09-gallery.Rmd

# Venndir Gallery

```{r setup-09, include=FALSE}
suppressPackageStartupMessages(library(venndir))
suppressPackageStartupMessages(library(patchwork))
```

## Figure Boosting

One of the best hobby activities while developing Venndir has been
"figure sniping", which is loosely translated,
"Can Venndir make that figure?"

There are two main motivations:

1. Can it be done? *A test of creativity, a test of wills.*
2. Can it be done better? *Dare we try?*

There are plenty of graphics tools with which someone could just
create their own Venn diagram "by hand", such as Inkscape,
Adobe Illustrator, Microsoft Powerpoint.
For me, the fewer things I do "by hand" the better.
To be completely frank with myself, the fewer things I don't *want*
to do by hand the better.
Also me: Somtimes I do what I *want* to do.

The first example was shown in Figure \@ref(fig:items-2),
to recreate part of the nice Venn diagram in [@Salybekov_2021] Figure 2.


### Me - Electron

This example is straighforward, using a fun post on Reddit
[r/physicsmemes](https://www.reddit.com/r/physicsmemes/comments/v5adqq/you_and_i_arent_so_different/)
[@reddit2022electron].
The person "Me" and an "Electron" both start behaving differently
when observed. This example demonstrates some of the basics of
re-creating a figure, in order to make a similar figure, or to
enhance the concept of the figure.

The labels are clear, the colors are easily approximated.
The font looks like Times, so `fontfamily='serif'` should suffice.
The center wording is best represented as one item label,
split across multiple lines using newline `'\n'` character.

Figure \@ref(fig:me-electron) shows the outcome, relatively quick and easy!
Two more options are shown on the bottom row. The first (bottom left)
customizes the innerborder and outerborder. The second calls
`modify_venndir_overlap()` to highlight the center region.

(ref:me-electron) Target figure from r/physicsmemes (top left), re-created (top right), with two alternatives (bottom).

```{r me-electron, out.width="50%", fig.show="hold", fig.ncol=2, echo=FALSE, fig.width=7, fig.height=4.5, fig.cap="(ref:me-electron)", fig.alt="(ref:me-electron)", fig.subcap=c("Source figure.", "Re-creation.", "Alternate 1.", "Alternate 2.")}
knitr::include_graphics("images/MeElectron.png")
olist <- c("Me",
   "Electron",
   paste(collapse="\n",
      c("start",
         "behaving",
         "differently",
         "when",
         "observed"))
)
k <- -0.13;
vo <- venn_meme(olist,
   outerborder.lwd=1.5,
   innerborder.lwd=1.5,
   poly_alpha=0.7,
   # expand_fraction=c(-0.1 - k, -0.1, -0.1 + k, -0.1)*2.4,
   expand_fraction=k,
   item_cex_factor=c(0.8, 0.85, 0.9),
   fontfamily="serif",
   set_colors=c("#BFFAD6", "#C4D8F6"))

# fun alternate #3
k <- -0.13;
vo3 <- venn_meme(olist,
   outerborder.lwd=5, outerborder="#F5F5F7FF",
   innerborder.lwd=0.5,# innerborder="#88888833",
   border="#77777755", border.lwd=0,
   poly_alpha=0.7,
   xyratio=1,
   expand_fraction=k,
   item_cex_factor=c(0.8*1.2, 0.85, 0.9)*1.2,
   fontfamily="Bradley Hand",
   set_colors=c("#BFFAD6", "#C4D8F6"))

# fun alternate #4
olist4 <- olist;
olist4[1:2] <- paste0("{.gothic ", olist4[1:2], "}");
ms <- list(
   gothic=marquee::style(
      family="AcademyEngravedLetPlain",
      weight="normal", width="normal"))

vo4 <- venn_meme(olist4,
   do_plot=FALSE,
   marquee_styles=ms,
   outerborder.lwd=5, outerborder="#F5F5F7FF",
   innerborder.lwd=0.5,
   border="#77777755", border.lwd=0,
   poly_alpha=0.7,
   xyratio=1,
   expand_fraction=k,
   item_cex_factor=c(0.8*1.2, 0.85, 0.9)*1.2,
   fontfamily="AndrewScript",
   # fontfamily="Allura",
   set_colors=c("#BFFAD6", "#C4D8F6"))
vo4 <- modify_venndir_overlap(vo4,
   overlap_set="A&B",
   params=list(outerborder="#B3ECEF", outerborder.lwd=5,
      innerborder="#E0FFFF", innerborder.lwd=5))
vo4 <- modify_venndir_overlap(vo4,
   overlap_set="A",
   params=list(innerborder="#BFFAD6",
      innerborder.lwd=2, outerborder.lwd=0))
vo4 <- modify_venndir_overlap(vo4,
   overlap_set="B",
   params=list(innerborder="#C4D8F6",
      innerborder.lwd=2, outerborder.lwd=0, border=0))

plot(vo4,
   # fontfamily="Allura",
   fontfamily="La Parisienne Script",
   item_cex_factor=c(0.8*2.5, 0.85*1.3, 1.0)*1.2,
   expand_fraction=k)


