Fig1_Overview.Rmd

---
title: "Fig1_Overview"
author: "Bernard Mulvey"
date: "2024-10-25"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.height = 10, fig.width = 7, include = FALSE)
knitr::opts_chunk$set(fig.width = 7, fig.height = 10)
#### sets tab autocompletion for directories to begin from the directory containing the .Rproj session, instead of the script's directory if different
knitr::opts_knit$set(root.dir = here::here())

library(data.table) # Preferred data manipulation package
library(ggplot2) # Dependency for several plotting functions
library(ggtext) # more character types in ggplots
library(ggrastr) # avoid making raster images one can't even open
library(SpatialExperiment) # Visium data framework
library(SpatialFeatureExperiment) # Xenium data framework
library(spatialLIBD) # one option for plotting cluster assignments, but breaks when the in-tissue portion of a visium area is very un-square.
library(escheR) # alternative spotplotting function, at least for visium
library(viridis) # palettes
library(Polychrome) # better palettes
require(colorout) # Utility for RStudio
library(sf) # define the polygonal boundaries of xenium domains
ColorOut()

# code reformatting in Rstudio
options("styler.addins_style_transformer" = "biocthis::bioc_style()")

# set plotting defaults for ggplot
theme_set(theme_bw() + theme(axis.text.x = element_text(size = 8), axis.title.x = element_text(size = 9), axis.text.y = element_text(size = 8), axis.title.y = element_text(size = 9), plot.title = element_blank(), strip.text = element_text(size = 10), legend.text = element_text(size = 8), legend.title = element_text(size = 8, hjust = 0.5)))
```

## load visium, xenium experiments, cluster labels
```{r}
#### VISIUM ####
hyp2 <- readRDS("spatial_HYP/processed-data/03-QC_filters/hypN10_umi210_gene126_chrm50_spotsweeped_lognorm_rotsNmirrors_072224.RDS")

bscl <- fread("spatial_HYP/processed-data/06-BayesSpace/01-bayesspace60kiter_k15-20-31_out/BSpace_k15_HARMONYlmbna_nnsvg10.txt") # main visium clustering (bs=bayesspace)

setnames(bscl,2,"cl")
bscl[,cl:=paste0("Vis",cl)]
bscl[cl=="Vis7",cl:="VMH.1"]
bscl[cl=="Vis12",cl:="VMH.2"]
bscl[cl=="Vis4",cl:="ARC.1"]
bscl[cl=="Vis6",cl:="ARC.2"]
# WM (optic tract, OT): Clusters 3 5 and 10
bscl[cl=="Vis3",cl:="OT (3)"]
bscl[cl=="Vis10",cl:="OT (3)"]
bscl[cl=="Vis5",cl:="OT (3)"]
# others:
bscl[cl=="Vis1",cl:="GABA (2)"]
bscl[cl=="Vis2",cl:="PeriVN"]
bscl[cl=="Vis9",cl:="Vascular"]
bscl[cl=="Vis8",cl:="SON"]
bscl[cl=="Vis13",cl:="Portal Vasc."]
bscl[cl=="Vis14",cl:="Astro"]
bscl[cl=="Vis15",cl:="GABA (2)"]

## Vis11 is a donor/sample-specific cluster, so remove that one for plotting here.
bscl <- bscl[cl!="Vis11"]

bscl[,dom:=cl]
bscl[dom %in% c("VMH.1","VMH.2"),dom:="VMH"]
bscl[dom %in% c("ARC.1","ARC.2"),dom:="ARC"]
bscl[dom %in% c("OT"),dom:="OT"]
bscl[!(dom %in% c("VMH","ARC","OT")),dom:="Other"]

# subset the visium obj to non-cluster 11 spots for plotting
hyp2 <- hyp2[,colnames(hyp2) %in% bscl$rn]

# append col labels to rest of visium data
bscl<-DataFrame(bscl,row.names=bscl$rn)[colnames(hyp2),]
hyp2$k15dom <- bscl$cl
hyp2$`Visium Domain` <- factor(bscl$dom,levels=c("ARC","VMH","OT","Other"))
```

#### XENIUM setup ####
```{r}
hypx <- readRDS("xenium_HYP/processed-data/05_Banksy_M0lam0_res2_multisamp/01-sfe-in_staggered-coords_genetarg-only_NONlog-norm.RDS")

bksmooth <- fread("xenium_HYP/processed-data/07_Domains-subdomains_by_knnSmoothing_Banksytypes/03b-ARCVMHdomains_2stepsmooth_VMH-k10-0.2-VMH-k200-0.2_ARC-k50-0.1_ARC-k500-0.5.txt") ## xenium domain assignments after smoothing, by xenium cell

## but drop discarded cell clusters, too
bkcl <- fread("xenium_HYP/processed-data/05_Banksy_M0lam0_res2_multisamp/01-BanksyClusts_M0lam0_leiden_multisamp.txt")
setnames(bkcl,2,"cl")

bkanno <- fread("xenium_HYP/processed-data/05_Banksy_M0lam0_res2_multisamp/02-M0l0kg6_topClusMarkers_celltypes_annotated.txt")

## drop sample-specific clusters, append cluster annotations 
bkanno <- unique(bkanno[,.(clus,bjm_annot)])
bkanno <- bkanno[bjm_annot!="DISCARD"&bjm_annot!="VMH_4_DISCARD"]

bkcl <- merge.data.table(bkcl,bkanno,by.x="cl",by.y="clus")
## drop excluded clusters from xenium spe
hypx <- hypx[,colnames(hypx) %in% bkcl$rn]

## append cluster annots to xenium spe
bkcl <- DataFrame(bkcl,row.names=bkcl$rn)[colnames(hypx),]
bkcl$rn <- NULL
hypx$banksyclus <- bkcl$bjm_annot

