bioproject <- "PRJNA515063"
project <- "mickelsen2019_LHA"
cb_fpr <- 0.001
low_cutoff_gene <- 500
high_cutoff_gene <- NULL
high_cutoff_gene <- 7000
low_cutoff_umis <- NULL
low_cutoff_umis <- -Inf
high_cutoff_umis <- 25000
high_cutoff_pc_mt <- 15
high_cutoff_pc_ribo <- 15
high_cutoff_pc_hb <- 0.1
high_cutoff_doublet_score <- 0.33
high_cutoff_complexity <- 0.85srt <- LoadH5Seurat(
file = here(
data_dir,
sprintf("%s-whole_dataset-fpr_%s-clusters.h5Seurat", bioproject, cb_fpr)
)
)
srtAn object of class Seurat
66001 features across 9053 samples within 3 assays
Active assay: SCT (21785 features, 3500 variable features)
2 other assays present: RAW, RNA
2 dimensional reductions calculated: pca, umapneurons <- subset(srt, subset = Rbfox3 > 0 | Elavl4 > 0 | Snap25 > 0 | Stmn2 > 0)
onecut <- subset(neurons, subset = Onecut3 > 0 | Onecut2 > 0)
onecut3 <- subset(onecut, subset = Onecut3 > 0)Derive and filter matrix of Onecut3
mtx_oc3 <-
onecut3 %>%
GetAssayData("data", "RNA") %>%
as.data.frame() %>%
t()
rownames(mtx_oc3) <- colnames(onecut3)
# Filter features
filt_low_genes <-
colSums(mtx_oc3) %>%
.[. > quantile(., 0.4)] %>%
names()
mtx_oc3 %<>% .[, filt_low_genes]
min_filt_vector <-
mtx_oc3 %>%
as_tibble() %>%
select(all_of(filt_low_genes)) %>%
summarise(across(.fns = ~ quantile(.x, .1))) %>%
as.list() %>%
map(as.double) %>%
simplify() %>%
.[colnames(mtx_oc3)]
# Prepare table of intersection sets analysis
content_mtx_oc3 <-
(mtx_oc3 > min_filt_vector) %>%
as_tibble() %>%
mutate_all(as.numeric)Visualise intersections sets that we are going to use (highlighted)
upset(as.data.frame(content_mtx_oc3),
order.by = "freq",
sets.x.label = "Number of cells",
text.scale = c(2, 1.6, 2, 1.3, 2, 3),
nsets = 15,
sets = c("Gad1", "Gad2", "Slc32a1", "Slc17a6"),
queries = list(
list(
query = intersects,
params = list("Gad1", "Gad2", "Slc32a1"),
active = T
),
list(
query = intersects,
params = list("Slc17a6"),
active = T
)
),
nintersects = 60,
empty.intersections = "on"
)
upset(as.data.frame(content_mtx_oc3),
order.by = "freq",
sets.x.label = "Number of cells",
text.scale = c(2, 1.6, 2, 1.3, 2, 3),
nsets = 15,
sets = c("Th", "Trh", "Slc32a1", "Slc17a6"),
queries = list(
list(
query = intersects,
params = list("Th", "Slc32a1"),
active = T
),
list(
query = intersects,
params = list("Trh", "Slc17a6"),
active = T
)
),
nintersects = 60,
empty.intersections = "on"
)
Make subset of stable neurons
onecut3$gaba_status <-
content_mtx_oc3 %>%
select(Gad1, Gad2, Slc32a1) %>%
mutate(gaba = if_all(.fns = ~ .x > 0)) %>%
.$gaba
onecut3$gaba_occurs <-
content_mtx_oc3 %>%
select(Gad1, Gad2, Slc32a1) %>%
mutate(gaba = if_any(.fns = ~ .x > 0)) %>%
.$gaba
onecut3$glut_status <-
content_mtx_oc3 %>%
select(Slc17a6) %>%
mutate(glut = Slc17a6 > 0) %>%
.$glut
oc3_fin <-
subset(onecut3,
cells = union(
WhichCells(onecut3,
expression = gaba_status == TRUE & glut_status == FALSE
),
WhichCells(onecut3,
expression = glut_status == TRUE & gaba_occurs == FALSE
)
)
)oc3_fin@meta.data %>%
janitor::tabyl(glut_status, gaba_status)Make splits of neurons by neurotransmitter signature
oc3_fin$status <- oc3_fin$gaba_status %>%
if_else(true = "GABAergic",
false = "glutamatergic"
)
Idents(oc3_fin) <- "status"
SaveH5Seurat(
object = oc3_fin,
filename = here(data_dir, "oc3_fin"),
overwrite = TRUE,
verbose = TRUE
)
## Split on basis of neurotrans and test for difference
oc3_fin_neurotrans <- SplitObject(oc3_fin, split.by = "status")Expression of GABA receptors in GABAergic Onecut3 positive cells
DotPlot(
object = oc3_fin_neurotrans$GABAergic,
features = gabar,
group.by = "orig.ident",
cols = c("#adffff", "#0084ff"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of GABA receptors in glutamatergic Onecut3 positive cells
DotPlot(
object = oc3_fin_neurotrans$glutamatergic,
features = gabar,
group.by = "orig.ident",
cols = c("#ffc2c2", "#ff3c00"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of glutamate receptors in GABAergic Onecut3 positive cells
DotPlot(
object = oc3_fin_neurotrans$GABAergic,
features = glutr,
group.by = "orig.ident",
cols = c("#adffff", "#0084ff"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of glutamate receptors in glutamatergic Onecut3 positive cells
DotPlot(
object = oc3_fin_neurotrans$glutamatergic,
features = glutr,
group.by = "orig.ident",
cols = c("#ffc2c2", "#ff3c00"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of GABA receptors in GABAergic Onecut3 positive cells
DotPlot(
