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run_merFISH_pipeline.R
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run_merFISH_pipeline.R
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# run merFISH basic analysis
setwd("/nfs4/chaozhang/proj/Neuron/Aritra/merFISH/script")
# init
source("merFISH_pipeline.R")
source("functions.R")
update_geom_defaults("point", list(shape = 16))
### run pipeline per sample
#
merFISH_mouse0.1 = merFISH_pipeline("../data/20210911_PFCL1", "mouse0.1")
merFISH_mouse0.2 = merFISH_pipeline("../data/20211004_PFCL2", "mouse0.2")
#
merFISH_mouse1.1 = merFISH_pipeline("../data/20211208_PFCL5", "mouse1.1")
merFISH_mouse1.2 = merFISH_pipeline("../data/20211208_PFCL6", "mouse1.2")
#
merFISH_mouse2.1 = merFISH_pipeline("../data/20211024_PFCL1_Results_Pain-d1", "mouse2.1")
merFISH_mouse2.2 = merFISH_pipeline("../data/20211101_PFCL1_Results_Pain-d2", "mouse2.2")
#
merFISH_mouse3.1 = merFISH_pipeline("../data/20220103_PFCL7", "mouse3.1")
merFISH_mouse3.2 = merFISH_pipeline("../data/20220106_PFCL1_Results", "mouse3.2")
merFISH_mouse3.3 = merFISH_pipeline("../data/20220107_PFCL1_Results", "mouse3.3")
merFISH_mouse3.4 = merFISH_pipeline("../data/20220128_PFCL1_Results", "mouse3.4")
#
merFISH_pain1 = merFISH_pipeline("../data/20220218_PFCL1_Results_Pain", "Pain1")
merFISH_pain2 = merFISH_pipeline("../data/20220210_PFCL1_Results_Pain", "Pain2")
#
merFISH_pain3 = merFISH_pipeline("../data/20220227_PFCL1_Results_Cingulate and NAc", "Pain3")
#
merFISH_pain4 = merFISH_pipeline("../data/20220305_RESULTS_Pain and Control_Cingulate and NAc", "Pain4")
merFISH_pain5 = merFISH_pipeline("../data/20220416_PFCL1_Results", "Pain5")
merFISH_pain6 = merFISH_pipeline("../data/20220429_PFCL1_Results", "Pain6")
merFISH_pain7 = merFISH_pipeline("../data/20220430_PFCL1_Results", "Pain7")
merFISH_pain8 = merFISH_pipeline("../data/20220513_PFCL1_Results", "Pain8")
merFISH_pain9 = merFISH_pipeline("../data/20220514_PFCL1_Results", "Pain9")
### integrated
merFISH_integrated = merge(merFISH_mouse0.1, c(merFISH_mouse0.2,
merFISH_mouse1.1, merFISH_mouse1.2,
merFISH_mouse2.1, merFISH_mouse2.2,
merFISH_mouse3.1, merFISH_mouse3.2, merFISH_mouse3.3, merFISH_mouse3.4,
merFISH_pain1, merFISH_pain2, merFISH_pain3, merFISH_pain4, merFISH_pain5,
merFISH_pain6, merFISH_pain7, merFISH_pain8, merFISH_pain9))
saveRDS(merFISH_integrated, file = "./RDS/merFISH_integrated.seurat0627.rds")
rm(merFISH_mouse0.2,
merFISH_mouse1.1, merFISH_mouse1.2,
merFISH_mouse2.1, merFISH_mouse2.2,
merFISH_mouse3.1, merFISH_mouse3.2, merFISH_mouse3.3, merFISH_mouse3.4,
merFISH_pain1, merFISH_pain2, merFISH_pain3, merFISH_pain4, merFISH_pain5,
merFISH_pain6, merFISH_pain7, merFISH_pain8, merFISH_pain9)
gc()
merFISH_integrated = readRDS(file = "./RDS/merFISH_integrated.seurat0627.rds")
# remove low quality 2 samples Pain6 Pain7
merFISH_integrated = subset(merFISH_integrated, subset = orig.ident %in% c("Pain6", "Pain7"), invert = T)
# remove cells from striatum region
cell_remove = gate_points(merFISH_integrated, image = "image.12")
cell_remove1 = gate_points(merFISH_integrated, image = "image.13")
