Earth Mover’s Distance (EMD): A True Metric for Comparing Biomarker Expression Levels in Cell Populations

By D. Orlova, Noah Zimmerman, Stephen Meehan, Connor Meehan, Jeffrey Waters, E. Ghosn, A. Filatenkov, G. A. Kolyagin, Y. Gernez, S. Tsuda and 4 more. 2016

118 citations recorded by the source corpus.

Changes in the frequencies of cell subsets that (co)express characteristic biomarkers, or levels of the biomarkers on the subsets, are widely used as indices of drug response, disease prognosis, stem cell reconstitution, etc. However, although the currently available computational “gating” tools accurately reveal subset frequencies and marker expression levels, they fail to enable statistically reliable judgements as to whether these frequencies and expression levels differ significantly between/among subject groups. Here we introduce flow cytometry data analysis pipeline which includes the Earth Mover’s Distance (EMD) metric as solution to this problem. Well known as an informative quantitative measure of differences between distributions, we present three exemplary studies showing that EMD 1) reveals clinically-relevant shifts in two markers on blood basophils responding to an offending allergen; 2) shows that ablative tumor radiation induces significant changes in the murine colon cancer tumor microenvironment; and, 3) ranks immunological differences in mouse peritoneal cavity cells harvested from three genetically distinct mouse strains.

Subfields: Molecular Biology, Evaluation & Benchmarks, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. S. Cherry
2. Max Sommerfeld
3. Douwe Kiela
4. Axel Munk
5. Nazneen Rajani

In Evaluation & Benchmarks

1. Tristan Thrush
2. Douwe Kiela
3. Max Sommerfeld
4. Angelina McMillan-Major
5. Axel Munk
6. Yacine Jernite
7. Alexandra Sasha Luccioni
8. Margaret Mitchell

In Computational Biology & Bioinformatics

1. Nazneen Rajani

Citations in the corpus

• Measuring Data (2022, methodology): “Earth Mover’s distance has been used in applications ranging from information retrieval and pattern recognition to calculate the distances between data from different modalities with diverse types of features (e.g. (Peleg et al., 1989; Orlova et al., 2016)).”
• Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications (2020, methodology): “B cells from individuals in EMD group 1 were enriched for FlowSOM clusters 1, 5, and 6, all of which were increased in COVID-19 patients Mathew et al., Science 369, eabc8511 (2020) 4 September 2020 7 of 17 Spearman correlation of COVID-19 patients is shown.”
• Inference for empirical Wasserstein distances on finite spaces (2016, background): “, 2009), cell biology (Orlova et al., 2016) and model validation (Halder and Bhattacharya, 2011).”