object = oc3_fin_neurotrans$GABAergic,
features = gabar,
cols = c("#adffff", "#0084ff"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of GABA receptors in glutamatergic Onecut3 positive cells
DotPlot(
object = oc3_fin_neurotrans$glutamatergic,
features = gabar,
cols = c("#ffc2c2", "#ff3c00"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of glutamate receptors in GABAergic Onecut3 positive cells
DotPlot(
object = oc3_fin_neurotrans$GABAergic,
features = glutr,
cols = c("#adffff", "#0084ff"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of glutamate receptors in glutamatergic Onecut3 positive cells
DotPlot(
object = oc3_fin_neurotrans$glutamatergic,
features = glutr,
cols = c("#ffc2c2", "#ff3c00"),
col.min = -1, col.max = 1
) + RotatedAxis()
sbs_mtx_oc <-
onecut %>%
GetAssayData("data", "RNA") %>%
as.data.frame() %>%
t()
rownames(sbs_mtx_oc) <- colnames(onecut)
# Filter features
filt_low_genes2 <-
colSums(sbs_mtx_oc) %>%
.[. > quantile(., 0.4)] %>%
names()
sbs_mtx_oc %<>% .[, filt_low_genes2]
min_filt_vector2 <-
sbs_mtx_oc %>%
as_tibble() %>%
select(all_of(filt_low_genes2)) %>%
summarise(across(.fns = ~ quantile(.x, .005))) %>%
as.list() %>%
map(as.double) %>%
simplify() %>%
.[filt_low_genes2]
# Prepare table of intersection sets analysis
content_sbs_mtx_oc <-
(sbs_mtx_oc > min_filt_vector2) %>%
as_tibble() %>%
mutate_all(as.numeric)
onecut$gaba_status <-
content_sbs_mtx_oc %>%
select(Gad1, Gad2, Slc32a1) %>%
mutate(gaba = if_all(.fns = ~ .x > 0)) %>%
.$gaba
onecut$gaba_occurs <-
content_sbs_mtx_oc %>%
select(Gad1, Gad2, Slc32a1) %>%
mutate(gaba = if_any(.fns = ~ .x > 0)) %>%
.$gaba
onecut$glut_status <-
content_sbs_mtx_oc %>%
select(Slc17a6) %>%
mutate(glut = Slc17a6 > 0) %>%
.$glut
oc_fin <-
subset(onecut,
cells = union(
WhichCells(onecut,
expression = gaba_status == TRUE & glut_status == FALSE
),
WhichCells(onecut,
expression = glut_status == TRUE & gaba_occurs == FALSE
)
)
)
oc_fin$status <- oc_fin$gaba_status %>%
if_else(true = "GABAergic",
false = "glutamatergic"
)
Idents(oc_fin) <- "status"
sbs_mtx_oc <-
oc_fin %>%
GetAssayData("data", "RNA") %>%
as.data.frame() %>%
t()
rownames(sbs_mtx_oc) <- colnames(oc_fin)
# Filter features
filt_low_genes2 <-
colSums(sbs_mtx_oc) %>%
.[. > quantile(., 0.4)] %>%
names()
sbs_mtx_oc %<>% .[, filt_low_genes2]
min_filt_vector2 <-
sbs_mtx_oc %>%
as_tibble() %>%
select(all_of(filt_low_genes2)) %>%
summarise(across(.fns = ~ quantile(.x, .005))) %>%
as.list() %>%
map(as.double) %>%
simplify() %>%
.[filt_low_genes2]
# Prepare table of intersection sets analysis
content_sbs_mtx_oc <-
(sbs_mtx_oc > min_filt_vector2) %>%
as_tibble() %>%
mutate_all(as.numeric)upset(as.data.frame(content_sbs_mtx_oc),
order.by = "freq",
sets.x.label = "Number of cells",
text.scale = c(2, 1.6, 2, 1.3, 2, 3),
nsets = 15,
sets = c("Gad1", "Gad2", "Slc32a1", "Slc17a6", "Onecut3"),
queries = list(
list(
query = intersects,
params = list("Gad1", "Gad2", "Slc32a1", "Onecut3"),
active = T
),
list(
query = intersects,
params = list("Slc17a6", "Onecut3"),
active = T
)
),
nintersects = 20,
empty.intersections = NULL
)
upset(as.data.frame(content_sbs_mtx_oc),
order.by = "freq",
sets.x.label = "Number of cells",
text.scale = c(2, 1.6, 2, 1.3, 2, 3),
nsets = 15,
sets = c("Th", "Trh", "Slc32a1", "Slc17a6", "Onecut3"),
queries = list(
list(
query = intersects,
params = list("Th", "Slc32a1", "Onecut3"),
active = T
),
list(
query = intersects,
params = list("Trh", "Slc17a6", "Onecut3"),
active = T
)
),
nintersects = 20,
empty.intersections = NULL
)
sbs_mtx_oc_full <- content_sbs_mtx_oc |>
select(any_of(c(neurotrans, glutr, gabar, "Trh", "Th", "Onecut3"))) |>
dplyr::bind_cols(oc_fin@meta.data)
sbs_mtx_oc_full |> glimpse()Rows: 384
Columns: 95
$ Slc17a6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Slc1a1 <dbl> 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,…
$ Slc1a2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Slc1a6 <dbl> 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
$ Gad1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Slc32a1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Slc6a1 <dbl> 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gria1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,…
$ Gria2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gria3 <dbl> 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gria4 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grid1 <dbl> 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1,…