## assign the domain labels to the xenium cells
bksmooth[dualVMHARC4=="other",dualVMHARC4:="Other"]
bksmooth <- DataFrame(bksmooth,row.names=bksmooth$rn)[colnames(hypx),]
hypx$dom <- bksmooth$dualVMHARC4
```

The colors slotted in 1 and 3 are also picked to have the same luminance so that gene expression coloration fill in escheR grayscale doesn't look falsely darker or lighter in one spot type vs. the other 
```{r}
pals <- readRDS("manuscript_plot_code/domain_and_xencluster_palettes_CHECKPALNAMESBEFOREUS.RDS")

pal <- pals[[1]]

fullpal <- readRDS("manuscript_plot_code/14color_palette.RDS")
 
#### FOR PLOTTING INDIVIDUAL VISIUM OT/GABA CLUSTERS, NOT USED: set levels for the domain labels so they appear in the same order
# hyp2$k15dom <- factor(hyp2$k15dom,levels=c("VMH.1","VMH.2","ARC.1","ARC.2","OT.1","OT.2","OT.3","GABA.1","GABA.2","SON","Vascular","Astro","PeriVN","Portal Vasc."))

## subset the 14 color pal to 11 colors (matching the xenium clustergroup palette for oligos (orange) and GABAs (deep pink)) to account for merging OT clusters or GABA clusters together, rename pal and factor the spe cluster column

fullpal <- c(fullpal[c("VMH.1","VMH.2","ARC.1","ARC.2")],
             pals[["xen_cellgroups"]][c("Oligo (4)","Other GABAergic Neurons (2)")],
             fullpal[c("SON","Vascular","Astro","PeriVN","Portal Vasc.")]) 

names(fullpal)[c(5,6)] <- c("OT (3)","GABA (2)")

stopifnot(all(names(fullpal) %in% unique(hyp2$k15dom)))
hyp2$k15dom <- factor(hyp2$k15dom,levels=names(fullpal))
### also load manuscript plotting ids
mscriptids <- fread("standardized_sampleids_for_plotting.txt")
```

###### TIP: LEGEND ORDER ######
### i would've been up shit creek without this handy post https://stackoverflow.com/questions/76397512/ggplot-ordering-legends-with-guides-changes-continuous-legend-to-discrete
#################################

Panels A and B are experimental overviews/schematics.
Panel C is an exemplary Visium sample with ALL domain labels.
Our exemplary sample here is V12D05-350_C1 (xen rep Br8741,X99_8741C looks better) 

## (another option: V13Y24-346_C1 (Br1225, xenium rep X99_1225B looks better)
```{r}
# panc <- hyp2[,hyp2$sample_id=="V12D05-350_C1"]
panc <- hyp2[,hyp2$sample_id=="V13Y24-346_C1"]

p <- make_escheR(panc)
p <- p |> add_fill("k15dom",size=0.25,point_size = 0.7)

pdf("manuscript_plots/Fig1/1C-V13Y24-346_C1_Visdomains.pdf",height=2,width=2)
p+
    scale_fill_manual(values=fullpal,na.value = NA)+
    # ggtitle(mscriptids[sample_id==unique(panc$sample_id),manuscript_id])+
    guides(color="none",fill=guide_legend(title = NULL,override.aes=list(size=1.5)))+
    #labs(fill="Visium\nCluster(s)\n(N if >1)")+
    theme(
       #plot.title.position = "plot",
       #plot.title = element_markdown(size=9,hjust=0.5),
       legend.key.size = ggplot2::unit(0.075,"in"),legend.title=element_text(size=7,hjust=0.5),plot.margin = margin(0,0.05,0,-0.05,unit = "in"),legend.margin = margin(0,0,0,-0.14,"in"),legend.text=element_text(size=6))
dev.off()

rm(panc,p)
```

### ## create supercluster labels to group related cell types together and use the previously saved + loaded xenium palette for cell groups for the next panel.
```{r}
bkcl <- as.data.table(bkcl,keep.rownames=T)
bkcl[,supercl:="ifthisisplottedigoofed"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="VMH",value=T),supercl:="VMH (4)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="ARC",value=T),supercl:="ARC (5)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="Tanyc|Alar",value=T),supercl:="Tanycytes,<br>Portal Vasc. (4)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="Oligo",value=T),supercl:="Oligo (4)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="Astro",value=T),supercl:="Astro (2)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="Microg",value=T),supercl:="Microglia (3)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="Supraopt",value=T),supercl:="SON (2)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="Unsure_AVP",value=T),supercl:="Non-SON<br>*AVP*+*OXT*+ (1)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="Macrop|Periph|Vascul|Endothel",value=T),supercl:="Vascular, Periph.<br>Immune (5)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="GABA",value=T),supercl:="Other GABAergic (2)"]
bkcl[bjm_annot %in% grep(bjm_annot,pattern="Periven",value=T),supercl:="PeriVN<br>(Excitatory) (1)"]
### the parenthetical numbers add up to 33, so we're good.


### lastly, in the case of the xenium spotplot of all cell groups, we need to put them in a specific order for them to fit within the plot space alongside 1225B: VMH, ARC, SON, Astro, Oligo, Microglia, PeriVN, Non-SON AVP+OXT+, Tanycytes n portal, Other GABA, Vasc n periph
levs <- c("VMH (4)","ARC (5)","SON (2)","Astro (2)","Oligo (4)","Microglia (3)","PeriVN<br>(Excitatory) (1)","Non-SON<br>*AVP*+*OXT*+ (1)","Tanycytes,<br>Portal Vasc. (4)","Other GABAergic (2)","Vascular, Periph.<br>Immune (5)")
stopifnot(all(levs %in% unique(bkcl$supercl)))

bkcl$supercl <- factor(bkcl$supercl,levels=c("VMH (4)","ARC (5)","SON (2)","Astro (2)","Oligo (4)","Microglia (3)","PeriVN<br>(Excitatory) (1)","Non-SON<br>*AVP*+*OXT*+ (1)","Tanycytes,<br>Portal Vasc. (4)","Other GABAergic (2)","Vascular, Periph.<br>Immune (5)"))
```

1E previous code to plot usign data.table (results in skewed dimensions; implemented a fix for it here but then switched to escher for consistency with other panels in this figure anyhow.)
```{r}
## make plotting table of spatial coords, super cluster assignments
# panealt <- as.data.table(cbind(colData(hypx[,hypx$sample_id=="X99_1225B"]),spatialCoords(hypx[,hypx$sample_id=="X99_1225B"])))

