Variance component analysis to assess protein quantification in biomarker validation: application to selected reaction monitoring-mass spectrometry

Table 4 Variance decomposition of Model 1S

Source of variation	DF	Adjusted sum of squares
Theoretical concentration and interaction	J	SS(x + x^∗D\|D)=RSS(Model 3S) − RSS(Model 1S)
Two-day measurement and interaction	2(J-1)	SS(D + x^∗D\|x)=RSS(Model 2S) − RSS(Model 1S)
Interaction	(J-1)	SS(x^∗D\|x, D)=RSS(Model 4S) − RSS(Model 1S)
Residual variation	IJR-2 J	\( RSS\left(\mathrm{Model}\ 1S\right)=\sum \limits_{ijr}{\left({y}_{ijr}-{\widehat{y}}_{ijr}\right)}^2 \)
Measurement error	(R-1)IJ	\( \sum \limits_{ij r}{\left({y}_{ij r}-{\overline{y}}_{ij\bullet}\right)}^2 \)
Lack of fit	IJ-2 J	\( \sum \limits_{ij r}{\left({\widehat{y}}_{ij r}-{\overline{y}}_{ij\bullet}\right)}^2 \)

DF degrees of freedom, I number of samples, J number of couples of days, R number of digestion-injections -\( {\overline{y}}_{ij\bullet } \): mean of digestion-injection replicate measurements of each sample and each couple of days - \( {\widehat{y}}_{ijr} \): predicted measurements - RSS: residual sum of squares - SS: sum of squares

ISSN: 1471-2105