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
