Identifying the role of Wdr37 by position and molecular phenotype of expressing neurons in the mouse brain

Load data and setup parameters

Code

# Load tidyverse infrastructure packages
library(here)
library(tidyverse)
library(RColorBrewer)
library(glmnet)

# Load packages for scRNA-seq analysis and visualisation
library(Seurat)
library(SeuratWrappers)
library(SeuratDisk)
library(scCustomize)
library(swne)
library(ggplot2)
library(cowplot)
library(UpSetR)
library(patchwork)
library(Nebulosa)
library(schex)

Set paths

Code

set_here()
data_dir <- here("data")
output_dir <- here("outputs")
plots_dir <- here(output_dir, "figures")
tables_dir <- here(output_dir, "tables")

Load helper functions and gene-sets

Code

source(here("functions.R"))
source(here("genes.R"))

Set fixed variables

Code

# set seed
reseed <- 42
set.seed(seed = reseed)

# available RAM in kB
ram <- check_ram()

# available cores
n.cores <- available_cores(prop2use = .1)

# Parameters for parallel execution
plan("multisession", workers = n.cores)
options(
  future.globals.maxSize = 100000 * 1024^2,
  future.rng.onMisuse = "ignore"
)
plan()

multisession:
- args: function (..., workers = 8, envir = parent.frame())
- tweaked: TRUE
- call: plan("multisession", workers = n.cores)

Load data from Zeisel et al. (2018)

Code

l6.neurons <- Connect(filename = here("l6_r1_neurons.loom"), mode = "r")
l6.neurons

Class: loom
Filename: /data/l6_r1_neurons.loom
Access type: H5F_ACC_RDONLY
Attributes: last_modified
Listing:
       name    obj_type  dataset.dims dataset.type_class
      attrs   H5I_GROUP          <NA>               <NA>
  col_attrs   H5I_GROUP          <NA>               <NA>
 col_graphs   H5I_GROUP          <NA>               <NA>
     layers   H5I_GROUP          <NA>               <NA>
     matrix H5I_DATASET 74539 x 27998          H5T_FLOAT
  row_attrs   H5I_GROUP          <NA>               <NA>
 row_graphs   H5I_GROUP          <NA>               <NA>

Code

l6.srt <- as.Seurat(l6.neurons)
l6.neurons$close_all()

show srt object

Code

l6.srt <-
  Store_Palette_Seurat(
    seurat_object = l6.srt,
    palette = rev(brewer.pal(n = 11, name = "Spectral")),
    palette_name = "div_Colour_Pal"
  )

l6.srt

An object of class Seurat 
27998 features across 74539 samples within 1 assay 
Active assay: RNA (27998 features, 0 variable features)

show metadata

Code

skimr::skim(l6.srt@meta.data)

Data summary

Name	l6.srt@meta.data
Number of rows	74539
Number of columns	129
_______________________
Column type frequency:
character	78
factor	1
numeric	50
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Age	0	1	1	8	0	22	0
AnalysisPool	0	1	0	15	48185	12	0
AnalysisProject	0	1	0	10	48185	2	0
Bucket	0	1	47	65	0	19	0
CellConc	0	1	0	4	48185	7	0
Cell_Conc	0	1	0	4	48185	8	0
ChipID	0	1	0	5	48185	23	0
Class	0	1	7	7	0	1	0
ClusterName	0	1	2	7	0	214	0
Comment	0	1	3	81	0	55	0
Comments	0	1	0	168	48185	30	0
DateCaptured	0	1	0	19	48185	22	0
Date_Captured	0	1	0	10	48185	22	0
Description	0	1	14	68	0	93	0
Developmental_compartment	0	1	11	15	0	6	0
DonorID	0	1	0	25	48185	24	0
Estimated.Number.of.Cells	0	1	0	5	70736	13	0
Flowcell	0	1	0	19	48185	20	0
Fraction.Reads.in.Cells	0	1	0	5	48185	61	0
Label	0	1	0	9	73578	6	0
Location_based_on	0	1	0	40	11871	175	0
Mean.Reads.per.Cell	0	1	0	7	48185	73	0
Median.Genes.per.Cell	0	1	0	5	48185	72	0
Median.UMI.Counts.per.Cell	0	1	0	6	69156	15	0
NGI_PlateWell	0	1	0	0	74539	1	0
Neurotransmitter	0	1	0	47	2875	38	0
NumPooledAnimals	0	1	0	3	48185	4	0
Num_Pooled_Animals	0	1	0	1	48185	4	0
Number.of.Reads	0	1	0	11	48185	73	0
PCRCycles	0	1	0	4	48185	4	0
PCR_Cycles	0	1	0	2	48185	4	0
PassedQC	0	1	0	2	48185	2	0
PlugDate	0	1	0	3	48185	2	0
Plug_Date	0	1	0	0	74539	1	0
Probable_location	0	1	3	128	0	133	0
Project	0	1	0	10	48185	3	0
Q30.Bases.in.Barcode	0	1	0	5	48185	53	0
Q30.Bases.in.RNA.Read	0	1	0	5	70736	13	0
Q30.Bases.in.Sample.Index	0	1	0	5	70736	12	0
Q30.Bases.in.UMI	0	1	0	5	48185	51	0
Reads.Mapped.Confidently.to.Exonic.Regions	0	1	0	5	48185	67	0
Reads.Mapped.Confidently.to.Intergenic.Regions	0	1	0	5	48185	35	0
Reads.Mapped.Confidently.to.Intronic.Regions	0	1	0	5	48185	62	0
Reads.Mapped.Confidently.to.Transcriptome	0	1	0	5	48185	68	0
Region	0	1	4	48	0	25	0
SampleID	0	1	7	7	0	131	0
SampleIndex	0	1	0	9	48185	67	0
SampleOK	0	1	0	2	73468	2	0
Sample_Index	0	1	0	9	59706	31	0
SeqComment	0	1	0	3	48185	2	0
SeqLibDate	0	1	0	19	48185	4	0
SeqLibOk	0	1	0	3	48185	3	0
Seq_Comment	0	1	0	0	74539	1	0
Seq_Lib_Date	0	1	0	9	73661	3	0
Seq_Lib_Ok	0	1	0	1	73661	2	0
Sequencing.Saturation	0	1	0	5	70736	13	0
Serial_Number	0	1	0	3	48185	73	0
Sex	0	1	1	5	0	12	0
Species	0	1	0	2	48185	2	0
Strain	0	1	0	27	48185	5	0
Subclass	0	1	7	15	0	2	0
TargetNumCells	0	1	0	5	48185	8	0
Target_Num_Cells	0	1	0	5	48185	9	0
TaxonomyRank1	0	1	7	7	0	1	0
TaxonomyRank2	0	1	11	11	0	2	0
TaxonomyRank3	0	1	15	50	0	11	0
TaxonomyRank4	0	1	15	43	0	21	0
TaxonomySymbol	0	1	4	4	0	21	0
Taxonomy_group	0	1	15	43	0	21	0
TimepointPool	0	1	0	3	48185	2	0
Tissue	0	1	2	10	0	23	0
Total.Genes.Detected	0	1	0	6	70736	13	0
Transcriptome	0	1	0	4	48185	2	0
Valid.Barcodes	0	1	0	5	48185	41	0
cDNAConcNanogramPerMicroliter	0	1	0	18	48185	56	0
cDNALibOk	0	1	0	9	48185	4	0
cDNA_Lib_Ok	0	1	0	9	54253	3	0
ngperul_cDNA	0	1	0	4	48185	56	0

