Package index • OAR

Wrapper Functions

These are all the functions you need to run the full analysis on your data.

oar(): Single line pipeline to run complete analysis

oar_by_factor(): Generate OAR score within each cluster and add them to full objects metadata

oar_deg(): Generate DEGs based on OAR score

get_missing_pattern_genes(): Create list of which genes participate in each pattern.

Visualize results

A few convenient functions to visualize the results of your analysis.

scatter_score_missing(): Create scatter plot of OAR score vs percent missing

oar_missing_data_plot(): Plot identified missing data patterns

Step-by-step functions

Run these in this order to achieve the same results as with the wrapper functions.

oar_preprocess_data(): Prepare data for oar fold functions

oar_hamming_distance(): Calculate hamming distances between genes

oar_missing_data_patterns(): Identify missing data patterns allowing for mismatch

oar_base(): Generate scores and p-values to determine heterogeneity of data by looking at whether missingness is observed-at-random (OAR)

Utility functions

Called within other functions in the package.

missing_pattern_pval_kw(): Kruskal-Wallis test to generate a per cell p-value based on missing data patterns

oar_missing_data_graph(): Group missing data patterns based on tolerance with a graph