Reduce abstract < 200 words for NAR

knitr/effadj.Rnw

@@ -82,16 +82,15 @@ Accepted March 1, 2009}
 
 
 \begin{abstract}
-{Accurate adjustment for the amplification efficiency (AE) has become an important part of real-time quantitative polymerase chain reaction (qPCR) experiments.
-The most commonly used correction strategy is to estimate the AE by dilution experiments and use this as a plug-in when efficiency correcting the $\ddcq$.
-Currently it is recommended to determine the AE with high precision as the plug-in approach does not account for its uncertainty,
+{Accurate adjustment for the amplification efficiency (AE) is an important part of real-time quantitative polymerase chain reaction (qPCR) experiments.
+A commonly used strategy is to estimate the AE by dilution experiments and use this as a plug-in when efficiency correcting the $\ddcq$.
+It is recommended to determine the AE with high precision as this approach disregards its uncertainty,
 implicitly assuming an infinitely precise AE estimate.
 Determining the AE with such precision, however, requires tedious laboratory work and vast amounts of biological material.
-Violation of the assumption leads to overly optimistic standard errors of the $\ddcq$, confidence intervals, and $p$-values which ultimately increase the type I error rate beyond the expected significance level.
-As qPCR is often used for validation it should be a high priority to account for the uncertainty of the AE estimate and thereby properly bounding the type I error rate and achieve the desired significance level.
+Violation of the assumption imply overly optimistic standard errors, confidence intervals, and $p$-values; ultimately increasing the type I error rate beyond the expected significance level.
+As qPCR is often used for validation it should be a high priority to account for the uncertainty of the AE estimate and thereby properly bounding the type I error rate.
 We suggest methods founded in a linear mixed effects model (LMM) to obtain variance approximations of the efficiency adjusted $\ddcq$ using the statistical delta method, Monte Carlo integration, or bootstrapping.
-We illustrate the impact of AE uncertainty in three qPCR data sets and use the methods to validate findings suggesting that \textit{MGST1} is differentially expressed between high and low abundance culture initiating cells in multiple myeloma and that microRNA-127 is differentially expressed between testicular and nodal lymphomas.
-Finally, we benchmark our methods in a simulation study.
+We illustrate and benchmark the impact of AE uncertainty in three qPCR datasets and use the methods to validate findings suggesting that \textit{MGST1} is differentially expressed between subgroups in multiple myeloma and that \textit{microRNA-127} is differentially expressed between testicular and nodal lymphomas.
 }
 % % Keywords
 % {amplification efficiency; delta-delta Cq; $\ddcq$; efficiency adjusted; power calculation; qPCR}