## zero-base the coordinates
# panealt[,sdimx:=0.1+sdimx-(min(sdimx))]
# panealt[,sdimy:=0.1+sdimy-(min(sdimy))]
# stopifnot(nrow(panealt[sdimx>0&sdimy>0])==nrow(panealt))

# factor supercl here to match the leveling from the source SPE 
# panealt[,supercl:=factor(supercl,levels=levs)]

# to plot appropriately, need x and y axes to be same range. since this section is taller than wide, get the difference to tack onto the coordinate limits so that the tissue spots fall in the center of the plot area along the x axis
# halfdiff <- (8000-max(panealt$sdimy))/2

# pdf("manuscript_plots/Fig1/1Ealt-Xenium cell group spotplot.pdf",height=2,width=3.25)
# ggplot(panealt[sdimy<8000],aes(x=sdimx,y=sdimy,col=supercl))+
#    #guides(color="none")+
#    labs(color="Cell group\n(N clusters)")+
#    ggtitle(mscriptids[sample_id=="X99_1225B",manuscript_id])+
#    guides(color=guide_legend(override.aes=list(size=1.5)))+
#    scale_color_manual(values=xenpal)+
#    #scale_x_continuous(limits=c(halfdiff,max(panealt$sdimx)+abs(halfdiff)),expand=c(0,0))+
#    # scale_y_continuous(limits=c(0,8000,expand=c(0,0)))+
#    theme(panel.grid = element_blank(),
#          axis.text.x=element_blank(),
#          axis.text.y=element_blank(),
#          axis.title.x=element_blank(),
#          axis.title.y=element_blank(),
#          axis.ticks = element_blank(),
#          legend.text = element_markdown(size=6,margin=margin(0,0,0,0,"in")),
#          legend.key.size = ggplot2::unit(0.075,"in"),
#          legend.title=element_text(size=7,hjust=0.5),
#          legend.background = element_blank(),
#          # legend.title=element_text(size=7,margin=margin(0,0,0.0525,-0.65,"in"),hjust=0.5),
#          legend.box = element_blank(),
#          legend.box.spacing = ggplot2::unit(0,"in"),
#          legend.key = element_blank(),
#          legend.key.height=ggplot2::unit(0.0125,"in"),
#          legend.key.width=ggplot2::unit(0.0125,"in"),
#          legend.key.spacing.y = ggplot2::unit(0.05,"in"))+
#          # legend.position.inside = c(0.19,0.165),
#          # legend.position="inside")+
#    rasterize(geom_point(size=0.2,stroke=0),layers="Points",dpi=900,dev="cairo_png")
```

1E continued: xenium "spot"plot with superclusters labeled. fix the palette names to match the tweaked names above (done to keep legend from taking up too much width)
```{r}
## make palette names match tweaks from previous block
xenpal <- pals$xen_cellgroups
names(xenpal)[names(xenpal)=="PeriVN (Excitatory) (1)"] <- "PeriVN<br>(Excitatory) (1)"
names(xenpal)[names(xenpal)=="Non-SON AVP+OXT+ (1)"] <- "Non-SON<br>*AVP*+*OXT*+ (1)"
names(xenpal)[names(xenpal)=="Tanycytes, Portal Vasc. (4)"] <- "Tanycytes,<br>Portal Vasc. (4)"
names(xenpal)[names(xenpal)=="Vascular and Peripheral Immune (5)"] <- "Vascular, Periph.<br>Immune (5)"
names(xenpal)[names(xenpal)=="Other GABAergic Neurons (2)"] <- "Other GABAergic (2)"

stopifnot(all(names(xenpal) %in% unique(bkcl$supercl)))

## assign superclusters to SPE
hypx$supercl <- DataFrame(bkcl,row.names=bkcl$rn)[colnames(hypx),]$supercl

# get sample
panealt <- hypx[,hypx$sample_id=="X99_1225B"]


pane <- make_escheR(panealt,y_reverse=FALSE)
pane <- pane |> add_fill("supercl",size=0.2,point_size = 0.2)

pdf("manuscript_plots/Fig1/1Ealt-Xenium cell group spotplot.pdf",height=2,width=3.25)
pane+   #guides(color="none")+
   labs(fill="Cell group\n(N clusters)")+
   # ggtitle(mscriptids[sample_id=="X99_1225B",manuscript_id])+
   guides(fill=guide_legend(override.aes=list(size=1.5)))+
   scale_fill_manual(values=xenpal)+
   #scale_x_continuous(limits=c(halfdiff,max(panealt$sdimx)+abs(halfdiff)),expand=c(0,0))+
   # scale_y_continuous(limits=c(0,8000,expand=c(0,0)))+
   theme(panel.grid = element_blank(),
         axis.text.x=element_blank(),
         axis.text.y=element_blank(),
         axis.title.x=element_blank(),
         axis.title.y=element_blank(),
         axis.ticks = element_blank(),
         legend.text = element_markdown(size=6,margin=margin(0,0,0,0,"in")),
         legend.key.size = ggplot2::unit(0.075,"in"),
         legend.title=element_text(size=7,hjust=0.5),
         legend.background = element_blank(),
         # legend.title=element_text(size=7,margin=margin(0,0,0.0525,-0.65,"in"),hjust=0.5),
         legend.box = element_blank(),
         legend.box.spacing = ggplot2::unit(0,"in"),
         legend.key = element_blank(),
         legend.key.height=ggplot2::unit(0.0125,"in"),
         legend.key.width=ggplot2::unit(0.0125,"in"),
         legend.key.spacing.y = ggplot2::unit(0.04,"in"))
         # legend.position.inside = c(0.19,0.165),
         # legend.position="inside")

