opti_preprocess_plot: Plot function to determine optimal pre-processing method.

In PatZeis/AssessMe: A cluster assessment tool for preprocessing and clustering optimization

Plot function to determine optimal pre-processing method.

Description

After cluster assessment, this function serves to identify optimal pre-processing method, independent of the number of clusters, plotting the following criteria: average F1-Score, average Entropy, average number of outlier genes across cluster and average number of enriched features across clusters.

Usage

opti_preprocess_plot(
  assesment_list2,
  cex = 1,
  lcol = c("red"),
  map = T,
  leg = T
)

Arguments

cex	= numeric, graphical parameter indicating the amount by which the line connecting the data points should be scaled. Default = 1
lcol	= vector of colors used for highlighting objects of list of assessments, for each list of lists of assessment one color: e.g. list of resolution optimizations for e.g. normalization A, and another color for list of resolution optimization for e.g. normalization B.
map	= logical. If T, then legend is shown. Default = T.
leg	= logical. If T, then the legend is shown. Default = T.
assessment_list2	list of assessment lists exhibiting different assessments, for example: different normalization methods and for each increasing resolution parameters.
f1_thr	numeric, threshold used to calculate how many clusters have at least 1 gene with F1-score above this threshold for different cluster partitions assessed. Default = 0.5.
max_leng	numeric, calculation of number of clusters with at least max_leng genes with minimal F1-score of f1_thr for the different cluster partitions assessed. Default = 3.

Value

plot with 6 graphs, plotting information about cluster partition against number of clusters and number of assessed genes, as well as plotting number of clusters against average F1-score, average Entropy, average number of enriched features assessed and average No. of outlier genes across clusters.

output_list

with with data.frame for every list of assessments, with with different resolutions/cluster partitions as rows and the following columns: “No.cluster” = number of assessed clusters, “mean_F1” = mean F1_Score across genes, “mean_Entropy” = mean Entropy across genes, “mean_No.cell_outlg1” = mean number of cells with 1 outlier gene expression across clusters, "mean_enriched_features" = mean numer of enriched feautres across clusters of assessed features and "assessed_features" = Number of assessed features.

Examples

opti_preprocess_plot(assessment_list, lcol = c("red", "blue", "green"))

PatZeis/AssessMe documentation built on Nov. 19, 2022, 6:03 a.m.