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different from the majority of the compounds and thus are outliers. Strong assay artifacts may also behave as outliers. Thus, outliers are not uncommon in HTS experiments. The regular versions of z-score and SSMD are sensitive to outliers and can be problematic. Consequently, robust methods such as the z*-score method,
229:
There are many metrics used for hit selection in primary screens without replicates. The easily interpretable ones are fold change, mean difference, percent inhibition, and percent activity. However, the drawback common to all of these metrics is that they do not capture data variability effectively.
282:
SSMD can overcome the drawback of average fold change not being able to capture data variability. On the other hand, because SSMD is the ratio of mean to standard deviation, we may get a large SSMD value when the standard deviation is very small, even if the mean is small. In some cases, a too small
204:
HTS experiments have the ability to screen tens of thousands (or even millions) of compounds rapidly. Hence, it is a challenge to glean chemical/biochemical significance from mounds of data in the process of hit selection. To address this challenge, appropriate analytic methods have been adopted for
237:
The z-score method is based on the assumption that the measured values (usually fluorescent intensity in log scale) of all investigated compounds in a plate have a normal distribution. SSMD also works the best under the normality assumption. However, true hits with large effects should behave very
245:
In a primary screen without replicates, every compound is measured only once. Consequently, we cannot directly estimate the data variability for each compound. Instead, we indirectly estimate data variability by making a strong assumption that every compound has the same variability as a negative
216:
There are two major types of HTS experiments, one without replicates (usually in primary screens) and one with replicates (usually in confirmatory screens). The analytic methods for hit selection differ in those two types of HTS experiments. For example, the z-score method is suitable for screens
254:
In a screen with replicates, we can directly estimate data variability for each compound, and thus we can use more powerful methods, such as SSMD for cases with replicates and t-statistic that does not rely on the strong assumption that the z-score and z*-score rely on. One issue with the use of
295:
versus average log fold-change (or average percent inhibition/activation) on the y- and x-axes, respectively, for all compounds investigated in an experiment. With the dual-flashlight plot, we can see how the genes or compounds are distributed into each category in effect sizes, as shown in the
255:
t-statistic and associated p-values is that they are affected by both sample size and effect size. They come from testing for no mean difference, thus are not designed to measure the size of small molecule or siRNA effects. For hit selection, the major interest is the size of effect in a tested
926:
Zhang XH, Yang XC, Chung N, Gates A, Stec E, Kunapuli P, Holder DJ, Ferrer M, Espeseth AS (2006). "Robust statistical methods for hit selection in RNA interference high-throughput screening experiments".
213:. The other strategy is to test whether a compound has effects strong enough to reach a pre-set level. In this strategy, false-negative rates (FNRs) and/or false-positive rates (FPRs) must be controlled.
1150:"Inhibition of calcineurin-mediated endocytosis and alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors prevents amyloid beta oligomer-induced synaptic disruption"
192:, et al.) with a desired size of inhibition or activation effects. A compound with a desired size of effects in an HTS screen is called a hit. The process of selecting hits is called
283:
mean value may not have a biological impact. As such, the compounds with large SSMD values (or differentiations) but too small mean values may not be of interest. The concept of
378:
Birmingham A, Selfors LM, Forster T, Wrobel D, Kennedy CJ, Shanks E, Santoyo-Lopez J, Dunican DJ, Long A, Kelleher D, Smith Q, Beijersbergen RL, Ghazal P, Shamu CE (2009).
246:
reference in a plate in the screen. The z-score, z*-score and B-score relies on this strong assumption; so are the SSMD and SSMD* for cases without replicates.
838:"A new method with flexible and balanced control of false negatives and false positives for hit selection in RNA interference high-throughput screening assays"
521:
Zhang XH, Kuan PF, Ferrer M, Shu X, Liu YC, Gates AT, Kunapuli P, Stec EM, Xu M, Marine SD, Holder DJ, Stulovici B, Heyse JF, Espeseth AS (2009).
1204:"Optimal High-Throughput Screening: Practical Experimental Design and Data Analysis for Genome-scale RNAi Research, Cambridge University Press"
1073:
Zhang XHD (2010). "Strictly standardized mean difference, standardized mean difference and classical t-test for the comparison of two groups".
205:
hit selection. There are two main strategies of selecting hits with large effects. One is to use certain metric(s) to rank and/or classify the
362:
1148:
Zhao WQ, Santini F, Breese R, Ross D, Zhang XD, Stone DJ, Ferrer M, Townsend M, Wolfe AL, Seager MA, Kinney GG, Shughrue PJ, Ray WJ (2010).
1038:
Zhang XHD (2009). "A method for effectively comparing gene effects in multiple conditions in RNAi and expression-profiling research".
879:"The use of strictly standardized mean difference for hit selection in primary RNA interference high-throughput screening experiments"
242:*, B-score method, and quantile-based method have been proposed and adopted for hit selection in primary screens without replicates.