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
orig.ident	0	1	FALSE	35	10X: 7100, 10X: 6905, 10X: 6766, 10X: 6515

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
nCount_RNA	0	1	4184.82	3644.92	160.00	1805.00	3199.00	5396.00	73358.00	▇▁▁▁▁
nFeature_RNA	0	1	1926.62	1029.78	88.00	1109.00	1776.00	2597.00	7490.00	▇▇▂▁▁
ClassProbability_Astrocyte	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.28	▇▁▁▁▁
ClassProbability_Astrocyte.Immune	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.22	▇▁▁▁▁
ClassProbability_Astrocyte.Neurons	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.35	▇▁▁▁▁
ClassProbability_Astrocyte.Oligos	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
ClassProbability_Astrocyte.Vascular	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.04	▇▁▁▁▁
ClassProbability_Bergmann.glia	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.22	▇▁▁▁▁
ClassProbability_Blood	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.36	▇▁▁▁▁
ClassProbability_Blood.Vascular	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.46	▇▁▁▁▁
ClassProbability_Enteric.glia	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.80	▇▁▁▁▁
ClassProbability_Enteric.glia.Cycling	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.01	▇▁▁▁▁
ClassProbability_Ependymal	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.40	▇▁▁▁▁
ClassProbability_Ex.Neurons	0	1	0.00	0.00	0.00	0.00	0.00	0.01	0.09	▇▁▁▁▁
ClassProbability_Ex.Vascular	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.11	▇▁▁▁▁
ClassProbability_Immune	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.40	▇▁▁▁▁
ClassProbability_Immune.Neurons	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.55	▇▁▁▁▁
ClassProbability_Immune.Oligos	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.01	▇▁▁▁▁
ClassProbability_Neurons	0	1	0.97	0.10	0.00	0.98	0.99	0.99	1.00	▁▁▁▁▇
ClassProbability_Neurons.Cycling	0	1	0.01	0.10	0.00	0.00	0.00	0.00	1.00	▇▁▁▁▁
ClassProbability_Neurons.Oligos	0	1	0.01	0.02	0.00	0.00	0.00	0.01	0.49	▇▁▁▁▁
ClassProbability_Neurons.Satellite.glia	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.39	▇▁▁▁▁
ClassProbability_Neurons.Vascular	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.27	▇▁▁▁▁
ClassProbability_OEC	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.33	▇▁▁▁▁
ClassProbability_Oligos	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.45	▇▁▁▁▁
ClassProbability_Oligos.Cycling	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.28	▇▁▁▁▁
ClassProbability_Oligos.Vascular	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.02	▇▁▁▁▁
ClassProbability_Satellite.glia	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.01	▇▁▁▁▁
ClassProbability_Satellite.glia.Cycling	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.01	▇▁▁▁▁
ClassProbability_Satellite.glia.Schwann	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▇▁▁▁▁
ClassProbability_Schwann	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▇▁▁▁▁
ClassProbability_Ttr	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.30	▇▁▁▁▁
ClassProbability_Vascular	0	1	0.00	0.01	0.00	0.00	0.00	0.00	0.33	▇▁▁▁▁
Clusters	0	1	108.63	52.78	0.00	71.00	94.00	160.00	213.00	▃▇▆▇▃
LeafOrder	0	1	70.80	61.76	1.00	24.00	42.00	128.00	214.00	▇▃▁▂▂
MitoRiboRatio	0	1	0.44	1.54	0.00	0.14	0.23	0.37	132.75	▇▁▁▁▁
OriginalClusters	0	1	7.55	6.40	0.00	2.00	6.00	12.00	24.00	▇▅▂▃▁
Outliers	0	1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	▁▁▇▁▁
X_KMeans_10	0	1	0.96	1.74	0.00	0.00	0.00	1.00	10.00	▇▁▁▁▁
X_LogCV	0	1	1.27	1.77	0.00	0.00	0.00	3.06	6.27	▇▁▂▂▁
X_LogMean	0	1	-1.22	1.82	-6.33	-2.80	0.00	0.00	1.39	▁▂▂▁▇
X_NGenes	0	1	1926.62	1029.78	88.00	1109.00	1776.00	2597.00	7490.00	▇▇▂▁▁
X_PC1	0	1	0.27	6.90	-81.88	0.00	0.00	0.00	108.10	▁▁▇▁▁
X_PC2	0	1	0.00	0.41	-13.24	0.00	0.00	0.00	12.79	▁▁▇▁▁
X_Total	0	1	4184.82	3644.92	160.00	1805.00	3199.00	5396.00	73358.00	▇▁▁▁▁
X_Valid	0	1	1.00	0.00	1.00	1.00	1.00	1.00	1.00	▁▁▇▁▁
X_X	0	1	0.00	15.34	-32.75	-12.21	-0.39	12.18	33.68	▃▆▇▇▃
X_Y	0	1	0.00	15.08	-34.33	-10.69	0.47	11.77	32.94	▂▆▇▆▂
X_tSNE1	0	1	0.11	12.08	-53.06	0.00	0.00	0.00	54.05	▁▁▇▁▁
X_tSNE2	0	1	1.59	11.60	-51.77	0.00	0.00	0.00	52.87	▁▁▇▁▁