dev.off()
```

## Panel d: Visium with key domains labeled. Don't show OT.
```{r}
pand <- hyp2[,hyp2$sample_id=="V13Y24-346_C1"]
pand$`Visium Domain` <- as.character(pand$`Visium Domain`)
pand$`Visium Domain`[pand$`Visium Domain`=="OT"] <- "Other"
pand$`Visium Domain` <- factor(pand$`Visium Domain`,levels=c("ARC","VMH","Other"))

p <- make_escheR(pand)
p <- p |> add_fill("Visium Domain",size=0.8,point_size = 0.8)

pdf("manuscript_plots/Fig1/1D-V13Y24-346_C1_keydomains.pdf",height=2,width=2)
p+
    scale_fill_manual(values=pal,na.value = NA)+
    # ggtitle(mscriptids[sample_id=="V13Y24-346_C1",manuscript_id])+
    # guides(color=guide_legend(override.aes=list(size=1.5,stroke=1)))+
    guides(fill="none")+
    # labs(color="Visium\nDomain")+
    theme(legend.text = element_text(size=7),
          # plot.title.position = "plot",
          # plot.title = element_markdown(size=9,hjust=0.5),
          # plot.title = element_text(size=9,hjust=0.5),
          # legend.key.size = ggplot2::unit(0.075,"in"),
          # legend.title=element_text(size=7.5,hjust=0.5),
          plot.margin = margin(0,0,0,0,unit = "in"))
          # legend.margin = margin(0,0,0,-0.14,"in"))
dev.off()

rm(p,pand)
```



### Panel F part 1: VMH marker expression (NR5A1). For consistency with the xenium plots, we first want to make an OUTLINE around the domain, rather than fill it in. We want the most precise polygons possible here, so we will not use hull functions and instead use the "Jarvis March" or gift-wrapping algorithm, which exhaustively searches for boundary points that fall within a query set. 
   # Credit for the approach to github copilot chat on IntelliJ IDEA in response to the prompt "How do I calculate the coordinates of all points defining a 2D polygon boundary from a set of x,y coordinates without needing to tune parameters? I want all points defining the boundary to be from within the set of query points. Concave and convex hull functions, like concaveman, chull, alphahull, and functions from the sf package are not appropriate."

```{r}
## set visium rownames  to gene symbols
rownames(hyp2) <- rowData(hyp2)$gene_name

panf <- hyp2["NR5A1",hyp2$sample_id=="V13Y24-346_C1"]
colData(panf)$`log counts` <- as.numeric(logcounts(panf)["NR5A1",])

# drop OT for now
panf$`Visium Domain` <- as.character(panf$`Visium Domain`)
panf$`Visium Domain`[panf$`Visium Domain`=="OT"] <- "Other"
panf$`Visium Domain` <- factor(panf$`Visium Domain`,levels=c("ARC","VMH","Other"))

## now we need to sidetrack and get the points defining the perimeter of the ARC and VMH. modifying some template code from copilot:

# subFunction to find the orientation of the triplet (p, q, r)
# Function to find the orientation of the triplet (p, q, r)
orientation <- function(p, q, r) {
  val <- (q[2] - p[2]) * (r[1] - q[1]) - (q[1] - p[1]) * (r[2] - q[2])
  if (val == 0) return(0)  # collinear
  if (val > 0) return(1)   # clockwise
  return(2)                # counterclockwise
}

# Function to check if point q is on segment pr
on_segment <- function(p, q, r) {
  if (q[1] <= max(p[1], r[1]) && q[1] >= min(p[1], r[1]) &&
      q[2] <= max(p[2], r[2]) && q[2] >= min(p[2], r[2])) {
    return(TRUE)
  }
  return(FALSE)
}

# Function to implement the gift wrapping algorithm allowing for potential concavity in polygons
gift_wrapping_concave <- function(points) {
  n <- nrow(points)
  if (n < 3) stop("There must be at least 3 points")

  # Initialize result
  hull <- list()

  # Find the leftmost point
  l <- which.min(points[, 1])

  p <- l
  repeat {
    # Add current point to result
    hull <- append(hull, list(points[p, ]))

    # Search for a point 'q' such that orientation(p, q, x) is counterclockwise
    q <- (p + 1) %% n
    for (i in 1:n) {
      if (orientation(points[p, ], points[i, ], points[q, ]) == 2) {
        q <- i
      }
    }

    # Now q is the most counterclockwise with respect to p
    p <- q

    # While we don't come to the first point
    if (p == l) break
  }

  # Convert list to matrix
  hull <- do.call(rbind, hull)

  # Remove points that are inside the concave polygon
  is_inside <- function(pt, hull) {
    n <- nrow(hull)
    count <- 0
    for (i in 1:n) {
      next_i <- ifelse(i == n, 1, i + 1)
      if (on_segment(hull[i, ], pt, hull[next_i, ])) return(FALSE)
      if (orientation(hull[i, ], pt, hull[next_i, ]) == 0) return(FALSE)
      if (orientation(hull[i, ], pt, hull[next_i, ]) == 2) count <- count + 1
    }
    return(count %% 2 == 1)
  }

  hull <- hull[!apply(points, 1, is_inside, hull = hull), ]

  return(hull)
}


## attach the coordinate info to colData so we can subset to VMH points and wrap this gift. it'll take a bit
tmpcoldata <- as.data.table(as.data.frame(cbind(colData(panf),spatialCoords(panf))))