$ Grid2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grik1 <dbl> 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,…
$ Grik2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grik3 <dbl> 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,…
$ Grik4 <dbl> 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0,…
$ Grik5 <dbl> 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1,…
$ Grin1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,…
$ Grin2a <dbl> 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,…
$ Grin2b <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grin2d <dbl> 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1,…
$ Grin3a <dbl> 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1,…
$ Grm1 <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,…
$ Grm5 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,…
$ Grm2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Grm3 <dbl> 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1,…
$ Grm4 <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,…
$ Grm7 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,…
$ Grm8 <dbl> 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,…
$ Gabra1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,…
$ Gabra2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabra3 <dbl> 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,…
$ Gabra4 <dbl> 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0,…
$ Gabra5 <dbl> 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,…
$ Gabrb1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabrb2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabrb3 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabrg1 <dbl> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,…
$ Gabrg2 <dbl> 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabrg3 <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabre <dbl> 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Gabrq <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,…
$ Gabbr1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,…
$ Gabbr2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,…
$ Trh <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
$ Th <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,…
$ Onecut3 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,…
$ nCount_RAW <dbl> 19623, 12483, 12675, 13940, 15363, 15310,…
$ nFeature_RAW <int> 6025, 4802, 4842, 4980, 5118, 5211, 5297,…
$ nCount_RNA <dbl> 19162, 12185, 12373, 13618, 14957, 15002,…
$ nFeature_RNA <int> 5978, 4781, 4818, 4958, 5081, 5193, 5268,…
$ orig.ident <chr> "SRR8441476", "SRR8441476", "SRR8441476",…
$ nFeature_Diff <int> 47, 21, 24, 22, 37, 18, 29, 29, 31, 31, 1…
$ nCount_Diff <dbl> 461, 298, 302, 322, 406, 308, 376, 361, 3…
$ percent_mito <dbl> 4.075775, 3.947476, 2.327649, 5.742400, 1…
$ percent_ribo <dbl> 5.3908778, 8.1657776, 5.5524125, 6.865912…
$ percent_mito_ribo <dbl> 9.466653, 12.113254, 7.880061, 12.608313,…
$ percent_hb <dbl> 0.000000000, 0.000000000, 0.000000000, 0.…
$ log10GenesPerUMI <dbl> 0.8818700, 0.9005570, 0.8999119, 0.893857…
$ cell_name <chr> "SRR8441476_AAATGCCAGGCGACAT-1", "SRR8441…
$ barcode <chr> "AAATGCCAGGCGACAT-1", "AACCGCGAGCGGATCA-1…
$ latent_RT_efficiency <dbl> 3.283587, 2.502992, 2.562110, 2.646928, 2…
$ latent_cell_probability <dbl> 0.9999477, 0.9998801, 0.9999301, 0.999928…
$ latent_scale <dbl> 4695.590, 4008.599, 3921.173, 4068.829, 4…
$ doublet_score <dbl> 0.04804282, 0.07987788, 0.05905781, 0.068…
$ predicted_doublets <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ QC <chr> "Pass", "Pass", "Pass", "Pass", "Pass", "…
$ var_regex <dbl> 10.766100, 13.254001, 9.140871, 14.003525…
$ RNA_snn_res.0.5 <fct> 4, 5, 11, 5, 4, 5, 4, 4, 4, 4, 4, 4, 4, 1…
$ RNA_snn_res.0.74082856775745 <fct> 3, 6, 13, 6, 3, 9, 3, 3, 3, 3, 3, 3, 3, 4…
$ RNA_snn_res.1.24733796060143 <fct> 5, 6, 16, 6, 5, 11, 5, 9, 9, 28, 28, 9, 9…
$ RNA_snn_res.3.000001 <fct> 11, 13, 17, 13, 11, 12, 11, 8, 8, 27, 27,…
$ seurat_clusters <fct> 3, 20, 27, 20, 30, 19, 10, 3, 3, 10, 11, …
$ k_tree <fct> 9, 5, 23, 5, 1, 14, 1, 9, 9, 1, 1, 9, 4, …
$ comb_clstr1 <fct> 3, 6, 13, 6, 3, 9, 3, 3, 3, 3, 3, 3, 3, 4…
$ S.Score <dbl> 0.001711877, -0.019196776, -0.038780572, …
$ G2M.Score <dbl> 0.022455905, 0.001625043, -0.014130781, -…
$ Phase <chr> "G2M", "G2M", "G1", "S", "G2M", "G1", "G2…
$ nCount_SCT <dbl> 5786, 6661, 6617, 6344, 6031, 6118, 6219,…
