Package: DGP4LCF 1.0.0

DGP4LCF: Dependent Gaussian Processes for Longitudinal Correlated Factors

Model high-dimensional gene expression trajectories using dynamic factor analysis with dependent Gaussian processes

Authors:Jiachen Cai [aut, cre]

DGP4LCF_1.0.0.tar.gz
DGP4LCF_1.0.0.zip(r-4.7)DGP4LCF_1.0.0.zip(r-4.6)DGP4LCF_1.0.0.zip(r-4.5)
DGP4LCF_1.0.0.tgz(r-4.6-x86_64)DGP4LCF_1.0.0.tgz(r-4.6-arm64)DGP4LCF_1.0.0.tgz(r-4.5-x86_64)DGP4LCF_1.0.0.tgz(r-4.5-arm64)
DGP4LCF_1.0.0.tar.gz(r-4.7-arm64)DGP4LCF_1.0.0.tar.gz(r-4.7-x86_64)DGP4LCF_1.0.0.tar.gz(r-4.6-arm64)DGP4LCF_1.0.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
DGP4LCF/json (API)

# Install 'DGP4LCF' in R:

install.packages('DGP4LCF', repos = c('https://jcai-1122.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jcai-1122/dgp4lcf/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

sim_fcs_init - Initials values.
sim_fcs_results_irregular_6_8 - Results when people have irregularly observed time points (some 6 while others 8).
sim_fcs_results_regular_8 - Results when people are observed at common 8 time points.
sim_fcs_truth - Truth of simulated data.

On CRAN:

openblas cpp

3.30 score 3 scripts 592 downloads 13 exports 77 dependencies

Last updated from:b33082b0d4. Checks:11 ERROR, 1 OK, 1 FAIL. Indexed: yes.

Target	Result	Time
linux-devel-arm64	ERROR	181
linux-devel-x86_64	ERROR	195
source / vignettes	OK	263
linux-release-arm64	ERROR	182
linux-release-x86_64	ERROR	190
macos-release-arm64	ERROR	166
macos-release-x86_64	ERROR	254
macos-oldrel-arm64	ERROR	149
macos-oldrel-x86_64	ERROR	296
windows-devel	ERROR	218
windows-release	ERROR	182
windows-oldrel	ERROR	196
wasm-release	FAIL	148

Exports:factor_loading_heatmap factor_score_trajectory gibbs_after_mcem_algorithm gibbs_after_mcem_combine_chains gibbs_after_mcem_diff_initials gibbs_after_mcem_load_chains mcem_algorithm mcem_cov_plot mcem_parameter_setup numerics_summary_do_not_need_alignment numerics_summary_need_alignment subject_specific_objects table_generator

Dependencies:ash bitops cli cluster coda codetools colorspace combinat corrplot cpp11 deldir deSolve doParallel dotCall64 evaluate factor.switching farver fda fda.usc fds fields FNN foreach ggplot2 glue GPFDA gtable HDInterval hdrcde highr interp isoband iterators kernlab KernSmooth knitr ks kSamples labeling lattice lifecycle locfit lpSolve maps MASS Matrix MatrixModels mclust mcmc MCMCpack mgcv multicool mvtnorm nlme pcaPP pheatmap pracma quantreg R6 rainbow RColorBrewer Rcpp RcppArmadillo RcppEigen RCurl rlang S7 scales spam SparseM SuppDists survival vctrs viridisLite withr xfun yaml

An Example of Irregular Data Analysis

Rendered frombsfadgp_irregular_data_example.Rmdusingknitr::rmarkdown

Last update: 2023-06-26
Started: 2023-05-17

An Example of Regular Data Analysis

Rendered frombsfadgp_regular_data_example.Rmdusingknitr::rmarkdown

Last update: 2023-06-26
Started: 2023-05-17

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Displaying significant factor loadings in the heatmap.	factor_loading_heatmap
Plotting figures for factor score trajectory.	factor_score_trajectory
Generating posterior samples for parameters (other than DGP parameters) in the model and predicted gene expression for one chain.	gibbs_after_mcem_algorithm
Combining from all chains the posterior samples for parameters in the model and predicted gene expressions.	gibbs_after_mcem_combine_chains
Generating different initials for multiple chains.	gibbs_after_mcem_diff_initials
Loading the saved posterior samples for parameters in the model and predicted gene expressions.	gibbs_after_mcem_load_chains
Monte Carlo Expectation Maximization (MCEM) algorithm to return the Maximum Likelihood Estimate (MLE) of DGP Parameters.	mcem_algorithm
Visualizing cross-correlations among factors.	mcem_cov_plot
Parameters' setup and initial value assignment for the Monte Carlo Expectation Maximization (MCEM) algorithm.	mcem_parameter_setup
Numerical summary for important continuous variables that do not need alignment.	numerics_summary_do_not_need_alignment
Numerical summary for factor loadings and factor scores, which need alignment.	numerics_summary_need_alignment
Initials values.	sim_fcs_init
Results when people have irregularly observed time points (some 6 while others 8).	sim_fcs_results_irregular_6_8
Results when people are observed at common 8 time points.	sim_fcs_results_regular_8
Truth of simulated data.	sim_fcs_truth
Constructing subject-specific objects required for Gibbs sampler (for subjects with incomplete observations only).	subject_specific_objects
Generating a table listing all possible combinations of the binary variables for one gene.	table_generator