ORA_phenotype: Performs an overrepresentation analysis (ORA) across all...

In visualneurosciencelab/PlasticityPhenotypes: Creates a plasticity phenotype using features that correlate with PCA basis vectors

Description

ORA between groups and visualizes the significantly different features in a plasticity phenotype.

Usage

ORA_phenotype(
  features_df_row = features_df_row,
  condition_list = condition_list,
  group_label = group_label,
  reference_group = reference_group,
  percentiles = percentiles
)

Arguments

features_df_row	A dataframe that contains all features to be explored using an ORA. Specify row names for each observation must contain the experimental group.
condition_list	A list of factors indicating the group identifiers as they appear in the data frame (row names)
group_label	X-axis title.
reference_group	A character string of the reference group as it appears in the row name. This indicates the group against which an ORA is performed.
percentiles	A list of two values (in ascending order) that act as thresholds for the ORA. These values range between [0,1], with default values of c(0.25,0.75). The first number acts as the lower percentile for the experimental group, while the second acts as the upper percentile.

Value

Plots an ORA phenotype for each group, where coloured boxes indicate that a feature is outside of the 90

Examples

ORA_phenotype(features_df_row  = tsne.processed.4[,2:10], # Data frame for bootstrap analysis
             condition_list = as.list(c('Normal 1\n C,P,M',
                                         'LT BV 1\n C,P,M',
                                         'MD 1\n P,M',
                                         'LT BV 5\n P,M',
                                         'LT BV 4\n P,M',
                                         'RO 2\n C,P,M',
                                         'ST BV 3\n C,P,M',
                                         'MD 3\n C,P',
                                         'ST BV 5\n P',
                                         'ST BV 1\n C,P,M',
                                         'LT BV 6\n P',
                                         'BD 3\n C,P,M',
                                         'BD 6\n P')), # List of subclusters as they appear in row names of "features_df_row"
              reference_group =  'Normal 1\n C,P,M', # Name of reference group as it appears in the row names of "features_df_row"
              group_label  = "\nSubclusters",
              percentiles = c(0.1,0.9)
)

visualneurosciencelab/PlasticityPhenotypes documentation built on Sept. 7, 2020, 2:18 p.m.