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Question: outliers in XP-EHH
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Whenever you perform an XP-EHH analysis involving populations, you tend to perform many pairwise XP-EHH calculations. During those scenarios, what is the best way to detect outliers? Currently, for all pairwise comparison, the XP-EHH scores were calculated per chromosome. the scores were converted to z-scores which were then converted to absolute Z-score. All the chromosomes were joined to get the whole genome XP-EHH plot.

What is the correct way to assign a threshold to detect outliers?

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The 1.5xIQR is a rule of thumb (and generally a good one, although it may not always be appropriate) by John Tukey for determining outliers. Outliers here are defined as observations that fall below Q1 − 1.5*IQR or above Q3 + 1.5*IQR, where IQR = Q3-Q1 and Q1 is first quartile and Q3 is third quartile. In a boxplot, the highest and lowest occurring value within this limit are indicated by whiskers of the box and any outliers as individual points.

ADD REPLYlink 9 months ago
a.zielezinski
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Thanks for this..the thing is somehow there is no one appropriate way to detect outliers for this kind of analysis..many papers have used different techniques..could not find a consensus.

ADD REPLYlink 9 months ago
prasundutta87
• 330

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