Check Violin plots for the expression of the gene of interest (goi) in the different clusters and tissues

Code

Idents(l6.srt) <- "ClusterName"
Stacked_VlnPlot(
  seurat_object = l6.srt, features = goi, x_lab_rotate = TRUE,
  color_seed = reseed
)

Code

Idents(l6.srt) <- "Tissue"
Stacked_VlnPlot(
  seurat_object = l6.srt, features = goi, x_lab_rotate = TRUE,
  color_seed = reseed
)

Code

DotPlot_scCustom(
  seurat_object = l6.srt,
  assay = "RNA",
  features = goi,
  flip_axes = TRUE,
  x_lab_rotate = TRUE,
  group.by = "Tissue"
)

Code

DotPlot_scCustom(
  seurat_object = l6.srt,
  assay = "RNA",
  features = goi,
  flip_axes = TRUE,
  x_lab_rotate = TRUE,
  group.by = "ClusterName"
)

Extact UMAP and tSNE coordinates from the srt object

Code

umap <- l6.srt@meta.data %>%
  select(X_X, X_Y) %>%
  as.matrix()
rownames(umap) <- colnames(l6.srt)
colnames(umap) <- paste0("UMAP_", 1:2)
l6.srt[["umap"]] <- CreateDimReducObject(embeddings = umap, key = "UMAP_", assay = DefaultAssay(l6.srt))

tsne <- l6.srt@meta.data %>%
  select(X_tSNE1, X_tSNE2) %>%
  as.matrix()
rownames(tsne) <- colnames(l6.srt)
colnames(tsne) <- paste0("tSNE_", 1:2)
l6.srt[["tsne"]] <- CreateDimReducObject(embeddings = tsne, key = "tSNE_", assay = DefaultAssay(l6.srt))

Exploratory data analysis

Use standradised pipeline

Code

l6.srt <- NormalizeData(l6.srt)
l6.srt <- FindVariableFeatures(l6.srt, selection.method = "vst", nfeatures = 3500)
l6.srt <- ScaleData(l6.srt)
var.genes <- VariableFeatures(l6.srt)
cell.clusters <- Idents(l6.srt)

Check UMAP and tSNE plots for the expression of the gene of interest (goi) in the different clusters and tissues

tSNE

Code

DimPlot_scCustom(
  l6.srt,
  reduction = "tsne",
  pt.size = 0.5,
  label = TRUE,
  repel = TRUE,
  figure_plot = TRUE,
  color_seed = reseed
) |
  DimPlot_scCustom(
    l6.srt,
    reduction = "tsne",
    pt.size = 0.5,
    group.by = "ClusterName",
    label = TRUE,
    repel = TRUE,
    figure_plot = TRUE,
    color_seed = reseed
  ) & NoLegend()

UMAP

Code

DimPlot_scCustom(
  l6.srt,
  reduction = "umap",
  pt.size = 0.5,
  label = TRUE,
  repel = TRUE,
  figure_plot = TRUE,
  color_seed = reseed
) |
  DimPlot_scCustom(
    l6.srt,
    reduction = "umap",
    pt.size = 0.5,
    group.by = "ClusterName",
    label = TRUE,
    repel = TRUE,
    figure_plot = TRUE,
    color_seed = reseed
  ) & NoLegend()

Code

FeaturePlot_scCustom(l6.srt, goi, num_columns = 1, pt.size = 1, order = T, colors_use = viridis::viridis(n = 100, alpha = .6, direction = -1, option = "E"))

Code

Plot_Density_Custom(seurat_object = l6.srt, features = "Wdr37", custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Custom(seurat_object = l6.srt, features = "Pacs1", custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Custom(seurat_object = l6.srt, features = "Pacs2", custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Custom(seurat_object = l6.srt, features = "Grm5", custom_palette = l6.srt@misc$div_Colour_Pal)

UMAP density plots of interactions between Wdr37 and other features

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Wdr37", "Pacs2"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Wdr37", "Grm5"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Grm5", "Pacs2"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Wdr37", "Penk"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Penk", "Ctsl"), custom_palette = l6.srt@misc$div_Colour_Pal)

UMAP density plots of neurotransmitter phenotype with Wdr37 and other features

GABA

Code

Plot_Density_Custom(seurat_object = l6.srt, features = c("Slc32a1"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Custom(seurat_object = l6.srt, features = c("Gad1"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Custom(seurat_object = l6.srt, features = c("Gad2"), custom_palette = l6.srt@misc$div_Colour_Pal)

