Package: promor 0.2.3

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_0.2.3.tar.gz
promor_0.2.3.zip(r-4.7-any)promor_0.2.3.zip(r-4.6-any)promor_0.2.3.zip(r-4.5-any)
promor_0.2.3.tgz(r-4.6-any)promor_0.2.3.tgz(r-4.5-any)
promor_0.2.3.tar.gz(r-4.7-any)promor_0.2.3.tar.gz(r-4.6-any)
promor_0.2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
promor/json (API)

# Install 'promor' in R:

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

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

Pkgdown/docs site:https://caranathunge.github.io

Datasets:

covid_fit_df - Suvarna et al 2021 LFQ data
covid_norm_df - Suvarna et al 2021 LFQ data
ecoli_fit_df - Cox et al 2014 LFQ data
ecoli_norm_df - Cox et al 2014 LFQ data

On CRAN:

biomarkers differential-expression lfq machine-learning mass-spectrometry modeling proteomics

5.58 score 16 stars 16 scripts 357 downloads 23 exports 104 dependencies

Last updated from:2ecc78e2bc. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	226
source / vignettes	OK	230
linux-release	OK	229
macos-release	OK	231
macos-oldrel	OK	187
windows-devel	OK	169
windows-release	OK	167
windows-oldrel	OK	170
wasm-release	OK	126

Exports:aver_techreps corr_plot create_df feature_plot filterbygroup_na find_dep heatmap_de heatmap_na impute_na impute_plot norm_plot normalize_data onegroup_only performance_plot pre_process rem_feature rem_sample roc_plot split_data test_models train_models varimp_plot volcano_plot

Dependencies:Biobase BiocGenerics boot caret class cli clock codetools colorspace cpp11 data.table DEoptimR diagram digest doRNG dplyr e1071 farver foreach future future.apply generics ggplot2 ggrepel globals glue gower gridExtra gtable hardhat ipred isoband iterators itertools jsonlite kernlab KernSmooth labeling laeken lattice lava lifecycle limma listenv lmtest lubridate magrittr MASS Matrix missForest ModelMetrics naivebayes nlme nnet numDeriv parallelly pcaMethods pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 randomForest ranger rbibutils RColorBrewer Rcpp RcppEigen Rdpack recipes reshape2 rlang rngtools robustbase rpart S7 scales shape sp sparsevctrs SQUAREM statmod stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vcd vctrs VIM viridis viridisLite withr xgboost zoo

Introduction to promor

Rendered fromintro_to_promor.Rmdusingknitr::rmarkdown

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

Help page	Topics
Compute average intensity	aver_techreps
Correlation between technical replicates	corr_plot
Suvarna et al 2021 LFQ data (fit object)	covid_fit_df
Suvarna et al 2021 LFQ data (normalized)	covid_norm_df
Create a data frame of protein intensities	create_df
Cox et al 2014 LFQ data (fit object)	ecoli_fit_df
Cox et al 2014 LFQ data (normalized)	ecoli_norm_df
Visualize feature (protein) variation among conditions	feature_plot
Filter proteins by group level missing data	filterbygroup_na
Identify differentially expressed proteins between groups	find_dep
Heatmap of differentially expressed proteins	heatmap_de
Visualize missing data	heatmap_na
Impute missing values	impute_na
Visualize the impact of imputation	impute_plot
Visualize the effect of normalization	norm_plot
Normalize intensity data	normalize_data
Proteins that are only expressed in a given group	onegroup_only
Model performance plot	performance_plot
Pre-process protein intensity data for modeling	pre_process
Remove user-specified proteins (features) from a data frame	rem_feature
Remove user-specified samples	rem_sample
ROC plot	roc_plot
Split the data frame to create training and test data	split_data
Test machine learning models on test data	test_models
Train machine learning models on training data	train_models
Variable importance plot	varimp_plot
Volcano plot	volcano_plot

Package: promor 0.2.3

promor: Proteomics Data Analysis and Modeling Tools

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