130.160.63.55 at 18:47, 30 September 2014

2014-09-30T18:47:49Z

85.211.96.68: /* Autoxidation in food */

2013-08-31T01:49:13Z

Autoxidation in food

← Older revision		Revision as of 03:49, 31 August 2013
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	~~Alyson~~ is ~~what my husband loves~~ to ~~call me but I don't like when people~~ use ~~my complete~~ name. ~~To perform lacross~~ is the ~~thing I adore most~~ of ~~all~~. ~~Some time~~ in the ~~past he chose~~ to ~~reside~~ in ~~North Carolina~~ and ~~he doesn't strategy on changing~~ it. ~~He is an information officer~~.<br><br>~~My site :: psychics online~~ ([http://~~bigpolis~~.~~com~~/~~blogs~~/~~post~~/~~6503 bigpolis~~.~~com~~])		{{distinguish\|z-score\|z-value}}
			{{about\|the Z-factor in statistics\|the gas compressibility factor\|Compressibility factor}}

			The '''Z-factor''' is a measure of [[statistics\|statistical]] [[effect size]]. It has been proposed for use in [[high-throughput screening]] (where it is also known as Z-prime,<ref>http://planetorbitrap.com/data/uploads/4fb692e73c07b.pdf</ref> and commonly written as Z') to judge whether the response in a particular [[assay]] is large enough to warrant further attention.

			==Background==
			In high-throughput screens, experimenters often compare a large number (hundreds of thousands to tens of millions) of single measurements of unknown samples to positive and negative [[scientific control\|control]] samples. The particular choice of experimental conditions and measurements is called an assay. Large screens are expensive in time and resources. Therefore, prior to starting a large screen, smaller test (or pilot) screens are used to assess the quality of an assay, in an attempt to predict if it would be useful in a high-throughput setting. The Z-factor is an attempt to quantify the suitability of a particular assay for use in a full-scale, high-throughput screen.

			==Definition==
			The Z-factor is defined in terms of four parameters: the [[expected value\|mean]]s and [[standard deviation]]s of both the positive (p) and negative (n) controls (<math>\mu_p</math>, <math>\sigma_p</math>, and <math>\mu_n</math>, <math>\sigma_n</math>). Given these values, the Z-factor is defined as:

			:<math>\text{Z-factor} = 1 - {3 (\sigma_p + \sigma_n) \over \| \mu_p - \mu_n \|}.</math>

			In practice, the Z-factor is estimated from the [[arithmetic mean\|sample mean]]s and sample standard deviations

			:<math>\text{Estimated Z-factor} = 1 - {3 (\hat{\sigma}_p + \hat{\sigma}_n) \over \| \hat{\mu}_p - \hat{\mu}_n \|}.</math>

			==Interpretation==
			The following interpretations for the Z-factor are taken from:<ref name=ZhangJHetal1999>{{cite journal \|author=Zhang JH, Chung TDY, Oldenburg KR
			\|title=A simple statistical parameter for use in evaluation and validation of high throughput screening assays
			\|journal=Journal of Biomolecular Screening \|volume=4 \|issue= \|pages=67–73
			\|year=1999 \|month= \|pmid=10838414 \|doi=10.1177/108705719900400206 \|url=}}</ref>

			{\| class="wikitable"
			! Z-factor !! Interpretation
			\|-
			\|1.0\|\|Ideal. Z-factors can never exceed 1.
			\|-
			\|between 0.5 and 1.0\|\|An excellent assay. Note that if <math>\sigma_p = \sigma_n</math>, 0.5 is equivalent to a separation of '''12''' standard deviations between <math>\mu_p</math> and <math>\mu_n</math>.
			\|-
			\|between 0 and 0.5\|\|A marginal assay.
			\|-
			\|less than 0\|\|There is too much overlap between the positive and negative controls for the assay to be useful.
			\|}

			Note that by the standards of many types of experiments, a zero Z-factor would suggest a large effect size, rather than a borderline useless result as suggested above. For example, if σ<sub>p</sub>=σ<sub>n</sub>=1, then μ<sub>p</sub>=6 and μ<sub>n</sub>=0 gives a zero Z-factor. But for normally-distributed data with these parameters, the probability that the positive control value would be less than the negative control value is less than 1 in 10<sup>5</sup>. Extreme conservatism is used in high throughput screening due to the large number of tests performed.

