Type:	Package
Title:	Collaborative Graphical Lasso - Multi-Omics Network Reconstruction
Version:	1.0.2
Description:	Reconstruct networks from multi-omics data sets with the collaborative graphical lasso (coglasso) algorithm described in Albanese, A., Kohlen, W., and Behrouzi, P. (2024) <doi:10.48550/arXiv.2403.18602>. Build multiple networks using the coglasso() function, select the best one with stars_coglasso().
URL:	https://github.com/DrQuestion/coglasso, https://drquestion.github.io/coglasso/
BugReports:	https://github.com/DrQuestion/coglasso/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Imports:	Matrix, Rcpp (≥ 1.0.11), stats, utils
LinkingTo:	Rcpp, RcppEigen
Depends:	R (≥ 2.10)
LazyData:	true
Encoding:	UTF-8
RoxygenNote:	7.2.3
Suggests:	igraph, knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	yes
Packaged:	2024-04-03 10:39:10 UTC; aless
Author:	Alessio Albanese [aut, cre, cph], Pariya Behrouzi [aut]
Maintainer:	Alessio Albanese <alessio.albanese@wur.nl>
Repository:	CRAN
Date/Publication:	2024-04-03 20:02:59 UTC

coglasso: Collaborative Graphical Lasso - Multi-Omics Network Reconstruction

Description

Reconstruct networks from multi-omics data sets with the collaborative graphical lasso (coglasso) algorithm described in Albanese, A., Kohlen, W., and Behrouzi, P. (2024) arXiv:2403.18602. Build multiple networks using the 'coglasso' function, select the best one with 'stars_coglasso'.

Author(s)

Maintainer: Alessio Albanese alessio.albanese@wur.nl (ORCID) [copyright holder]

Authors:

• Pariya Behrouzi pariya.behrouzi@wur.nl (ORCID)

See Also

Useful links:

• https://github.com/DrQuestion/coglasso

• https://drquestion.github.io/coglasso/

• Report bugs at https://github.com/DrQuestion/coglasso/issues

Estimate networks from a multi-omics data set

Description

coglasso() estimates multiple multi-omics networks with the algorithm collaborative graphical lasso, one for each combination of input values for the hyperparameters \lambda_w, \lambda_b and c.

Usage

coglasso(
  data,
  pX,
  lambda_w = NULL,
  lambda_b = NULL,
  c = NULL,
  nlambda_w = NULL,
  nlambda_b = NULL,
  nc = NULL,
  lambda_w_max = NULL,
  lambda_b_max = NULL,
  c_max = NULL,
  lambda_w_min_ratio = NULL,
  lambda_b_min_ratio = NULL,
  c_min_ratio = NULL,
  cov_output = FALSE,
  verbose = TRUE
)

Arguments

Value

coglasso() returns a list containing several elements:

• loglik is a numerical vector containing the log likelihoods of all the estimated networks.

• density is a numerical vector containing a measure of the density of all the estimated networks.

• df is an integer vector containing the degrees of freedom of all the estimated networks.

• convergence is a binary vector containing whether a network was successfully estimated for the given combination of hyperparameters or not.

• path is a list containing the adjacency matrices of all the estimated networks.

• icov is a list containing the inverse covariance matrices of all the estimated networks.

• nexploded is the number of combinations of hyperparameters for which coglasso() failed to converge.

• data is the input multi-omics data set.

• hpars is the ordered table of all the combinations of hyperparameters given as input to coglasso(), with \alpha(\lambda_w+\lambda_b) being the key to sort rows.

• lambda_w is a numerical vector with all the \lambda_w values coglasso() used.

• lambda_b is a numerical vector with all the \lambda_b values coglasso() used.

• c is a numerical vector with all the c values coglasso() used.

• pX is the number of variables of the first data set.

• cov optional, returned when cov_output is TRUE, is a list containing the variance-covariance matrices of all the estimated networks.

Examples

# Typical usage: set the number of hyperparameters to explore
cg <- coglasso(multi_omics_sd_micro, pX = 4, nlambda_w = 3, nlambda_b = 3, nc = 3, verbose = FALSE)

Multi-omics dataset of sleep deprivation in mouse

Description

A dataset containing transcript and metabolite values analysed in Albanese et al. 2023, subset of the multi-omics data set published in Jan, M., Gobet, N., Diessler, S. et al. A multi-omics digital research object for the genetics of sleep regulation. Sci Data 6, 258 (2019).

multi_omics_sd_small is a smaller version, limited to the transcript Cirbp and the transcripts and metabolites belonging to its neighborhood as described in Albanese et al. 2023

multi_omics_sd_micro is a minimal version with Cirbp and a selection of its neighborhood.

