Technical Series

Finding Untargeted Metabolomics Intensity Differences

Dealing with High-Dimensional Data

In biomedical research using high-throughput technology (i.e., -omics), short and wide occurs. It presents huge problemsfrom having a very large number of ways of analyzing the many biological measurements.

This technical report introduces a
new approach for analyzing LC/MS untargeted metabolomics data that is automatic and unbiased

Dealing with High-Dimensional Data
Supplement #1: Example with Microbiome Data

Finding Distinct Subgroups of Samples
Using Microbiome Taxa Count Data

In Technical Report 2 we showed through simulations that all pairwise distances become identical as
the number of dimensions approaches infinity.
In this Supplement, we demonstrate this theory with real microbiome data.

In this first Report we show how cluster analysis
is highly subjective with results changing for different inputs, present using the Dirichlet multinomial
distribution for microbiome data, and
finally show an example of this analysis using HMP stool samples.

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