Package: promor 0.2.1

promor: Proteomics Data Analysis and Modeling Tools

A comprehensive, user-friendly package for label-free proteomics data analysis and machine learning-based modeling. Data generated from 'MaxQuant' can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) <doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).

Authors:Chathurani Ranathunge [aut, cre, cph]

promor_0.2.1.tar.gz
promor_0.2.1.zip(r-4.5)promor_0.2.1.zip(r-4.4)promor_0.2.1.zip(r-4.3)
promor_0.2.1.tgz(r-4.4-any)promor_0.2.1.tgz(r-4.3-any)
promor_0.2.1.tar.gz(r-4.5-noble)promor_0.2.1.tar.gz(r-4.4-noble)
promor_0.2.1.tgz(r-4.4-emscripten)promor_0.2.1.tgz(r-4.3-emscripten)
promor.pdf |promor.html
promor/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/caranathunge/promor/issues

Datasets:

On CRAN:

biomarkersdifferential-expressionlfqmachine-learningmass-spectrometrymodelingproteomics

23 exports 13 stars 1.65 score 120 dependencies 14 scripts 286 downloads

Last updated 1 years agofrom:f3ef8441fc. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 09 2024
R-4.5-winNOTESep 09 2024
R-4.5-linuxNOTESep 09 2024
R-4.4-winNOTESep 09 2024
R-4.4-macNOTESep 09 2024
R-4.3-winOKSep 09 2024
R-4.3-macOKSep 09 2024

Exports:aver_techrepscorr_plotcreate_dffeature_plotfilterbygroup_nafind_depheatmap_deheatmap_naimpute_naimpute_plotnorm_plotnormalize_dataonegroup_onlyperformance_plotpre_processrem_featurerem_sampleroc_plotsplit_datatest_modelstrain_modelsvarimp_plotvolcano_plot

Dependencies:abindbackportsBiobaseBiocGenericsbootbroomcarcarDatacaretclasscliclockcodetoolscolorspacecowplotcpp11data.tableDEoptimRDerivdiagramdigestdoBydoRNGdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2ggrepelglobalsgluegowergridExtragtablehardhatipredisobanditeratorsitertoolsjsonlitekernlabKernSmoothlabelinglaekenlatticelavalifecyclelimmalistenvlme4lmtestlubridatemagrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamissForestModelMetricsmodelrmunsellnaivebayesnlmenloptrnnetnumDerivparallellypbkrtestpcaMethodspillarpkgconfigplyrpROCprodlimprogressrproxypurrrquantregR6randomForestrangerRColorBrewerRcppRcppEigenrecipesreshape2rlangrngtoolsrobustbaserpartscalesshapespSparseMSQUAREMstatmodstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vcdvctrsVIMviridisviridisLitewithrxgboostzoo

Introduction to promor

Rendered fromintro_to_promor.Rmdusingknitr::rmarkdownon Sep 09 2024.

Last update: 2023-07-11
Started: 2022-04-26