common technique for both drug discovery and basic research, but researchers often struggle with how best to derive hits from HTS data. While a wide range of hit identification techniques exist, little information is available about their sensitivity and specificity, especially in comparison to each other. To address this, we have developed the open-source NoiseMaker software tool for generation of realistically noisy virtual screens. By applying potential hit identification methods to NoiseMaker-simulated data and determining how many of the pre-defined true hits are recovered (as well as how many known non-hits are misidentified as hits), one can draw conclusions about the likely performance of these techniques on real data containing unknown true hits. Such simulations apply to a range of screens, such as those using small molecules, siRNAs, shRNAs, miRNA mimics or inhibitors, or gene over-expression; we demonstrate this utility by using it to explain apparently conflicting reports about the performance of the B score hit identification method.

Availability and implementation: NoiseMaker is written in C#, an ECMA and ISO standard language with compilers for multiple operating systems. Source code, a Windows installer and complete unit tests are available at http://sourceforge.net/projects/noisemaker. Full documentation and support are provided via an extensive help file and tool-tips, and the developers welcome user suggestions.

Contact: moc.rehsifomreht@mahgnimrib.adnama

Supplementary information: Supplementary data are available at Bioinformatics online.

1 INTRODUCTION

Data analysis and hit identification are points of confusion for many screeners (Birmingham et al., 2009). Those asking questions such as ‘Which method identifies the most “true hits” for my particular screen circumstances?’ or ‘What will the false positive rate of my chosen method be?’ are frequently stymied, since answering these requires them to know the identity of the real hits. However, developing a list of the anticipated real biological hits for any given assay is extremely challenging and is likely to be both noisy and incomplete, especially for medium- to weak-strength effects.

The difficulty in assessing the performance of hit identification methods can be avoided by moving to in silico-based strategies. In the computational environment, one can generate a virtual screen containing defined true hits at known locations, and then perturb these true values with varying degrees and types of noise (both systematically biased and random) to simulate the variation inherent in biological screens; statistical techniques can then be evaluated based on their ability to identify known true positives and true negatives. These evaluations will be valid to the extent that the in silico hit distributions and types of noise are congruent with those of the real system. This approach offers both speed and flexibility, providing the opportunity to profile a method's performance in many different realistic screening scenarios as well as the ability to simulate whole screens within minutes.

To enable such in silico testing, we have developed the NoiseMaker tool for generating simulated high-throughput screening datasets. A NoiseMaker user selects a realistic scenario for his or her simulated screen, including a range of hit properties as well as noise characteristics, derived from previous screens or assay development work (Supplementary Appendix 1); the software then randomly assigns ‘true hits’ conforming to this scenario and generates noisy replicates of the screen. The user applies potential analysis approaches to this noisy data, using the known true hits to calculate metrics of interest (such as sensitivity, specificity or positive predictive value), and selects the most effective method.

2 MAIN FEATURES

This simulation software offers two main features: (i) the ability to generate a random set of ‘true hits’ that conform to expected characteristics and (ii) the ability to apply user-specified noise to a list of true hits to model realistically messy screening results.

On the tab for generation of true hits (Fig. 1A), the user inputs a tab-delimited plate map file containing reagent identifiers represented by one row per well and a default ‘true’ value to be assigned to all reagents that are not treated as hits or controls. Controls are specified by reagent identifier and assigned a name (such as ‘up-regulating positive control’) and a true value. The user may specify as many types of controls as desired as long as each has a unique name; e.g. an siRNA-based screen might have lipid controls, negative controls for transfection and positive controls for both up- and down-regulation, all with different identifiers and different expected values. All instances of a control's reagent identifier in the plate map will be assigned the value specified for that control type.

Fig. 1.
(A) Hit and (B) Noise tabs of the NoiseMaker interface.

Hits may represent either an increase or a decrease from the default value. They are specified by their unique name, strength and frequency; the latter number can be either an absolute value (e.g. eight wells) or a percentage of the non-control wells (e.g. 8% of the wells). For each hit type, the NoiseMaker software will randomly select, without replacement, the appropriate number of non-control wells and assign them the value specified for that hit type. The Hit Type input can also be used to model random equipment or assay failures that could be mistaken for hits.