Glutamate

Code

Plot_Density_Custom(seurat_object = l6.srt, features = c("Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Wdr37", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Pacs2", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Grm5", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Penk", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal)

Code

Plot_Density_Joint_Only(seurat_object = l6.srt, features = c("Ctsl", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal)

Focused data analysis of Wdr37+ brain regions

Subset CA1 only

Code

ca1.srt <- subset(l6.srt, idents = "CA1")
ca1.srt <- FindVariableFeatures(ca1.srt, selection.method = "vst", nfeatures = 3500)
ca1.srt <- ScaleData(ca1.srt)
l6.srt <- subset(l6.srt, idents = c("CA1", "Hypoth", "MBd", "MBv", "Medulla", "Pons", "Thal"))
l6.srt <- FindVariableFeatures(l6.srt, selection.method = "vst", nfeatures = 3500)
l6.srt <- ScaleData(l6.srt)
var.genes <- VariableFeatures(l6.srt)
cell.clusters <- Idents(l6.srt)

Run SWNE for CA1

Code

## Run SWNE
l6.srt <- RunSWNE(l6.srt,
  k = 20, genes.embed = goi,
  return.format = "seurat"
)

calculating variance fit ... using gam [1] "3500 variable genes to use"
Initial stress        : 0.23280
stress after  10 iters: 0.06662, magic = 0.500
stress after  20 iters: 0.06507, magic = 0.500
stress after  30 iters: 0.06492, magic = 0.500
stress after  40 iters: 0.06489, magic = 0.500

Code

ca1.srt <- RunSWNE(ca1.srt,
  k = 20, genes.embed = goi,
  return.format = "seurat"
)

calculating variance fit ... using gam [1] "3500 variable genes to use"
Initial stress        : 0.23150
stress after  10 iters: 0.07290, magic = 0.500
stress after  20 iters: 0.07265, magic = 0.500
stress after  30 iters: 0.07254, magic = 0.500
stress after  40 iters: 0.07251, magic = 0.500

Plot SWNE of CA1

Code

DimPlot_scCustom(
  ca1.srt,
  reduction = "swne",
  pt.size = 0.5,
  group.by = "ClusterName",
  label = TRUE,
  repel = TRUE,
  figure_plot = TRUE,
  color_seed = reseed
) & NoLegend()

GABA

Code

Plot_Density_Custom(
  seurat_object = ca1.srt,
  features = c("Slc32a1"),
  custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne")

Code

Plot_Density_Custom(
  seurat_object = ca1.srt,
  features = c("Gad1"),
  custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne")

Code

Plot_Density_Custom(
  seurat_object = ca1.srt,
  features = c("Gad2"),
  custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne")

Glutamate

Code

Plot_Density_Custom(
  seurat_object = ca1.srt,
  features = c("Slc17a6"),
  custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne")

SWNE density plots of interactions between Wdr37 and other features

Code

Plot_Density_Joint_Only(
  seurat_object = ca1.srt, features = c("Wdr37", "Pacs2"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = ca1.srt, features = c("Wdr37", "Grm5"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = ca1.srt, features = c("Grm5", "Pacs2"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = ca1.srt, features = c("Wdr37", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = ca1.srt, features = c("Pacs2", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = ca1.srt, features = c("Grm5", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Plot SWNE of selected regions: CA1, Hypothalamus, Midbrain, Medulla, Pons, Thalamus

Code

DimPlot_scCustom(
  l6.srt,
  reduction = "swne",
  pt.size = 0.5,
  label = TRUE,
  repel = TRUE,
  figure_plot = TRUE,
  color_seed = reseed
) |
  DimPlot_scCustom(
    l6.srt,
    reduction = "swne",
    pt.size = 0.5,
    group.by = "ClusterName",
    label = TRUE,
    repel = TRUE,
    figure_plot = TRUE,
    color_seed = reseed
  ) & NoLegend()

SWNE density plots of interactions between Wdr37 and other features

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Wdr37", "Pacs2"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Wdr37", "Grm5"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Grm5", "Pacs2"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Wdr37", "Penk"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Wdr37", "Ctsl"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Wdr37", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Pacs2", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Grm5", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Penk", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Code

Plot_Density_Joint_Only(
  seurat_object = l6.srt, features = c("Ctsl", "Slc17a6"), custom_palette = l6.srt@misc$div_Colour_Pal,
  reduction = "swne"
)

Calculate hexagon cell representation to quantify interactions between Wdr37 and other features in the SWNE space

Code

l6.srt <- make_hexbin(l6.srt,
  nbins = 42,
  dimension_reduction = "swne", use_dims = c(1, 2)
)

Plot hexagon cell representation

Code

plot_hexbin_density(l6.srt) + ggsci::scale_fill_material("amber")

Code

plot_hexbin_feature_plus(l6.srt,
  col = "Tissue",
  mod = "RNA", type = "data", feature = "Wdr37",
  action = "median", xlab = "SWNE1", ylab = "SWNE2",
  title = paste0("Median of mRNA expression of Wdr37 in logarithmic scale")
) + ggsci::scale_fill_material("amber")

Code

plot_hexbin_feature_plus(l6.srt,
  col = "Tissue",
  mod = "RNA", type = "data", feature = "Pacs2",
  action = "median", xlab = "SWNE1", ylab = "SWNE2",
  title = paste0("Median of mRNA expression of Pacs2 in logarithmic scale")
) + ggsci::scale_fill_material("amber")

