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Editorial
. 2016 Dec;4(24):490.
doi: 10.21037/atm.2016.12.05.

Circulating microRNA biomarkers for cardiovascular risk prediction: are we approaching clinical application?

Affiliations
Editorial

Circulating microRNA biomarkers for cardiovascular risk prediction: are we approaching clinical application?

Maurice W J de Ronde et al. Ann Transl Med. 2016 Dec.
No abstract available

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Pitfalls of microRNA data handling. The following pitfalls can be expected from wrong data handling. (A) shows the effect of using an unrealistically low value to substitute Ct values ≥40. Due to a large gap between the lowest measurable ∆Ct of 24.99 and the substituted ∆Ct of 40, there is more variation in the data, creating bias in the analysis. Dots are fictional data with mean (black line) and standard deviation (grey whiskers). This is solved by using a more appropriate ∆Ct to substitute these low values such as a ∆Ct of 26; (B) shows the effect of excluding missing values due to low microRNA expression. When these values are excluded, this falsely leads to increased expression levels. Therefore, missing values due to low microRNA expression must be substituted with a low value (e.g., 0); (C) shows the effect of excluding missing values that occurred due to technical errors. Excluding instead of imputing these missing values from the analysis will lead to an increased standard error of the mean and may therefore mask significance. In (B) and (C), dots are fictional data with mean (black line) and standard error of the mean (grey whiskers).

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References

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