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
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DGP4LCF.pdf |DGP4LCF.html✨
DGP4LCF/json (API)

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

Peer review:

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

3.30 score 3 scripts 461 downloads 13 exports 77 dependencies

Last updated 6 months agofrom:b33082b0d4. Checks:OK: 1 ERROR: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-win-x86_64ERROROct 26 2024
R-4.5-linux-x86_64ERROROct 26 2024
R-4.4-win-x86_64ERROROct 26 2024
R-4.4-mac-x86_64ERROROct 26 2024
R-4.4-mac-aarch64ERROROct 26 2024
R-4.3-win-x86_64ERROROct 26 2024
R-4.3-mac-x86_64ERROROct 26 2024
R-4.3-mac-aarch64ERROROct 26 2024

Exports:factor_loading_heatmapfactor_score_trajectorygibbs_after_mcem_algorithmgibbs_after_mcem_combine_chainsgibbs_after_mcem_diff_initialsgibbs_after_mcem_load_chainsmcem_algorithmmcem_cov_plotmcem_parameter_setupnumerics_summary_do_not_need_alignmentnumerics_summary_need_alignmentsubject_specific_objectstable_generator

Dependencies:ashbitopscliclustercodacodetoolscolorspacecombinatcorrplotdeldirdeSolvedoParalleldotCall64factor.switchingfansifarverfdafda.uscfdsfieldsFNNforeachggplot2glueGPFDAgtableHDIntervalhdrcdeinterpisobanditeratorskernlabKernSmoothkskSampleslabelinglatticelifecyclelocfitlpSolvemagrittrmapsMASSMatrixMatrixModelsmclustmcmcMCMCpackmgcvmulticoolmunsellmvtnormnlmepcaPPpheatmappillarpkgconfigpracmaquantregR6rainbowRColorBrewerRcppRcppArmadilloRcppEigenRCurlrlangscalesspamSparseMSuppDistssurvivaltibbleutf8vctrsviridisLitewithr

An Example of Irregular Data Analysis

Rendered frombsfadgp_irregular_data_example.Rmdusingknitr::rmarkdownon Oct 26 2024.

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

An Example of Regular Data Analysis

Rendered frombsfadgp_regular_data_example.Rmdusingknitr::rmarkdownon Oct 26 2024.

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

Readme and manuals

Help Manual

Help pageTopics
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