ENVS 567 - Advanced Project

Christian Ayala

Chapter 1 Introduction

My advanced project involves performing a differential abundance analysis of metabolomics data using simple custom functions as well as differential expression analysis of gene count data using the widely used r package DESeq2 (Love, Huber, and Anders 2014).

1.1 Differential abundance analysis (DA) in metabolomics

Differential abundance analysis in metabolomics and proteomics involves identifying which metabolites/proteins (features) have significantly different abundances between two experimental groups.

1.2 Differential expression analysis (DE) in meta/transcriptomics

Differential expression analysis for meta/transcriptomics involves taking the normalised read count data and performing statistical analysis to discover quantitative changes in expression levels between experimental groups.

References

Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2.” Genome Biology 15 (12): 1–21.