Function reference • promor

Proteomics Data Analysis

Pre-processing

create_df(): Create a data frame of protein intensities

aver_techreps(): Compute average intensity

Quality Control

filterbygroup_na(): Filter proteins by group level missing data

rem_sample(): Remove user-specified samples

impute_na(): Impute missing values

normalize_data(): Normalize intensity data

Differential Expression Analysis

find_dep(): Identify differentially expressed proteins between groups

Visualization

corr_plot(): Correlation between technical replicates

heatmap_na(): Visualize missing data

impute_plot(): Visualize the impact of imputation

norm_plot(): Visualize the effect of normalization

volcano_plot(): Volcano plot

heatmap_de(): Heatmap of differentially expressed proteins

Miscellaneous

onegroup_only(): Proteins that are only expressed in a given group

Building Models

Pre-processing

pre_process(): Pre-process protein intensity data for modeling

rem_feature(): Remove user-specified proteins (features) from a data frame

split_data(): Split the data frame to create training and test data

Modeling

train_models(): Train machine learning models on training data

test_models(): Test machine learning models on test data

Visualization

feature_plot(): Visualize feature (protein) variation among conditions

varimp_plot(): Variable importance plot

performance_plot(): Model performance plot

roc_plot(): ROC plot

Data

covid_fit_df: Suvarna et al 2021 LFQ data (fit object)

covid_norm_df: Suvarna et al 2021 LFQ data (normalized)

ecoli_norm_df: Cox et al 2014 LFQ data (normalized)

ecoli_fit_df: Cox et al 2014 LFQ data (fit object)