```

```{r me-electron-show, eval=FALSE, fig.width=7, fig.height=6, fig.cap="(ref:me-electron)", fig.alt="(ref:me-electron)"}
olist <- c("Me",
   "Electron",
   paste(collapse="\n",
      c("start", "behaving",
         "differently", "when",
         "observed"))
)
venn_meme(olist,
   outerborder.lwd=2, innerborder.lwd=2,
   poly_alpha=0.7,
   item_cex_factor=c(0.8, 0.8, 0.9),
   fontfamily="serif",
   set_colors=c("#BFFAD6", "#C4D8F6"))
```


### eulerGlyphs

(ref:eulerglyph) Target diagram from eulerGlyphs.

```{r eulerglyph, echo=FALSE, out.width="90%", fig.cap="(ref:eulerglyph)", fig.alt="(ref:eulerglyph)"}
knitr::include_graphics("images/eulerGlyphs.png")
```

Figure \@ref(fig:eulerglyph) shows a fantastic figure created by
[eulerGlyphs](https://www.eulerdiagrams.com/eulerGlyphs/) [@Micallef_2012],
an application designed to create proportional Euler diagrams.
The data represents [breast cancer screening statistics](10.1109/TVCG.2012.199),
and is a common reference dataset to study the visual perception of statistics.

A brief summary of the data follows:

* 10 out of 1,000 women age 40 have breast cancer.
* 8 of every10 women *with* breast cancer got a positive test result.
* 95 of every 990 women *without* breast cancer got a positive test result.

Figure \@ref(fig:euler-glyphs-1) shows the initial attempt, using
`overlap_type="agreement"`, then visualizing items with only the
sign, which for agreement uses `'='` the equals sign. Items are
rotated with `jitter_degrees` to provide some visual randomness.

(ref:euler-glyphs-1) Initial attempt at re-creating the EulerGlyphs figure.

```{r euler-glyphs-1, fig.height=5, fig.width=8, fig.cap="(ref:euler-glyphs-1)", fig.alt="(ref:euler-glyphs-1)"}
mammo_counts <- c(
   wob=895,
   wwbc=2,
   "wob&wwpm"=95,
   "wwbc&wwpm"=8)
mammo_list <- counts2setlist(mammo_counts)
mammo_labels <- c(
   wob="women without\nbreast cancer",
   wwbc="women with\nbreast cancer",
   wwpm="women with\npositive mammography")
mammo_colors=c("#AEAEFF", "#FF9D9D", "#896699")
v_mammo <- venndir(mammo_list,
   overlap_type="agreement",
   poly_alpha=0.3,
   set_colors=mammo_colors,
   setlist_labels=mammo_labels,
   xyratio=0.4,
   show_labels="Ni",
   show_items="sign",
   jitter_degrees=45,
   item_buffer=-0.01, width_buffer=0.05,
   item_cex=c(1, 1, 1, 1, 1),
   segment_distance=0.02,
   expand_fraction=c(-0.1, -0.2, -0.1, 0),
   rotate_degrees=180,
   draw_legend=FALSE,
   proportional=TRUE)
```

The first pass fills the space with `'='` symbols,
rotates the `eulerr` output, and placed the circles quite well.
The argument `xyratio=0.4` placed the `'='` symbols closer
together than default.

