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{{Expert
[[Image:wiki.dualflashlight.png|thumb|350px|Dual-flashlight plot showing a high-throughput screening dataset.]]
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As a whole, the points in a dual-flashlight plot look like the beams of a flashlight with two heads, hence the name dual-flashlight plot.<ref name="ZhangPharmacogenomics2010"/>
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 figure. Meanwhile, we can also see the average fold-change for each gene or compound. The dual-flashlight plot is similar to the [[volcano plot (statistics)|volcano plot]]. In a [[volcano plot (statistics)|volcano plot]], the [[p-value]] (or q-value{{clarify|date=June 2012}}), instead of SMCV or SSMD, is plotted against average fold-change <ref name=Jin2001>{{cite journal |
|journal= Nature Genetics |volume=29 |issue= |pages=389–95
|year=2001 |month= |pmid= |doi= 10.1038/ng766 |url=}}</ref>
.<ref name=Cui2003>{{cite journal |
|journal= Genome Biology |volume=4 |issue= 4|pages=210
|year=2003 |month= |pmid=12702200 |doi= 10.1186/gb-2003-4-4-210|url= |pmc=154570}}</ref> The advantage of using SMCV over p-value (or q-value) is that, if there exist any non-zero true effects for a gene or compound, the estimated SMCV goes to its population value whereas the p-value (or q-value) for testing no mean difference (or zero contrast mean) goes to zero when the sample size increases
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