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