Another approach could improve the figure, using a
the Unicode `'U+25CF'` filled circle with
the method described in [Customize the Symbols].
This symbol would match the font color, which can be edited
to match the source figure.

(In a pinch, the items themselves could be edited in the `Venndir` object:
`v_mammo@label_df$items`. The items could be replaced with the Unicode
symbol as one option.)

Figure \@ref(fig:euler-glyphs-3) shows Unicode filled circles,
and colors assigned to approximate the colors in eulerGlyphs.
The set labels are nudged.

(ref:euler-glyphs-3) Second attempt at the EulerGlyphs figure. It already looks cleaner.

```{r euler-glyphs-3, fig.height=5, fig.width=8, fig.cap="(ref:euler-glyphs-3)", fig.alt="(ref:euler-glyphs-3)"}
curate_df3 <- get_venndir_curate_df();
agg3 <- which(curate_df3$from %in% "agreement")
curate_df3[agg3, "sign"] <- "\u25CF";

# create a new Venndir
v_mammo3 <- venndir(mammo_list,
   do_plot=FALSE,
   circle_nudge=list(
      wwbc=c(-1.8, 0),
      wwpm=c(-1.6, 0)),
   innerborder.lwd=1, outerborder.lwd=1,
   overlap_type="agreement",
   poly_alpha=0.1,
   set_colors=mammo_colors,
   curate_df=curate_df3,
   setlist_labels=mammo_labels,
   xyratio=0.6,
   fontfaces=list(overlap="plain"),
   show_labels="Ni",
   show_items="sign",
   segment_buffer=-0.05,
   jitter_cex=0, jitter_color=0,
   font_cex=0.8,
   item_buffer=-0.02,
   item_cex=c(1, 1, 1, 1, 1) * 1,
   segment_distance=0.02,
   rotate_degrees=180,
   draw_legend=FALSE,
   proportional=TRUE)

# edit the label colors
v_mammo3@label_df["wob", "color"] <- "blue1";
v_mammo3@label_df["wwbc", "color"] <- "red2";
v_mammo3@label_df["wob.agreement", "color"] <- mammo_colors[1];
v_mammo3@label_df["wob&wwpm.agreement", "color"] <- "royalblue";
v_mammo3@label_df["wwbc&wwpm.agreement", "color"] <- "#DD6666";
v_mammo3@label_df["wwbc.agreement", "color"] <- mammo_colors[2];

# nudge labels
v_mammo3n <- nudge_venndir_label(v_mammo3,
   label_location="outside",
   offset_list=list(wwbc=c(0.0, 0.03),
      wwpm=c(-0.07, 0.1),
      wob=c(0, -0.06)))

# visualize
plot(v_mammo3n,
   jitter_color=0, width_buffer=0.02,
   L_lo=80, L_hi=85, C_floor=50,
   expand_fraction=c(-0.1, -0.10, -0.1, -0.05),
   innerborder.lwd=0, outerborder.lwd=0.7)
```

Both previous attempts showed "quick and easy" approximations,
however the spirit of Figure Boosting is to re-create the
image as closely as possible.

The eulerGlyphs figure used points colored
to convey true breast cancer status, with black border to
indicate a positive mammography test result.
To mimic this effect requires using proper points.

The steps required:

1. Create the Venndir object without item labels.
2. Nudge the set labels, apply custom colors.
3. Extract the `JamPolygon` object.
4. Call `label_fill_JamPolygon()` for each overlap.
5. Render `grid::pointsGrob()` in the correct `viewport`.

Steps 1 and 2 are shown below:

```{r euler-glyphs-4-prep-1}
# create a new Venndir
v_mammo4 <- venndir(mammo_list,
   do_plot=FALSE,
   circle_nudge=list(
      wwbc=c(-1.4, 0),
      wwpm=c(-1.6, 0)),
   innerborder.lwd=1, outerborder.lwd=1,
   overlap_type="agreement",
   poly_alpha=0.1,
   set_colors=mammo_colors,
   setlist_labels=mammo_labels,
   fontfaces=list(overlap="plain"),
   show_labels="N", keep_items=TRUE,
   segment_buffer=-0.05,
   font_cex=0.8,
   segment_distance=0.02,
   rotate_degrees=180,
   draw_legend=FALSE,
   proportional=TRUE)
# edit the label colors
k <- c("wob", "wwbc")
v_mammo4@label_df[k, "color"] <- c("blue", "red")
# nudge labels
v_mammo4n <- nudge_venndir_label(v_mammo4,
   label_location="outside",
   offset_list=list(wwbc=c(0.01, 0.045),
      wwpm=c(-0.09, 0.06),
      wob=c(-0.01, -0.06)))
```

