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 |