PSEA-Quant statistically assesses the enrichment of proteins with high abundance and well reproduced abundance measurements across a set of replicates in protein sets from GO and MSigDB. Such protein sets are of interest because they represent proteins with highly reliable abundance measurements of great amplitude that are likely to be linked ...

2014年12月5日 · To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as ...

2016年3月24日 · PSEA-Quant analyzes quantitative mass spectrometry-based proteomics datasets to identify enrichments of annotations contained in repositories such as the Gene Ontology and Molecular Signature databases. It allows users to identify the annotations that are significantly enriched for reproducibly quan …

2016年3月24日 · PSEA-Quant analyzes quantitative mass spectrometry–based proteomics datasets to identify enrichments of annotations contained in repositories such as the Gene Ontology and Molecular Signature databases. It allows users to identify the annotations that are significantly enriched for reproducibly quantified high abundance proteins.

2023年4月4日 · We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates.

PSEA-Quant uses a permutation scheme, which models both protein abundance dependencies and annotation biases. We show that PSEA-Quant works with both label-free and label-based protein quantification methods and yields complementary results to classic GO enrichment analysis tools (GOrilla and Ontologizer ). When PSEA-Quant was applied to a ...

To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as ...

2014年9月1日 · We propose a statistical approach to computationally identify protein sets (e.g. Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification...

PSEA-Quant uses a permutation scheme, which models both protein abundance dependencies and annotation biases. We show that PSEA-Quant works with both label-free and label-based protein quantification methods and yields complementary results to classic GO enrichment analysis tools (GOrilla10 and Ontologizer7). When PSEA-Quant was applied to a ...

2020年10月14日 · Mathieu Lavallée-Adam和他的团队为我们开发了一种内部工具——PSEA-Quant，专门用于蛋白质集富集分析，是专为基因集富集分析而开发的，但已针对无标记和基于标记的蛋白质定量数据进行了优化。