plot_diff_map: Plots differential map.

In rz6/CopulaHiC: Hi-C differential analysis via dependency modelling of chromatin interactions with generalized linear models

Description

Draws differential map (i.e. one with positive and negative entries).

Usage

plot_diff_map(
  mtx.dense,
  zeros.na = TRUE,
  breaks = 10,
  colors.pal = c("blue", "white", "red"),
  color.range = NULL,
  sqrt.transform = FALSE,
  na.color = "black",
  neg.inf.color = "gold",
  pos.inf.color = "darkgreen",
  useRaster = TRUE,
  ...
)

Arguments

mtx.dense	numeric matrix with positive and negative entries; if matrix does not contain any negative values use plot_contact_map function
zeros.na	logical if TRUE convert zero cells to NA
breaks	numeric number of breaks on color scale
colors.pal	colors for pallette
color.range	numeric vector of length 2 or NULL; if specified gives minimum and maximum values for color scale; this is manual adjustment of scale
sqrt.transform	logical if TRUE apply sqrt transformation: sqrt(pos) to positive elements of matrix and -sqrt(abs(neg)) to negative elements of matrix
useRaster	logical if TRUE (default) lower quality image is produced, but the process is much faster and resulting file uses little space

See Also

image_plot_na for function handling heatmap plotting

Examples

# get sample contact map (MSC replicate 1) for chromosome 18
mtx1.sparse <- DIADEM::sample_hic_maps[["MSC-HindIII-1_40kb-raw"]][["18"]]
# get another sample contact map (MSC replicate 2) for chromosome 18
mtx2.sparse <- DIADEM::sample_hic_maps[["MSC-HindIII-2_40kb-raw"]][["18"]]
# make differential map
merged <- base::merge(mtx1.sparse, mtx2.sparse, by = c("i", "j"))
merged$difference <- merged$val.x - merged$val.y
dense <- sparse2dense(merged[c("i","j","difference")], N = 1952)
# plot
plot_diff_map(dense)
# plot with sqrt transformation of data
plot_diff_map(dense, sqrt.transform = TRUE)

rz6/CopulaHiC documentation built on Dec. 31, 2019, 9:19 a.m.