Code

plot_hexbin_feature_plus(l6.srt,
  col = "Tissue",
  mod = "RNA", type = "data", feature = "Grm5",
  action = "median", xlab = "SWNE1", ylab = "SWNE2",
  title = paste0("Median of mRNA expression of Grm5 in logarithmic scale")
) + ggsci::scale_fill_material("amber")

Code

plot_hexbin_feature_plus(l6.srt,
  col = "Tissue",
  mod = "RNA", type = "data", feature = "Slc32a1",
  action = "median", xlab = "SWNE1", ylab = "SWNE2",
  title = paste0("Median of mRNA expression of Slc32a1 in logarithmic scale")
) + ggsci::scale_fill_material("amber")

Code

plot_hexbin_feature_plus(l6.srt,
  col = "Tissue",
  mod = "RNA", type = "data", feature = "Gad1",
  action = "median", xlab = "SWNE1", ylab = "SWNE2",
  title = paste0("Median of mRNA expression of Gad1 in logarithmic scale")
) + ggsci::scale_fill_material("amber")

Code

plot_hexbin_feature_plus(l6.srt,
  col = "Tissue",
  mod = "RNA", type = "data", feature = "Gad2",
  action = "median", xlab = "SWNE1", ylab = "SWNE2",
  title = paste0("Median of mRNA expression of Gad2 in logarithmic scale")
) + ggsci::scale_fill_material("amber")

Code

plot_hexbin_feature_plus(l6.srt,
  col = "Tissue",
  mod = "RNA", type = "data", feature = "Slc17a6",
  action = "median", xlab = "SWNE1", ylab = "SWNE2",
  title = paste0("Median of mRNA expression of Slc17a6 in logarithmic scale")
) + ggsci::scale_fill_material("amber")

Quantify and plot hexagon representation of feature interactions with Spearman’s correlation

Code

plot_hexbin_interact(l6.srt,
  type = c("data", "data"),
  mod = c("RNA", "RNA"), feature = c("Wdr37", "Pacs2"), interact = "corr_spearman",
  ylab = "SWNE2", xlab = "SWNE1",
  title = "Interaction between mRNA expression of Wdr37 and Pacs2/nSpearman rho in hexagonal cells representation"
) +
  scale_fill_gradient2(
    midpoint = 0, space = "Lab",
    low = "navy",
    mid = "wheat1",
    high = "orangered1"
  )

Code

plot_hexbin_interact(l6.srt,
  type = c("data", "data"),
  mod = c("RNA", "RNA"), feature = c("Wdr37", "Grm5"), interact = "corr_spearman",
  ylab = "SWNE2", xlab = "SWNE1",
  title = "Interaction between mRNA expression of Wdr37 and Grm5/nSpearman rho in hexagonal cells representation"
) +
  scale_fill_gradient2(
    midpoint = 0, space = "Lab",
    low = "navy",
    mid = "wheat1",
    high = "orangered1"
  )

Code

plot_hexbin_interact(l6.srt,
  type = c("data", "data"),
  mod = c("RNA", "RNA"), feature = c("Wdr37", "Slc17a6"), interact = "corr_spearman",
  ylab = "SWNE2", xlab = "SWNE1",
  title = "Interaction between mRNA expression of Wdr37 and Slc17a6/nSpearman rho in hexagonal cells representation"
) +
  scale_fill_gradient2(
    midpoint = 0, space = "Lab",
    low = "navy",
    mid = "wheat1",
    high = "orangered1"
  )

Code

plot_hexbin_interact(l6.srt,
  type = c("data", "data"),
  mod = c("RNA", "RNA"), feature = c("Pacs2", "Slc17a6"), interact = "corr_spearman",
  ylab = "SWNE2", xlab = "SWNE1",
  title = "Interaction between mRNA expression of Pacs2 and Slc17a6/nSpearman rho in hexagonal cells representation"
) +
  scale_fill_gradient2(
    midpoint = 0, space = "Lab",
    low = "navy",
    mid = "wheat1",
    high = "orangered1"
  )

Code

plot_hexbin_interact(l6.srt,
  type = c("data", "data"),
  mod = c("RNA", "RNA"), feature = c("Grm5", "Slc17a6"), interact = "corr_spearman",
  ylab = "SWNE2", xlab = "SWNE1",
  title = "Interaction between mRNA expression of Grm5 and Slc17a6/nSpearman rho in hexagonal cells representation"
) +
  scale_fill_gradient2(
    midpoint = 0, space = "Lab",
    low = "navy",
    mid = "wheat1",
    high = "orangered1"
  )

Save results

Code

write_rds(l6.srt, file = here("l6.srt.rds"))

