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@@ -1101,7 +1101,8 @@ noprefix "false"
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However, the steep slope of the sigmoid transformation near 0 and 1 tends
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to over-exaggerate small differences in β values near those extremes, which
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in turn amplifies the error in those values, leading to a U-shaped trend
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- in the mean-variance curve.
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+ in the mean-variance curve: extreme values have higher variances than values
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+ near the middle.
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This mean-variance dependency must be accounted for when fitting the linear
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model for differential methylation, or else the variance will be systematically
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overestimated for probes with moderate M-values and underestimated for
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@@ -1109,7 +1110,7 @@ However, the steep slope of the sigmoid transformation near 0 and 1 tends
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\end_layout
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\begin_layout Subsubsection
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-The voom method for RNA-seq data can model this heteroskedasticity
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+The voom method for RNA-seq data can model M-value heteroskedasticity
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\end_layout
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\begin_layout Standard
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@@ -1133,8 +1134,8 @@ literal "false"
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Hence, the method is sufficiently general to model the mean-variance relationsh
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ip in methylation array data.
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However, the standard implementation of voom assumes that the input is
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- given in raw read counts, and minor adjustments are required to run it
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- on methylation M-values.
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+ given in raw read counts, and it must be adapted to run on methylation
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+ M-values.
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\end_layout
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\begin_layout Standard
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