get_evogram: get_evogram Collect information to plot dendrogram
In MathOnco/EvoFreq: Clonal Dynamic Visualizations

Description Usage Arguments Value Examples

get_evogram

Collect information to plot dendrogram

get_evogram(size_df, clones, parents, fill_value = NULL,
  fill_range = NULL, time_pt = NULL, clone_cmap = NULL,
  threshold = 0.01, data_type = "size", fill_gaps_in_size = FALSE,
  test_links = TRUE, link_type = "elbow",
  rescale_after_thresholding = FALSE, shuffle_colors = FALSE)

`size_df`	Dataframe in a wide format, where each row corrsponds to a single clone, and the columns are the sizes of that clone at each timepoint
`clones`	Array containing the clone ids. The index of each clone must correspond to the same index of the row in `size_df` that contains the sizes of that clone over time
`parents`	Array containing the ids of the parent of each clone in the `clones` array.
`fill_value`	Array containing information that can be used to color each clone. If NULL (the default), each clone is assigned a color. If values are a clone attribute, e.g. fitness, then the colors are assigned according to those values. The user can also provide custom colors in 3 ways: 1) hexcode; 2) rgb values as a string, with each value being a the intensity of the color channel, each separated by commas, e.g. "255, 10, 128"; 3) Any of the named in colors in R, which can be found with `colors`
`fill_range`	Array containing the minimum and maximum values to set the range of colors. If NULL (the default), the range is determined directly from `fill_value`.
`time_pt`	Timepoint with which the dendrgram should be drawn
`clone_cmap`	Colormap to use for the clones. For a list of available colormaps, see https://github.com/bhaskarvk/colormap.
`threshold`	The minimum frequency of clones to be plotted. Clones with with a frequency below this value will not be plotted
`data_type`	String defining what kind of information is in size_df. If "size", then the values in `size_df` are the population sizes. If "mutation", the values are the frequencies, between 0 and 1, of each mutation in the population over time
`fill_gaps_in_size`	Boolean defining whether or not missing sizes should be filled in
`test_links`	Make sure clone does not have the same id as it's parent. If true, it can cause infinite recursion.
`link_type`	Defines the shape of the edges berween nodes: "straight" draws straight lines between parents and childrend, while "elbow" draws a step from parent to child
`rescale_after_thresholding`	Boolean determining if frequencies should be rescaled after thresholding, so that frequencies are based on what was above the threshold.
`shuffle_colors`	Boolean determining if colors should be shuffled before being assigned to each clone. Only applies when fill_value = NULL

List containing two dataframes: "dendro_pos" contains the positions of the nodes, "links" contains the positions of the edges between nodes

data("example.easy.wide")
### Split dataframe into clone info and size info using fact timepoint column names can be converted to numeric values
time_col_idx <- suppressWarnings(which(! is.na(as.numeric(colnames(example.easy.wide)))))
attribute_col_idx <- suppressWarnings(which(is.na(as.numeric(colnames(example.easy.wide)))))
size_df <- example.easy.wide[, time_col_idx]
parents <- example.easy.wide$parent
clones <- example.easy.wide$clone

tree_info <- get_evogram(size_df, parents = parents, clones = clones)
tree_pos <- tree_info$dendro_pos
elbow_links <- tree_info$links
tree_p <- plot_evogram(tree_pos, elbow_links)

### Can also plot with straight links
tree_info <- get_evogram(size_df, parents = parents, clones = clones, link_type = "straight")
tree_pos <- tree_info$dendro_pos
straight_links <- tree_info$links
tree_straight_p <- plot_evogram(tree_pos, straight_links)

### Can also set nodes to be colored by attribute
data("example.easy.wide.with.attributes")
### Split dataframe into clone info and size info using fact timepoint column names can be converted to numeric values
time_col_idx <- suppressWarnings(which(! is.na(as.numeric(colnames(example.easy.wide.with.attributes)))))
attribute_col_idx <- suppressWarnings(which(is.na(as.numeric(colnames(example.easy.wide.with.attributes)))))
attr_size_df <- example.easy.wide.with.attributes[, time_col_idx]
attr_parents <- example.easy.wide.with.attributes$parent
attr_clones <- example.easy.wide.with.attributes$clone
fitness <- example.easy.wide.with.attributes$fitness

attribute_dendro_df <- get_evogram(attr_size_df, attr_clones, attr_parents, fill_value = fitness)
attribute_tree_pos <- attribute_dendro_df$dendro_pos
attribute_elbow_links <- attribute_dendro_df$links
attribute_tree_elbow_p <- plot_evogram(attribute_tree_pos, attribute_elbow_links, scale_by_node_size = TRUE)