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rTensor - Tools for Tensor Analysis and Decomposition

A set of tools for creation, manipulation, and modeling of tensors with arbitrary number of modes. A tensor in the context of data analysis is a multidimensional array. rTensor does this by providing a S4 class 'Tensor' that wraps around the base 'array' class. rTensor provides common tensor operations as methods, including matrix unfolding, summing/averaging across modes, calculating the Frobenius norm, and taking the inner product between two tensors. Familiar array operations are overloaded, such as index subsetting via '[' and element-wise operations. rTensor also implements various tensor decomposition, including CP, GLRAM, MPCA, PVD, Tucker, INDSCAL, RESCAL, DEDICOM, PARAFAC2, and 2DLDA. For tensors with 3 modes, rTensor also implements transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some auxiliary functions include the Khatri-Rao product, Kronecker product, and the Hadamard product for a list of matrices.

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8.52 score 7 stars 32 dependents 523 scripts 1.6k downloads

nnTensor - Non-Negative Tensor Decomposition

Some functions for performing non-negative matrix factorization, non-negative CANDECOMP/PARAFAC (CP) decomposition, non-negative Tucker decomposition, and generating toy model data. See Andrzej Cichock et al (2009) and the reference section of GitHub README.md <https://github.com/rikenbit/nnTensor>, for details of the methods.

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7.00 score 21 stars 4 dependents 8 scripts 733 downloads

iTensor - ICA-Based Matrix/Tensor Decomposition

Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented. ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively. For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.

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5.73 score 2 stars 1 dependents 2 scripts 230 downloads

Vicus - Exploiting Local Structures to Improve Network-Based Analysis of Biological Data

Compared with the similar graph embedding method such as Laplacian Eigenmaps, 'Vicus' can exploit more local structures of graph data. For the details of the methods, see the reference section of 'GitHub' README.md <https://github.com/rikenbit/Vicus>.

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5.18 score 2 stars 5 dependents 330 downloads

dcTensor - Discrete Matrix/Tensor Decomposition

Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/dcTensor>.

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5.08 score 3 stars 180 downloads

MeSHDbi - DBI to construct MeSH-related package from sqlite file

The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db.

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annotationannotationdatainfrastructure

4.87 score 3 dependents 41 scripts 541 downloads

meshr - Tools for conducting enrichment analysis of MeSH

A set of annotation maps describing the entire MeSH assembled using data from MeSH.

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annotationdatafunctionalannotationbioinformaticsstatisticsannotationmultiplecomparisonsmeshdb

4.73 score 1 stars 1 dependents 15 scripts 476 downloads

DelayedTensor - R package for sparse and out-of-core arithmetic and decomposition of Tensor

DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum.

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softwareinfrastructuredatarepresentationdimensionreduction

4.51 score 4 stars 3 scripts 346 downloads

guidedPLS - Supervised Dimensional Reduction by Guided Partial Least Squares

Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). This package provides implementations of PLS-SVD, guided-PLS, and guided-PCA for supervised dimensionality reduction. The guided-PCA function (new in v1.1.0) automatically handles mixed data types (continuous and categorical) in the supervision matrix and provides detailed contribution analysis for interpretability. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.

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4.30 score 1 stars 2 scripts 485 downloads

otTensor - Optimal Tensor Transport

An optimal transport (OT) method, which can handle tensors of any order by learning possibly multiple transport plans. For the details of the methods, see Kerdoncuff et al. (2022) <doi:10.1609/aaai.v36i7.20695>.

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4.30 score

scTensor - Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition

The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.

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dimensionreductionsinglecellsoftwaregeneexpression

4.18 score 2 scripts 334 downloads

mwTensor - Multi-Way Component Analysis

For single tensor data, any matrix factorization method can be specified the matricised tensor in each dimension by Multi-way Component Analysis (MWCA). An originally extended MWCA is also implemented to specify and decompose multiple matrices and tensors simultaneously (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.

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4.00 score 3 scripts 241 downloads

scTGIF - Cell type annotation for unannotated single-cell RNA-Seq data

scTGIF connects the cells and the related gene functions without cell type label.

