scholarly journals Deep Unfolding for Non-Negative Matrix Factorization with Application to Mutational Signature Analysis

2022 ◽  
Author(s):  
Rami Nasser ◽  
Yonina C. Eldar ◽  
Roded Sharan
Keyword(s):  
Matrix Factorization ◽  
Signature Analysis ◽  
Mutational Signature ◽  
Non Negative Matrix Factorization

scholarly journals Mutational signature learning with supervised negative binomial non-negative matrix factorization

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i154-i160 ◽  
Author(s):  
Xinrui Lyu ◽  
Jean Garret ◽  
Gunnar Rätsch ◽  
Kjong-Van Lehmann
Keyword(s):  
Matrix Factorization ◽  
Negative Binomial ◽  
Extraction Methods ◽  
Supplementary Information ◽  
Cancer Type ◽  
Mutational Signatures ◽  
Signature Extraction ◽  
Mutational Signature ◽  
Mutational Processes ◽  
Non Negative Matrix Factorization

Abstract Motivation Understanding the underlying mutational processes of cancer patients has been a long-standing goal in the community and promises to provide new insights that could improve cancer diagnoses and treatments. Mutational signatures are summaries of the mutational processes, and improving the derivation of mutational signatures can yield new discoveries previously obscured by technical and biological confounders. Results from existing mutational signature extraction methods depend on the size of available patient cohort and solely focus on the analysis of mutation count data without considering the exploitation of metadata. Results Here we present a supervised method that utilizes cancer type as metadata to extract more distinctive signatures. More specifically, we use a negative binomial non-negative matrix factorization and add a support vector machine loss. We show that mutational signatures extracted by our proposed method have a lower reconstruction error and are designed to be more predictive of cancer type than those generated by unsupervised methods. This design reduces the need for elaborate post-processing strategies in order to recover most of the known signatures unlike the existing unsupervised signature extraction methods. Signatures extracted by a supervised model used in conjunction with cancer-type labels are also more robust, especially when using small and potentially cancer-type limited patient cohorts. Finally, we adapted our model such that molecular features can be utilized to derive an according mutational signature. We used APOBEC expression and MUTYH mutation status to demonstrate the possibilities that arise from this ability. We conclude that our method, which exploits available metadata, improves the quality of mutational signatures as well as helps derive more interpretable representations. Availability and implementation https://github.com/ratschlab/SNBNMF-mutsig-public. Supplementary information Supplementary data are available at Bioinformatics online.

Mutational Signature Analysis Reveals NTHL1 Deficiency to Cause a Multi-Tumor Phenotype Including a Predisposition to Colon and Breast Cancer

2018 ◽  
Author(s):  
Judith E. Grolleman ◽  
Richarda M. de Voer ◽  
Fadwa A. Elsayed ◽  
Maartje Nielsen ◽  
Robbert D. A. Weren ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Signature Analysis ◽  
Mutational Signature ◽  
Tumor Phenotype
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