			==Limitations==
			The constant factor 3 in the definition of the Z-factor is motivated by the [[normal distribution]], for which more than 99% of values occur within 3 standard deviations of the mean. If the data follow a strongly non-normal distribution, the reference points (e.g. the meaning of a negative value) may be misleading. Another issue is that the usual estimates of the mean and standard deviation are not [[robust statistics\|robust]]; accordingly many users in the high-throughput screening community prefer "Robust Z-prime".<ref>{{cite journal \|author=Birmingham, Amanda, et. al.
			\|title= Statistical Methods for Analysis of High-Throughput RNA Interference Screens
			\|journal=Nat Methods \|volume=6 \|issue=8 \|pages=569–575
			\|date=August 2009 \|doi= 10.1038/nmeth.1351 \|pmc=2789971}}</ref> Extreme values (outliers) in either the positive or negative controls can adversely affect the Z-factor, potentially leading to an apparently unfavorable Z-factor even when the assay would perform well in actual screening
			.<ref name=Sui2007>{{cite journal \|author=Sui Y, Wu Z
			\|title= Alternative Statistical Parameter for High-Throughput Screening Assay Quality Assessment
			\|journal=Journal of Biomolecular Screening \|volume=12 \|issue= \|pages=229–34
			\|year=2007 \|month= \|pmid=17218666 \|doi= 10.1177/1087057106296498 \|url=}}</ref>
			In addition, the application of the single Z-factor-based criterion to two or more positive controls with different strengths in the same assay will lead to misleading results
			.<ref name=ZhangetalJBS2008>{{cite journal \|author=Zhang XHD, Espeseth AS, Johnson E, Chin J, Gates A, Mitnaul L, Marine SD, Tian J, Stec EM, Kunapuli P, Holder DJ, Heyse JF, Stulovici B, Ferrer M
			\|title= Integrating experimental and analytic approaches to improve data quality in genome-wide RNAi screens
			\|journal=Journal of Biomolecular Screening \|volume=13 \|issue= \|pages= 378–89
			\|year=2008 \|month= \|pmid=18480473 \|doi= 10.1177/1087057108317145 \|url=}}</ref> The absolute sign in the Z-factor makes it inconvenient to derive the statistical inference of Z-factor mathematically
			<ref name=ZhangGenomics2007>{{cite journal \|author=Zhang XHD
			\|title=A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays
			\|journal=Genomics \|volume=89 \|issue= \|pages=552–61
			\|year=2007 \|month= \|pmid=17276655 \|doi=10.1016/j.ygeno.2006.12.014 \|url=}}</ref>
			. A recently proposed statistical parameter, strictly standardized mean difference ([[SSMD]]), can address these issues <ref name="ZhangetalJBS2008"/>
			<ref name=ZhangGenomics2007>{{cite journal \|author=Zhang XHD
			\|title=A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays
			\|journal=Genomics \|volume=89 \|issue= \|pages=552–61
			\|year=2007 \|month= \|pmid= 17276655\|doi=10.1016/j.ygeno.2006.12.014 \|url=}}</ref>
			<ref name=ZhangJBS2008>{{cite journal \|author=Zhang XHD
			\|title= Novel analytic criteria and effective plate designs for quality control in genome-wide RNAi screens
			\|journal=Journal of Biomolecular Screening \|volume=13 \|issue= \|pages= 363–77
			\|year=2008 \|month= \|pmid=18567841\|doi= 10.1177/1087057108317062 \|url=}}</ref>
			. One estimate of [[SSMD]] is robust to outliers.

			==See also==
			* [[Z-score]] or [[Standard score]]
			* [[high-throughput screening]]
			* [[SSMD]]

			==References==
			{{reflist}}

			==Further reading==
			* Kraybill, B. (2005) [http://iccb.med.harvard.edu/screening/Quantitative%20Assay%20Evaluation%20and%20Optimization%20complete%20(3).pdf "Quantitative Assay Evaluation and Optimization"] (unpublished note)
			* Zhang XHD (2011) [http://www.cambridge.org/9780521734448 "Optimal High-Throughput Screening: Practical Experimental Design and Data Analysis for Genome-scale RNAi Research, Cambridge University Press"]

			{{DEFAULTSORT:Z-Factor}}
			[[Category:Change detection]]
			[[Category:Effect size]]

en>Rjwilmsi: /* Autoxidation in food */Format plain DOIs using AWB (8087)

2012-07-02T18:47:09Z

Autoxidation in food: Format plain DOIs using AWB (8087)

New page

Alyson is what my husband loves to call me but I don't like when people use my complete name. To perform lacross is the thing I adore most of all. Some time in the past he chose to reside in North Carolina and he doesn't strategy on changing it. He is an information officer.<br><br>My site :: psychics online ([http://bigpolis.com/blogs/post/6503 bigpolis.com])

Autoxidation - Revision history

130.160.63.55 at 18:47, 30 September 2014

85.211.96.68: /* Autoxidation in food */

en>Rjwilmsi: /* Autoxidation in food */Format plain DOIs using AWB (8087)