Code

sessioninfo::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.2 (2022-10-31)
 os       Ubuntu 22.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language en_US:en
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2023-08-02
 pandoc   2.19.2 @ /opt/python/3.8.8/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version    date (UTC) lib source
 abind                  1.4-5      2016-07-21 [1] RSPM (R 4.2.0)
 askpass                1.1        2019-01-13 [1] RSPM (R 4.2.0)
 base64enc              0.1-3      2015-07-28 [1] RSPM (R 4.2.0)
 beeswarm               0.4.0      2021-06-01 [1] RSPM (R 4.2.0)
 Biobase              * 2.58.0     2022-11-01 [1] RSPM (R 4.2.2)
 BiocGenerics         * 0.44.0     2022-11-01 [1] RSPM (R 4.2.2)
 BiocManager            1.30.20    2023-02-24 [1] RSPM (R 4.2.0)
 bit                    4.0.5      2022-11-15 [1] RSPM (R 4.2.0)
 bit64                  4.0.5      2020-08-30 [1] RSPM (R 4.2.0)
 bitops                 1.0-7      2021-04-24 [1] RSPM (R 4.2.0)
 circlize               0.4.15     2022-05-10 [1] RSPM (R 4.2.0)
 cli                    3.6.1      2023-03-23 [1] RSPM (R 4.2.0)
 cluster                2.1.4      2022-08-22 [1] RSPM (R 4.2.0)
 codetools              0.2-19     2023-02-01 [1] RSPM (R 4.2.0)
 colorspace             2.1-0      2023-01-23 [1] RSPM (R 4.2.0)
 concaveman             1.1.0      2020-05-11 [1] RSPM (R 4.2.0)
 cowplot              * 1.1.1      2020-12-30 [1] RSPM (R 4.2.0)
 crayon                 1.5.2      2022-09-29 [1] RSPM (R 4.2.0)
 curl                   5.0.0      2023-01-12 [1] RSPM (R 4.2.0)
 data.table             1.14.8     2023-02-17 [1] RSPM (R 4.2.0)
 DelayedArray           0.24.0     2022-11-01 [1] RSPM (R 4.2.2)
 deldir                 1.0-6      2021-10-23 [1] RSPM (R 4.2.0)
 digest                 0.6.31     2022-12-11 [1] RSPM (R 4.2.0)
 dplyr                * 1.1.2      2023-04-20 [1] RSPM (R 4.2.2)
 ellipsis               0.3.2      2021-04-29 [1] RSPM (R 4.2.0)
 entropy                1.3.1      2021-10-02 [1] RSPM (R 4.2.0)
 evaluate               0.20       2023-01-17 [1] RSPM (R 4.2.0)
 fansi                  1.0.4      2023-01-22 [1] RSPM (R 4.2.0)
 farver                 2.1.1      2022-07-06 [1] RSPM (R 4.2.0)
 fastmap                1.1.1      2023-02-24 [1] RSPM (R 4.2.0)
 fitdistrplus           1.1-11     2023-04-25 [1] RSPM (R 4.2.2)
 FNN                    1.1.3.2    2023-03-20 [1] RSPM (R 4.2.0)
 forcats              * 1.0.0      2023-01-29 [1] RSPM (R 4.2.0)
 foreach                1.5.2      2022-02-02 [1] RSPM (R 4.2.0)
 future                 1.32.0     2023-03-07 [1] RSPM (R 4.2.0)
 future.apply           1.10.0     2022-11-05 [1] RSPM (R 4.2.0)
 generics               0.1.3      2022-07-05 [1] RSPM (R 4.2.0)
 GenomeInfoDb         * 1.34.9     2023-02-02 [1] RSPM (R 4.2.2)
 GenomeInfoDbData       1.2.9      2023-04-30 [1] RSPM (R 4.2.2)
 GenomicRanges        * 1.50.2     2022-12-16 [1] RSPM (R 4.2.2)
 ggbeeswarm             0.7.2      2023-04-30 [1] Github (eclarke/ggbeeswarm@3cf58a9)
 ggforce                0.4.1.9000 2023-04-30 [1] Github (thomasp85/ggforce@9be635c)
 ggplot2              * 3.4.2      2023-04-03 [1] RSPM (R 4.2.0)
 ggprism                1.0.4      2023-04-30 [1] Github (csdaw/ggprism@0e411f4)
 ggrastr                1.0.1      2023-04-30 [1] Github (VPetukhov/ggrastr@7aed9af)
 ggrepel                0.9.2.9999 2023-04-30 [1] Github (slowkow/ggrepel@fe3b5c3)
 ggridges               0.5.4      2022-09-26 [1] RSPM (R 4.2.0)
 ggsci                  3.0.0      2023-04-30 [1] Github (nanxstats/ggsci@028b373)
 glmnet               * 4.1-7      2023-03-23 [1] RSPM (R 4.2.0)
 GlobalOptions          0.1.2      2020-06-10 [1] RSPM (R 4.2.0)
 globals                0.16.2     2022-11-21 [1] RSPM (R 4.2.0)
 glue                   1.6.2      2022-02-24 [1] RSPM (R 4.2.0)
 goftest                1.2-3      2021-10-07 [1] RSPM (R 4.2.0)
 gridExtra              2.3        2017-09-09 [1] RSPM (R 4.2.0)
 gtable                 0.3.3      2023-03-21 [1] RSPM (R 4.2.0)
 hdf5r                  1.3.8      2023-01-21 [1] RSPM (R 4.2.2)
 here                 * 1.0.1      2020-12-13 [1] RSPM (R 4.2.0)
 hexbin                 1.28.3     2023-03-21 [1] RSPM (R 4.2.0)
 hms                    1.1.3      2023-03-21 [1] RSPM (R 4.2.0)
 htmltools              0.5.5      2023-03-23 [1] RSPM (R 4.2.0)
