The Use of Strictly Standardized Mean Difference for Hit Selection in Primary RNA Interference High-Throughput Screening Experiments

Zhang, Xiaohua Douglas; Ferrer, Marc; Espeseth, Amy S.; Marine, Shane; Stec, Erica; Crackower, Michael A.; Holder, Daniel; Heyse, Joseph F.; Strulovici, Berta

doi:10.1177/1087057107300646

Cited by 87 publications

(88 citation statements)

References 17 publications

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“…Another example of fairly strong control is epidermal growth factor receptor (EGFR) in a mucin screen. 40 …”

Section: Guideline For Selecting Biological Controlsmentioning

confidence: 99%

“…12,17,[21][22][23][24][25][26][27] However, the probabilistic and statistical basis of Z factor is not as strong as that of strictly standardized mean difference (SSMD). [28][29][30] The Z-factor-based criterion is unable to obtain consistent evaluation results for positive controls with different strengths of effects. 22 In RNAi HTS experiments with no replicate for sample siRNAs in a plate, it is important to take into account the strength of positive controls.…”

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confidence: 99%

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Integrating Experimental and Analytic Approaches to Improve Data Quality in Genome-wide RNAi Screens

Zhang¹,

Espeseth²,

Johnson³

et al. 2008

SLAS Discovery

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“…Another example of fairly strong control is epidermal growth factor receptor (EGFR) in a mucin screen. 40 …”

Section: Guideline For Selecting Biological Controlsmentioning

confidence: 99%

mentioning

confidence: 99%

Integrating Experimental and Analytic Approaches to Improve Data Quality in Genome-wide RNAi Screens

Zhang¹,

Espeseth²,

Johnson³

et al. 2008

SLAS Discovery

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“…SSMD can also be applied to one siRNA pool or one siRNA (Zhang, 2010a(Zhang, , 2007Zhang et al, 2007Zhang et al, , 2010. To distinguish these two types of SSMD, we use cSSMD to denote the SSMD for collective activity of multiple siRNAs.…”

Section: Introductionmentioning

confidence: 99%

cSSMD: assessing collective activity for addressing off-target effects in genome-scale RNA interference screens

et al. 2011

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Motivation: Off-target activity commonly exists in RNA interference (RNAi) screens and often generates false positives. Existing analytic methods for addressing the off-target effects are demonstrably inadequate in RNAi confirmatory screens. Results: Here, we present an analytic method assessing the collective activity of multiple short interfering RNAs (siRNAs) targeting a gene. Using this method, we can not only reduce the impact of off-target activities, but also evaluate the specific effect of an siRNA, thus providing information about potential off-target effects. Using in-house RNAi screens, we demonstrate that our method obtains more reasonable and sensible results than current methods such as the redundant siRNA activity (RSA) method, the RNAi gene enrichment ranking (RIGER) method, the frequency approach and the t-test.

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“…Strictly standardized mean difference (SSMD) has been proposed and adopted for both quality control [4][5][6] and assessment of siRNA effects in RNAi high-throughput screening (HTS) assays. [7][8][9][10] Two clear advantages of using SSMD to asses siRNA effects are that (1) SSMD has both an original and probability meaning, and (2) its value is comparable across experiments. 4,8,10 Based on SSMD, an error control method has been proposed to maintain a flexible and balanced control of the false-negative rate (FNR), in which the siRNAs with large effects are not selected as hits, and the restricted false-positive rate (RFPR), in which the siRNAs with small or no effects are selected as hits.…”

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confidence: 99%

“…In the mucin RNAi HTS experiment described in Zhang et al, 9 there is 1 negative control and 1 inhibition control. We use the inhibition control to calculate empirical FNR as follows: for a given cutoff b * , the empirical FNR is the proportion of wells for the inhibition control withb < b * .…”

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Error Rates and Powers in Genome-Scale RNAi Screens

2009

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For hit selection in genome-scale RNAi research, we do not want to miss small interfering RNAs (siRNAs) with large effects; meanwhile, we do not want to include siRNAs with small or no effects in the list of selected hits. There is a strong need to control both the false-negative rate (FNR), in which the siRNAs with large effects are not selected as hits, and the restricted false-positive rate (RFPR), in which the siRNAs with no or small effects are selected as hits. An error control method based on strictly standardized mean difference (SSMD) has been proposed to maintain a flexible and balanced control of FNR and RFPR. In this article, the authors illustrate how to maintain a balanced control of both FNR and RFPR using the plot of error rate versus SSMD as well as how to keep high powers using the plot of power versus SSMD in RNAi high-throughput screening experiments. There are relationships among FNR, RFPR, Type I and II errors, and power. Understanding the differences and links among these concepts is essential for people to use statistical terminology correctly and effectively for data analysis in genome-scale RNAi screens. Here the authors explore these differences and links. (Journal of Biomolecular Screening 2009:230-238) Key words: restricted false-positive rate, false-negative rate, strictly standardized mean difference, Type I error, Type II error, power INTRODUCTION R NAI IS A MECHANISM THAT KNOCKS DOWN GENES with complementary nucleotide sequences of doublestranded RNA. [1][2][3] In genome-scale RNAi screen experiments, the primary interest is (1) the assessment of the magnitude of impact on a biological response related to the knockdown of a gene and (2) the selection of siRNAs with large effects on the biological response of interest. The key is to search an analytic metric to effectively quantify knockdown effect and then to construct a selection criterion based on this metric to control false-positive and false-negative rates. Strictly standardized mean difference (SSMD) has been proposed and adopted for both quality control [4][5][6] and assessment of siRNA effects in RNAi high-throughput screening (HTS) assays. [7][8][9][10] Two clear advantages of using SSMD to asses siRNA effects are that (1) SSMD has both an original and probability meaning, and (2) its value is comparable across experiments. 4,8,10 Based on SSMD, an error control method has been proposed to maintain a flexible and balanced control of the false-negative rate (FNR), in which the siRNAs with large effects are not selected as hits, and the restricted false-positive rate (RFPR), in which the siRNAs with small or no effects are selected as hits. 8 In this article, we illustrate how to maintain a balanced control of both FNR and RFPR using the plot of error rate versus SSMD cutoff (or critical value) 10 as well as how to keep high powers using the plot of power versus SSMD critical value in RNAi HTS experiments. FNR and RFPR may be linked to Type I and II error rates under certain conditions, and they are also related to stat...

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The Use of Strictly Standardized Mean Difference for Hit Selection in Primary RNA Interference High-Throughput Screening Experiments

Cited by 87 publications

References 17 publications

Integrating Experimental and Analytic Approaches to Improve Data Quality in Genome-wide RNAi Screens

Integrating Experimental and Analytic Approaches to Improve Data Quality in Genome-wide RNAi Screens

cSSMD: assessing collective activity for addressing off-target effects in genome-scale RNA interference screens

Error Rates and Powers in Genome-Scale RNAi Screens

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