The internal function `label_fill_JamPolygon()` defines
coordinates for item labels inside a `JamPolygon`.
The example iterates each overlap region that contains items,
then stores item coordinates to use later.
The point fill color and border are defined for each region as well.

```{r euler-glyphs-4-prep-2}
# JamPolygon
v_items <- jamba::rmNULL(v_mammo4@label_df$items)
v_colors <- mammo_colors[c(1, 2, 1, 2)];
v_borders <- c(NA, NA, "black", "black")
v_buffers <- c(0.01, -0.2, 0, -0.15)
xy <- jamba::rbindList(lapply(seq_along(v_items), function(i){
   which_jp <- match(gsub("[|].+", "", names(v_items)[i]),
      names(v_mammo4@jps))
   xy <- label_fill_JamPolygon(jp=v_mammo4@jps[which_jp],
      width_buffer=0.01,
      buffer=v_buffers[i], xyratio=0.5,
      labels=seq_along(v_items[[i]]))$items_df;
   xy$color <- v_colors[i];
   xy$border <- v_borders[i];
   xy;
}))
```

Finally, the item coordinates are used with `grid::pointsGrob()`
with some visual noise added by `rnorm()` for visual flair.

Figure \@ref(fig:euler-glyphs-4) shows the result from the final
steps, drawing the points in the correct `viewport`.

(ref:euler-glyphs-4) Third attempt at re-creating the eulerGlyphs figure.

```{r euler-glyphs-4, out.width="100%", fig.height=5, fig.width=8, fig.cap="(ref:euler-glyphs-4)", fig.alt="(ref:euler-glyphs-4)"}
# plot the Venndir
v_mammo4p <- plot(v_mammo4n,
   expand_fraction=c(-0.1, -0.10,
      -0.1, -0.05))
# extract the viewport adjustments
vp <- attr(v_mammo4p, "viewport");
adjx <- attr(v_mammo4p, "adjx");
adjy <- attr(v_mammo4p, "adjy");
# create pointsGrob
set.seed(123);
pts <- grid::grid.points(
   x=adjx(xy$x + rnorm(1000)/6),
   draw=FALSE,
   y=adjy(xy$y + rnorm(1000)/6),
   pch=21,
   gp=grid::gpar(col=xy$border,
      fill=xy$color, cex=0.6),
   vp=attr(v_mammo4p, "viewport"),
   default.units="native")
# draw inside the viewport
grid::pushViewport(vp)
grid::grid.draw(pts)
grid::popViewport()
```

:::: {.tipbox data-latex=""}

**Tips:**

* The `Venndir` object must be plotted in order to define the `viewport`,
since it depends upon the `expand_fraction` adjustments.
* The viewport must be pushed before drawing points.

::::

The result turned out better than expected, and the workflow could
be re-used for other datasets.

**Can it be improved?**

While creating the figure, the first question that arose:  
**"How many points are in each region?"**  
In truth, it took some effort to discover these values, despite
being the focal point of the study (and the study about the study).

Figure \@ref(fig:euler-glyphs-5) shows some potential improvement,
labeling each section with the number of points.