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Zhang XH, Lacson R, Yang R, Marine SD, McCampbell, Toolan DM, Hare TR, Kajdas J, Berger JP, Holder DJ, Heyse JF, Ferrer M (2010).
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Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R (2006). "Statistical practice in high-throughput screening data analysis".
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Klinghoffer RA, Frazier J, Annis J, Berndt JD, Roberts BS, Arthur WT, Lacson R, Zhang XH, Ferrer M, Moon RT, Cleary MA (2010).
572:"A lentivirus-mediated genetic screen identifies dihydrofolate reductase (DHFR) as a modulator of beta-catenin/GSK3 signaling"
35:
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Optimal High-Throughput
Screening: Practical Experimental Design and Data Analysis for Genome-scale RNAi Research
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Zhang XH, Ferrer M, Espeseth AS, Marine SD, Stec EM, Crackower MA, Holder DJ, Heyse JF, Strulovici B (2007).
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by their effects and then to select the largest number of potent compounds that is practical for validation
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Zhang XHD (2010). "Assessing the size of gene or RNAi effects in multifactor high-throughput experiments".
623:"An effective method controlling false discoveries and false non-discoveries in genome-scale RNAi screens"
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without replicates whereas the t-statistic is suitable for screens with replicate. The calculation of
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713:"The use of SSMD-based false discovery and false non-discovery rates in genome-scale RNAi screens"
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is comparable across experiments and thus we can use the same cutoff for the population value of
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has also been shown to be better than other commonly used effect sizes. The population value of
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Quon K, Kassner PD (2009). "RNA interference screening for the discovery of oncology targets".
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Please expand the article to include this information. Further details may exist on the
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It may require cleanup to comply with
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429:"Genome-wide screens for effective siRNAs through assessing the size of siRNA effects"
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for screens without replicates also differs from that for screens with replicates.
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523:"Hit selection with false discovery rate control in genome-scale RNAi screens"
380:"Statistical methods for analysis of high-throughput RNA interference screens"
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figure. Meanwhile, we can also see the average fold-change for each compound.
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Malo N, Hanley JA, Carlile G, Liu J, Pelletier J, Thomas D, Nadon R (2010).
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about small molecule hit selection concepts (clustering, singletons, etc.).
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To address this issue, researchers then turned to the z-score method or
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176:(HTS), one of the major goals is to select compounds (including
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234:, which can capture data variability in negative references.
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Cohen J (1994). "The Earth Is Round (P-Less-Than.05)".
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A major contributor to this article appears to have a
797:"Error rates and power in genome-scale RNAi screens"
962:Brideau C, Gunter G, Pikounis B, Liaw A (2003).
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287:has been proposed to address this issue. In a
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50:Learn how and when to remove these messages
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142:Learn how and when to remove this message
1075:Statistics in Biopharmaceutical Research
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267:directly assesses the size of effects.
795:Zhang XH, Marine SD, Ferrer M (2009).
279:to measure the size of siRNA effects.
754:Expert Opinion on Therapeutic Targets
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200:Methods for hit selection in general
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968:Journal of Biomolecular Screening
883:Journal of Biomolecular Screening
842:Journal of Biomolecular Screening
801:Journal of Biomolecular Screening
717:Journal of Biomolecular Screening
668:Journal of Biomolecular Screening
627:Journal of Biomolecular Screening
31:This article has multiple issues.
122:. Please discuss further on the
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1154:Journal of Biological Chemistry
39:or discuss these issues on the
357:. Cambridge University Press.
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589:10.1371/journal.pone.0006892
1017:10.1037/0003-066X.49.12.997
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225:Screens without replicates
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311:high-throughput screening
174:high-throughput screening
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855:10.1177/1087057107300645
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681:10.1177/1087057110377497
640:10.1177/1087057110381783
1167:10.1074/jbc.M109.057182
250:Screens with replicates
527:Nucleic Acids Research
446:10.1186/1756-0500-1-33
72:is missing information
1087:10.1198/sbr.2009.0074
1005:American Psychologist
120:neutral point of view
480:Nature Biotechnology
331:Dual-flashlight plot
289:dual-flashlight plot
285:dual-flashlight plot
1127:10.2217/PGS.09.136
836:Zhang XHD (2007).
621:Zhang XHD (2010).
539:10.1093/nar/gkn435
433:BMC Research Notes
427:Zhang XHD (2010).
396:10.1038/nmeth.1351
353:Zhang XHD (2011).
1224:Clinical research
1202:Zhang XHD (2011)
364:978-0-521-73444-8
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306:Effect size
1213:Categories
337:References
36:improve it
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1025:0003-066X
207:compounds
132:June 2013
124:talk page
86:June 2013
78:talk page
42:talk page
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300:See also
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261:siRNA
190:genes
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359:ISBN
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259:or
172:In
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52:)
48:(
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