# Proteomics Data Analysis and Modeling Tools

## Documentation for package ‘promor’ version 0.2.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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