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Fig2_Atlasing_Novelties.Rmd
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---
title: "Fig2_Atlasing_Novelties"
output: html_document
date: "2024-10-08"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.height = 10,fig.width = 7,include = FALSE)
#### sets tab autocompletion for directories to begin from the directory containing the .Rproj session, instead of the script's directory if different
knitr::opts_knit$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
library(ggstance) # for y axis dodge
require(colorout) # Utility for RStudio
library(sf) # define the polygonal boundaries of xenium domains
library(ggbreak) # x axis breaks in ggplots
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)))
## last of all, unload the base package datasets, whose data keep getting in the way of autocompletions (e.g., Theoph is priortized over TRUE)
unloadNamespace("datasets")
```
#### VISIUM setup ####
```{r}
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"]
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("VMH","ARC")),dom:="Other"]
bscl<-DataFrame(bscl,row.names=bscl$rn)[colnames(hyp2),]
hyp2$k15dom <- bscl$cl
hyp2$`Visium Domain` <- factor(bscl$dom,levels=c("ARC","VMH","Other"))
```
### XENIUM setup ###
```{r}
hypx <- readRDS("xenium_HYP/processed-data/05_Banksy_M0lam0_res2_multisamp/01-sfe-in_staggered-coords_genetarg-only_NONlog-norm.RDS")
bksmooth <- fread("xenium_HYP/processed-data/07_Domains-subdomains_by_knnSmoothing_Banksytypes/03b-ARCVMHdomains_2stepsmooth_VMH-k10-0.2-VMH-k200-0.2_ARC-k50-0.1_ARC-k500-0.5.txt") ## xenium domain assignments after smoothing, by xenium cell
## but drop discarded cell clusters, too
bkcl <- fread("xenium_HYP/processed-data/05_Banksy_M0lam0_res2_multisamp/01-BanksyClusts_M0lam0_leiden_multisamp.txt")
setnames(bkcl,2,"cl")
bkanno <- fread("xenium_HYP/processed-data/05_Banksy_M0lam0_res2_multisamp/02-M0l0kg6_topClusMarkers_celltypes_annotated.txt")
## drop sample-specific clusters, append cluster annotations
bkanno <- unique(bkanno[,.(clus,bjm_annot)])
bkanno <- bkanno[bjm_annot!="DISCARD"&bjm_annot!="VMH_4_DISCARD"]
## convert ARC_1..etc to more descriptive cluster IDs defined based on ARC/VMH specific cluster marker analyses
arcvmhanno <- fread("manuscript_plot_code/xARCxVMH_cluster_detailedlabs_andplotformatnames.txt")
## append these to the other annots and replace bjm_annot with the ARC/VMH cell type labels where applicable
bkanno <- merge.data.table(bkanno,arcvmhanno,by="bjm_annot",all.x=T)
bkanno[!is.na(plotclusname),bjm_annot:=plotclusname]
## this drops rows that were assigned one of the discarded types (ie no longer in the cell type annotations)
bkcl <- merge.data.table(bkcl,bkanno,by.x="cl",by.y="clus")
## and now we can drop excluded clusters from xenium spe
hypx <- hypx[,colnames(hypx) %in% bkcl$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$dualVMHARC4
## load manuscript sample ids
mscriptids <- fread("standardized_sampleids_for_plotting.txt")
```
## palette set up (shared)
```{r}
pals <- readRDS("manuscript_plot_code/domain_and_xencluster_palettes_CHECKPALNAMESBEFOREUS.RDS")
pal <- pals[["Domains"]]
vmhpal <- pals[["XenVMH"]]
arcpal <- pals[["XenARC"]]
stopifnot(sum(names(vmhpal)%in%unique(hypx$banksyclus))==length(names(vmhpal)))
stopifnot(sum(names(arcpal)%in%unique(hypx$banksyclus))==length(names(arcpal)))
```
To show the robustness of the dataset, a different sample will be used on a per-figure basis. So here, let's use the classic V12Y31-080_A1 and its xenium counterpart, X86 reg1.
The last bit of setup we need to do here is to define the polygonal boundaries of the VMH and ARC in Xenium. the sf_concave_hull function actually works for this sample's ARC, mostly, so we can define this once pretty quickly and use it for all the visium plots.
