## EMDomics: Earth Mover's Distance for Differential Analysis of Genomics
Data

The EMDomics algorithm is used to perform a supervised two-class
analysis to measure the magnitude and statistical significance of observed
continuous genomics data between two groups. Usually the data will be gene
expression values from array-based or sequence-based experiments, but data
from other types of experiments can also be analyzed (e.g. copy number
variation). Traditional methods like Significance Analysis of Microarrays
(SAM) and Linear Models for Microarray Data (LIMMA) use significance tests
based on summary statistics (mean and standard deviation) of the two
distributions. This approach lacks power to identify expression differences
between groups that show high levels of intra-group heterogeneity. The Earth
Mover's Distance (EMD) algorithm instead computes the "work" needed to
transform one distribution into the other, thus providing a metric of the
overall difference in shape between two distributions. Permutation of sample
labels is used to generate q-values for the observed EMD scores.

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