2019
DOI: 10.1101/661496
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proDA: Probabilistic Dropout Analysis for Identifying Differentially Abundant Proteins in Label-Free Mass Spectrometry

Abstract: Protein mass spectrometry with label-free quantification (LFQ) is widely used for quantitative proteomics studies. Nevertheless, well-principled statistical inference procedures are still lacking, and most practitioners adopt methods from transcriptomics. These, however, cannot properly treat the principal complication of label-free proteomics, namely many non-randomly missing values.We present proDA, a method to perform statistical tests for differential abundance of proteins. It models missing values in an i… Show more

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Cited by 38 publications
(54 citation statements)
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“…This method aims to combine the sigmoidal dropout curve for missing values with the information from the observed values without direct value imputation. This allows for a more robust analysis that combines both the information from measured and missing values [ 22 ].…”
Section: Resultsmentioning
confidence: 99%
“…This method aims to combine the sigmoidal dropout curve for missing values with the information from the observed values without direct value imputation. This allows for a more robust analysis that combines both the information from measured and missing values [ 22 ].…”
Section: Resultsmentioning
confidence: 99%
“…Hypotheses, measures, and our analytical plan were pre-registered with the Open Science Framework: https://osf.io/rfndu/. All statistical analyses were performed in R studio (R Core Team, 2008), including the following packages: apaTables (Stanley, 2017), dplyr (Wickham, 2016), ggplot2 (Wickham, 2009), lemon (Edwards, 2017), ggsignif (Ahlmann-Eltze, 2017), psych (Revelle, 2015), reshape2 (Wickham, 2007), and Rmisc (Hope, 2013). The R script used for all data processing and analyses is available as an Electronic Supplement to this article, as is the dataset and an information sheet about the included variables.…”
Section: Methodsmentioning
confidence: 99%
“…The statistical tests were performed on R Studio using “PMCMRPlus” ( Kessler et al, 2020 ) and “FSA” (Derek H. Ogle et al, 2021 ) packages. Data were plotted using R Studio “ggplot2” ( Hadley, 2016 ) and “ggsignif” ( Ahlmann-Eltze, 2019 ) packages. Cells with gene expression level < 0.2 were exclude from the violin plots and statistical analyses.…”
Section: Methodsmentioning
confidence: 99%