$ nFeature_SCT <int> 3074, 3904, 3858, 3495, 3117, 3251, 3442,…
$ SCT_snn_res.1 <fct> 9, 5, 20, 5, 4, 13, 4, 9, 9, 4, 4, 9, 4, …
$ SCT_snn_res.1.10569909553377 <fct> 9, 5, 20, 5, 4, 12, 4, 9, 9, 4, 4, 9, 6, …
$ SCT_snn_res.1.23112793047964 <fct> 9, 5, 22, 5, 2, 12, 2, 9, 9, 2, 2, 9, 6, …
$ SCT_snn_res.1.38237925427927 <fct> 9, 5, 23, 5, 2, 12, 2, 9, 9, 2, 2, 9, 6, …
$ SCT_snn_res.1.5683423560766 <fct> 9, 5, 22, 5, 2, 12, 2, 9, 9, 2, 2, 9, 6, …
$ SCT_snn_res.1.80251553997986 <fct> 9, 5, 23, 5, 1, 14, 1, 9, 9, 1, 1, 9, 4, …
$ SCT_snn_res.2.10643815972253 <fct> 7, 19, 26, 19, 2, 13, 2, 7, 7, 2, 2, 7, 4…
$ SCT_snn_res.2.51672603000175 <fct> 6, 20, 26, 20, 4, 14, 4, 6, 6, 4, 7, 6, 7…
$ SCT_snn_res.3.10106021549219 <fct> 6, 20, 26, 20, 5, 12, 5, 6, 6, 9, 9, 6, 9…
$ SCT_snn_res.4.00000100000001 <fct> 3, 20, 27, 20, 30, 19, 10, 3, 3, 10, 11, …
$ gaba_status <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
$ gaba_occurs <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
$ glut_status <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ status <chr> "GABAergic", "GABAergic", "GABAergic", "G…sbs_mtx_oc_full$modulator <-
sbs_mtx_oc_full %>%
select(Trh, Th) %>%
mutate(modulator = if_any(.fns = ~ .x > 0)) %>%
.$modulator
sbs_mtx_oc_full$oc3 <-
(sbs_mtx_oc_full$Onecut3 > 0)
library(ggstatsplot)
# for reproducibility
set.seed(123)
## plot
# ggpiestats(
# data = sbs_mtx_oc_full,
# x = modulator,
# y = status,
# perc.k = 1,
# package = "ggsci",
# palette = "category10_d3",
# ) + # further modification with `{ggplot2}` commands
# ggplot2::theme(
# plot.title = ggplot2::element_text(
# color = "black",
# size = 14,
# hjust = 0
# )
# )
# plot
grouped_ggpiestats(
# arguments relevant for `ggpiestats()`
data = sbs_mtx_oc_full,
x = modulator,
y = oc3,
grouping.var = status,
perc.k = 1,
package = "ggsci",
palette = "category10_d3",
# arguments relevant for `combine_plots()`
title.text = "Neuromodulator specification of onecut-driven hypothalamic neuronal lineages by Onecut3 and main neurotransmitter expression",
caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
plotgrid.args = list(nrow = 2)
)
(instead of neurons subset)
Derive and filter matrix of neurons
mtx_neurons <-
neurons %>%
GetAssayData("data", "RNA") %>%
as.data.frame() %>%
t()
rownames(mtx_neurons) <- colnames(neurons)
# Filter features
filt_low_genes <-
colSums(mtx_neurons) %>%
.[. > quantile(., 0.4)] %>%
names()
mtx_neurons %<>% .[, filt_low_genes]
min_filt_vector <-
mtx_neurons %>%
as_tibble() %>%
select(all_of(filt_low_genes)) %>%
summarise(across(.fns = ~ quantile(.x, .1))) %>%
as.list() %>%
map(as.double) %>%
simplify() %>%
.[colnames(mtx_neurons)]
# Prepare table of intersection sets analysis
content_mtx_neuro <-
(mtx_neurons > min_filt_vector) %>%
as_tibble() %>%
mutate_all(as.numeric)Visualise intersections sets that we are going to use (highlighted)
upset(as.data.frame(content_mtx_neuro),
order.by = "freq",
sets.x.label = "Number of cells",
text.scale = c(2, 1.6, 2, 1.3, 2, 3),
nsets = 15,
sets = c("Gad1", "Gad2", "Slc32a1", "Slc17a6"),
queries = list(
list(
query = intersects,
params = list("Gad1", "Gad2", "Slc32a1"),
active = T
),
list(
query = intersects,
params = list("Slc17a6"),
active = T
)
),
nintersects = 60,
empty.intersections = "on"
)
upset(as.data.frame(content_mtx_neuro),
order.by = "freq",
sets.x.label = "Number of cells",
text.scale = c(2, 1.6, 2, 1.3, 2, 3),
nsets = 15,
sets = c("Th", "Trh", "Slc32a1", "Slc17a6"),
queries = list(
list(
query = intersects,
params = list("Th", "Slc32a1"),
active = T
),
list(
query = intersects,
params = list("Trh", "Slc17a6"),
active = T
)
),
nintersects = 60,
empty.intersections = "on"
)
Make subset of stable neurons
neurons$gaba_status <-
content_mtx_neuro %>%
select(Gad1, Gad2, Slc32a1) %>%
mutate(gaba = if_all(.fns = ~ .x > 0)) %>%
.$gaba
neurons$gaba_expr <-
content_mtx_neuro %>%
select(Gad1, Gad2, Slc32a1) %>%
mutate(gaba = if_any(.fns = ~ .x > 0)) %>%
.$gaba
neurons$glut_status <-
content_mtx_neuro %>%
select(Slc17a6) %>%
mutate(glut = Slc17a6 > 0) %>%
.$glut
neuro_fin <-
subset(neurons,
cells = union(
WhichCells(neurons,
expression = gaba_status == TRUE & glut_status == FALSE
),
WhichCells(neurons,
expression = glut_status == TRUE & gaba_expr == FALSE
)
)
)neuro_fin@meta.data %>%
janitor::tabyl(glut_status, gaba_status)Make splits of neurons by neurotransmitter signature
neuro_fin$status <- neuro_fin$gaba_status %>%
