Package: LongDat 1.1.3

LongDat: A Tool for 'Covariate'-Sensitive Longitudinal Analysis on 'omics' Data

This tool takes longitudinal dataset as input and analyzes if there is significant change of the features over time (a proxy for treatments), while detects and controls for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input, including count, proportion, binary, ordinal and continuous data. The output table contains p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.

Authors:Chia-Yu Chen [aut, cre], Sofia Forslund [ctb]

LongDat_1.1.3.tar.gz
LongDat_1.1.3.zip(r-4.7-any)LongDat_1.1.3.zip(r-4.6-any)LongDat_1.1.3.zip(r-4.5-any)
LongDat_1.1.3.tgz(r-4.6-any)LongDat_1.1.3.tgz(r-4.5-any)
LongDat_1.1.3.tar.gz(r-4.7-any)LongDat_1.1.3.tar.gz(r-4.6-any)
LongDat_1.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
LongDat/json (API)

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

Bug tracker:https://github.com/ccy-dev/longdat/issues

Datasets:

On CRAN:

Conda:

4.48 score 3 stars 8 scripts 393 downloads 5 exports 123 dependencies

Last updated from:e992370baf. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK185
source / vignettesOK243
linux-releaseOK216
macos-releaseOK250
macos-oldrelOK223
windows-develOK122
windows-releaseOK141
windows-oldrelOK174
wasm-releaseOK135

Exports:cuneiform_plotlongdat_contlongdat_discmake_master_tabletheta_plot

Dependencies:abindbackportsbestNormalizebootbroombutchercarcarDataclasscliclockcodetoolscolorspacecorrplotcowplotcpp11crayondata.tableDerivdiagramdigestdoBydoParalleldoRNGdplyreffsizeemmeansestimabilityfarverforeachforecastFormulafracdifffuturefuture.applygenericsggplot2glmmTMBglobalsgluegowergtablehardhathmsipredisobanditeratorsKernSmoothlabelingLambertWlamWlatticelavalifecyclelistenvlme4lmtestlobstrlubridatemagrittrMASSMatrixMatrixModelsmgcvminqamodelrmvtnormnlmenloptrnnetnortestnumDerivparallellypatchworkpbkrtestpillarpkgconfigplyrprettyunitsprodlimprogressprogressrpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRdpackrecipesreformulasreshape2rlangrngtoolsrpartrstatixRTMBS7sandwichscalesshapeSparseMsparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDateTMBtzdburcautf8vctrsviridisLitewithrzoo

Longitudinal analysis pipeline with longdat_cont()
Introduction | Explaining the input data frame format | Preparing the input data frame with make_master_table() | Run longdat_cont() | Results | Result table | Covariate table | Result interpretation | Plotting the result | Wrap-up
Rendered fromLongDat_cont_tutorial.Rmdusingknitr::rmarkdown

Last update: 2023-04-17
Started: 2021-08-13

Longitudinal analysis pipeline with longdat_disc()
Introduction | Explaining the input data frame format | Preparing the input data frame with make_master_table() | Run longdat_disc() | Results | Result table | Covariate table | Result interpretation | Plotting the result | Wrap-up
Rendered fromLongDat_disc_tutorial.Rmdusingknitr::rmarkdown

Last update: 2023-04-17
Started: 2021-08-13

Readme and manuals

Help Manual

Help pageTopics
Effect size (Cliff's delta) calculation in longdat_disc() pipelinecliff_cal
Covariate model test in longdat_cont() pipelineConModelTest_cont
Covariate model test in longdat_disc() pipelineConModelTest_disc
Post-hoc test based on correlation test for longdat_cont().correlation_posthoc
Create cuneiform plots of result table from longdat_disc() or longdat_cont()cuneiform_plot
Data preprocessingdata_preprocess
Calculate the p values for every factor (used for selecting factors later)factor_p_cal
Generate result table as output in longdat_cont()final_result_summarize_cont
Generate result table as output in longdat_disc()final_result_summarize_disc
Replace the symbols in variable and covariate names in raw inputfix_name_fun
Longitudinal analysis with time as continuous variablelongdat_cont
data/LongDat_cont_feature_table.RData documentationLongDat_cont_feature_table
data/LongDat_cont_master_table.RData documentationLongDat_cont_master_table
data/LongDat_cont_metadata_table.RData documentationLongDat_cont_metadata_table
Longitudinal analysis with time as discrete variablelongdat_disc
data/LongDat_disc_feature_table.RData documentationLongDat_disc_feature_table
data/LongDat_disc_master_table.RData documentationLongDat_disc_master_table
data/LongDat_disc_metadata_table.RData documentationLongDat_disc_metadata_table
Create input master table from metadata and feature tables for longdat_disc() and longdat_cont()make_master_table
Null Model Test and post-hoc Test in longdat_cont() pipelineNuModelTest_cont
Null Model Test and post-hoc Test in longdat_disc() pipelineNuModelTest_disc
Randomized negative control for count data in longdat_cont()random_neg_ctrl_cont
Randomized negative control for count data in longdat_disc()random_neg_ctrl_disc
Remove the dependent variables that are below the threshold of sparsity when the data type is count data in longdat_cont()rm_sparse_cont
Remove the dependent variables that are below the threshold of sparsity when the data type is count data in longdat_disc()rm_sparse_disc
Plot theta values of negative binomial models versus non-zero count for count datatheta_plot
Unlist confound (covariate) and inverse confound (covariate) tables, turn them into tablesunlist_table
Wilcoxon post-hoc testwilcox_posthoc