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, and Tucker. 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.

Last updated 2 years ago

6 stars 7.63 score 0 dependencies 30 dependents

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.

Last updated 5 months ago

14 stars 6.53 score 37 dependencies 4 dependents

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>.

Last updated 5 months ago

3 stars 5.08 score 38 dependencies

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>.

Last updated 1 years ago

1 stars 4.95 score 63 dependencies 1 dependents

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.

Last updated 5 months ago

annotationannotationdatainfrastructure

4.73 score 38 dependencies 3 dependents

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.

Last updated 15 days ago

softwareinfrastructuredatarepresentationdimensionreduction

4 stars 4.68 score 41 dependencies

meshr - Tools for conducting enrichment analysis of MeSH

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

Last updated 5 months ago

annotationdatafunctionalannotationbioinformaticsstatisticsannotationmultiplecomparisonsmeshdb

4.56 score 75 dependencies 1 dependents

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.

Last updated 5 months ago

dimensionreductionsinglecellsoftwaregeneexpression

4.18 score 225 dependencies

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

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

Last updated 5 months ago

dimensionreductionqualitycontrolsinglecellsoftwaregeneexpression

4.00 score 139 dependencies

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). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.

Last updated 2 years ago

4.00 score 3 dependencies

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>.

Last updated 2 years ago

1 stars 3.70 score 6 dependencies

LRBaseDbi - DBI to construct LRBase-related package

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

Last updated 5 months ago

infrastructure

3.48 score 38 dependencies

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.

Last updated 2 years ago

3.48 score 19 dependencies 2 dependents

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

Last updated 5 months ago

rnaseqdifferentialexpressionsequencingimmunooncology

3.30 score 7 dependencies

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.

Last updated 1 years ago

3 stars 3.18 score 5 dependencies

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.

Last updated 1 years ago

2.70 score 73 dependencies