Usage

multi_omics_sd

multi_omics_sd_small

multi_omics_sd_micro

Format

multi_omics_sd

A data frame with 30 rows and 238 variables (162 transcripts and 76 metabolites):

Plin4 to Tfrc

log2 CPM values of 162 transcripts in mouse cortex under sleep deprivation (-4.52–10.46)

Ala to SM C24:1

abundance values of 76 metabolites (0.02–1112.67)

multi_omics_sd_small

A data frame with 30 rows and 19 variables (14 transcripts and 5 metabolites)

Cirbp to Stip1

log2 CPM values of 14 transcripts in mouse cortex under sleep deprivation (4.24–9.31)

Phe to PC ae C32:2

Abundance values of 5 metabolites (0.17–145.33)

multi_omics_sd_micro

A data frame with 30 rows and 6 variables (4 transcripts and 2 metabolites)

Cirbp to Dnajb11

log2 CPM values of 4 transcripts in mouse cortex under sleep deprivation (4.78–9.31)

Trp to PC aa C36:3

Abundance values of 2 metabolites (58.80–145.33)

Source

Jan, M., Gobet, N., Diessler, S. et al. A multi-omics digital research object for the genetics of sleep regulation. Sci Data 6, 258 (2019) doi:10.1038/s41597-019-0171-x

Figshare folder of the original manuscript: https://figshare.com/articles/dataset/Input_data_for_systems_genetics_of_sleep_regulation/7797434

Stability selection of the best coglasso network

Description

stars_coglasso() selects the combination of hyperparameters given to coglasso() yielding the most stable, yet sparse network. Stability is computed upon network estimation from subsamples of the multi-omics data set, allowing repetition. Subsamples are collected for a fixed amount of times (rep_num), and with a fixed proportion of the total number of samples (stars_subsample_ratio).

Usage

stars_coglasso(
  coglasso_obj,
  stars_thresh = 0.1,
  stars_subsample_ratio = NULL,
  rep_num = 20,
  max_iter = 10,
  verbose = TRUE
)

Arguments

Details

StARS for collaborative graphical regression is an adaptation of the method published by Liu, H. et al. (2010): Stability Approach to Regularization Selection (StARS). StARS was developed for network estimation regulated by a single penalty parameter, while collaborative graphical lasso needs to explore three different hyperparameters. In particular, two of these are penalty parameters with a direct influence on network sparsity, hence on stability. For every c parameter, stars_coglasso() explores one of the two penalty parameters (\lambda_w or \lambda_b), keeping the other one fixed at its previous best estimate, using the normal, one-dimentional StARS approach, until finding the best couple. It then selects the c parameter for which the best (\lambda_w, \lambda_b) couple yielded the most stable, yet sparse network.

Value

stars_coglasso() returns a list containing the results of the selection procedure, built upon the list returned by coglasso().

• ... are the same elements returned by coglasso().

• merge_lw and merge_lb are lists with as many elements as the number of c parameters explored. Every element is in turn a list of as many matrices as the number of \lambda_w (or \lambda_b) values explored. Each matrix is the "merged" adjacency matrix, the average of all the adjacency matrices estimated for those specific c and \lambda_w (or \lambda_b) values across all the subsampling in the last path explored before convergence, the one when the final combination of \lambda_w and \lambda_b is selected for the given c value.

• variability_lw and variability_lb are lists with as many elements as the number of c parameters explored. Every element is a numeric vector of as many items as the number of \lambda_w (or \lambda_b) values explored. Each item is the variability of the network estimated for those specific c and \lambda_w (or \lambda_b) values in the last path explored before convergence, the one when the final combination of \lambda_w and \lambda_b is selected for the given c value.

• opt_adj is a list of the adjacency matrices finally selected for each c parameter explored.

• opt_variability is a numerical vector containing the variabilities associated to the adjacency matrices in opt_adj.

• opt_index_lw and opt_index_lb are integer vectors containing the index of the selected \lambda_ws (or \lambda_bs) for each c parameters explored.

• opt_lambda_w and opt_lambda_b are vectors containing the selected \lambda_ws (or \lambda_bs) for each c parameters explored.

• sel_index_c, sel_index_lw and sel_index_lb are the indexes of the final selected parameters c, \lambda_w and \lambda_b leading to the most stable sparse network.

• sel_c, sel_lambda_w and sel_lambda_b are the final selected parameters c, \lambda_w and \lambda_b leading to the most stable sparse network.

• sel_adj is the adjacency matrix of the final selected network.

• sel_density is the density of the final selected network.

• sel_icov is the inverse covariance matrix of the final selected network.

Examples

cg <- coglasso(multi_omics_sd_micro, pX = 4, nlambda_w = 3, nlambda_b = 3, nc = 3, verbose = FALSE)

# Takes around 20 seconds
sel_cg <- stars_coglasso(cg, verbose = FALSE)