 htmlwidgets            1.6.2      2023-03-17 [1] RSPM (R 4.2.0)
 httpuv                 1.6.9      2023-02-14 [1] RSPM (R 4.2.0)
 httr                   1.4.5      2023-02-24 [1] RSPM (R 4.2.0)
 ica                    1.0-3      2022-07-08 [1] RSPM (R 4.2.0)
 igraph                 1.4.1      2023-02-24 [1] RSPM (R 4.2.0)
 IRanges              * 2.32.0     2022-11-01 [1] RSPM (R 4.2.2)
 irlba                  2.3.5.1    2022-10-03 [1] RSPM (R 4.2.0)
 iterators              1.0.14     2022-02-05 [1] RSPM (R 4.2.0)
 janitor                2.2.0.9000 2023-04-30 [1] Github (sfirke/janitor@d64c8bb)
 jsonlite               1.8.4      2022-12-06 [1] RSPM (R 4.2.0)
 KernSmooth             2.23-20    2021-05-03 [1] RSPM (R 4.2.0)
 knitr                  1.42       2023-01-25 [1] RSPM (R 4.2.0)
 ks                     1.14.0     2022-11-24 [1] RSPM (R 4.2.0)
 labeling               0.4.2      2020-10-20 [1] RSPM (R 4.2.0)
 later                  1.3.0      2021-08-18 [1] RSPM (R 4.2.0)
 lattice                0.21-8     2023-04-05 [1] RSPM (R 4.2.0)
 lazyeval               0.2.2      2019-03-15 [1] RSPM (R 4.2.0)
 leiden                 0.4.3      2022-09-10 [1] RSPM (R 4.2.0)
 lifecycle              1.0.3      2022-10-07 [1] RSPM (R 4.2.0)
 liger                  2.0.1      2023-04-30 [1] Github (JEFworks/liger@406dfa4)
 listenv                0.9.0      2022-12-16 [1] RSPM (R 4.2.0)
 lmtest                 0.9-40     2022-03-21 [1] RSPM (R 4.2.0)
 lubridate            * 1.9.2      2023-02-10 [1] RSPM (R 4.2.0)
 magrittr               2.0.3      2022-03-30 [1] RSPM (R 4.2.0)
 MASS                   7.3-58.1   2022-08-03 [1] CRAN (R 4.2.2)
 Matrix               * 1.5-4      2023-04-04 [1] RSPM (R 4.2.0)
 MatrixGenerics       * 1.10.0     2022-11-01 [1] RSPM (R 4.2.2)
 matrixStats          * 0.63.0     2022-11-18 [1] RSPM (R 4.2.0)
 mclust                 6.0.0      2022-10-31 [1] RSPM (R 4.2.0)
 mgcv                   1.8-42     2023-03-02 [1] RSPM (R 4.2.0)
 mime                   0.12       2021-09-28 [1] RSPM (R 4.2.0)
 miniUI                 0.1.1.1    2018-05-18 [1] RSPM (R 4.2.0)
 munsell                0.5.0      2018-06-12 [1] RSPM (R 4.2.0)
 mvtnorm                1.1-3      2021-10-08 [1] RSPM (R 4.2.0)
 Nebulosa             * 1.8.0      2022-11-01 [1] RSPM (R 4.2.2)
 nlme                   3.1-162    2023-01-31 [1] RSPM (R 4.2.0)
 NNLM                   0.4.4      2023-04-30 [1] Github (linxihui/NNLM@4574bca)
 openssl                2.0.6      2023-03-09 [1] RSPM (R 4.2.0)
 paletteer              1.5.0      2022-10-19 [1] RSPM (R 4.2.0)
 parallelly             1.35.0     2023-03-23 [1] RSPM (R 4.2.0)
 patchwork            * 1.1.2.9000 2023-04-30 [1] Github (thomasp85/patchwork@c14c960)
 pbapply                1.7-0      2023-01-13 [1] RSPM (R 4.2.0)
 pillar                 1.9.0      2023-03-22 [1] RSPM (R 4.2.0)
 pkgconfig              2.0.3      2019-09-22 [1] RSPM (R 4.2.0)
 plotly                 4.10.1     2022-11-07 [1] RSPM (R 4.2.0)
 plyr                   1.8.8      2022-11-11 [1] RSPM (R 4.2.0)
 png                    0.1-8      2022-11-29 [1] RSPM (R 4.2.0)
 polyclip               1.10-4     2022-10-20 [1] RSPM (R 4.2.0)
 pracma                 2.4.2      2022-09-22 [1] RSPM (R 4.2.0)
 prismatic              1.1.1      2022-08-15 [1] RSPM (R 4.2.0)
 progressr              0.13.0     2023-01-10 [1] RSPM (R 4.2.0)
 promises               1.2.0.1    2021-02-11 [1] RSPM (R 4.2.0)
 proxy                  0.4-27     2022-06-09 [1] RSPM (R 4.2.0)
 purrr                * 1.0.1      2023-01-10 [1] RSPM (R 4.2.0)
 R.methodsS3            1.8.2      2022-06-13 [1] RSPM (R 4.2.0)
 R.oo                   1.25.0     2022-06-12 [1] RSPM (R 4.2.0)
 R.utils                2.12.2     2022-11-11 [1] RSPM (R 4.2.0)
 R6                     2.5.1      2021-08-19 [1] RSPM (R 4.2.0)
 RANN                   2.6.1      2019-01-08 [1] RSPM (R 4.2.0)
 RColorBrewer         * 1.1-3      2022-04-03 [1] RSPM (R 4.2.0)
 Rcpp                   1.0.10     2023-01-22 [1] RSPM (R 4.2.0)
 RcppAnnoy              0.0.20     2022-10-27 [1] RSPM (R 4.2.0)
 RCurl                  1.98-1.12  2023-03-27 [1] RSPM (R 4.2.0)
 readr                * 2.1.4      2023-02-10 [1] RSPM (R 4.2.0)
 rematch2               2.1.2      2020-05-01 [1] RSPM (R 4.2.0)
 remotes                2.4.2      2021-11-30 [1] RSPM (R 4.2.0)
 repr                   1.1.6      2023-01-26 [1] RSPM (R 4.2.0)
