Tidyproteomics: an open-source R package and data object for quantitative proteomics post analysis and visualization

Table 2 Suggested data normalization and imputation strategies for various proteomics experiments

Experimental design	Filter	Normalization	Imputation	Refs.
Small change between two groups (e.g. gene knockdown/out, mutation, disease, drug response, biomarker discovery)	n/a	ANY	Randomforest BETWEEN	[36, 58]
Difference between separate samples from the same organism (e.g. different organs, tissue sections, etc.)	n/a	Median shift	Randomforest BETWEEN	[36, 58]
Co-cultured multi-organism competitive study with or without environmental changes	IN single organism	Median shift	Randomforest BETWEEN	[59]
Affinity capture (flowthrough/capture)	OUT common contaminants	None, or linear based on bait subset	Minimum WITHIN	[60]
Antibody purification (flowthrough/capture)	OUT common contaminants	n/a	Minimum WITHIN	[60]
Protein over-expression	n/a	Median shift	Minimum WITHIN	[60]

These suggestions only reflect the opinions and experiences of the authors, have not been derived from examination of any specific literature, and do not come with any comparison testing. They are intended only as a starting point, adequate domain knowledge for each experimental design listed is expected

ISSN: 1471-2105