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Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis

Y-H. Taguchi, Turki Turki
2021 Genes  
In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address this difficult problem.  ...  Genes selected based on TD-based unsupervised FE are also significantly related to reasonable biological roles.  ...  Then, higher-order singular-value decomposition (HOSVD), which is a type of TD, is applied to the tensor.  ... 
doi:10.3390/genes12091442 pmid:34573424 pmcid:PMC8468466 fatcat:7m4wm5pi7jhlbjwni56wqo3axa

Unsupervised tensor decomposition-based method to extract candidate transcription factors as histone modification bookmarks in post-mitotic transcriptional reactivation

Y-h. Taguchi, Turki Turki, Andrei Chernov
2021 PLoS ONE  
We developed a novel computational approach comprising tensor decomposition (TD)-based unsupervised feature extraction (FE) to identify transcription factors (TFs) that bind to genes associated with reactivated  ...  The histone group added to a gene sequence must be removed during mitosis to halt transcription during the DNA replication stage of the cell cycle.  ...  We select singular value vectors attributed to genomic regions that share G with larger absolute values with the singular value vectors selected in the process mentioned earlier, because these singular  ... 
doi:10.1371/journal.pone.0251032 pmid:34032804 fatcat:253gthbfbfa5jmksp6je6huraq

Unsupervised tensor decomposition-based method to extract candidate transcription factors as histone modification bookmarks in post-mitotic transcriptional reactivation [article]

Y-h. Taguchi, Turki Turki
2020 bioRxiv   pre-print
We developed a novel computational approach comprising tensor decomposition (TD)-based unsupervised feature extraction (FE) to identify transcription factors (TFs) that bind to genes associated with reactivated  ...  The histone group added to a gene sequence must be released during mitosis to halt transcription during the DNA replication stage of the cell cycle.  ...  INTRODUCTION During the cell division process, gene transcription must be initially terminated and then reactivated Tensor Decomposition 64 Higher-order singular value decomposition (HOSVD) (Taguchi,  ... 
doi:10.1101/2020.09.23.309633 fatcat:y3mtngzrezclnhop3xsazwa5gq

rCUR: an R package for CUR matrix decomposition

András Bodor, István Csabai, Michael W Mahoney, Norbert Solymosi
2012 BMC Bioinformatics  
Many methods for dimensionality reduction of large data sets such as those generated in microarray studies boil down to the Singular Value Decomposition (SVD).  ...  In gene expression studies, it gives an additional way of analysis of differential expression and discriminant gene selection based on the use of statistical leverage scores.  ...  Acknowledgments We want to thank Gábor Tusnády who suggested the usage of method for our work. The function CUR in package uses the function ginv from package MASS [14] .  ... 
doi:10.1186/1471-2105-13-103 pmid:22594948 pmcid:PMC3546429 fatcat:2zdb3ugqqre4voghqsatrhvkl4

Identification of Transcription Factors, Biological Pathways, and Diseases as Mediated by N6-methyladenosine Using Tensor Decomposition-Based Unsupervised Feature Extraction

Y-h. Taguchi, S. Akila Parvathy Dharshini, M. Michael Gromiha
2020 Applied Sciences  
In this study, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to a dataset composed of mouse embryonic stem cells (mESC) and a human cancer cell line (HEC-1-A) and successfully  ...  These significantly overlapped genes occupy at most 10% genes from both gene sets.  ...  Tensor Decomposition-Based Unsupervised Feature Extraction Higher order singular value decomposition [18] (HOSVD) was applied to x ijks to derive tensor decomposition x ijks = 4 ∑ 1 =1 K ∑ 2 =1 2 ∑ 3  ... 
doi:10.3390/app11010213 fatcat:sbilpzjv7nahzivrij2mmbq7l4

Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies

Andrew E. Teschendorff, Joanna Zhuang, Martin Widschwendter
2011 Computer applications in the biosciences : CABIOS  
Thus, ISVA should be useful as a feature selection tool in studies that are subject to confounding. Availability: An R-package isva is available from www.cran.r-project .org.  ...  Motivation: A common difficulty in large-scale microarray studies is the presence of confounding factors, which may significantly skew estimates of statistical significance, cause unreliable feature selection  ...  If the number of selected features is less than 500, we select the top 500 features (based on P-values).  ... 
doi:10.1093/bioinformatics/btr171 pmid:21471010 fatcat:zvj2zjalbratla6gejegcyky74

Identification of Candidate Drugs for Heart Failure using Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Integrated Analysis of Gene Expression between Heart Failure and DrugMatrix Datasets [article]

Y-h. Taguchi
2017 bioRxiv   pre-print
In this paper, I apply tensor decomposition-based unsupervised feature extraction to the integrated analysis of gene expression between heart failure and the DrugMatrix dataset where comprehensive data  ...  I found that this strategy, in a fully unsupervised manner, enables us to identify a combined set of genes and compounds, for which various associations with heart failure were reported.  ...  Top most significant ENCODE TF-ChiP-seq 2015 is POLR2A_heart_mm9; POLR2A is a transcription factor (TF) reported to be a stable reference gene for gene expression alteration in gene expression studies  ... 
doi:10.1101/117465 fatcat:q6tkhjolfbdpnktk7f3xwhvpjy

Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

Y-h. Taguchi, Turki Turki
2019 Frontiers in Genetics  
In this study, a tensor decomposition (TD)-based unsupervised feature extraction (FE) was applied to the integration of two scRNA-seq expression profiles that measure human and mouse midbrain development  ...  One possible drawback of this strategy is that the outcome is highly dependent upon genes selected for the usage of clustering.  ...  In this case, TD is equivalent to singular value decomposition (SVD).  ... 
doi:10.3389/fgene.2019.00864 pmid:31608111 pmcid:PMC6761323 fatcat:samakueigbalxmqxf3psfn5bju

SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs

Shankara Anand, Laurynas Kalesinskas, Craig Smail, Yosuke Tanigawa
2019 Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  
We systematically characterized latent factors by applying singular value decomposition to ChIP-seq tracks of lymphoblastoid cell lines, and annotated the biological function of each latent factor using  ...  Advances in chromatin immunoprecipitation sequencing (ChIP-seq) have made large-scale repositories of epigenetic data available, allowing investigation of coordinated mechanisms of epigenetic markers and  ...  Y.T. is supported by the Funai Overseas Scholarship from Funai Foundation for Information Technology and the Stanford School of Medicine.  ... 
pmid:30864321 pmcid:PMC6417821 fatcat:ipjtbpezrje6daw3t5wfdwhctu

SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs

Shankara Anand, Laurynas Kalesinskas, Craig Smail, Yosuke Tanigawa
2018 Biocomputing 2019  
We systematically characterized latent factors by applying singular value decomposition to 652 ChIP-seq tracks of lymphoblastoid cell lines, and annotated the biological function of each latent factor  ...  Advances in chromatin immunoprecipitation sequencing (ChIP-seq) have made large-scale repositories of epigenetic data available, allowing investigation of coordinated mechanisms of epigenetic markers and  ...  Availability All the source code used in this project as well as pre-processed reference dataset are available in our GitHub repository: https://github.com/lkalesinskas/SNPs2ChIP.  ... 
doi:10.1142/9789813279827_0017 fatcat:v3gwaqbfrvghxn6tett5dtok7u

Tensor decomposition and principal component analysis-based unsupervised feature extraction outperforms state-of-the-art methods when applied to histone modification profiles [article]

Sanjiban Sekhar Roy, Y-h. Taguchi
2022 bioRxiv   pre-print
In this study, tensor decomposition and principal component analysis based unsupervised feature extraction with optimized standard deviation, which were successfully applied to gene expression and DNA  ...  Although multiple methods have been developed to identify histone modification, most of these methods are not specific for histone modification, but are general methods that aim to identify protein binding  ...  )u ℓ1 (9) where G ∈ R N ×M ×K and i u ℓ1i u ℓ ′ 1 i = δ ℓ1ℓ ′ 1 ( 10 ) j u ℓ2j u ℓ ′ 2 j = δ ℓ2ℓ ′ 2 (11) k u ℓ3k u ℓ ′ 3 k = δ ℓ3ℓ ′ 3 ( 12 ) with higher order singular value decomposition (HOSVD [13  ... 
doi:10.1101/2022.04.29.490081 fatcat:ter2zjkkj5f27mgi3up2q4nabi

Tensor decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis [article]

Y-h Taguchi, Turki Turki
2019 bioRxiv   pre-print
In this study, a tensor decomposition (TD) based unsupervised feature extraction (FE) was applied to the integration of two scRNA-seq expression profiles that measure human and mouse midbrain development  ...  One possible drawback of this strategy is that the outcome is highly dependent upon genes selected for the usage of clustering.  ...  In this case, TD is equivalent to singular value 90 decomposition (SVD).  ... 
doi:10.1101/684225 fatcat:odwrsm7gfzclrp3rhyt46bv5ve

Robust and accurate cancer classification with gene expression profiling

Haifeng Li, Keshu Zhang, Tao Jiang
2005 Proceedings. IEEE Computational Systems Bioinformatics Conference  
Unfortunately, Sw is always singular in the case of cancer classification due to the small sample size problem.  ...  Our method is able to map gene expression data into a very low dimensional space and thus meets the recommended samples to features per class ratio.  ...  Devise a fast algorithm to efficiently calculate S t + and the eigenvectors of S t + S b via singular value decomposition (SVD). with Thus, we can obtain the eigenvalues and corresponding eigenvectors  ... 
pmid:16447988 fatcat:p5jxany2mfao5gnrrm66hgddve

A new framework for identifying combinatorial regulation of transcription factors: A case study of the yeast cell cycle

Junbai Wang
2007 Journal of Biomedical Informatics  
using a singular value decomposition method and reducing high-dimensional input gene space by considering genomic properties of gene clusters.  ...  By integrating heterogeneous functional genomic datasets, we have developed a new framework for detecting combinatorial control of gene expression, which includes estimating transcription factor activities  ...  Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/ j.jbi.2007.02.003.  ... 
doi:10.1016/j.jbi.2007.02.003 pmid:17418646 fatcat:h7ogpscr3bbuffsa3og757vqiq

Combining transcriptional datasets using the generalized singular value decomposition

Andreas W Schreiber, Neil J Shirley, Rachel A Burton, Geoffrey B Fincher
2008 BMC Bioinformatics  
Conclusion: We show that the generalized singular value decomposition provides a viable tool for a combined analysis of two gene expression datasets with only partial overlap of both gene sets and experimental  ...  Results: We show here that the generalized singular value decomposition provides a practical tool for merging a small, targeted dataset obtained by quantitative real-time PCR of specific genes with a much  ...  singular value decomposition (GSVD) [37] [38] [39] .  ... 
doi:10.1186/1471-2105-9-335 pmid:18687147 pmcid:PMC2562393 fatcat:amkbzxmkynbjnh66baxguc3dma
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