merFISH_integrated = merFISH_integrated[, !colnames(merFISH_integrated) %in% c(cell_remove, cell_remove1)]
SpatialFeaturePlot(merFISH_integrated, features = "nCount_RNA", images = "image.12")
# scale data
all.genes = rownames(merFISH_integrated)
merFISH_integrated = FindVariableFeatures(merFISH_integrated, selection.method = "vst")
merFISH_integrated = ScaleData(merFISH_integrated, vars.to.regress = c("abs_volume"))
merFISH_integrated = RunPCA(merFISH_integrated, npcs = 50, verbose = FALSE)
ElbowPlot(merFISH_integrated, ndims=50)
# determine how many PCs
determinePCA(merFISH_integrated) # 39
# normalize batch effects
# harmony
require(harmony)
merFISH_integrated <- RunHarmony(
object = merFISH_integrated,
group.by.vars = 'orig.ident',
assay.use = "RNA",
dims.use = 1:39,
plot_convergence = TRUE
)
library(pals)
DimPlot(object = merFISH_integrated, reduction = "pca",
pt.size = .1,
raster=FALSE, shuffle = T,
group.by = "orig.ident") &
scale_color_manual(values = as.vector(alphabet()))
DimPlot(object = merFISH_integrated, reduction = "harmony",
pt.size = .1,
raster=FALSE, shuffle = T,
group.by = "orig.ident") &
scale_color_manual(values = as.vector(alphabet()))
merFISH_integrated <- RunUMAP(merFISH_integrated, dims = 1:39, reduction = 'harmony')
DimPlot(object = merFISH_integrated,
pt.size = .01,
raster=FALSE,shuffle = T,
split.by = "predicted_doulets") &
scale_color_manual(values = as.vector(alphabet())) & NoAxes()
FeaturePlot(object = merFISH_integrated,
features = c("nCount_RNA", "abs_volume"),
pt.size = .01,
raster=FALSE) &
NoAxes() &
scale_color_gradientn(colours = rainbow(10)[3:10])
scale_color_gradientn(colours = c( "grey95", "blue"))
FeaturePlot(merFISH_integrated, features = "centroid_3", raster=FALSE) &
scale_color_gradientn(colours = rainbow(10)) & NoAxes()
abs_volume_outliar = quantile(merFISH_integrated$abs_volume, probs = c(0.98))
FeaturePlot(subset(merFISH_integrated, subset = abs_volume > abs_volume_outliar[[1]]),
raster=FALSE, features = "abs_volume") &
scale_color_gradientn(colours = rainbow(10)) & NoAxes()
VlnPlot(object = merFISH_integrated, features = c('nCount_RNA', "nFeature_RNA"), pt.size = -1)
merFISH_integrated = subset(merFISH_integrated, subset = abs_volume < abs_volume_outliar[[1]] & predicted_doulets == FALSE) # 490733
#
# clustering
merFISH_integrated <- FindNeighbors(merFISH_integrated, reduction = "harmony", dims = 1:39, k.param = 10)
merFISH_integrated <- FindClusters(merFISH_integrated,
resolution = 0.3,
method = "igraph",
algorithm = 4,
group.singletons = FALSE,
verbose = FALSE)
saveRDS(merFISH_integrated, file = "./RDS/merFISH_integrated.seurat.rds")
merFISH_integrated = readRDS(file = "./RDS/merFISH_integrated.seurat.rds")
# remove cell cluster that contains less than 10 cells
cluster_select = table(merFISH_integrated$seurat_clusters)
cluster_select = cluster_select[cluster_select > 10]
DimPlot(object = subset(merFISH_integrated, subset = seurat_clusters %in% names(cluster_select)),
pt.size = .01,
label = T) &
scale_color_manual(values = as.vector(polychrome())) & NoAxes()
VlnPlot(subset(merFISH_integrated, subset = seurat_clusters %in% names(cluster_select)),