if_else(true = "GABAergic",
false = "glutamatergic"
)
Idents(neuro_fin) <- "status"
SaveH5Seurat(
object = neuro_fin,
filename = here(data_dir, "neuro_fin"),
overwrite = TRUE,
verbose = TRUE
)
## Split on basis of neurotrans and test for difference
neuro_fin_neurotrans <- SplitObject(neuro_fin, split.by = "status")Expression of GABA receptors in GABAergic Onecut3 positive cells
DotPlot(
object = neuro_fin_neurotrans$GABAergic,
features = gabar,
group.by = "orig.ident",
cols = c("#adffff", "#0084ff"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of GABA receptors in glutamatergic Onecut3 positive cells
DotPlot(
object = neuro_fin_neurotrans$glutamatergic,
features = gabar,
group.by = "orig.ident",
cols = c("#ffc2c2", "#ff3c00"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of glutamate receptors in GABAergic Onecut3 positive cells
DotPlot(
object = neuro_fin_neurotrans$GABAergic,
features = glutr,
group.by = "orig.ident",
cols = c("#adffff", "#0084ff"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of glutamate receptors in glutamatergic Onecut3 positive cells
DotPlot(
object = neuro_fin_neurotrans$glutamatergic,
features = glutr,
group.by = "orig.ident",
cols = c("#ffc2c2", "#ff3c00"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of GABA receptors in GABAergic Onecut3 positive cells
DotPlot(
object = neuro_fin_neurotrans$GABAergic,
features = gabar,
cols = c("#adffff", "#0084ff"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of GABA receptors in glutamatergic Onecut3 positive cells
DotPlot(
object = neuro_fin_neurotrans$glutamatergic,
features = gabar,
cols = c("#ffc2c2", "#ff3c00"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of glutamate receptors in GABAergic Onecut3 positive cells
DotPlot(
object = neuro_fin_neurotrans$GABAergic,
features = glutr,
cols = c("#adffff", "#0084ff"),
col.min = -1, col.max = 1
) + RotatedAxis()
Expression of glutamate receptors in glutamatergic Onecut3 positive cells
DotPlot(
object = neuro_fin_neurotrans$glutamatergic,
features = glutr,
cols = c("#ffc2c2", "#ff3c00"),
col.min = -1, col.max = 1
) + RotatedAxis()
sbs_mtx_neuro <-
neurons %>%
GetAssayData("data", "RNA") %>%
as.data.frame() %>%
t()
rownames(sbs_mtx_neuro) <- colnames(neurons)
# Filter features
filt_low_genes2 <-
colSums(sbs_mtx_neuro) %>%
.[. > quantile(., 0.4)] %>%
names()
sbs_mtx_neuro %<>% .[, filt_low_genes2]
min_filt_vector2 <-
sbs_mtx_neuro %>%
as_tibble() %>%
select(all_of(filt_low_genes2)) %>%
summarise(across(.fns = ~ quantile(.x, .005))) %>%
as.list() %>%
map(as.double) %>%
simplify() %>%
.[filt_low_genes2]
# Prepare table of intersection sets analysis
content_sbs_mtx_neuro <-
(sbs_mtx_neuro > min_filt_vector2) %>%
as_tibble() %>%
mutate_all(as.numeric)
neurons$gaba_status <-
content_sbs_mtx_neuro %>%
select(Gad1, Gad2, Slc32a1) %>%
mutate(gaba = if_all(.fns = ~ .x > 0)) %>%
.$gaba
neurons$gaba_expr <-
content_sbs_mtx_neuro %>%
select(Gad1, Gad2, Slc32a1) %>%
mutate(gaba = if_any(.fns = ~ .x > 0)) %>%
.$gaba
neurons$glut_status <-
content_sbs_mtx_neuro %>%
select(Slc17a6) %>%
mutate(glut = Slc17a6 > 0) %>%
.$glut
neuro_fin <-
subset(neurons,
cells = union(
WhichCells(neurons,
expression = gaba_status == TRUE & glut_status == FALSE
),
WhichCells(neurons,
expression = glut_status == TRUE & gaba_expr == FALSE
)
)
)
neuro_fin$status <- neuro_fin$gaba_status %>%
if_else(true = "GABAergic",
false = "glutamatergic"
)
Idents(neuro_fin) <- "status"
sbs_mtx_neuro <-
neuro_fin %>%
GetAssayData("data", "RNA") %>%
as.data.frame() %>%
t()
rownames(sbs_mtx_neuro) <- colnames(neuro_fin)
# Filter features
filt_low_genes2 <-
colSums(sbs_mtx_neuro) %>%
.[. > quantile(., 0.4)] %>%
names()
sbs_mtx_neuro %<>% .[, filt_low_genes2]
min_filt_vector2 <-
sbs_mtx_neuro %>%
as_tibble() %>%
select(all_of(filt_low_genes2)) %>%
summarise(across(.fns = ~ quantile(.x, .005))) %>%
as.list() %>%
map(as.double) %>%
simplify() %>%
.[filt_low_genes2]
# Prepare table of intersection sets analysis
content_sbs_mtx_neuro <-
(sbs_mtx_neuro > min_filt_vector2) %>%
as_tibble() %>%
mutate_all(as.numeric)upset(as.data.frame(content_sbs_mtx_neuro),
order.by = "freq",
sets.x.label = "Number of cells",
text.scale = c(2, 1.6, 2, 1.3, 2, 3),
nsets = 15,
sets = c("Gad1", "Gad2", "Slc32a1", "Slc17a6", "Onecut3"),
queries = list(
list(
query = intersects,
params = list("Gad1", "Gad2", "Slc32a1", "Onecut3"),
active = T
),
list(
query = intersects,
