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Integrated Bioanalytics

figures & captions

One of the first components in preparing a scientific manuscript is creating high quality figures. Considering the following for your figures:

General Appearance:

Create plots that are clean, professional, and easy to view from a distance. Ensure axes tick labels are clear, non-overlapping, and utilize the available space efficiently for enhanced readability and precision. Use an appealing (and color blind-friendly) color palette to differentiate data points or categories. Tailor axes labels to be descriptive, and select an appropriate theme that complements the data and maintains professionalism.

Representing Data:

Appropriate Geoms and Annotations: Choose geoms that best represent the data and help the viewer evaluate the hypothesis or make the desired comparison. Include raw data points where possible for detailed data distribution understanding. Consider apply statistical transformations like smoothing lines or histograms where appropriate to provide deeper insights into the data. Consider using facets for visualizing multiple categories or groups, allowing for easier comparison while maintaining a consistent scale and layout. Adhere to specific standards or conventions relevant to your field, including the representation of data, error bars, or statistical significance markers.

Here are some tips for figures, beyond the creation of individual plots:

figures

insets

5.1.0.1 zoomed insets

Zoom in on certain plot regions

p <- ggplot(mpg, aes(displ, hwy, colour = factor(cyl))) +
  geom_point() 

data.tb <- 
  tibble(x = 7, y = 44, 
         plot = list(p + 
                       coord_cartesian(xlim = c(4.9, 6.2), 
                                       ylim = c(13, 21)) +
                       labs(x = NULL, y = NULL) +
                       theme_bw(8) +
                       scale_colour_discrete(guide = "none")))

ggplot(mpg, aes(displ, hwy, colour = factor(cyl))) +
  geom_plot(data = data.tb, aes(x, y, label = plot)) +
  annotate(geom = "rect", 
           xmin = 4.9, xmax = 6.2, ymin = 13, ymax = 21,
           linetype = "dotted", fill = NA, colour = "black") +
  geom_point()

5.1.0.2 plot insets 

p <- ggplot(mpg, aes(factor(cyl), hwy, fill = factor(cyl))) +
  stat_summary(geom = "col", fun = mean, width = 2/3) +
  labs(x = "Number of cylinders", y = NULL, title = "Means") +
  scale_fill_discrete(guide = "none")

data.tb <- tibble(x = 7, y = 44, 
                  plot = list(p +
                                theme_bw(8)))

ggplot(mpg, aes(displ, hwy, colour = factor(cyl))) +
  geom_plot(data = data.tb, aes(x, y, label = plot)) +
  geom_point() +
  labs(x = "Engine displacement (l)", y = "Fuel use efficiency (MPG)",
       colour = "Engine cylinders\n(number)") +
  theme_bw()

5.1.0.3 image insets 

Isoquercitin_synthase <- magick::image_read("https://thebustalab.github.io/integrated_bioanalytics/images/homology2.png")
grobs.tb <- tibble(x = c(0, 10, 20, 40), y = c(4, 5, 6, 9),
                   width = c(0.05, 0.05, 0.01, 1),
                   height =  c(0.05, 0.05, 0.01, 0.3),
                   grob = list(grid::circleGrob(), 
                               grid::rectGrob(), 
                               grid::textGrob("I am a Grob"),
                               grid::rasterGrob(image = Isoquercitin_synthase)))

ggplot() +
  geom_grob(data = grobs.tb, 
            aes(x, y, label = grob, vp.width = width, vp.height = height),
            hjust = 0.7, vjust = 0.55) +
  scale_y_continuous(expand = expansion(mult = 0.3, add = 0)) +
  scale_x_continuous(expand = expansion(mult = 0.2, add = 0)) +
  theme_bw(12)
# ggplot() +
#   annotate("grob", x = 1, y = 3, vp.width = 0.5,
#            label = grid::rasterGrob(image = Isoquercitin_synthase, width = 1)) +
#   theme_bw(12)
# bloom_example_pics <- ggplot(data = data.frame(x = c(0,1), y = c(0.5,0.5))) +
#   geom_point(aes(x = x, y = y), color = "white") +
#   theme_void() +
#   annotation_custom(
#       rasterGrob(
#           png::readPNG(
#               "https://thebustalab.github.io/integrated_bioanalytics/images/homology2.png"
#           ), interpolate=TRUE
#       ), xmin=0, xmax=1, ymin=0, ymax=1
#   )

