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MultIS (version 0.6.2)

Reconstruction of Clones from Integration Site Readouts and Visualization

Description

Tools necessary to reconstruct clonal affiliations from temporally and/or spatially separated measurements of viral integration sites. For this means it utilizes correlations present in the relative readouts of the integration sites. Furthermore, facilities for filtering of the data and visualization of different steps in the pipeline are provided with the package.

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Version

Install

install.packages('MultIS')

Monthly Downloads

204

Version

0.6.2

License

LGPL

Maintainer

Sebastian Wagner

Last Published

August 6th, 2021

Functions in MultIS (0.6.2)

filter_at_tp_biggest_n

Filters a matrix of readouts for the n biggest IS at a certain measurement
bw

Calculate the bw index
evaluate_clustering_dunn

Evaluate a clustering using the dunn index
evaluate_clustering_custom

Evaluate a clustering using a custom evaluation function
evaluate_clustering_ptbiserial

Evaluate a clustering using the point-biserial index
filter_names

Filters a vector of names and returns the shortest common prefix.
evaluate_clustering_sdindex

Evaluate a clustering using the SD-index
convert_columnwise_relative

Converts a matrix to relative abundances
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
weighted_spring_model

Plot the relationship of integration sites as a graph.
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.ISSimilarity

Plots the similarity of integration sites
evaluate_clustering_silhouette

Evaluate a clustering using the silhouette index
find_best_nr_cluster

Finds the best number of clusters according to silhouette
filter_zero_columns

Removes columns that only contain 0 or NA.
filter_zero_rows

Removes rows that only contain 0 or NA.
evaluate_clustering

Evaluate a clustering using the given method
get_similarity_matrix

Generate a similarity matrix
filter_nr_tp_min

Filters for a minimum number of time points/measurements
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
reconstruct_kmedoid

Calculate the k-medoids clustering for a given time course.
plot.clusterObj

Plots the clustering based on a clustering object
plot.timeseries

Plots time series data, which consists of multiple measurements over time / place (cols) of different clones / integration sites (rows).
filter_measurement_names

Splits a vector of strings by a given regexp, selects and rearranges the parts and joins them again
plot_rsquare

Plots R^2 of two integration sites
filter_match

Filters for columns containing a certain substring.
reconstruct_recursive

Apply a clustering algorithm recursively to a given time course.
reconstruct

Apply a clustering algorithm to a given time course.
evaluate_clustering_bw

Evaluate a clustering using the bw index
bushmanplot

Create a stacked area plot that represents the abundance of integration sites over time.
filter_at_tp_min

Filters a matrix of readouts for IS that have a minimum occurrence in some measurement