(ref:euler-glyphs-5) Update which labels the number of points in each region. The title reveals each point represents one woman tested per 1,000 in the study.

```{r euler-glyphs-5, echo=FALSE, out.width="100%", fig.height=5, fig.width=8, fig.cap="(ref:euler-glyphs-5)", fig.alt="(ref:euler-glyphs-5)"}
# create a different Venndir
v_mammo5 <- venndir(mammo_list,
   do_plot=FALSE,
   circle_nudge=list(
      wwbc=c(-1.4, 0),
      wwpm=c(-1.6, 0)),
   innerborder.lwd=1, outerborder.lwd=1,
   overlap_type="agreement",
   poly_alpha=0.1,
   set_colors=mammo_colors,
   setlist_labels=mammo_labels,
   fontfaces=list(overlap="plain"),
   show_labels="NC", keep_items=TRUE,
   segment_buffer=-0.05,
   font_cex=c(0.8, 1.1, 0.8),
   segment_distance=0.02,
   rotate_degrees=180,
   draw_legend=FALSE,
   proportional=TRUE)
# edit the label colors
k <- c("wob", "wwbc")
v_mammo5@label_df[k, "color"] <- c("blue", "red")
# nudge labels
v_mammo5n <- nudge_venndir_label(v_mammo5,
   label_location="outside",
   offset_list=list(wwbc=c(0.01, 0.0505),
      wwpm=c(-0.09, 0.06),
      "wwbc&wwpm"=c(-0.12, 0.14),
      "wob&wwpm"=c(-0.09, -0.55),
      wob=c(-0.01, -0.06)))
# plot the Venndir
v_mammo5p <- plot(v_mammo5n,
   main="Mammography test outcomes per 1,000 women.",
   segment_buffer=-0.4,
   expand_fraction=c(-0.1, -0.10, -0.1, -0.05))

# draw inside the viewport
grid::pushViewport(vp)
grid::grid.draw(pts)
grid::popViewport()
```


## Venndir Case Studies

While many stylistic techniques were described earlier in the text,
putting them together in an artistic way is not always straightforward.

The following case studies serve two purposes:

1. Illustrate some stylistic options while showing the associated techniques.
2. Demonstrate the utility of directional Venn diagrams.

In both cases, it often works best to use real world data.


### Seehawer Kmt2 Gene Venns

**Context**

Many genomics studies compare results across experimental conditions
using Venn or Euler diagrams—for instance, to identify genes affected
by a perturbation such as a treatment, toxin exposure, or disease
onset. These studies often aim to uncover the genetic basis of a
perturbation, in hopes of revealing ways to mitigate or prevent its
effects.

There are thousands of studies that identify "genes affected",
and many of them have associated direction of change.
For example, "up" may be recognized as "up-regulated" or "increased function",
and "down" may be recognized as "down-regulated" or "decreased function."

When two studies are compared, they often focus on the genes involved
without regard to the direction of change.
A common alternative is to compare
only the concordant gene changes: For example, comparing up-regulated
genes in two studies, and separately comparing down-regulated genes
in two studies.

The underlying question is that the genes in each set represent the
biological mechanism involved in the study, and that shared genes
may therefore represent shared biology.

If the genes represent the process, surely the direction of change
represents the effect on the process?
Venndir proposes the important distinction that the concordance of
direction represents whether the underlying process is also affected
in the same way.

Direction is important in science and medicine, where it often means the
difference between disease and treatment.

1. Overlap alone is not enough.
2. Directionality matters.

To date, no Venn software tool indicates overlap and
directionality together.


**Example Data**

A scientific paper by
[Seehawer et al](https://www.nature.com/articles/s41556-024-01446-3)
[@Seehawer_2024] studied brain cancer metastasis, 
the migration property of cancer cells associated with
poor clinical prognosis.

They studied the effect of losing either of two genes,
*Kmt2c* and *Kmt2d*, and they included two very different
cell types: "168FARN" and "67NR".

Figure \@ref(fig:seehawer-0) shows the target figure.
Each Venn diagram compares the genes affected in two cell types:
"168FARN" and "67NR".

* The left panels focus on *Kmt2c*, the right panels focus on *Kmt2d*.
* The top panels compare only up-regulated genes,
the bottom panels compare only down-regulated genes.

The authors concluded that the two cell lines indicated a shared
genetic mechanism, even in very different cell types,
due to the concordant gene changes shown in each Venn diagram.
However, these figures did not indicate discordant changes.

(ref:seehawer-0) Target Extended Figure 4h from Seehawer *et al* 2024.

```{r seehawer-0, echo=FALSE, out.width="90%", fig.cap="(ref:seehawer-0)", fig.alt="(ref:seehawer-0)"}
knitr::include_graphics("images/SeehawerVenns.png")
```

Data for Extended Figure 4h were available via Supplemental Table 4,
filtered for adjusted P-value below 0.1 and absolute log2 fold change
of at least 0.6.
The gene symbol and fold change sign were used in Venndir.

