Novel Analytic Criteria and Effective Plate Designs for Quality Control in Genome-Scale RNAi Screens

Zhang, Xiaohua Douglas

doi:10.1177/1087057108317062

Cited by 70 publications

(85 citation statements)

References 30 publications

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“…Various plate designs with controls placed systematically within plates according to different configurations have also been suggested for normalizing row and column effects in RNAi screens. 27 This approach has the advantage of not requiring control plates, but to be effective it requires that controls be interspersed within the entire plate, which is not always feasible. Other disadvantages are that the plate designs estimate row and column effects based on relatively few wells and do not estimate individual well biases.…”

Section: Discussionmentioning

confidence: 99%

Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Murie¹,

Barette²,

Lafanechère³

et al. 2014

SLAS Discovery

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Systematic error is present in all high-throughput screens, lowering measurement accuracy. Because screening occurs at the early stages of research projects, measurement inaccuracy leads to following up inactive features and failing to follow up active features. Current normalization methods take advantage of the fact that most primary-screen features (e.g., compounds) within each plate are inactive, which permits robust estimates of row and column systematic-error effects. Screens that contain a majority of potentially active features pose a more difficult challenge because even the most robust normalization methods will remove at least some of the biological signal. Control plates that contain the same feature in all wells can provide a solution to this problem by providing well-by-well estimates of systematic error, which can then be removed from the treatment plates. We introduce the robust control-plate regression (CPR) method, which uses this approach. CPR's performance is compared to a high-performing primary-screen normalization method in four experiments. These data were also perturbed to simulate screens with large numbers of active features to further assess CPR's performance. CPR performs almost as well as the best performing normalization methods with primary screens and outperforms the Z-score and equivalent methods with screens containing a large proportion of active features.

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Section: Discussionmentioning

confidence: 99%

Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Murie¹,

Barette²,

Lafanechère³

et al. 2014

SLAS Discovery

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“…Mean difference, fold change, percent viability, p-value, and other similar measures cannot effectively measure the size of siRNA effects, whereas SSMD effectively measures the size of siRNA effects. 4,6,8,10 SSMD (denoted as b) is defined as the ratio of mean to standard deviation of the difference between the 2 groupsnamely, b = . It has been mentioned recently 12 that the square of SSMD is Fisher's discrimination ratio.…”

Section: Error Rates and Powers In Genome-scale Rnai Screensmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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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“…Thus, the second process is to construct and adopt effective plate designs. Zhang 18 presents multiple effective plate designs and guidelines. After selecting effective biological controls and adjusting for systematic errors, effective QC metrics are needed to measure the degree of differentiation so that assays with inferior data quality can be identified.…”

mentioning

confidence: 99%

“…The existence of systematic errors of measurement is not uncommon in HTS experiments. [14][15][16][17][18][19][20][21] A good plate design helps to identify systematic errors (especially those linked with well position) and to determine what normalization should be used to remove/reduce the impact of systematic errors on both quality control (QC) and hit selection. Thus, the second process is to construct and adopt effective plate designs.…”

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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Novel Analytic Criteria and Effective Plate Designs for Quality Control in Genome-Scale RNAi Screens

Cited by 70 publications

References 30 publications

Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Error Rates and Powers in Genome-Scale RNAi Screens

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

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