### these end up looking wacky without some stray spots removed, so we need to manually drop a handful of singletons
# tmpcoldata <- tmpcoldata[Visium.Domain %in% c("ARC","VMH")]
# this will be easier to figure out using the array row/col values on a temporary plot with some lines overlaid to help pinpoint array coords to remove from the tracing. this doesn't account for the rotations or mirroring of the visium data, so grab a screenshot and rotate so that the x and y coords are not changed relative to the values we'll use to filter the table.
ggplot(tmpcoldata,aes(x=array_row,y=array_col,color=Visium.Domain))+
   geom_point(size=1.5)+
   geom_vline(xintercept=seq(0,max(tmpcoldata$array_row),by=5))+
   geom_hline(yintercept=seq(0,max(tmpcoldata$array_col),5))+scale_x_reverse()
dev.off()
# ok three conditionals should do it: ARC points that are to the right of an array_row/col line col = 5.8*row - 230 and for array_row>=50 & array_row<=55 or at array_col>40 where array_row<40, or anything with array_col>101 or array_row<20. for VMH, points right of the line 10.8*row-184
tmpcoldata <- rbind(tmpcoldata[Visium.Domain=="ARC"&!((array_row<40&array_col>40)|(array_row>=50&array_row<=55&array_col>5.8*array_row-230)|array_col>101|array_row<21)],
                    tmpcoldata[Visium.Domain=="VMH"&!(array_col>10.8*array_row-184)])

## for plotting with geom_path, we need to add the first point at the end of the sequence as well. this function is a little glitchy in that it returns a matrix of equal dimensions to the input but now with a bunch of NA rows so dump those after
vhull <- gift_wrapping_concave(as.data.frame(tmpcoldata[Visium.Domain=="VMH",.(array_col,array_row)]))
vhull <- vhull[rowSums(is.na(vhull))==0,]
vhull <- as.data.frame(rbind(vhull,vhull[1,]))
vhull$dom <- "VMH"
rownames(vhull) <- as.numeric(rownames(vhull))
rownames(vhull) <- paste0("pt",seq(1,nrow(vhull),by=1))


ahull <- gift_wrapping_concave(as.data.frame(tmpcoldata[Visium.Domain=="ARC",.(array_col,array_row)]))
ahull <- ahull[rowSums(is.na(ahull))==0,]

# the ARC hull points still come out a little goofy. after array_col 37, array _row 27, we need to add the following points: col40row36,col38row40,col40row42,col45row45,52-49,7454

addpoints <- as.data.frame(cbind(c(40,38,40,45,52,74),c(36,40,42,45,49,54)))
colnames(addpoints) <- c("array_col","array_row")
insertrow <- which(ahull[,1]==37&ahull[,2]==27)
ahull <- rbind(ahull[c(1:insertrow),],addpoints,ahull[c(1+insertrow:nrow(ahull)),])

# then add the first point to the end of the sequence again
ahull <- rbind(ahull,ahull[1,])
ahull$dom <- "ARC"
rownames(ahull) <- paste0("pt",seq((1+nrow(vhull)),nrow(vhull)+nrow(ahull),by=1))


dompoly <- rbind(vhull,ahull)

## get the pixel-resolution values corresponding to these spots as pixel res is what escher uses to plot
dompoly2 <- merge.data.table(as.data.table(dompoly,keep.rownames=T),tmpcoldata,by=c("array_col","array_row"))
dompoly2 <- dompoly2[,.(rn,array_col,array_row,pxl_col_in_fullres,pxl_row_in_fullres,Visium.Domain)]
dompoly2[,rn:=as.numeric(gsub(rn,pattern="pt",replacement=""))]
setorderv(dompoly2,"rn")

rm(vhull,ahull,dompoly,tmpcoldata)
```

now plot it
```{r}
p <- make_escheR(panf)
p <- p |> add_fill("log counts",size=0.8,point_size = 0.8)
# p <- p |> add_ground(var = "Visium Domain",stroke=0.55,point_size = 0.5)

pdf("manuscript_plots/Fig1/1F-V13Y24-346_C1_NR5A1.pdf",height=2,width=2)
p+
   geom_path(data=dompoly2,aes(x=pxl_col_in_fullres,y=pxl_row_in_fullres,color=Visium.Domain,group=Visium.Domain),size=0.4)+
   scale_color_manual(values=pal,na.value = NA)+
   scale_fill_continuous("log\ncounts",low= "#FFFFFF",high="#000000")+
   ggtitle("*NR5A1*")+
   guides(color=guide_legend(override.aes=list(size=1.5,stroke=1)))+
   labs(color="Visium\nDomain")+
   guides(color="none")+
   theme(plot.title.position = "plot",plot.title = element_markdown(size=9,hjust=0.5),legend.text = element_text(size=7),legend.key.size = ggplot2::unit(0.075,"in"),legend.title=element_text(size=7.5,hjust=0.5),plot.margin = margin(0,0.05,0,-0.05,unit = "in"),legend.margin = margin(0,0,0,-0.14,"in"))
dev.off()

rm(p,panf)
```


# panels G is a representative ARC marker in our Visium sample : POMC
```{r}
## we need to set rownames of the visium object to gene symbols instead of ensembl identifiers for ease here
rownames(hyp2) <- rowData(hyp2)$gene_name

## for escheR, we put the assay value into the colData for it to use
pang <- hyp2["POMC",hyp2$sample_id=="V13Y24-346_C1"]
colData(pang)$`log counts` <- as.numeric(logcounts(pang)["POMC",])

p <- make_escheR(pang)
p <- p |> add_fill("log counts",size=0.8,point_size = 0.8)
# p <- p |> add_ground(var = "Visium Domain",stroke=0.15,point_size = 0.5)

pdf("manuscript_plots/Fig1/1G-V13Y24-346_C1_POMC.pdf",height=2,width=2)
p+
    geom_path(data=dompoly2,aes(x=pxl_col_in_fullres,y=pxl_row_in_fullres,color=Visium.Domain,group=Visium.Domain),size=0.4)+
    scale_color_manual(values=pal,na.value = NA)+
    scale_fill_continuous("log\ncounts",low= "#FFFFFF",high="#000000")+
    #scale_fill_grey()+
    ggtitle("*POMC*")+
    # guides(color=guide_legend(override.aes=list(size=1.5,stroke=1)))+
    # labs(color="Visium\nDomain")+
    guides(color="none")+
    theme(plot.title.position = "plot",plot.title = element_markdown(size=9,hjust=0.5),legend.text = element_text(size=7),legend.key.size = ggplot2::unit(0.075,"in"),legend.title=element_text(size=7.5,hjust=0.5),plot.margin = margin(0,0.05,0,-0.05,unit = "in"),legend.margin = margin(0,0,0,-0.14,"in"))
dev.off()

rm(p,pang)
```

Panel I is the xenium domains in the same donor (Br1225 , sample X99_1225B).

### Panel I: xenium counterpart to C (X99_1225B)
```{r}
pani <- hypx[,hypx$sample_id=="X99_1225B"]

