Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics.

By F. Dieterle, A. Ross, G. Schlotterbeck, Hans Senn. Analytical Chemistry, 2006

1,707 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/45aa9bda64a06029d4b4035334d16cc47175c865

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Citations in the corpus

Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis (2016, methodology): “MetaboAnalyst offers five options for sample normalization— no normalization, normalization by sum, normalization by median (Hendriks et al., 2007), normalization by a reference sample (Dieterle et al., 2006), normalization by a reference feature, and sample-specific normalization.”
Metabolomics reveals unhealthy alterations in rumen metabolism with increased proportion of cereal grain in the diet of dairy cows (2010, background): “These studies allow one to explore the symbiotic complexity between the host and its microbiota as affected by diet, and to unravel the effects of diet on the development of metabolic diseases (Dumas et al. 2006).”
Fat, Fiber and Cancer Risk in African Americans and Rural Africans (2015, positive): “Probabilistic quotient normalization was subsequently performed on the resulting datasets in order to account for dilution of complex biological mixtures 69 .”
MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis (2012, positive): “0 now supports 11 different procedures for data transformation and normalization (14,15) all of which can be interactively visualized and assessed using various diagnostic plotting tools.”
Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis (2019, methodology): “Both methods are based on the concept of probabilistic quotient normalization (PQN; Dieterle et al., 2006).”
A new approach to toxicity testing in Daphnia magna: application of high throughput FT-ICR mass spectrometry metabolomics (2009, methodology): “Next, the samples were normalised using probabilistic quotient normalisation (PQN) to minimise the effects of particularly high and low intensity peaks ( Dieterle et al. 2006 ) and then subject to the generalised log transformation (glog; Parsons et al. 2007).”
Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling (2016, methodology): “This method was adapted by Dieterle et al. (2006).”
MetaboAnalyst: a web server for metabolomic data analysis and interpretation (2009, methodology): “…been implemented in MetaboAnalyst, including normalization to a constant sum, normalization to a reference sample (probabilistic quotient normalization) (17), normalization to a reference feature (creatinine or an internal standard) and sample-speciﬁc normalization (dry weight or tissue volume).”