```{r}
lamp <- hyp2[,hyp2$sample_id=="V12D05-348_D1"]
## 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(lamp),spatialCoords(lamp))))
### 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_col,y=array_row,color=Visium.Domain))+
geom_point(size=1.5)+
geom_vline(xintercept=seq(0,max(tmpcoldata$array_col),by=5))+
geom_hline(yintercept=seq(0,max(tmpcoldata$array_row),5))
dev.off()
# ok just a couple easy drops here: ARC: array_col>35&array_row>50
tmpcoldata <- rbind(tmpcoldata[Visium.Domain=="ARC"&!((array_col>35&array_row>50))],
tmpcoldata[Visium.Domain=="VMH"])
## for plotting with geom_path, we need to add the first point at the end of the sequence as well. for vmh, we can use convex hull because..well, its a convex shape
tmpcoldata.sf <- st_as_sf(tmpcoldata,coords=c("array_col","array_row"))
vhull <- st_convex_hull(st_union(tmpcoldata.sf[tmpcoldata.sf$Visium.Domain=="VMH",]))
vhull <- vhull[[1]][[1]]
vhull <- rbind(vhull,vhull[1,])
rownames(vhull) <- rownames(vhull) <- paste0("pt",seq((1+nrow(vhull)),nrow(vhull)+nrow(vhull),by=1))
vhull <- as.data.table(vhull,keep.rownames=T)
ahull <- st_concave_hull(st_union(tmpcoldata.sf[tmpcoldata.sf$Visium.Domain=="ARC",]),ratio = 0.1)
ahull <- ahull[[1]][[1]]
ahull <- rbind(ahull,ahull[1,])
rownames(ahull) <- paste0("pt",seq((1+nrow(vhull)),nrow(vhull)+nrow(ahull),by=1))
ahull <- as.data.table(ahull,keep.rownames=T)
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(dompoly,tmpcoldata,by.x=c("V1","V2"),by.y=c("array_col","array_row"))
dompoly2[,rn:=as.numeric(gsub(rn,pattern="pt",replacement=""))]
setorderv(dompoly2,"rn")
rm(vhull,ahull,dompoly,tmpcoldata)
```
Panel A: Visium GHRH
```{r}
pana <- hyp2[,hyp2$sample_id=="V12D05-348_D1"]
rownames(pana) <- rowData(pana)$gene_name
colData(pana)$`log counts` <- as.numeric(logcounts(pana)["GHRH",])
p <- make_escheR(pana)
p <- p |> add_fill("log counts",size=0.64,point_size = 0.64)
#p <- p |> add_ground(var = "Visium Domain",stroke=0.15,point_size = 0.6)
pdf("manuscript_plots/Fig2/2A-GHRH_visium.pdf",height=1.6,width=1.75)
p+
geom_path(data=dompoly2,aes(x=pxl_col_in_fullres,y=pxl_row_in_fullres,group=Visium.Domain,col=Visium.Domain),size=0.25)+
scale_color_manual(values=pal,na.value = NA)+
scale_fill_gradient(low="white",high="black")+
ggtitle(paste0("*GHRH* ",mscriptids[sample_id=="V12D05-348_D1",manuscript_id]))+
guides(color="none")+
labs(fill="log\ncounts")+
theme(plot.title.position = "plot",plot.title = element_markdown(size=10,hjust=0.5),legend.text = element_text(size=7),legend.key.size = ggplot2::unit(0.125,"in") ,legend.title=element_text(size=7.5,hjust=0.5),plot.margin = margin(-0.05,0.03,-0.05,0,unit = "in"))
dev.off()
rm(pana,p)
```
Panel B: Visium TAC3
```{r}
panb <- hyp2[,hyp2$sample_id=="V12D05-348_D1"]
rownames(panb) <- rowData(panb)$gene_name
colData(panb)$`log counts` <- as.numeric(logcounts(panb)["TAC3",])
p <- make_escheR(panb)
p <- p |> add_fill("log counts",size=0.64,point_size = 0.64)
# p <- p |> add_ground(var = "Visium Domain",stroke=0.15,point_size = 0.6)
pdf("manuscript_plots/Fig2/2O-TAC3_visium.pdf",height=1.6,width=1.75)
p+
geom_path(data=dompoly2,aes(x=pxl_col_in_fullres,y=pxl_row_in_fullres,group=Visium.Domain,col=Visium.Domain),size=0.25)+
scale_color_manual(values=pal,na.value = NA)+
scale_fill_gradient(low="white",high="black")+
ggtitle(paste0("*TAC3* ",mscriptids[sample_id=="V12D05-348_D1",manuscript_id]))+
guides(color="none")+
labs(fill="log\ncounts")+
theme(plot.title.position = "plot",plot.title = element_markdown(size=10,hjust=0.5),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(-0.05,0.03,-0.05,0,unit = "in"))
dev.off()
rm(panb,p)
```
for xenium panels: need domain boundaries
```{r}
panc <- hypx[,hypx$sample_id=="X97_reg3"]
## attach the coordinate info to colData so we can make an sf object out of this for hull extraction. see well-annotated code from DE benchmarking of domain boundaries in xenium_HYP/code/07/03c-DE benchmarks...
