count_outliers: Count up the outlier information for each of the groups you...

In blacksheepr: Outlier Analysis for pairwise differential comparison

Description

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.

Usage

count_outliers(groupings, outliertab,
    aggregate_features = FALSE, feature_delineator = "\\.")

Arguments

groupings	table generated by the comparison_groupings function
outliertab	outlier table generated by make_outlier_table
aggregate_features	DEFAULT: FALSE; Toggle the Aggregate feature, which will aggregate features in your table based on the given delineator. Aggregation will output counts for the TOTAL number of outliers and non- outliers across ALL sites you aggregate across.
feature_delineator	DEFAULT: <"\.">; What character delineates the separation between primary and secondary features. NOTE: to use proper R syntax with escape characters if necessary Ex) Protein1.Phosphosite1 uses "\." to aggregate on Protein1

Value

the tabulated information of outliers per group

Examples

data("sample_phosphodata")
reftable_function_out <- make_outlier_table(sample_phosphodata[1:1000,])
outliertab <- reftable_function_out$outliertab

data("sample_annotationdata")
groupings <- comparison_groupings(sample_annotationdata)

count_outliers_out <- count_outliers(groupings, outliertab,
    aggregate_features = FALSE)
grouptablist <- count_outliers_out$grouptablist
fractiontab <- count_outliers_out$fractiontab

blacksheepr documentation built on Nov. 8, 2020, 7:57 p.m.