 reshape2               1.4.4      2020-04-09 [1] RSPM (R 4.2.0)
 reticulate             1.28-9000  2023-04-30 [1] Github (rstudio/reticulate@442c49f)
 rlang                  1.1.0      2023-03-14 [1] RSPM (R 4.2.0)
 rmarkdown              2.21       2023-03-26 [1] RSPM (R 4.2.0)
 ROCR                   1.0-11     2020-05-02 [1] RSPM (R 4.2.0)
 rprojroot              2.0.3      2022-04-02 [1] RSPM (R 4.2.0)
 RSpectra               0.16-1     2022-04-24 [1] RSPM (R 4.2.0)
 rsvd                   1.0.5      2021-04-16 [1] RSPM (R 4.2.0)
 Rtsne                  0.16       2022-04-17 [1] RSPM (R 4.2.0)
 S4Vectors            * 0.36.2     2023-02-26 [1] RSPM (R 4.2.2)
 scales                 1.2.1      2022-08-20 [1] RSPM (R 4.2.0)
 scattermore            0.8        2022-02-14 [1] RSPM (R 4.2.0)
 scCustomize          * 1.1.1      2023-04-30 [1] Github (samuel-marsh/scCustomize@d08268d)
 schex                * 1.12.0     2022-11-01 [1] RSPM (R 4.2.2)
 sctransform            0.3.5      2022-09-21 [1] RSPM (R 4.2.0)
 sessioninfo            1.2.2      2021-12-06 [1] RSPM (R 4.2.0)
 Seurat               * 4.3.0      2022-11-18 [1] RSPM (R 4.2.2)
 SeuratDisk           * 0.0.0.9020 2023-04-30 [1] Github (mojaveazure/seurat-disk@9b89970)
 SeuratObject         * 4.1.3      2022-11-07 [1] RSPM (R 4.2.0)
 SeuratWrappers       * 0.3.1      2023-04-30 [1] Github (satijalab/seurat-wrappers@d28512f)
 shape                  1.4.6      2021-05-19 [1] RSPM (R 4.2.0)
 shiny                * 1.7.4      2022-12-15 [1] RSPM (R 4.2.0)
 SingleCellExperiment * 1.20.1     2023-03-17 [1] RSPM (R 4.2.2)
 skimr                  2.1.5      2023-04-30 [1] Github (ropensci/skimr@d5126aa)
 snakecase              0.11.0     2019-05-25 [1] RSPM (R 4.2.0)
 snow                   0.4-4      2021-10-27 [1] RSPM (R 4.2.0)
 sp                     1.6-0      2023-01-19 [1] RSPM (R 4.2.0)
 spatstat.data          3.0-1      2023-03-12 [1] RSPM (R 4.2.0)
 spatstat.explore       3.1-0      2023-03-14 [1] RSPM (R 4.2.0)
 spatstat.geom          3.1-0      2023-03-12 [1] RSPM (R 4.2.0)
 spatstat.random        3.1-4      2023-03-13 [1] RSPM (R 4.2.0)
 spatstat.sparse        3.0-1      2023-03-12 [1] RSPM (R 4.2.0)
 spatstat.utils         3.0-2      2023-03-11 [1] RSPM (R 4.2.0)
 stringi                1.7.12     2023-01-11 [1] RSPM (R 4.2.0)
 stringr              * 1.5.0      2022-12-02 [1] RSPM (R 4.2.0)
 SummarizedExperiment * 1.28.0     2022-11-01 [1] RSPM (R 4.2.2)
 survival               3.5-5      2023-03-12 [1] RSPM (R 4.2.0)
 swne                 * 0.6.20     2023-04-30 [1] Github (yanwu2014/swne@05fc3ee)
 systemfonts            1.0.4      2022-02-11 [1] RSPM (R 4.2.0)
 tensor                 1.5        2012-05-05 [1] RSPM (R 4.2.0)
 tibble               * 3.2.1      2023-03-20 [1] RSPM (R 4.2.0)
 tidyr                * 1.3.0      2023-01-24 [1] RSPM (R 4.2.0)
 tidyselect             1.2.0      2022-10-10 [1] RSPM (R 4.2.0)
 tidyverse            * 2.0.0.9000 2023-04-30 [1] Github (tidyverse/tidyverse@8ec2e1f)
 timechange             0.2.0      2023-01-11 [1] RSPM (R 4.2.0)
 tweenr                 2.0.2      2022-09-06 [1] RSPM (R 4.2.0)
 tzdb                   0.3.0      2022-03-28 [1] RSPM (R 4.2.0)
 umap                   0.2.10.0   2023-02-01 [1] RSPM (R 4.2.0)
 UpSetR               * 1.4.0      2023-04-30 [1] Github (hms-dbmi/UpSetR@b14854a)
 usedist                0.4.0      2020-03-01 [1] RSPM (R 4.2.0)
 utf8                   1.2.3      2023-01-31 [1] RSPM (R 4.2.0)
 uwot                   0.1.14     2022-08-22 [1] RSPM (R 4.2.0)
 V8                     4.2.2      2022-11-03 [1] RSPM (R 4.2.0)
 vctrs                  0.6.2      2023-04-19 [1] RSPM (R 4.2.2)
 vipor                  0.4.5      2017-03-22 [1] RSPM (R 4.2.0)
 viridis                0.6.2      2021-10-13 [1] RSPM (R 4.2.0)
 viridisLite            0.4.1      2022-08-22 [1] RSPM (R 4.2.0)
 withr                  2.5.0      2022-03-03 [1] RSPM (R 4.2.0)
 xfun                   0.39       2023-04-20 [1] RSPM (R 4.2.2)
 xtable                 1.8-4      2019-04-21 [1] RSPM (R 4.2.0)
 XVector                0.38.0     2022-11-01 [1] RSPM (R 4.2.2)
 yaml                   2.3.7      2023-01-23 [1] RSPM (R 4.2.0)
 zlibbioc               1.44.0     2022-11-01 [1] RSPM (R 4.2.2)
 zoo                    1.8-12     2023-04-13 [1] RSPM (R 4.2.2)

 [1] /opt/R/4.2.2/lib/R/library

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