tmpcoldata <- cbind(colData(panc),spatialCoords(panc))
pancv <- st_as_sf(as.data.frame(tmpcoldata[tmpcoldata$dom=="VMH",]),coords = c("sdimx","sdimy"))
pancv <- st_convex_hull(st_union(pancv))
panca <- st_as_sf(as.data.frame(tmpcoldata[tmpcoldata$dom=="ARC",]),coords=c("sdimx","sdimy"))
panca <- st_concave_hull(st_union(panca),ratio = 0.1)
## create the data table we will use for overlaying the boundary polygons
dompoly <- cbind(pancv[[1]][[1]],rep("VMH",nrow(pancv[[1]][[1]])))
dompoly <- rbind(dompoly,cbind(panca[[1]][[1]],rep("ARC",nrow(panca[[1]][[1]]))))
dompoly <- as.data.table(dompoly)
setnames(dompoly,c("xpol","ypol","dompol"))
dompoly[,xpol:=as.numeric(xpol)]
dompoly[,ypol:=as.numeric(ypol)]
rm(panca,pancv)
```
panel C: xenium GHRH, TAC3, and dual-positive cells color coded
```{r}
panc <- panc[,panc$dom=="ARC"]
tmpcd <- as.data.table(colData(panc),keep.rownames=T)
tmpcd[!(banksyclus %in% c("*GAL*-*TRH*-*GHRH*","*TAC3*-*ESR1*")),banksyclus:=NA]
tmpcd <- DataFrame(tmpcd,row.names=tmpcd$rn)[colnames(panc),]
colData(panc) <- tmpcd
panc$ghrhpos <- ifelse(counts(panc["GHRH",])>0,1,0)
panc$tacpos <- ifelse(counts(panc["TAC3",])>0,1,0)
panc$bothpos <- ifelse(panc$ghrhpos+panc$tacpos==2,1,0)
panc$mkxpr <- ifelse(panc$bothpos==1,"Both","Neither")
panc$mkxpr <- ifelse(panc$bothpos==0&panc$ghrhpos==1,"*GHRH*",panc$mkxpr)
panc$mkxpr <- ifelse(panc$bothpos==0&panc$ghrhpos==0&panc$tacpos==1,"*TAC3*",panc$mkxpr)
panc$mkxpr <- factor(panc$mkxpr,levels=c("*GHRH*","Neither","*TAC3*","Both"))
p <- make_escheR(panc,y_reverse=FALSE)
p <- p |> add_fill("mkxpr",size=.25,point_size = .25)
## at 1.6h x 1.75w with the various point size settings margins etc below, the NA gets chopped off the legend perfectly.
pancpal <- c("#09979B","gray","#F0B","darkorange")
names(pancpal) <- c("*GHRH*","Neither","*TAC3*","Both")
tmpdompoly <- dompoly[dompol=="ARC"]
p <- p+
geom_path(data=tmpdompoly,aes(x=xpol,y=ypol,group=dompol,col=dompol),linewidth = 0.25)+
scale_color_manual(values=pal["ARC"],na.value = NA)+
scale_fill_manual(values=pancpal,na.value=NA)+
guides(color="none",fill=guide_legend(ncol=2,override.aes = list(size=2)))+
labs(fill="Counts >0 for")+
theme(plot.title.position = "plot",
plot.title = element_markdown(size=9,hjust=0.5,margin=margin(0.15,0,-0.15,0,"in")),
legend.text = element_markdown(size=7),
legend.key.size = ggplot2::unit(0.125,"in"),
legend.title=element_text(size=7.5,hjust=0.5),
plot.margin = margin(-0.17,0,-0.17,-0.05,unit = "in"),
legend.margin=margin(-0.075,0,0,0,"in"),
legend.position="bottom",legend.direction = "vertical")
pdf("manuscript_plots/Fig2/2C-X97_reg3_GHRH-TAC3_detection.pdf",height=1.6,width=1.75)
p
dev.off()
rm(p)
