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Robust biclustering by sparse singular value decomposition incorporating stability selection
2011
Computer applications in the biosciences : CABIOS
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Despite of huge diversity regarding the mathematical concepts of the different biclustering methods, many of them can be related to the singular value decomposition (SVD). ...
Recently, a sparse SVD approach (SSVD) has been proposed to reveal biclusters in gene expression data. In this article, we propose to incorporate stability selection to improve this method. ...
By incorporating the stability selection, a stopping criterion can be defined. ...
doi:10.1093/bioinformatics/btr322
pmid:21636597
fatcat:6yo7f4hbubbg5mwju2ikaoniti
Robust Integrative Biclustering for Multi-view Data
[article]
2021
arXiv
pre-print
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Our method, integrative sparse singular value decomposition (iSSVD), incorporates stability selection to control Type I error rates, estimates the probability of samples and variables to belong to a bicluster ...
We extend an existing biclustering method based on sparse singular value decomposition for single-view data to data from multiple views. ...
Finally, we incorporate the concept of stability selection to identify robust biclusters and control Type 1 error rates of falsely selecting samples and variables in a bicluster. ...
arXiv:2111.06209v1
fatcat:xezdt5n5yzchvooji6qoztiezq
bioNMF: a versatile tool for non-negative matrix factorization in biology
2006
BMC Bioinformatics
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This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. ...
Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types ...
Acknowledgements This work has been partially funded by the Spanish grants CICYT BFU2004-00217/BMC, GEN2003-20235-c05-05, CYTED-505PI0058, TIN2005-5619, PR27/05-13964-BSCH and a collaborative grant between ...
doi:10.1186/1471-2105-7-366
pmid:16875499
pmcid:PMC1550731
fatcat:5tyxoqbuezbfjbgaq6dg6xjvgy
Scalable Interpretable Multi-Response Regression via SEED
[article]
2016
arXiv
pre-print
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In this paper, we suggest a scalable procedure called sequential estimation with eigen-decomposition (SEED) which needs only a single top-r singular value decomposition to find the optimal low-rank and ...
sparse matrix by solving a sparse generalized eigenvalue problem. ...
In fact, the above decomposition (2) for C * coincides with the singular value decomposition of XC * through different scalings on the singular vectors. ...
arXiv:1608.03686v1
fatcat:q5wibugdsrh2dp4l4pxva77gbe
A New Algorithm for Convex Biclustering and Its Extension to the Compositional Data
[article]
2021
arXiv
pre-print
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biclusters required by existing convex biclustering algorithms. ...
Multiple biclustering algorithms have been developed in the past two decades, among which the convex biclustering can guarantee a global optimum by formulating in as a convex optimization problem. ...
Acknowledgement Hu and Li's research is partially supported by a National Institute of Health (NIH) grant (5R01DK110014). ...
arXiv:2011.12182v2
fatcat:onylus2refexhmvanvlebuv5va
Biclustering on expression data: A review
2015
Journal of Biomedical Informatics
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Most of biclustering approaches use a measure or cost function that determines the quality of biclusters. ...
Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. ...
Acknowledgement This research has been supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2011-28956. ...
doi:10.1016/j.jbi.2015.06.028
pmid:26160444
fatcat:3w5p45w4zzebxmnphdue67ueny
Non-Negative Factorization for Clustering of Microarray Data
2014
International Journal of Computers Communications & Control
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Typically, gene expression data are formed by thousands of genes associated to tens or hundreds of samples. ...
One solution for improving the class discovery efficiency is provided by data dimensionality reduction, where data is decomposed into lower dimensional factors, so that those factors approximate original ...
Approach One of the frequently used dimensionality reduction approaches is given by singular values decomposition (SVD). ...
doi:10.15837/ijccc.2014.1.866
fatcat:dlbifvalnjfulhn5a6ece3wh6m
Matrix Reordering Methods for Table and Network Visualization
2016
Computer graphics forum (Print)
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Value Decomposition (SVD) step to derive a permutation. ...
(d) Single Value Decomposition: Liu et al.
rowMean = mean(M).
Figure 11 : 11 Heuristic Approaches for Matrix Reordering.
Figure 12 : 12 Example for Heuristic Matrix Reorderings. ...
They found that for feature retrieval tasks, the Manhattan Distance is a robust choice and outperforms Jensen-Shannon Divergence and even the Euclidean Distance. ...
doi:10.1111/cgf.12935
fatcat:oaqptigpdja4fbemtowma3zpcm
On Bayesian new edge prediction and anomaly detection in computer networks
2019
Annals of Applied Statistics
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This work was supported in part by The Alan Turing Institute and the Lloyds Register Foundation Programme on Data-Centric Engineering. ...
