Update dna_segs and comparisons sequentially in plotting order

Takes a list of dna_seg and a list of comparison objects, and then updates both sequentially, in plotting order. This can be used to update the positions of the comparisons, as well as the color and region_plot attributes of both the dna_segs and comparisons. It does this by taking the values of features for these attributes and transferring those over to comparisons directly connected to it, and then to the features connected to those comparisons. For example, if a feature from a single dna_seg has red as its fill attribute, the comparisons that can be linked to this feature will become red as well. This is then followed up by updating any dna_seg features linked to those comparisons, and so on.

Usage

sequential_updates(
  dna_segs,
  comparisons,
  seg_id = "locus_id",
  comparison_id = "auto",
  color_var = NULL,
  default_color = "grey80",
  update_region_plot = TRUE,
  update_positions = FALSE,
  both_directions = TRUE
)

Arguments

dna_segs: A list of dna_seg objects.
comparisons: A list of comparison objects.
seg_id: The name of a dna_seg column, whose values will be used to make the links to the comparisons.
comparison_id: The shared name of the comparison columns, whose values will be used to make the links to the dna_segs. See details.
color_var: A character string denoting which color attribute to update, must be one of: "fill", "col", or left as NULL, which means no color will be updated.
default_color: A character string providing a default color, must be either NULL or a valid color. The color given by this argument will be ignored when updating, never overwriting any other color.
update_region_plot: Logical. If TRUE, updates the region_plot attribute of dna_segs (and of comparisons but only to pass this information to the dna_segs that follow), which determines whether the neighborhood of these features is plotted in a regional plot.
update_positions: Logical. If TRUE, updates the plotted positions of the comparisons to match the dna_segs. start1 and end1 will be updated using the dna_seg above the comparison in plotting order, while start2 and end2 will be updated using the dna_seg under the comparison in plotting order.
both_directions: Logical. If FALSE, updates are applied sequentially in plotting order, starting from the first dna_seg. When both_directions is TRUE, it will then additionally update each dna_seg and comparison in reverse plotting order.

Value

A list with 2 named elements: dna_segs and comparisons, which are both lists containing the dna_seg and comparison objects, respectively.

Details

When updating colors, the existing colors from the input dna_seg and comparison objects are transferred over to the next object in the plotting order, with the exception of their default colors, provided by default_color. As comparison objects only have a single color attribute col, those will be updated using the column provided by color_var from the dna_segs, while the dna_segs themselves will be updated using the col column from the comparisons regardless of color_var (unless it is left as NULL to avoid updating colors entirely).

The objects are linked together through shared values. The columns for these shared values are determined by the seg_id and comparison_id arguments, for the dna_segs and comparisons, respectively. comparison_id refers to 2 columns, and defaults to "auto", which will attempt to determine which columns to use automatically. If for example, comparison_id is set as "name", it will look for the "name1" and "name2" columns to match to the seg_id in the dna_segs above, and under it, respectively.

There are some circumstances with very interconnected comparisons where you might want to set both_directions to FALSE to avoid transferring over the region_plot attribute to too many features.

Author

Mike Puijk

Examples

## Prepare dna_seg
names1 <- c("1A", "1B", "1C")
names2 <- c("2A", "2C", "2B")
names3 <- c("3B", "3A", "3C")

## Make dna_segs with some alternate colors
dna_seg1 <- dna_seg(data.frame(name=names1, start=(1:3)*3, end=(1:3)*3+2,
                               strand=rep(1, 3),
                               fill=c("darkred", "grey80", "darkblue")))
dna_seg2 <- dna_seg(data.frame(name=names2, start=(1:3)*3, end=(1:3)*3+2,
                               strand=rep(1, 3),
                               fill=c("grey80", "grey80", "darkgreen")))
dna_seg3 <- dna_seg(data.frame(name=names3, start=(1:3)*3, end=(1:3)*3+2,
                               strand=rep(1, 3)))

## Make comparisons
comp1 <- comparison(data.frame(start1=c(3,6,9), end1=c(5,8,11),
                               start2=c(3,9,6), end2=c(5,11,8),
                               name1=c("1A", "1B", "1C"), 
                               name2=c("2A", "2B", "2C"),
                               direction=c(1,1,1)))
comp2 <- comparison(data.frame(start1=c(3,9,6), end1=c(5,11,8),
                               start2=c(6,3,9), end2=c(8,5,11),
                               name1=c("2A", "2B", "2C"), 
                               name2=c("3A", "3B", "3C"),
                               direction=c(1,1,1)))

## Before applying sequential_updates
plot_gene_map(dna_segs=list(dna_seg1, dna_seg2, dna_seg3), 
              comparisons=list(comp1, comp2),
              alpha_comparisons=0.6)


## Applying sequential_updates without updating in both directions
full_data <- sequential_updates(list(dna_seg1, dna_seg2, dna_seg3),
                                comparisons=list(comp1, comp2),
                                seg_id = "name",
                                color_var = "fill",
                                both_directions = FALSE)
plot_gene_map(dna_segs=full_data$dna_segs,
              comparisons=full_data$comparisons,
              alpha_comparisons=0.6)


## Applying sequential_updates using both directions
full_data <- sequential_updates(list(dna_seg1, dna_seg2, dna_seg3),
                                comparisons=list(comp1, comp2),
                                seg_id = "name",
                                color_var = "fill")
plot_gene_map(dna_segs=full_data$dna_segs, 
              comparisons=full_data$comparisons,
              alpha_comparisons=0.6)