Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 406 - Advances of Statistical Methodologies in Proteogenomics Research
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #309878
Title: Extracting Biological Information from Extreme Values in Proteogenomics Data
Author(s): Wenke Liu* and Kelly Ruggles and David Fenyo
Companies: and NYU school of medicine and NYU school of medicine
Keywords: proteomics

Multi-omics datasets span large biological feature spaces with limited number of samples. Dimensionality of data and technical noise impose great challenge to exploratory analysis that seeks to identify phenotype-related molecular signatures. Here we summarize applications of two statistical methods in proteogenomics data that focuses on extreme values. We utilized non-parametric outlier analysis to identify aberrant molecules enriched in functional subgroups without assuming underlying feature distribution. We also applied independent component analysis (ICA) to proteogenomics data, hypothesizing that each observed sample is the mixture of super-Gaussian signal sources arises from different biological processes. Annotation based on known markers and pathways suggests that these two methods were able to extract mechanistically relevant information from high dimensional multi-omics data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program