bushmanplot |
Create a stacked area plot that represents the abundance of integration sites over time. |
bw |
Calculate the bw index |
convert_columnwise_relative |
Converts a matrix to relative abundances |
evaluate_clustering |
Evaluate a clustering using the given method |
evaluate_clustering_bw |
Evaluate a clustering using the bw index |
evaluate_clustering_custom |
Evaluate a clustering using a custom evaluation function |
evaluate_clustering_dunn |
Evaluate a clustering using the dunn index |
evaluate_clustering_ptbiserial |
Evaluate a clustering using the point-biserial index |
evaluate_clustering_sdindex |
Evaluate a clustering using the SD-index |
evaluate_clustering_silhouette |
Evaluate a clustering using the silhouette index |
filter_at_tp_biggest_n |
Filters a matrix of readouts for the n biggest IS at a certain measurement |
filter_at_tp_min |
Filters a matrix of readouts for IS that have a minimum occurrence in some measurement |
filter_combine_measurements |
Combines columns that have the same name. The columns are joined additively. |
filter_is_names |
Shortens the rownames of a readout matrix to the shortest distinct prefix |
filter_match |
Filters for columns containing a certain substring. |
filter_measurement_names |
Splits a vector of strings by a given regexp, selects and rearranges the parts and joins them again |
filter_names |
Filters a vector of names and returns the shortest common prefix. |
filter_nr_tp_min |
Filters for a minimum number of time points/measurements |
filter_zero_columns |
Removes columns that only contain 0 or NA. |
filter_zero_rows |
Removes rows that only contain 0 or NA. |
find_best_nr_cluster |
Finds the best number of clusters according to silhouette |
get_similarity_matrix |
Generate a similarity matrix |
ggplot_colors |
Get the default ggplot color palette or a color palette based on the ggplot palette, but with sub-colors that differ in their luminance |
lineplot_split_clone |
Show line plots of all integration sites over time, split into facets by their respective clone. |
normalize_timecourse |
Normalizes a time course using a given mapping from integration sites to clones. |
plot.clusterObj |
Plots the clustering based on a clustering object |
plot.ISSimilarity |
Plots the similarity of integration sites |
plot.timeseries |
Plots time series data, which consists of multiple measurements over time / place (cols) of different clones / integration sites (rows). |
plot_rsquare |
Plots R^2 of two integration sites |
reconstruct |
Apply a clustering algorithm to a given time course. |
reconstruct_kmedoid |
Calculate the k-medoids clustering for a given time course. |
reconstruct_recursive |
Apply a clustering algorithm recursively to a given time course. |
weighted_spring_model |
Plot the relationship of integration sites as a graph. |