The output of the ‘true hit’ generation is a plate map file that is annotated with the true values for every well and the type of value for that well (which is either ‘Default’ or the well's assigned control or hit name). For convenience, this file's name is automatically copied to the input field of the ‘Noise’ tab (Fig. 1B).

Noise can also be added to true values files not created with NoiseMaker, as long as they conform to the expected format; this can be useful for modeling the effect of noise on ‘clumped’ hit distributions such as those from non-randomly plated screening libraries. On the Noise tab, the user specifies the plate dimensions and number of replicates and then describes the systematic noise to be applied. Noise can be applied at the level of several different elements of the screen, including the entire screen, the edges of every plate, an individual plate in the screen, a particular row on every plate, a particular column on every plate and/or a particular well on every plate. This allows the user to simulate a wide variety of realistic outcomes, from evaporation of reagents in edge wells to a blocked dispensing tip at a single well position.

Noise definitions are additive; e.g. if one is specified for the entire screen, one for Plate 2 and one for Row 5, then all values in Row 5 of Plate 2 will be permuted with the screen noise, the plate noise, and the row noise combined. This simulates the convergence of disparate systematic effects in real screens. All Noise definitions model noise as a Gaussian perturbance of the true values. They adjust the well values away from their initial values by approximately the amount assigned to the Noise definition's mean change value, with the exact amount of adjustment being randomly chosen from a Gaussian distribution centered on the mean change value and with the specified standard deviation (SD) value. This ensures that each Noise definition produces realistically noisy adjustments even as it introduces the intended systematic effects. Noise can also be limited to a specific range (such as that simulating an instrument's detection range) using optional floor and/or ceiling values. The output file contains the input true values and one column of noisy values for each simulated replicate.

Currently NoiseMaker is limited to Gaussian noise distributions, additive noise and linear positional effects. Future development will address non-Gaussian and multiplicative noise, as well as bowl-shaped (non-linear) positional biases.

3 SAMPLE APPLICATION

The B score (Brideau et al., 2003) is a normalization and hit identification method employing Tukey's median polish, and has been proposed for use in screens displaying within-plate positional effects such as row and/or column biases. However, Makarenkov et al. (2007) have reported that it failed to recover correct hits in a scenario with ‘noisy standard normal data with systematic error stemming from row × column interactions which are constant across plates’. To address this apparent inconsistency in the literature and demonstrate how NoiseMaker can be applied in evaluating the performance of statistical techniques, we evaluated the B score in scenarios with different types of row and column positional effects: varying size of SD (Group A), varying size of mean change (Group B) and varying size of both mean change and SD (Group C).

After data sets with appropriate noise were created by NoiseMaker, we calculated B scores for all wells and identified the wells whose scores were in the top 1% as positives. We found that the true positive rates of datasets in Group A decrease and the false positive rates slightly increase as SD of the row and column noise increases (Supplementary Appendix II). However, the true positive rates and false positive rates of datasets in Group B remain steady regardless of the amount of mean change of the row and column noise, while the true positive rates and false positive rates of data sets in Group C behave similarly to those in Group A. These results suggest that B score is an appropriate choice for correction of systemic influences that primarily affect mean rather than variance. Notably, Makarenkov's work examined simulated data with varying SDs, which is consistent with this finding.

4 CONCLUSION

NoiseMaker is simulation software for creating realistic, virtual high-throughput screens that can be used to evaluate hit identification methods and quality criteria. We establish its power by using it to clarify the utility of the B score under various screening conditions. This tool will be useful for broader comparisons of available hit identification methods, and is freely available for download and use by others interested in modeling screens in silico.

Supplementary Material

Supplementary Data:

ACKNOWLEDGEMENTS

The authors thank Gabor Bakos of Trinity College, Dublin, and members of the RNAi Global Initiative for suggestions and helpful discussions, and Kevin Sullivan and John Quinn for code-review.

Conflict of Interest: none declared.

REFERENCES

  • Birmingham A, et al. Statistical methods for analysis of high-throughput RNA interference screens. Nat. Methods. 2009;6:569–575. [PMC free article] [PubMed]
  • Brideau C, et al. Improved statistical methods for hit selection in high-throughput screening. J. Biomol. Screen. 2003;8:634–647. [PubMed]
  • Makarenkov V, et al. An efficient method for the detection and elimination of systematic error in high-throughput screening. Bioinformatics. 2007;23:1648–1657. [PubMed]

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