# y_reverse is an argument to unexported escheR internals that we don't want set to TRUE, since we have oriented all the tissue the way we want it already.
p <- make_escheR(pani,y_reverse=FALSE)
p <- p |> add_fill("dom",size=0.05,point_size = 0.2)

pdf("manuscript_plots/Fig1/1I-X99_1225B_Xendomains.pdf",height=2,width=2)
p+
    scale_fill_manual(values=pal,na.value = NA)+
    # ggtitle(mscriptids[sample_id=="X99_1225B",manuscript_id])+
    # guides(color="none",fill=guide_legend(override.aes=list(size=1.5)))+
    # labs(fill="Xenium\nDomain")+
   guides(color="none",fill="none")+
   theme(plot.title.position = "plot",plot.title = element_markdown(size=9,hjust=0.5,margin=margin(0.12,0,-0.10,0,"in")),plot.margin = margin(-0.11,-0.4,-0.12,-0.4,unit = "in"))
dev.off()

rm(pani,p)

```


### xenium panels: no WM "domain" defined, so change the domain color palette to exclude WM. also remove the "other" category, since we only have VMH/ARC outlines colored. (expression is filled with grayscale.)
```{r}
pal <- pal[1:2]
```

### The xenium expression panels (I, J,K) are going to be tricky -- there's so many spots we can't really use the line-color / fill heuristic. we'll have to add an outline around the domain instead.

### J K L are the same genes in the xenium counterpart, X99_1225B
```{r}
panj <- hypx["NR5A1",hypx$sample_id=="X99_1225B"]
# 
## attach the coordinate info to colData so we can make an sf object out of this for hull extraction. see well-annotated code from DE benchmarking of domain boundaries in xenium_HYP/code/07/03c-DE benchmarks...
tmpcoldata <- cbind(colData(panj),spatialCoords(panj))
panjv <- st_as_sf(as.data.frame(tmpcoldata[tmpcoldata$dom=="VMH",]),coords = c("sdimx","sdimy"))
panjv <- st_convex_hull(st_union(panjv))

panja <- st_as_sf(as.data.frame(tmpcoldata[tmpcoldata$dom=="ARC",]),coords=c("sdimx","sdimy"))
panja <- st_concave_hull(st_union(panja),ratio = 0.1)

## create the data table we will use for overlaying the boundary polygons
dompoly <- cbind(panjv[[1]][[1]],rep("VMH",nrow(panjv[[1]][[1]])))
dompoly <- rbind(dompoly,cbind(panja[[1]][[1]],rep("ARC",nrow(panja[[1]][[1]]))))
dompoly <- as.data.table(dompoly)
setnames(dompoly,c("xpol","ypol","dompol"))
dompoly[,xpol:=as.numeric(xpol)]
dompoly[,ypol:=as.numeric(ypol)]

rm(panja,panjv)
```

### finally, append the counts for our gene of interest (NR5A1) and make panel J
```{r}
colData(panj)$`log counts` <- as.numeric(logcounts(panj)["NR5A1",])

p <- make_escheR(panj,y_reverse=FALSE)
p <- p |> add_fill("log counts",size=0.2,point_size = 0.225)

pdf("manuscript_plots/Fig1/1J-X99_1225B_NR5A1.pdf",height=2,width=2)
p+
    scale_fill_continuous("log\ncounts",low= "#FFFFFF",high="#000000")+
    geom_path(data=dompoly,aes(x=xpol,y=ypol,group=dompol,col=dompol),linewidth = 0.25)+
    scale_color_manual(values=pal,na.value = NA)+
    ggtitle("*NR5A1*")+
    guides(color="none")+
    # labs(col="Xenium\nDomain")+
    theme(plot.title.position = "plot",plot.title = element_markdown(size=9,hjust=0.5,margin=margin(0.12,0,-0.12,0,"in")),legend.text = element_text(size=7),legend.key.size = ggplot2::unit(0.125,"in"),legend.title=element_text(size=7.5,hjust=0.5),plot.margin = margin(-0.11,-0.4,-0.12,-0.4,unit = "in"),legend.margin=margin(0,-0.2,0,0,"in"),legend.background = element_blank(),legend.box = element_blank(),legend.key = element_blank())
dev.off()

# rm(panj,p,tmpcoldata)

```

K: POMC in X99_1225B
```{r}
pank <- hypx["POMC",hypx$sample_id=="X99_1225B"]
colData(pank)$`log counts` <- as.numeric(logcounts(pank)["POMC",])

p <- make_escheR(pank,y_reverse=FALSE)
p <- p |> add_fill("log counts",size=0.2,point_size = 0.225)

pdf("manuscript_plots/Fig1/1K-X99_1225B_POMC.pdf",height=2,width=2)
p+
    scale_fill_continuous("log\ncounts",low= "#FFFFFF",high="#000000")+
    geom_path(data=dompoly,aes(x=xpol,y=ypol,group=dompol,col=dompol),linewidth = 0.25)+
    scale_color_manual(values=pal,na.value = NA)+
    ggtitle("*POMC*")+
    guides(color="none")+
    # labs(col="Xenium\nDomain")+
    theme(plot.title.position = "plot",plot.title = element_markdown(size=9,hjust=0.5,margin=margin(0.12,0,-0.12,0,"in")),legend.text = element_text(size=7),legend.key.size = ggplot2::unit(0.125,"in"),legend.title=element_text(size=7.5,hjust=0.5),plot.margin = margin(-0.11,-0.4,-0.12,-0.4,unit = "in"),legend.margin=margin(0,-0.2,0,0,"in"),legend.background = element_blank(),legend.box = element_blank(),legend.key = element_blank())
dev.off()

