Statistical Inference of Omics Data with Variable-Selection

Explore statistical inference techniques for omics data analysis with variable selection in this comprehensive lecture. Dive into the world of liquid chromatography with tandem mass spectrometry (LC-MS/MS) and its application in characterizing biological samples. Learn about computational approaches, including chemometrics and deep-learning methods, used to enhance spectral matching and predict molecular structures from large MS spectra databases. Discover how in-silico fingerprints can numerically represent molecular structures and assist in matching target compounds. The lecture covers various topics, including data structure, multivariate tests, disadvantages of current methods, and the Meta Toolbox. Gain insights into Principal Component Analysis (PCA), Manhattan plots, and both simulation and experimental results. Conclude with a Q&A session to deepen your understanding of this cutting-edge field in omics data analysis.

Introduction
Context
Response
Data structure
Multivariate test
Disadvantages
Meta Toolbox
PCA
Manhattan plots
Simulation results
Experimental results
Conclusions
Questions