Figure \@ref(fig:seehawer-2) shows the Venndir re-creation
of Seehawer Extended Figure 4h, with separate up- and down-regulated
Venn diagrams [@Seehawer_2024].
The Venndir output is nearly identical,
differing only by `1` gene each in the 67NR-specific overlap -
most likely a discrepancy by rounding error.

```{r seehawer-1, echo=FALSE}
kmdf2 <- data.table::fread(file="data/seehawer.txt", data.table=FALSE)
kmlist2 <- lapply(split(kmdf2, kmdf2$Set), function(idf){
   jamba::nameVector(idf$Sign, idf$Gene)
})
# split into up-regulated and down-regulated genes
kmlist_up2 <- lapply(kmlist2, function(i){ i[i > 0] })
kmlist_dn2 <- lapply(kmlist2, function(i){ i[i < 0] })
# define colors
km_colors2 <- c("dodgerblue", "#FFF400", "dodgerblue", "#FFF400")

make_km_venn <- function
(sets=c(1, 3),
 dir=c("all", "up", "down"),
 do_plot=FALSE,
 proportional=TRUE,
 font_cex=c(1, 1, 0.5),
 overlap_type="overlap",
 ...)
{
   #
   dir <- match.arg(dir)
   main <- gsub("(Kmt2[cd])[.]", "*\\1*_",
      paste(unique(gsub("^.+ ", "", names(kmlist2)[sets])),
         collapse=" vs "))
   if ("all" %in% dir) {
      use_list <- kmlist2;
   } else if ("up" %in% dir) {
      use_list <- kmlist_up2;
      main <- paste0(main, "  \nUpregulated genes")
   } else {
      use_list <- kmlist_dn2;
      main <- paste0(main, "  \nDownregulated genes")
   }
   km_labels2 <- gsub(" .+", "", names(kmlist2))
   v <- venndir(use_list,
      proportional=proportional,
      overlap_type=overlap_type,
      setlist_labels=km_labels2,
      main=main,
      font_cex=font_cex,
      show_segments=FALSE,
      do_plot=do_plot,
      set_colors=km_colors2,
      sets=sets,
      ...)
   return(invisible(v))
}
```

