Package: MOTL 0.99.1

Morgane Térézol

MOTL: Multi-omics matrix factorization with transfer learning

A transfer learning algorithm for multi-omics matrix factorization called 'MOTL' (Multi-Omics Transfer Learning). 'MOTL' is a Bayesian transfer learning method, based on 'MOFA'. 'MOTL' infers latent factor values for a multi-omics target dataset, consisting of a small number of samples, by incorporating latent factor values already inferred with a 'MOFA' factorization of a large, heterogeneous, learning dataset.

Authors:David Hirst [aut], Morgane Térézol [cre]

MOTL_0.99.1.tar.gz
MOTL_0.99.1.zip(r-4.7)MOTL_0.99.1.zip(r-4.6)MOTL_0.99.1.zip(r-4.5)
MOTL_0.99.1.tgz(r-4.6-any)MOTL_0.99.1.tgz(r-4.5-any)
MOTL_0.99.1.tar.gz(r-4.7-any)MOTL_0.99.1.tar.gz(r-4.6-any)
MOTL_0.99.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
MOTL/json (API)
NEWS

# Install 'MOTL' in R:

install.packages('MOTL', repos = c('https://biocstaging.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/moohtus/motl/issues

Datasets:

Lrn - Learning dataset
TL_param - Transfer learning parameters
Trg - Target datasets

On CRAN:

dimensionreduction featureextraction bayesian normalization

3.74 score 28 exports 92 dependencies

Last updated from:2b258d0a0a. Checks:8 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	203
linux-devel-x86_64	NOTE	299
source / vignettes	OK	311
linux-release-x86_64	NOTE	318
macos-release-arm64	NOTE	228
macos-oldrel-arm64	NOTE	215
windows-devel	NOTE	248
windows-release	NOTE	220
windows-oldrel	NOTE	263
wasm-release	OK	147

Exports:countsNormalization countsTransformation E_Z_SqE_W_Sq_update E_ZE_W_update E_ZSqE_WSq_update E_ZWSq_update ELBO_calculation GeoMeanFun GeoMeans_Lrn_init initTransferLearningParamaters intercepts_calculation mRNA_addVersion preprocessCountsData TargetDataPrefiltering TargetDataPreparation Tau_calculation Tau_init TauLn_calculation TCGATargetDataPrefiltering TCGATargetDataPreparation transferLearning_function VarExplFun W0_calculation WSq_calculation YGauss_calculation Zeta_calculation ZMu_calculation ZVar_calculation

Dependencies:abind basilisk BH Biobase BiocGenerics biocmake BiocParallel cli codetools corrplot cowplot cpp11 data.table DelayedArray DESeq2 dir.expiry dplyr dqrng farver filelock FNN forcats formatR futile.logger futile.options generics GenomicRanges ggplot2 ggrepel glue gtable h5mread HDF5Array here IRanges irlba isoband jsonlite labeling lambda.r lattice lifecycle locfit magrittr Matrix MatrixGenerics matrixStats MOFA2 pheatmap pillar pkgconfig plyr png purrr R6 rappdirs RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppProgress RcppTOML reshape2 reticulate rhdf5 rhdf5filters Rhdf5lib rlang rprojroot RSpectra Rtsne S4Arrays S4Vectors S7 scales Seqinfo sitmo snow SparseArray stringi stringr SummarizedExperiment tibble tidyr tidyselect utf8 uwot vctrs viridisLite withr XVector

How to run MOTL: basic example

Rendered fromMOTL.Rmdusingknitr::rmarkdownon Jun 21 2026.

Last update: 2026-05-07
Started: 2026-05-04

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Normalize counts data	countsNormalization
Log2 transform and select top data based on variance	countsTransformation
Calculate 'E_Z_SqE_W_Sq'	E_Z_SqE_W_Sq_update
Calculate 'E_ZE_W'	E_ZE_W_update
Calculate 'E_ZSqE_WSq'	E_ZSqE_WSq_update
Calculate 'E_ZWSq'	E_ZWSq_update
Calculate the ELBO value for the current view/iterations	ELBO_calculation
Calculate the Geometric mean of a vector	GeoMeanFun
Retrieve the Geometric means calculated for the learning dataset during counts normalization	GeoMeans_Lrn_init
Transfer learning parameters and data objects initialization	initTransferLearningParamaters
Intercepts calculation	intercepts_calculation
Learning dataset	Lrn
Format mRNA features to match with learning dataset	mRNA_addVersion
Preprocess counts data	preprocessCountsData
Prepare the target data for a given view	TargetDataPrefiltering
Target data preparation for transfer learning	TargetDataPreparation
Update Tau values for the current view	Tau_calculation
Initialization of the Tau values for each view	Tau_init
Initialization of the log(Tau) values	TauLn_calculation
Filter out TCGA target subset data according variance	TCGATargetDataPrefiltering
Prepare TCGA target dataset for transfer learning	TCGATargetDataPreparation
Transfer learning parameters	TL_param
Transfer Learning with Variational Inference	transferLearning_function
Target datasets	Trg
Calculate the variance explained by each factor for each view	VarExplFun
Initialization of feature weight intercept values	W0_calculation
Initialization of the squared weight values	WSq_calculation
Initialize or update pseudo Y values (YGauss)	YGauss_calculation
Calculate the Zeta matrix for the current data view	Zeta_calculation
Z matrix 'ZMu' calculation for the current data	ZMu_calculation
Calculation of the Z variances for the current data	ZVar_calculation