When performing sparse truncated-SVD incorporating stability selection, a dimensionality of K = 6 is automatically chosen. ...
The commonly used spectral biclustering algorithm of Dhillon (2001) and Cho et al. (2004) calculates a truncated-singular value decomposition of (5.2) D −1/2 X AD −1/2 Y . ...
doi:10.1214/19-aoas1286
fatcat:kshb6cj6qngcbgqwkuwz3q6naa
Three-way clustering of multi-tissue multi-individual gene expression data using semi-nonnegative tensor decomposition
2019
Annals of Applied Statistics
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Through simulation and application to the GTEx RNA-seq data from 53 human tissues, we show that MultiCluster identifies three-way interactions with high accuracy and robustness. ...
We further develop a tensor projection procedure which detects covariate-related genes with high power, demonstrating the advantage of tensor-based methods in incorporating information across similar tissues ...
This research is supported in part by a Math+X research grant from the Simons Foundation, a Packard Fellowship for Science and Engineering, and a National Institutes of Health grant R01-GM094402. ...
doi:10.1214/18-aoas1228
pmid:33381253
pmcid:PMC7771883
fatcat:bpq6h35zlnehrfhnhvauiaeeui
Inference of gene regulatory subnetworks from time course gene expression data
2012
BMC Bioinformatics
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Results: We propose the network and community identification (NCI) method for identifying multiple subnetworks from gene expression data by incorporating community structure information into GRN inference ...
Particularly, we apply sparse singular value decomposition (SSVD) [17] on a general L* to identify the communities in GRNs. The NCI method is summarized in Algorithm 1. ...
Consider the sigular value decompostion (SVD) of the matrix X X = U V T , (18) where U and V are orthogonal matrices consisting of singular vectors, and Σ is the diagnal matrix made up of the singular ...
doi:10.1186/1471-2105-13-s9-s3
pmid:22901088
pmcid:PMC3372453
fatcat:l6irizresjgjrjicdsq5sqtima
Learning the Structure of Biomedical Relationships from Unstructured Text
2015
PLoS Computational Biology
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Here we describe a novel algorithm, Ensemble Biclustering for Classification (EBC), that learns the structure of biomedical relationships automatically from text, overcoming differences in word choice ...
LSA uses the singular value decomposition (SVD) [37] instead of ITCC to accomplish a similar goal, and has been applied in at least one case to corpus-level relationship extraction (a technique called ...
Comparing EBC to Latent Semantic Analysis (LSA) To investigate how similar EBC's performance was to a more established method designed to solve a similar problem, we used the singular value decomposition ...
doi:10.1371/journal.pcbi.1004216
pmid:26219079
pmcid:PMC4517797
fatcat:lvc57v6l4jfehb2ppl7qyaim2u
Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)
2018
Bioinformatics and Biology Insights
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The normalized singular value represents the square root of the first eigenvalue. ...
Multiple factor analysis starts from a PCA on each block (type) of data and followed by jointly analyzing the singular-value normalized data using the global PCA. ...
Integration occurs at the subject level: the input data are an original expression or sequence variables from the same subject, data are merged by subject ID. ...
doi:10.1177/1177932218759292
pmid:29497285
pmcid:PMC5824897
fatcat:nbknjl4qq5awrldy7natmg3h6y
Bioinformatics: Organisms from Venus, Technology from Jupiter, Algorithms from Mars
2003
European Journal of Control
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In this paper, we discuss datasets that are being generated by microarray technology, which makes it possible to measure in parallel the activity or expression of thousands of genes simultaneously. ...
This year 2003 marks the 50 th anniversary of the discovery of the double helix structure of DNA, the basic building structure of all living organisms, by Crick and Watson, in the 1 page landmark paper ...
In this Section, we will elaborate on some often used algorithms in microarray data analysis, including Principal Component Analysis (or the Singular Value Decomposition) and some classical and newly developed ...
doi:10.3166/ejc.9.237-278
fatcat:w26h4jvugjg35abzgqctb2yubi
A Biclustering Framework for Consensus Problems
2014
SIAM Journal of Imaging Sciences
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2014 pre-print arXiv:1405.6159v2 fatcat:j6auynhd4zcfdkru5hcwmlc2qeOther Versions
Among many tools for bi-clustering, see [35] and references therein, the Penalized Matrix Decomposition (PMD) [49] and the Sparse Singular Value Decomposition (SSVD) [28] have shown great promise ...
Hence, the value of ε is not critical, our model being inherently robust to poor models, as shown in Section 2. ...
doi:10.1137/140967325
fatcat:umu2aqg6onaoxkq5yoea6ctjw4
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