(ref:seehawer-2) Venndir re-creation of Seehawer Ext. Fig 4h.

```{r seehawer-2, echo=FALSE, out.width="100%", fig.width=10.5, fig.height=10, fig.cap="(ref:seehawer-2)", fig.alt="(ref:seehawer-2)"}
suppressPackageStartupMessages(library(patchwork))

vc1 <- make_km_venn(sets=c(1, 3), dir="all")
vc2 <- make_km_venn(sets=c(1, 3), dir="all",
   font_cex=c(1, 1, 0.7),
   template="tall", overlap_type="concordance")
vc3 <- make_km_venn(sets=c(1, 3), dir="all",
   font_cex=c(1, 1, 0.8),
   template="tall", overlap_type="agreement")
v_up <- make_km_venn(sets=c(1, 3), dir="up")
v_dn <- make_km_venn(sets=c(1, 3), dir="down")
xk <- c(0.15, 0.15, 0.11, 0.05);
vc1_grobs <- plot(vc1,
   expand_fraction=xk,
   do_draw=FALSE)
vc2_grobs <- plot(vc2,
   expand_fraction=xk,
   do_draw=FALSE)
vc3_grobs <- plot(vc3,
   expand_fraction=xk,
   do_draw=FALSE)
v_up_grobs <- plot(v_up,
   expand_fraction=xk,
   do_draw=FALSE)
v_dn_grobs <- plot(v_dn,
   expand_fraction=xk,
   do_draw=FALSE)
suppressWarnings({
   vc1_gtree <- attr(vc1_grobs, "gtree")
   vc2_gtree <- attr(vc2_grobs, "gtree")
   vc3_gtree <- attr(vc3_grobs, "gtree")
   v_up_gtree <- attr(v_up_grobs, "gtree")
   v_dn_gtree <- attr(v_dn_grobs, "gtree")
})
v_updn_c <- (wrap_elements(v_up_gtree) /
   wrap_elements(v_dn_gtree)) +
   plot_layout(heights=c(1, 1));

vd1 <- make_km_venn(sets=c(2, 4), dir="all")
vd2 <- make_km_venn(sets=c(2, 4), dir="all",
   font_cex=c(1, 1, 0.7),
   template="tall", overlap_type="concordance")
vd3 <- make_km_venn(sets=c(2, 4), dir="all",
   font_cex=c(1, 1, 0.8),
   template="tall", overlap_type="agreement")
v_upd <- make_km_venn(sets=c(2, 4), dir="up")
v_dnd <- make_km_venn(sets=c(2, 4), dir="down")
vd1_grobs <- plot(vd1,
   expand_fraction=xk,
   do_draw=FALSE)
vd2_grobs <- plot(vd2,
   expand_fraction=xk,
   do_draw=FALSE)
vd3_grobs <- plot(vd3, 
   expand_fraction=xk,
   do_draw=FALSE)
v_up_grobsd <- plot(v_upd,
   expand_fraction=xk,
   do_draw=FALSE)
v_dn_grobsd <- plot(v_dnd,
   expand_fraction=xk,
   do_draw=FALSE)
suppressWarnings({
   vd1_gtree <- attr(vd1_grobs, "gtree")
   vd2_gtree <- attr(vd2_grobs, "gtree")
   vd3_gtree <- attr(vd3_grobs, "gtree")
   v_up_gtreed <- attr(v_up_grobsd, "gtree")
   v_dn_gtreed <- attr(v_dn_grobsd, "gtree")
})
v_updn_d <- (wrap_elements(v_up_gtreed) /
   wrap_elements(v_dn_gtreed)) +
   plot_layout(heights=c(1, 1));

v_updn_cd <- (wrap_elements(v_up_gtree) +
   wrap_elements(v_up_gtreed)) /
   (wrap_elements(v_dn_gtree) +
   wrap_elements(v_dn_gtreed)) +
   plot_layout(widths=c(1, 1),
      heights=c(1, 1));
plot(v_updn_cd)
```

However, the *next question* is whether the shared gene changes are
concordant or discordant in direction.

Figure \@ref(fig:seehawer-3) includes up- and down-regulated genes
together, with *Kmt2c*_KO (left) and *Kmt2d*_KO (right).

(ref:seehawer-3) Venndir re-creation of Seehawer Ext. Fig 4h, using the complete set of up- and down-regulated genes together. The top row shows 'overlap', the second row shows 'agreement', and the third row shows 'concordance'.

```{r seehawer-3, echo=FALSE, out.width="100%", fig.width=10.5, fig.height=15, fig.cap="(ref:seehawer-3)", fig.alt="(ref:seehawer-3)"}
vcd123 <- (wrap_elements(vc1_gtree) + wrap_elements(vd1_gtree)) /
   (wrap_elements(vc3_gtree) + wrap_elements(vd3_gtree)) /
   (wrap_elements(vc2_gtree) + wrap_elements(vd2_gtree)) +
   plot_layout(widths=c(1, 1),
      heights=c(1, 1, 1));
plot(vcd123)


```

**Findings:**

The first row of panels shows regulated genes without regard to direction.

* For *Kmt2c*_KO (left) and *Kmt2d*_KO (right), the shared genes
represent a higher proportion of the Venn diagrams than in Seehawer.
* This increase in shared genes suggests that the increase is caused by
discordant changes.

The second row summarizes the overlap direction by `'agreement'`, described in [Overlap Type], using `'='` for agreement, and `'X'` for disagreement.

* Nearly as many shared genes disagree in direction as those that agree.

The bottom row summarizes the overlap by `'concordance'`, using
arrows to indicate direction, and `'X'` for discordant changes.

**Conclusion:**

Directionality provides important and context which was previously
missing in the Seehawer data [@Seehawer_2024].
While it is true the two cell types shared some proportion of gene
changes, the directional concordance was not particularly strong.
That said, the "true answer" is also not known, the subset of
concordant genes may represent the shared components, or maybe
the discordant genes could play different functional roles in the
two cell types.
In any case, the concordance adds important context to the question
and interpretation of supporting data.