Table I. Various manipulation methods including 3 transformation, 18 pretreatment (2 centering, 4 scaling, and 12 normalization methods), and 7 imputation methods together with their purpose of data manipulation and/or corresponding statistical/biological assumptions. All manipulation methods were abbreviated using three-letter code. As a transformation method integrating with subsequent normalization technique, the VSN determined data-dependent transformation parameters by having a built-in transformation (47, 79).

Classes	Abb.	Manipulation Method	Purpose/Assumption of the Manipulation Method
Transformation	BOX	Box-Cox Transformation	Making asymmetric data fulfill the normality assumption in a regression model by converting the protein abundances into a more symmetric distribution (80).
	LOG	Log Transformation	Converting the distribution of ratios of abundance values of proteins into a more symmetric (almost normal distribution) and minimizing the effect of proteins with extreme abundance (39).
	VSN	Variance Stabilization Normalization	Having a built-in transformation (47) and making individual observations more directly comparable (81); assuming that most of the proteins across different samples are not differentially expressed (81).
Pretreatment
Centering	MEC	Mean-Centering	Converting all the intensities to fluctuations around zero instead of around the mean of the protein intensities; assuming all proteins are equally important (35).
	MDC	Median-Centering	Making all the intensities to fluctuations around zero instead of around the median of the protein intensities; assuming all proteins are equally important (35).
Scaling	ATO	Auto Scaling	Adjusting each protein abundance for systematic variance using the standard deviation of each protein of all samples as scaling factor (82); assuming all proteins are equally important (35).
	PAR	Pareto Scaling	Scaling each protein abundance for systematic variance using the square root of the standard deviation of each protein of all samples as scaling factor (83); assuming all proteins are equally important (35).
	VAS	Vast Scaling	Adjusting each protein abundance for systematic variance using the coefficient of variation of each protein of all samples as scaling factor (84); assuming all proteins are equally important (35).
	RAN	Range Scaling	Scaling each protein abundance for systematic variance using the abundance range of each protein of all samples as scaling factor (85); assuming all proteins are equally important (35).
Normalization	MEA	Mean Normalization	Ensuring the protein abundance values from all studied samples directly comparable with each other (86); assuming the mean level of the protein abundance is constant for all samples (27).
	MED	Median Normalization	Making the protein intensities from all individual samples directly comparable with each other (27, 86); assuming the median level of the protein abundance is constant for all samples (36).
	MAD	Median Absolute Deviation	Ensuring the comparability of protein intensities among all samples (86); assuming the median level of the protein abundance and the spread of abundances are the same in all samples (37).
	TIC	Total Ion Current	Making the protein intensities from all samples directly comparable with each other (86); assuming the total area under the protein abundance curve is constant among samples (38).
	CYC	Cyclic Loess	Assuming that the intensities of the vast majority of the proteins are not changed in control and case groups (33, 87) and the systematic bias is nonlinearly dependent on the protein abundances (27).
	LIN	Linear Baseline Scaling	Assuming that the abundances of the majority of the proteins in samples are unchanged under the studied condition (33, 88) and the systematic bias is linearly dependent on the protein intensities (39).
	RLR	Robust Linear Regression	Assuming that the intensities of the majority of the proteins are not changed in control and case groups (87, 88) and the systematic bias is linearly dependent on the magnitude of protein abundances (27).
	LOW	Locally Weighted Scatterplot Smoothing	Assuming that the abundances of the majority of the proteins are unchanged under the studies circumstances (40, 87) and the systematic bias is nonlinearly dependent on the protein intensities (40).
	EIG	EigenMS	Overcoming the problems caused by the heterogeneity in the protein intensities of studied samples (43, 89); Does not require any assumption about the relative strength of signals due to each source of variation (43).
	PQN	Probabilistic Quotient Normalization	Ensuring the comparability of protein intensities among all samples (86); assuming that the majority of the protein intensities does not vary for the studied classes (41).
	QUA	Quantile Normalization	Making the protein intensities from all samples directly comparable with each other (86); assuming that the majority of protein intensity signals are unchanged among samples (40).
	TMM	Trimmed Mean of M Values	Ensuring the protein abundance values from all studied samples directly comparable with each other (86); assuming the majority of proteins are not differentially expressed between control and case groups (42).
Imputation	BAK	Background Imputation	Assuming that the protein values are missing because of having small concentrations in the sample and thus cannot be detected during the MS run (27).
	BPC	Bayesian Principal Component Imputation	Imputing based on the variational Bayesian framework that does not force orthogonality between the principal components (90).
	CEN	Censored Imputation	Imputing the lowest intensity values in the dataset by assuming that the missing of protein values is because of being below detection capacity (27).
	KNN	K-nearest Neighbor Imputation	Finding k most similar proteins (k-nearest neighbors) and using a weighted average over these k proteins to estimate the missing protein values (27, 91).
	LLS	Local Least Squares Imputation	Representing a studied protein that has missing values as a linear combination of a number of proteins similar to this particular protein (92).
	SVD	Singular Value Decomposition	Applying this imputation method to the data to obtain sets of mutually orthogonal expression patterns of all proteins in the data (91).
	ZER	Zero Imputation	Imputing the missing intensities of the studied proteins by directly replacing these missing values with a number of zeros (27).