composite figures

Many high quality figures are composite figures in which there is more than one panel. Here is a simple way to make such figures in R. First, make each component of the composite figure and send the plot to a new object:

color_palette <- RColorBrewer::brewer.pal(11, "Paired")
names(color_palette) <- unique(alaska_lake_data$element)

plot1 <- ggplot(
  data = filter(alaska_lake_data, element_type == "bound"),
  aes(y = lake, x = mg_per_L)
) +
  geom_col(
    aes(fill = element), size = 0.5, position = "dodge",
    color = "black"
  ) +
  facet_grid(park~., scales = "free", space = "free") +
  theme_bw() + 
  scale_fill_manual(values = color_palette) +
  scale_y_discrete(name = "Lake Name") +
  scale_x_continuous(name = "Abundance mg/L)") +
  theme(
    text = element_text(size = 14)
  )

plot2 <- ggplot(
  data = filter(alaska_lake_data, element_type == "free"),
  aes(y = lake, x = mg_per_L)
) +
  geom_col(
    aes(fill = element), size = 0.5, position = "dodge",
    color = "black"
  ) +
  facet_grid(park~., scales = "free", space = "free") +
  theme_bw() + 
  scale_fill_manual(values = color_palette) +
  scale_y_discrete(name = "Lake Name") +
  scale_x_continuous(name = "Abundance mg/L)") +
  theme(
    text = element_text(size = 14)
  )

Now, add them together to lay them out. Let’s look at various ways to lay this out:

plot_grid(plot1, plot2)
plot_grid(plot1, plot2, ncol = 1)
plot_grid(plot_grid(plot1,plot2), plot1, ncol = 1)

exporting graphics

To export graphics from R, consider the code below. The should be something like: “C:\Desktop\the_file.png” (i.e. a path to a specific file with a .png suffix. It should be a file that does not yet exist - if it does already exist, it will be overwritten. You should adjust with height and width to get the image to look how you want, then once you have that dialed in, crank the resolution to 1200 or 2400 and export a final version.

plot <- ggplot(data, aes(x = x, y = y)) + geom_point()

png(filename = <path_to_file_you_want_to_create>, width = 8, height = 8, res = 600, units = "in")

plot

dev.off()
plot <- ggplot(data, aes(x = x, y = y)) + geom_point()

pdf(filename = <path_to_file_you_want_to_create>, width = 8, height = 8)

plot

dev.off()

captions

  1. Title - an overall description of the what is shown
  2. For each subplot:
  • The type of plot (line plot, bar chart, etc.)
  • Describe what is plotted as y vs x in words.
  • Describe what each bar, point, or error bar represents.
  • If applicable, describe the number of independent samples or measurements (sometimes called “replicates”) that underlie a given geometric feature or summary statistic.
  • Describe where the data are from.
  1. Avoid abbreviations, but if you do use any, specify what they mean.

An example:

(plot1 + plot2 + labs(caption = str_wrap("Figure 1: Carbon, nitrogen, and phosphorous in Alaskan lakes. A) A bar chart showing the abundance (in mg per L, x-axis) of the bound elements (C, N, and P) in various Alaskan lakes (lake names on y-axis) that are located in one of three parks in Alaska (park names on right y groupings). B) A bar chart showing the abundance (in mg per L, x-axis) of the free elements (Cl, S, F, Br, Na, K, Ca, and Mg) in various Alaskan lakes (lake names on y-axis) that are located in one of three parks in Alaska (park names on right y groupings). The data are from a public chemistry data repository. Each bar represents the result of a single measurement of a single analyte, the identity of which is coded using color as shown in the color legend. Abbreviations: BELA - Bering Land Bridge National Preserve, GAAR - Gates Of The Arctic National Park & Preserve, NOAT - Noatak National Preserve.", 90))) + plot_annotation(tag_levels = 'A') + 
  plot_layout(widths = c(1.1, 3), guides = 'collect') &
  theme(legend.position = 'right')

further reading

plot layout

One option for plot layout, patchwork. This one is quick and simple. patchwork, for plot layout

Another option for plot layout is cowplot. Cowplot is a bit more complicated, but is more versatile. cowplot on Github

Note: Second Edition is under construction 🏗

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