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dimensionreductionqualitycontrolsinglecellsoftwaregeneexpression

4.00 score 10 scripts 380 downloads

ttTensor - Tensor-Train Decomposition

Tensor-train is a compact representation for higher-order tensors. Some algorithms for performing tensor-train decomposition are available such as TT-SVD, TT-WOPT, and TT-Cross. For the details of the algorithms, see I. V. Oseledets (2011) <doi:10.1137/090752286>, Yuan Longao, et al (2017) <doi:10.48550/arXiv.1709.02641>, I. V. Oseledets (2010) <doi:10.1016/j.laa.2009.07.024>.

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3.60 score 4 stars 6 scripts 475 downloads

ccTensor - CUR/CX Tensor Decomposition

CUR/CX decomposition factorizes a matrix into two factor matrices and Multidimensional CX Decomposition factorizes a tensor into a core tensor and some factor matrices. See the reference section of GitHub README.md <https://github.com/rikenbit/ccTensor>, for details of the methods.

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3.48 score 2 dependents 233 downloads

LRBaseDbi - DBI to construct LRBase-related package

Interface to construct LRBase package (LRBase.XXX.eg.db).

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infrastructure

3.30 score 7 scripts 337 downloads

metaSeq - Meta-analysis of RNA-Seq count data in multiple studies

The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method

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rnaseqdifferentialexpressionsequencingimmunooncology

3.30 score 2 scripts 502 downloads

gcTensor - Generalized Coupled Tensor Factorization

Multiple matrices/tensors can be specified and decomposed simultaneously by Probabilistic Latent Tensor Factorisation (PLTF). See the reference section of GitHub README.md <https://github.com/rikenbit/gcTensor>, for details of the method.

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3.18 score 3 stars 125 downloads

otTensor - Optimal Tensor Transport

An optimal transport (OT) method, which can handle tensors of any order by learning possibly multiple transport plans. For the details of the methods, see Kerdoncuff et al. (2022) <doi:10.1609/aaai.v36i7.20695>.

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2.30 score

symTensor - Symmetric Matrix and Tensor Decomposition

Provides symmetric matrix and tensor operations and decomposition algorithms including symmetric NMF (symNMF), PageRank, Label Propagation, Higher-order Power Method, and TOPHITS. Designed to work with 'rTensor' objects. Methods are described in Kuang et al. (2012) <doi:10.1137/1.9781611972825.10>, Kolda and Mayo (2011) <doi:10.1137/100801482>, and Kolda and Bader (2006) <doi:10.1137/1.9781611972764.26>.

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1.48 score 1 dependents

hmmTensor - Hidden Markov Model by Matrix and Tensor Decomposition

Solves Hidden Markov Models (HMMs) via matrix and tensor decomposition. Converts observation sequences to co-occurrence matrices/tensors and applies Symmetric Non-negative Matrix Factorization (symNMF), Singular Value Decomposition (SVD), CANDECOMP/PARAFAC (CP) decomposition, or Tensor-Train (TT) decomposition to recover HMM parameters. Also provides standard HMM algorithms (Forward, Backward, Viterbi, Baum-Welch) for comparison. The spectral learning approach for HMMs is based on Hsu, Kakade, and Zhang (2012) <doi:10.1016/j.jcss.2011.12.025>. The symNMF method is described in Kuang, Yun, and Park (2015) <doi:10.1007/s10898-014-0247-2>. The Tensor-Train decomposition is described in Oseledets (2011) <doi:10.1137/090752286>.

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1.00 score 2 scripts

symTensor - Symmetric Matrix and Tensor Decomposition

Provides symmetric matrix and tensor operations and decomposition algorithms including symmetric NMF (symNMF), PageRank, Label Propagation, Higher-order Power Method, and TOPHITS. Designed to work with 'rTensor' objects. Methods are described in Kuang et al. (2012) <doi:10.1137/1.9781611972825.10>, Kolda and Mayo (2011) <doi:10.1137/100801482>, and Kolda and Bader (2006) <doi:10.1137/1.9781611972764.26>.

Last updated

1.00 score