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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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VarNMF: non-negative probabilistic factorization with source variation.

Ela Fallik1,2, Nir Friedman1,2

  • 1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel.

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|December 28, 2024
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Summary
This summary is machine-generated.

VarNMF, a novel probabilistic method, models variation in source values for genomic data. This approach enhances Non-negative matrix factorization (NMF) by revealing patient-specific disease behaviors and inter-tumor variability.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Non-negative matrix factorization (NMF) is widely used for analyzing mixed genomic samples, such as cell types in heterogeneous tissues.
  • NMF accounts for source proportions and observation noise but struggles with non-trivial variation in source contributions between samples.

Purpose of the Study:

  • To introduce VarNMF, a probabilistic extension of NMF designed to model and account for variation in source values.
  • To enable the recovery of source variation directly from mixed samples without direct observation of individual sources.

Main Methods:

  • VarNMF models sources as non-negative distributions, extending the standard NMF framework.
  • The method was applied to cell-free ChIP-seq data from cancer and healthy cohorts.

Main Results:

  • VarNMF provides improved estimation of data distribution compared to standard NMF.
  • The method successfully extracts cancer-associated source distributions, decoupling tumor characteristics from contribution amounts.
  • VarNMF identifies patient-specific disease behaviors and highlights obscured inter-tumor variability.

Conclusions:

  • VarNMF offers a powerful probabilistic extension to NMF for analyzing complex genomic data with source variation.
  • The approach enhances the understanding of tumor heterogeneity and patient-specific disease characteristics.