```
#### Then: the cell types in VMH and ARC.
## pand- VMH:
```{r}
pand <- hypx[,hypx$sample_id=="X97_reg3"&hypx$dom=="VMH"]
pand <- as.data.table(cbind(spatialCoords(pand),as.data.table(colData(pand))))[,.(sdimx,sdimy,banksyclus)]
pand <- pand[banksyclus %in% bkanno[subclus_domain=="VMH",bjm_annot]]
## save an with cell types faceted
pdf("manuscript_plots/Fig2/2D-VMH_celltypes_x6588C_facet.pdf",height=1.85,width=7.45)
ggplot(pand[sdimy<6500],aes(x=sdimx,y=sdimy,color=banksyclus))+
geom_point(size=0.075,stroke=0.375)+
scale_color_manual(values=vmhpal)+
facet_wrap(~banksyclus,nrow=1)+
#guides(color=guide_legend(override.aes = list(size=1)))+
guides(color="none")+
theme(axis.text.x=element_blank(),axis.text.y=element_blank(),axis.title.y=element_blank(),axis.title.x=element_blank(),axis.ticks = element_blank(),legend.background = element_blank(),legend.box=element_blank(),legend.margin=margin(0,0,0,-0.15,unit="in"),plot.margin = margin(0.01,0.02,0,0,unit="in"),legend.text = element_markdown(margin=margin(0,0,0,-0.075,"in")),strip.text=element_markdown(size=8),strip.background=element_blank(),strip.background.x = element_blank(),strip.background.y=element_blank())
dev.off()
rm(pand)
```
## pane - ARC:
```{r}
pane <- hypx[,hypx$sample_id=="X97_reg3"&hypx$dom=="ARC"]
pane <- as.data.table(cbind(spatialCoords(pane),as.data.table(colData(pane))))[,.(sdimx,sdimy,banksyclus)]
pane <- pane[banksyclus %in% bkanno[subclus_domain=="ARC",bjm_annot]]
## put TAC3-ESR1 first in the order
pane[,banksyclus:=factor(banksyclus,levels=c("*TAC3*-*ESR1*",unique(pane[banksyclus!="*TAC3*-*ESR1*",banksyclus])))]
## with cell types faceted
pdf("manuscript_plots/Fig2/2E-ARC_celltypes_x6588C_facet.pdf",height=1.85,width=7.45)
ggplot(pane[sdimy<6500],aes(x=sdimx,y=sdimy,color=banksyclus))+
geom_point(size=0.075,stroke=0.375)+
scale_color_manual(values=arcpal)+
facet_wrap(~banksyclus,nrow=1)+
#guides(color=guide_legend(override.aes = list(size=1)))+
guides(color="none")+
theme(axis.text.x=element_blank(),axis.text.y=element_blank(),axis.title.y=element_blank(),axis.title.x=element_blank(),axis.ticks = element_blank(),legend.background = element_blank(),legend.box=element_blank(),legend.margin=margin(0,0,0,-0.15,unit="in"),plot.margin = margin(0.01,0.02,0,0,unit="in"),legend.text = element_markdown(margin=margin(0,0,0,-0.075,"in")),strip.text=element_markdown(size=8),strip.background=element_blank(),strip.background.x = element_blank(),strip.background.y=element_blank())
dev.off()
rm(pane)
```
panel F: kiss1 rnascopes.