rm(pank,p)
```

```{r}
sessionInfo()
sessioninfo::session_info()
```
R version 4.4.1 RC (2024-06-06 r86719)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Chicago
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] sf_1.0-16                      colorout_1.3-0.2              
 [3] Polychrome_1.5.1               viridis_0.6.5                 
 [5] viridisLite_0.4.2              escheR_1.4.0                  
 [7] spatialLIBD_1.16.2             SpatialFeatureExperiment_1.6.1
 [9] SpatialExperiment_1.14.0       SingleCellExperiment_1.26.0   
[11] SummarizedExperiment_1.34.0    Biobase_2.64.0                
[13] GenomicRanges_1.56.1           GenomeInfoDb_1.40.1           
[15] IRanges_2.38.1                 S4Vectors_0.42.1              
[17] BiocGenerics_0.50.0            MatrixGenerics_1.16.0         
[19] matrixStats_1.3.0              ggrastr_1.0.2                 
[21] ggtext_0.1.2                   ggplot2_3.5.1                 
[23] data.table_1.15.4              rlang_1.1.4                   

loaded via a namespace (and not attached):
  [1] later_1.3.2               BiocIO_1.14.0             fields_16.2              
  [4] bitops_1.0-7              filelock_1.0.3            tibble_3.2.1             
  [7] R.oo_1.26.0               XML_3.99-0.17             lifecycle_1.0.4          
 [10] edgeR_4.2.0               doParallel_1.0.17         rprojroot_2.0.4          
 [13] lattice_0.22-6            magrittr_2.0.3            sass_0.4.9               
 [16] limma_3.60.3              plotly_4.10.4             rmarkdown_2.27           
 [19] jquerylib_0.1.4           yaml_2.3.9                httpuv_1.6.15            
 [22] spam_2.10-0               sessioninfo_1.2.2         sp_2.1-4                 
 [25] cowplot_1.1.3             DBI_1.2.3                 RColorBrewer_1.1-3       
 [28] golem_0.4.1               maps_3.4.2                abind_1.4-5              
 [31] zlibbioc_1.50.0           purrr_1.0.2               R.utils_2.12.3           
 [34] RCurl_1.98-1.14           rappdirs_0.3.3            GenomeInfoDbData_1.2.12  
 [37] ggrepel_0.9.5             irlba_2.3.5.1             terra_1.7-78             
 [40] units_0.8-5               dqrng_0.4.1               commonmark_1.9.1         
 [43] DelayedMatrixStats_1.26.0 codetools_0.2-20          DropletUtils_1.24.0      
 [46] DelayedArray_0.30.1       DT_0.33                   scuttle_1.14.0           
 [49] xml2_1.3.6                tidyselect_1.2.1          farver_2.1.2             
 [52] UCSC.utils_1.0.0          ScaledMatrix_1.12.0       shinyWidgets_0.8.6       
 [55] BiocFileCache_2.12.0      GenomicAlignments_1.40.0  jsonlite_1.8.8           
 [58] BiocNeighbors_1.22.0      e1071_1.7-14              scater_1.32.0            
 [61] iterators_1.0.14          foreach_1.5.2             tools_4.4.1              
 [64] Rcpp_1.0.13               glue_1.7.0                gridExtra_2.3            
 [67] SparseArray_1.4.8         xfun_0.45                 here_1.0.1               
 [70] EBImage_4.46.0            dplyr_1.1.4               HDF5Array_1.32.0         
 [73] withr_3.0.0               BiocManager_1.30.23       fastmap_1.2.0            
 [76] boot_1.3-30               rhdf5filters_1.16.0       fansi_1.0.6              
 [79] spData_2.3.1              rsvd_1.0.5                digest_0.6.36            
 [82] R6_2.5.1                  mime_0.12                 colorspace_2.1-0         
 [85] wk_0.9.2                  markdown_1.13             jpeg_0.1-10              
 [88] RSQLite_2.3.7             R.methodsS3_1.8.2         config_0.3.2             
 [91] utf8_1.2.4                tidyr_1.3.1               generics_0.1.3           
 [94] rtracklayer_1.64.0        class_7.3-22              httr_1.4.7               
 [97] htmlwidgets_1.6.4         S4Arrays_1.4.1            scatterplot3d_0.3-44     
[100] spdep_1.3-5               pkgconfig_2.0.3           gtable_0.3.5             
[103] blob_1.2.4                XVector_0.44.0            htmltools_0.5.8.1        
[106] dotCall64_1.1-1           fftwtools_0.9-11          scales_1.3.0             
[109] attempt_0.3.1             png_0.1-8                 knitr_1.48               
[112] rstudioapi_0.16.0         rjson_0.2.21              curl_5.2.1               
[115] proxy_0.4-27              cachem_1.1.0              rhdf5_2.48.0             
[118] stringr_1.5.1             BiocVersion_3.19.1        KernSmooth_2.23-24       
[121] parallel_4.4.1            vipor_0.4.7               AnnotationDbi_1.66.0     
[124] restfulr_0.0.15           s2_1.1.6                  pillar_1.9.0             
[127] grid_4.4.1                vctrs_0.6.5               promises_1.3.0           
[130] BiocSingular_1.20.0       dbplyr_2.5.0              beachmat_2.20.0          
[133] xtable_1.8-4              sfheaders_0.4.4           beeswarm_0.4.0           
[136] paletteer_1.6.0           evaluate_0.24.0           zeallot_0.1.0            
[139] magick_2.8.3              Rsamtools_2.20.0          cli_3.6.3                
[142] locfit_1.5-9.10           compiler_4.4.1            crayon_1.5.3             
[145] labeling_0.4.3            classInt_0.4-10           rematch2_2.1.2           
[148] ggbeeswarm_0.7.2          stringi_1.8.4             deldir_2.0-4             
[151] BiocParallel_1.38.0       munsell_0.5.1             Biostrings_2.72.1        