params = list("Slc17a6", "Onecut3"),
active = T
)
),
nintersects = 20,
empty.intersections = NULL
)
upset(as.data.frame(content_sbs_mtx_neuro),
order.by = "freq",
sets.x.label = "Number of cells",
text.scale = c(2, 1.6, 2, 1.3, 2, 3),
nsets = 15,
sets = c("Th", "Trh", "Slc32a1", "Slc17a6", "Onecut3"),
queries = list(
list(
query = intersects,
params = list("Th", "Slc32a1", "Onecut3"),
active = T
),
list(
query = intersects,
params = list("Trh", "Slc17a6", "Onecut3"),
active = T
)
),
nintersects = 20,
empty.intersections = NULL
)
sbs_mtx_neuro_full <- content_sbs_mtx_neuro |>
select(any_of(c(neurotrans, glutr, gabar, "Trh", "Th", "Onecut3"))) |>
dplyr::bind_cols(neuro_fin@meta.data)
sbs_mtx_neuro_full |> glimpse()Rows: 2,899
Columns: 95
$ Slc17a6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Slc1a1 <dbl> 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,…
$ Slc1a2 <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,…
$ Slc1a6 <dbl> 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,…
$ Gad1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Slc32a1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Slc6a1 <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,…
$ Gria1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gria2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gria3 <dbl> 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0,…
$ Gria4 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grid1 <dbl> 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,…
$ Grid2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grik1 <dbl> 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1,…
$ Grik2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grik3 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,…
$ Grik4 <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0,…
$ Grik5 <dbl> 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,…
$ Grin1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,…
$ Grin2a <dbl> 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,…
$ Grin2b <dbl> 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grin2d <dbl> 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0,…
$ Grin3a <dbl> 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0,…
$ Grm1 <dbl> 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,…
$ Grm5 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grm2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Grm3 <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,…
$ Grm4 <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
$ Grm7 <dbl> 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Grm8 <dbl> 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1,…
$ Gabra1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabra2 <dbl> 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,…
$ Gabra3 <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,…
$ Gabra4 <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
$ Gabra5 <dbl> 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0,…
$ Gabrb1 <dbl> 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabrb2 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,…
$ Gabrb3 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabrg1 <dbl> 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,…
$ Gabrg2 <dbl> 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabrg3 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabre <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,…
$ Gabrq <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,…
$ Gabbr1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Gabbr2 <dbl> 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ Trh <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Th <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,…
$ Onecut3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ nCount_RAW <dbl> 4764, 7946, 12205, 9419, 8672, 6217, 1962…
$ nFeature_RAW <int> 2674, 3655, 4803, 4055, 4055, 3133, 6025,…
$ nCount_RNA <dbl> 4520, 7717, 11914, 9177, 8428, 5999, 1916…
$ nFeature_RNA <int> 2625, 3638, 4774, 4034, 4028, 3111, 5978,…
$ orig.ident <chr> "SRR8441476", "SRR8441476", "SRR8441476",…
$ nFeature_Diff <int> 49, 17, 29, 21, 27, 22, 47, 54, 42, 16, 2…
$ nCount_Diff <dbl> 244, 229, 291, 242, 244, 218, 461, 443, 4…
$ percent_mito <dbl> 4.092920, 9.718803, 5.078059, 3.574153, 6…
$ percent_ribo <dbl> 1.349558, 1.943761, 6.253148, 6.331045, 3…
