Man pages for promor
Proteomics Data Analysis and Modeling Tools
| 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 |