[154] lazyeval_0.2.2            tiff_0.1-12               Matrix_1.7-0             
[157] ExperimentHub_2.12.0      benchmarkme_1.0.8         sparseMatrixStats_1.16.0 
[160] bit64_4.0.5               Rhdf5lib_1.26.0           KEGGREST_1.44.1          
[163] statmod_1.5.0             shiny_1.8.1.1             AnnotationHub_3.12.0     
[166] gridtext_0.1.5            memoise_2.0.1             bslib_0.7.0              
[169] benchmarkmeData_1.0.4     bit_4.0.5                
> sessioninfo::session_info()
─ Session info ──────────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 RC (2024-06-06 r86719)
 os       macOS Sonoma 14.5
 system   aarch64, darwin20
 ui       RStudio
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/Chicago
 date     2024-10-25
 rstudio  2024.07.0-daily+219 Cranberry Hibiscus (desktop)
 pandoc   3.1.11 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)

─ Packages ──────────────────────────────────────────────────────────────────────
 ! package                  * version   date (UTC) lib source
   abind                      1.4-5     2016-07-21 [1] CRAN (R 4.4.0)
   AnnotationDbi              1.66.0    2024-05-01 [1] Bioconductor 3.19 (R 4.4.0)
   AnnotationHub              3.12.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   attempt                    0.3.1     2020-05-03 [1] CRAN (R 4.4.0)
   beachmat                   2.20.0    2024-05-06 [1] Bioconductor 3.19 (R 4.4.0)
   beeswarm                   0.4.0     2021-06-01 [1] CRAN (R 4.4.0)
   benchmarkme                1.0.8     2022-06-12 [1] CRAN (R 4.4.0)
   benchmarkmeData            1.0.4     2020-04-23 [1] CRAN (R 4.4.0)
   Biobase                  * 2.64.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.1)
   BiocFileCache              2.12.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   BiocGenerics             * 0.50.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   BiocIO                     1.14.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   BiocManager                1.30.23   2024-05-04 [1] CRAN (R 4.4.0)
   BiocNeighbors              1.22.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   BiocParallel               1.38.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   BiocSingular               1.20.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   BiocVersion                3.19.1    2024-04-22 [1] Bioconductor 3.19 (R 4.4.0)
   Biostrings                 2.72.1    2024-06-02 [1] Bioconductor 3.19 (R 4.4.0)
   bit                        4.0.5     2022-11-15 [1] CRAN (R 4.4.0)
   bit64                      4.0.5     2020-08-30 [1] CRAN (R 4.4.0)
   bitops                     1.0-7     2021-04-24 [1] CRAN (R 4.4.0)
   blob                       1.2.4     2023-03-17 [1] CRAN (R 4.4.0)
   boot                       1.3-30    2024-02-26 [1] CRAN (R 4.4.1)
   bslib                      0.7.0     2024-03-29 [1] CRAN (R 4.4.0)
   cachem                     1.1.0     2024-05-16 [1] CRAN (R 4.4.0)
   class                      7.3-22    2023-05-03 [1] CRAN (R 4.4.1)
   classInt                   0.4-10    2023-09-05 [1] CRAN (R 4.4.0)
 P cli                        3.6.3     2024-06-21 [2] CRAN (R 4.4.0)
   codetools                  0.2-20    2024-03-31 [1] CRAN (R 4.4.1)
   colorout                 * 1.3-0.2   2024-05-01 [1] Github (jalvesaq/colorout@c6113a2)
   colorspace                 2.1-0     2023-01-23 [1] CRAN (R 4.4.0)
   commonmark                 1.9.1     2024-01-30 [1] CRAN (R 4.4.0)
   config                     0.3.2     2023-08-30 [1] CRAN (R 4.4.0)
   cowplot                    1.1.3     2024-01-22 [1] CRAN (R 4.4.0)
   crayon                     1.5.3     2024-06-20 [1] CRAN (R 4.4.0)
   curl                       5.2.1     2024-03-01 [1] CRAN (R 4.4.0)
   data.table               * 1.15.4    2024-03-30 [2] CRAN (R 4.4.0)
   DBI                        1.2.3     2024-06-02 [1] CRAN (R 4.4.0)
   dbplyr                     2.5.0     2024-03-19 [1] CRAN (R 4.4.0)
   DelayedArray               0.30.1    2024-05-07 [1] Bioconductor 3.19 (R 4.4.0)
   DelayedMatrixStats         1.26.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   deldir                     2.0-4     2024-02-28 [1] CRAN (R 4.4.0)
   digest                     0.6.36    2024-06-23 [1] CRAN (R 4.4.0)
   doParallel                 1.0.17    2022-02-07 [1] CRAN (R 4.4.1)
   dotCall64                  1.1-1     2023-11-28 [1] CRAN (R 4.4.0)
   dplyr                      1.1.4     2023-11-17 [1] CRAN (R 4.4.0)
   dqrng                      0.4.1     2024-05-28 [1] CRAN (R 4.4.0)
   DropletUtils               1.24.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   DT                         0.33      2024-04-04 [1] CRAN (R 4.4.0)
   e1071                      1.7-14    2023-12-06 [1] CRAN (R 4.4.0)
   EBImage                    4.46.0    2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   edgeR                      4.2.0     2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
   escheR                   * 1.4.0     2024-05-16 [1] Bioconductor 3.19 (R 4.4.0)
   evaluate                   0.24.0    2024-06-10 [1] CRAN (R 4.4.0)
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 [1] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
 [2] /Users/bmulvey/Library/R/arm64/4.4/library

 P ── Loaded and on-disk path mismatch.

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