Technical Series

Automating the Analysis of Untargeted LC/MS Metabolomics Data

Untargeted metabolomics measures metabolites in samples to find those that correlate with subgroups (e.g., disease or healthy tissue). Current analysis pipelines are time consuming and require analysts to make ad hoc subjective decisions. Software developed by our group automates the analysis, removes subjective decision making, and runs fast. Linear models are added which introduces a larger class of biostatistical models and methods for researchers.

Repeated Measures Method for Microbial Count Data

This technical report introduces a repeated measures analysis method for the microbiome data using
the generalized Dirichlet-multinomial model. We start by reviewing the concept of compositional data, explain the challenge of the repeated data analysis, present the method, and illustrate its performance in hypothesis testing using simulated data.

Microbiome Research: Moving from Exploratory to Regulatory

If you plan on going to a regulatory agency like the FDA or USDA for your microbiome product, you will need to have biostatistics in order to meet their requirements. This paper shows how to move from exploratory R&D to formally designed experiments to test hypotheses about microbiome data.

Finding Untargeted Metabolomics Intensity Differences

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

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.

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.

Finding Distinct Subgroups of Samples Using Microbiome Taxa Count 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.

(p) 314-633-1821


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