# Outlier Analysis for pairwise differential comparison

## Documentation for package ‘blacksheepr’ version 1.6.0

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

## Help Pages

annotationlist_builder | Create the annotation object for plotting in a heatmap
comparison_groupings | Create all of the groups based on the input metadata
count_outliers | Count up the outlier information for each of the groups you have made. If aggregating then you will have to turn the parameter on, but you still input the outliertable. Aggregate will count the total number of outliers AND nonoutliers in its operation, so it needs the original outlier table made by the <make_outlier_table> function.
create_heatmap | Plot out a heatmap
deva | Run the entire blacksheep Function from Start to finish
deva_normalization | Normalization of data to prepare for deva. Uses a Median of Ratio method followed by a log2 transformation.
deva_results | Utility function that allows easier grabbing of data
make_comparison_columns | Utility function that will take in columns with several subcategories, and output several columns each with binary classifications. ex) col1: A,B,C >> colA: A,notA,notA; colB: notB,B,notB; colC: notC,notC,C
make_outlier_table | Separate out the "i"th gene, take the bounds, and then create a column that says whether or not this gene is high, low, or none in a sample with regards to the other samples in the dataset. Repeat this for every gene to create a reference table.
outlier_analysis | With the grouptablist generated by count_outliers - run through and run a fisher exact test to get the p.value for the difference in outlier count for each feature in each of your comparisons
outlier_heatmap | With the grouptablist generated by count_outliers - run through and run a fisher exact test to get the p.value for the difference in outlier count for each feature in each of your comparisons
sample_annotationdata | sample_annotationdata
sample_phosphodata | sample_phosphodata
sample_rnadata | sample_rnadata