# reprod info
```{r}
sessionInfo()
sessioninfo::session_info()
```
> 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 2024-10-08
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)
aplot 0.2.3 2024-06-17 [1] CRAN (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)
ExperimentHub 2.12.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0)
farver 2.1.2 2024-05-13 [1] CRAN (R 4.4.0)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
fftwtools 0.9-11 2021-03-01 [1] CRAN (R 4.4.0)
fields 16.2 2024-06-27 [1] CRAN (R 4.4.0)
filelock 1.0.3 2023-12-11 [1] CRAN (R 4.4.0)
foreach 1.5.2 2022-02-02 [1] CRAN (R 4.4.0)
fs 1.6.4 2024-04-25 [1] CRAN (R 4.4.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.0)
GenomeInfoDb * 1.40.1 2024-05-24 [1] Bioconductor 3.19 (R 4.4.0)
GenomeInfoDbData 1.2.12 2024-05-01 [1] Bioconductor
GenomicAlignments 1.40.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
GenomicRanges * 1.56.1 2024-06-12 [1] Bioconductor 3.19 (R 4.4.1)
ggbeeswarm 0.7.2 2023-04-29 [1] CRAN (R 4.4.0)
ggbreak * 0.1.2 2023-06-26 [1] CRAN (R 4.4.0)
ggfun 0.1.5 2024-05-28 [1] CRAN (R 4.4.0)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.0)
ggplotify 0.1.2 2023-08-09 [1] CRAN (R 4.4.0)
ggrastr * 1.0.2 2023-06-01 [1] CRAN (R 4.4.0)
ggrepel 0.9.5 2024-01-10 [1] CRAN (R 4.4.0)
ggstance * 0.3.7 2024-04-05 [1] CRAN (R 4.4.0)
ggtext * 0.1.2 2022-09-16 [1] CRAN (R 4.4.0)
glue 1.7.0 2024-01-09 [1] CRAN (R 4.4.0)
golem 0.4.1 2023-06-05 [1] CRAN (R 4.4.0)
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.4.0)
gridGraphics 0.5-1 2020-12-13 [1] CRAN (R 4.4.0)
gridtext 0.1.5 2022-09-16 [1] CRAN (R 4.4.0)
gtable 0.3.5 2024-04-22 [1] CRAN (R 4.4.0)
HDF5Array 1.32.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
here 1.0.1 2020-12-13 [1] CRAN (R 4.4.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.0)
httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
IRanges * 2.38.1 2024-07-03 [1] Bioconductor 3.19 (R 4.4.1)
irlba 2.3.5.1 2022-10-03 [1] CRAN (R 4.4.0)
iterators 1.0.14 2022-02-05 [1] CRAN (R 4.4.0)
jpeg 0.1-10 2022-11-29 [1] CRAN (R 4.4.0)
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.4.0)
jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.4.0)
KEGGREST 1.44.1 2024-06-19 [1] Bioconductor 3.19 (R 4.4.0)
KernSmooth 2.23-24 2024-05-17 [1] CRAN (R 4.4.1)
knitr 1.48 2024-07-07 [1] CRAN (R 4.4.1)
labeling 0.4.3 2023-08-29 [1] CRAN (R 4.4.0)
later 1.3.2 2023-12-06 [1] CRAN (R 4.4.0)
lattice 0.22-6 2024-03-20 [1] CRAN (R 4.4.1)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.4.0)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
limma 3.60.3 2024-06-16 [1] Bioconductor 3.19 (R 4.4.0)
locfit 1.5-9.10 2024-06-24 [1] CRAN (R 4.4.0)
magick 2.8.3 2024-02-18 [1] CRAN (R 4.4.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
maps 3.4.2 2023-12-15 [1] CRAN (R 4.4.0)
markdown 1.13 2024-06-04 [1] CRAN (R 4.4.0)
Matrix 1.7-0 2024-04-26 [1] CRAN (R 4.4.1)
MatrixGenerics * 1.16.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
matrixStats * 1.3.0 2024-04-11 [1] CRAN (R 4.4.0)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.4.0)
munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.0)
paletteer 1.6.0 2024-01-21 [1] CRAN (R 4.4.0)
patchwork 1.2.0 2024-01-08 [1] CRAN (R 4.4.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
plotly 4.10.4 2024-01-13 [1] CRAN (R 4.4.0)
png 0.1-8 2022-11-29 [1] CRAN (R 4.4.0)
Polychrome * 1.5.1 2022-05-03 [1] CRAN (R 4.4.0)
promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.0)
proxy 0.4-27 2022-06-09 [1] CRAN (R 4.4.0)
purrr 1.0.2 2023-08-10 [1] CRAN (R 4.4.0)
R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.4.0)
R.oo 1.26.0 2024-01-24 [1] CRAN (R 4.4.0)