$ percent_mito_ribo <dbl> 5.442478, 11.662563, 11.331207, 9.905198,…
$ percent_hb <dbl> 0.000000000, 0.000000000, 0.008393487, 0.…
$ log10GenesPerUMI <dbl> 0.9354309, 0.9159897, 0.9025590, 0.909918…
$ cell_name <chr> "SRR8441476_AAACCTGTCACAATGC-1", "SRR8441…
$ barcode <chr> "AAACCTGTCACAATGC-1", "AAACGGGAGAGCTTCT-1…
$ latent_RT_efficiency <dbl> 1.244024, 1.791169, 2.429151, 2.070349, 1…
$ latent_cell_probability <dbl> 0.9998177, 0.9998643, 0.9998670, 0.999868…
$ latent_scale <dbl> 3184.321, 3566.985, 3987.859, 3686.131, 3…
$ doublet_score <dbl> 0.04078606, 0.07379372, 0.06347266, 0.046…
$ predicted_doublets <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ QC <chr> "Pass", "Pass", "Pass", "Pass", "Pass", "…
$ var_regex <dbl> 7.323009, 12.725152, 12.548263, 11.125640…
$ RNA_snn_res.0.5 <fct> 1, 5, 11, 4, 11, 4, 4, 4, 5, 4, 5, 5, 11,…
$ RNA_snn_res.0.74082856775745 <fct> 4, 6, 13, 3, 13, 3, 3, 3, 6, 3, 6, 6, 13,…
$ RNA_snn_res.1.24733796060143 <fct> 12, 6, 16, 9, 16, 28, 5, 5, 6, 9, 6, 6, 1…
$ RNA_snn_res.3.000001 <fct> 9, 13, 17, 8, 17, 27, 11, 11, 20, 8, 13, …
$ seurat_clusters <fct> 1, 20, 27, 3, 27, 11, 3, 30, 16, 3, 20, 2…
$ k_tree <fct> 4, 5, 23, 9, 23, 1, 9, 1, 5, 9, 5, 5, 23,…
$ comb_clstr1 <fct> 4, 6, 13, 3, 13, 3, 3, 3, 6, 3, 6, 6, 13,…
$ S.Score <dbl> -0.0003863379, -0.0321787304, 0.005341143…
$ G2M.Score <dbl> 0.0247065841, -0.0531857167, -0.004585026…
$ Phase <chr> "G2M", "G1", "S", "G1", "G1", "G1", "G2M"…
$ nCount_SCT <dbl> 4988, 6605, 6672, 6644, 6622, 5941, 5786,…
$ nFeature_SCT <int> 2606, 3625, 3957, 4000, 4008, 3096, 3074,…
$ SCT_snn_res.1 <fct> 7, 5, 20, 9, 20, 4, 9, 4, 5, 9, 5, 5, 20,…
$ SCT_snn_res.1.10569909553377 <fct> 6, 5, 20, 9, 20, 4, 9, 4, 5, 9, 5, 5, 20,…
$ SCT_snn_res.1.23112793047964 <fct> 6, 5, 22, 9, 22, 2, 9, 2, 5, 9, 5, 5, 22,…
$ SCT_snn_res.1.38237925427927 <fct> 6, 5, 23, 9, 23, 2, 9, 2, 5, 9, 5, 5, 23,…
$ SCT_snn_res.1.5683423560766 <fct> 6, 5, 22, 9, 22, 2, 9, 2, 5, 9, 5, 28, 22…
$ SCT_snn_res.1.80251553997986 <fct> 4, 5, 23, 9, 23, 1, 9, 1, 5, 9, 5, 30, 23…
$ SCT_snn_res.2.10643815972253 <fct> 4, 19, 26, 7, 26, 2, 7, 2, 11, 7, 19, 19,…
$ SCT_snn_res.2.51672603000175 <fct> 2, 20, 26, 6, 26, 7, 6, 4, 13, 6, 20, 20,…
$ SCT_snn_res.3.10106021549219 <fct> 2, 20, 26, 6, 26, 9, 6, 5, 13, 6, 20, 20,…
$ SCT_snn_res.4.00000100000001 <fct> 1, 20, 27, 3, 27, 11, 3, 30, 16, 3, 20, 2…
$ gaba_status <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
$ gaba_expr <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
$ glut_status <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ status <chr> "GABAergic", "GABAergic", "GABAergic", "G…sbs_mtx_neuro_full$modulator <-
sbs_mtx_neuro_full %>%
select(Trh, Th) %>%
mutate(modulator = if_any(.fns = ~ .x > 0)) %>%
.$modulator
sbs_mtx_neuro_full$neuro <-
(sbs_mtx_neuro_full$Onecut3 > 0)
library(ggstatsplot)
# for reproducibility
set.seed(reseed)
## plot
# ggpiestats(
# data = sbs_mtx_neuro_full,
# x = modulator,
# y = status,
# perc.k = 1,
# package = "ggsci",
# palette = "category10_d3",
# ) + # further modification with `{ggplot2}` commands
# ggplot2::theme(
# plot.title = ggplot2::element_text(
# color = "black",
# size = 14,
# hjust = 0
# )
# )
# plot
grouped_ggpiestats(
# arguments relevant for `ggpiestats()`
data = sbs_mtx_neuro_full,
x = modulator,
y = neuro,
grouping.var = status,
perc.k = 1,
package = "ggsci",
palette = "category10_d3",
# arguments relevant for `combine_plots()`
title.text = "Neuromodulator specification of neurons-driven hypothalamic neuronal lineages by Onecut3 and main neurotransmitter expression",
caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: non-significant",
plotgrid.args = list(nrow = 2)
)
devtools::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 2023-04-22
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)
base64enc 0.1-3 2015-07-28 [1] RSPM (R 4.2.0)
BayesFactor 0.9.12-4.4 2022-07-05 [1] RSPM (R 4.2.0)
bayestestR 0.13.0 2022-09-18 [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)
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)
cachem 1.0.7 2023-02-24 [1] RSPM (R 4.2.0)
callr 3.7.3 2022-11-02 [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)
coda 0.19-4 2020-09-30 [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)
correlation 0.8.4 2023-04-08 [1] Github (easystats/correlation@3d5cd1e)
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)
data.table 1.14.8 2023-02-17 [1] RSPM (R 4.2.0)