R.utils 2.12.3 2023-11-18 [1] CRAN (R 4.4.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.4.0)
RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.4.0)
Rcpp 1.0.13 2024-07-17 [1] CRAN (R 4.4.0)
RCurl 1.98-1.14 2024-01-09 [1] CRAN (R 4.4.0)
rematch2 2.1.2 2020-05-01 [1] CRAN (R 4.4.0)
restfulr 0.0.15 2022-06-16 [1] CRAN (R 4.4.0)
rhdf5 2.48.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
rhdf5filters 1.16.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
Rhdf5lib 1.26.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
rjson 0.2.21 2022-01-09 [1] CRAN (R 4.4.0)
P rlang * 1.1.4 2024-06-04 [2] CRAN (R 4.4.1)
rmarkdown 2.27 2024-05-17 [1] CRAN (R 4.4.0)
rprojroot 2.0.4 2023-11-05 [1] CRAN (R 4.4.0)
Rsamtools 2.20.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
RSQLite 2.3.7 2024-05-27 [1] CRAN (R 4.4.0)
rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.4.0)
rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.4.0)
rtracklayer 1.64.0 2024-05-06 [1] Bioconductor 3.19 (R 4.4.0)
s2 1.1.6 2023-12-19 [1] CRAN (R 4.4.0)
S4Arrays 1.4.1 2024-05-20 [1] Bioconductor 3.19 (R 4.4.0)
S4Vectors * 0.42.1 2024-07-03 [1] Bioconductor 3.19 (R 4.4.1)
sass 0.4.9 2024-03-15 [1] CRAN (R 4.4.0)
ScaledMatrix 1.12.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
scales 1.3.0 2023-11-28 [1] CRAN (R 4.4.0)
scater 1.32.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
scatterplot3d 0.3-44 2023-05-05 [1] CRAN (R 4.4.0)
scuttle 1.14.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0)
sf * 1.0-16 2024-03-24 [1] CRAN (R 4.4.0)
sfheaders 0.4.4 2024-01-17 [1] CRAN (R 4.4.0)
shiny 1.8.1.1 2024-04-02 [1] CRAN (R 4.4.0)
shinyWidgets 0.8.6 2024-04-24 [1] CRAN (R 4.4.0)
SingleCellExperiment * 1.26.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
sp 2.1-4 2024-04-30 [1] CRAN (R 4.4.0)
spam 2.10-0 2023-10-23 [1] CRAN (R 4.4.0)
SparseArray 1.4.8 2024-05-30 [1] Bioconductor 3.19 (R 4.4.0)
sparseMatrixStats 1.16.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
SpatialExperiment * 1.14.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
SpatialFeatureExperiment * 1.6.1 2024-05-15 [1] Bioconductor 3.19 (R 4.4.0)
spatialLIBD * 1.16.2 2024-05-28 [1] Bioconductor 3.19 (R 4.4.0)
spData 2.3.1 2024-05-31 [1] CRAN (R 4.4.0)
spdep 1.3-5 2024-06-10 [1] CRAN (R 4.4.0)
statmod 1.5.0 2023-01-06 [1] CRAN (R 4.4.0)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.0)
stringr 1.5.1 2023-11-14 [1] CRAN (R 4.4.0)
SummarizedExperiment * 1.34.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
terra 1.7-78 2024-05-22 [1] CRAN (R 4.4.0)
tibble 3.2.1 2023-03-20 [1] CRAN (R 4.4.0)
tidyr 1.3.1 2024-01-24 [1] CRAN (R 4.4.0)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
tiff 0.1-12 2023-11-28 [1] CRAN (R 4.4.0)
UCSC.utils 1.0.0 2024-05-06 [1] Bioconductor 3.19 (R 4.4.0)
units 0.8-5 2023-11-28 [1] CRAN (R 4.4.0)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.0)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
vipor 0.4.7 2023-12-18 [1] CRAN (R 4.4.0)
viridis * 0.6.5 2024-01-29 [1] CRAN (R 4.4.0)
viridisLite * 0.4.2 2023-05-02 [1] CRAN (R 4.4.0)
withr 3.0.0 2024-01-16 [1] CRAN (R 4.4.0)
wk 0.9.2 2024-07-09 [1] CRAN (R 4.4.0)
xfun 0.45 2024-06-16 [1] CRAN (R 4.4.0)
XML 3.99-0.17 2024-06-25 [1] CRAN (R 4.3.3)
xml2 1.3.6 2023-12-04 [1] CRAN (R 4.4.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.0)
XVector 0.44.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
yaml 2.3.9 2024-07-05 [1] CRAN (R 4.4.0)
yulab.utils 0.1.4 2024-01-28 [1] CRAN (R 4.4.0)
zeallot 0.1.0 2018-01-28 [1] CRAN (R 4.4.0)
zlibbioc 1.50.0 2024-04-30 [1] Bioconductor 3.19 (R 4.4.0)
[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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