datawizard 0.7.1 2023-04-03 [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)
devtools 2.4.5 2022-10-11 [1] RSPM (R 4.2.0)
digest 0.6.31 2022-12-11 [1] RSPM (R 4.2.0)
dplyr * 1.1.1 2023-03-22 [1] RSPM (R 4.2.0)
effectsize 0.8.3 2023-01-28 [1] RSPM (R 4.2.0)
ellipsis 0.3.2 2021-04-29 [1] RSPM (R 4.2.0)
emmeans 1.8.5 2023-03-08 [1] RSPM (R 4.2.0)
estimability 1.4.1 2022-08-05 [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 1.1-8 2022-03-10 [1] RSPM (R 4.2.0)
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fs 1.6.1 2023-02-06 [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)
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ggbeeswarm 0.7.1.9000 2023-04-08 [1] Github (eclarke/ggbeeswarm@5dc91cc)
ggmin 0.0.0.9000 2023-04-08 [1] Github (sjessa/ggmin@8ada274)
ggplot2 * 3.4.2 2023-04-03 [1] RSPM (R 4.2.0)
ggprism 1.0.4 2023-04-08 [1] Github (csdaw/ggprism@0e411f4)
ggrastr 1.0.1 2023-04-08 [1] Github (VPetukhov/ggrastr@7aed9af)
ggrepel 0.9.2.9999 2023-04-08 [1] Github (slowkow/ggrepel@fe3b5c3)
ggridges 0.5.4 2022-09-26 [1] RSPM (R 4.2.0)
ggstatsplot * 0.11.0.9000 2023-04-08 [1] Github (IndrajeetPatil/ggstatsplot@fe83ae6)
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)
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)
insight 0.19.1 2023-03-18 [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)
janitor 2.2.0.9000 2023-04-08 [1] Github (sfirke/janitor@d64c8bb)
jsonlite 1.8.4 2022-12-06 [1] RSPM (R 4.2.0)
kableExtra * 1.3.4 2021-02-20 [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)
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)
MatrixModels 0.5-1 2022-09-11 [1] RSPM (R 4.2.0)
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)
memoise 2.0.1 2021-11-26 [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)
multcomp 1.4-23 2023-03-09 [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)
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)
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patchwork * 1.1.2.9000 2023-04-08 [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)
pkgbuild 1.4.0 2022-11-27 [1] RSPM (R 4.2.0)
pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.2.0)
pkgload 1.3.2 2022-11-16 [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)
prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.2.0)
prismatic 1.1.1 2022-08-15 [1] RSPM (R 4.2.0)
processx 3.8.0 2022-10-26 [1] RSPM (R 4.2.0)
profvis 0.3.7 2020-11-02 [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)
ps 1.7.4 2023-04-02 [1] RSPM (R 4.2.0)
purrr * 1.0.1 2023-01-10 [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 2023-04-08 [1] Github (rstudio/reticulate@63a5c3e)
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)
rstudioapi 0.14 2022-08-22 [1] RSPM (R 4.2.0)
Rtsne 0.16 2022-04-17 [1] RSPM (R 4.2.0)
rvest 1.0.3 2022-08-19 [1] RSPM (R 4.2.0)
S4Vectors 0.36.2 2023-02-26 [1] RSPM (R 4.2.2)
sandwich 3.0-2 2022-06-15 [1] RSPM (R 4.2.0)
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 2023-04-08 [1] Github (samuel-marsh/scCustomize@d08268d)
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 2023-04-08 [1] Github (mojaveazure/seurat-disk@9b89970)
SeuratObject * 4.1.3 2022-11-07 [1] RSPM (R 4.2.0)
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 2023-04-08 [1] Github (ropensci/skimr@d5126aa)
snakecase 0.11.0 2019-05-25 [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)
statsExpressions 1.5.0 2023-02-19 [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)
svglite 2.1.1 2023-01-10 [1] RSPM (R 4.2.0)
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)
TH.data 1.1-1 2022-04-26 [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 2023-04-08 [1] Github (tidyverse/tidyverse@8ec2e1f)
timechange 0.2.0 2023-01-11 [1] RSPM (R 4.2.0)
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UpSetR * 1.4.0 2023-04-08 [1] Github (hms-dbmi/UpSetR@b14854a)
urlchecker 1.0.1 2021-11-30 [1] RSPM (R 4.2.0)
usethis 2.1.6 2022-05-25 [1] RSPM (R 4.2.0)
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withr 2.5.0 2022-03-03 [1] RSPM (R 4.2.0)
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xtable 1.8-4 2019-04-21 [1] RSPM (R 4.2.0)
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yaml 2.3.7 2023-01-23 [1] RSPM (R 4.2.0)
zeallot * 0.1.0 2018-01-28 [1] RSPM (R 4.2.0)
zlibbioc 1.44.0 2022-11-01 [1] RSPM (R 4.2.2)
zoo 1.8-11 2022-09-17 [1] RSPM (R 4.2.0)
[1] /opt/R/4.2.2/lib/R/library
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