features = c("doulet_scores", "nCount_RNA", "abs_volume"), pt.size = -1)
FeaturePlot(merFISH_integrated, features = c("centroid_3"),
raster=FALSE, pt.size = 0.1) &
NoAxes() &
scale_color_gradientn(colours = rainbow(10))
FeaturePlot(merFISH_integrated, features = c("Drd1", "Drd2"),
pt.size = 0.1) &
NoAxes() &
scale_color_gradientn(colours = c( "grey95", "red", "red4"))
SpatialPlot(subset(merFISH_integrated, idents = c(14)),
pt.size.factor=1,crop = T,
images = c("image.4", "image.5", "image.7"),
stroke=0)& theme(aspect.ratio = 1)
# remove cluster 4, which are MSN cells
cluster_select = cluster_select[names(cluster_select) != "13"]
# remove cluster 14, no specific marker, maybe incomplete cells
cluster_select = cluster_select[names(cluster_select) != "14"]
# remove singleton
cluster_select = cluster_select[names(cluster_select) != "singleton"]
table(merFISH_integrated$orig.ident, merFISH_integrated$seurat_clusters)[, names(cluster_select)]
merFISH_integrated = subset(merFISH_integrated, idents = names(cluster_select))
DimPlot(object = merFISH_integrated,
pt.size = .01,
label = T) &
scale_color_manual(values = as.vector(polychrome())) & NoAxes()
FeaturePlot(merFISH_integrated, features = c("Gjb6", "Cldn5", "C1qb", "Mog", "Pdgfra", "Serpinf1", "Gad1", "Grin2a", "Tshz2"),
pt.size = 0.1) &
NoAxes() &
scale_color_gradientn(colours = c( "grey95", "red", "red4"))
L1_cluster <- c("Excitatory","Inhibitory", "OPC", "Excitatory", "Excitatory", "Excitatory", "Endo", "Astrocyte", "Excitatory", "Inhibitory", "Oligo",
"Microglia")
names(L1_cluster) <- levels(merFISH_integrated)
merFISH_integrated <- RenameIdents(merFISH_integrated, L1_cluster)
merFISH_integrated@meta.data$L1_cluster = Idents(merFISH_integrated)
DimPlot(object = merFISH_integrated,
pt.size = .01,
raster=FALSE,
label = T) &
scale_color_manual(values = as.vector(alphabet2())) & NoAxes()
# rerun umap
#merFISH_integrated = ScaleData(merFISH_integrated, vars.to.regress = "abs_volume")
#merFISH_integrated = RunPCA(merFISH_integrated, npcs = 50, verbose = FALSE)
#merFISH_integrated <- RunHarmony(
# object = merFISH_integrated,
# group.by.vars = 'orig.ident',
# assay.use = "RNA",
# dims.use = 1:39,
# plot_convergence = TRUE
#)
merFISH_integrated <- RunUMAP(merFISH_integrated, dims = 1:39, n.neighbors = 10L,
reduction = 'harmony')
Idents(merFISH_integrated) <- factor(x = Idents(merFISH_integrated), levels = sort(levels(merFISH_integrated)))
merFISH_integrated@meta.data$L1_cluster = Idents(merFISH_integrated)
L1_color = c("#AA0DFE","#993F00",
pals::kelly()[c(5:8, 10:14)],
"maroon2", "yellow3")
DimPlot(object = merFISH_integrated,
pt.size = .01,
label.size = 5,
raster=FALSE,
label = T) &
scale_color_manual(values = L1_color) & NoAxes()
FeaturePlot(merFISH_integrated,
features = c("nCount_RNA", "abs_volume", "centroid_3", "doulet_scores"),
pt.size = 0.1) &
NoAxes() &
scale_color_gradientn(colours = rainbow(10)[4:10])
SpatialPlot(merFISH_integrated,
pt.size.factor=1.5,
crop = T,
images = "image.3",
stroke=0) & theme(aspect.ratio = 2) &
scale_fill_manual(values = L1_color) & NoAxes() &
theme(legend.position = "None")
DimPlot(object = merFISH_integrated, group.by = "orig.ident",
pt.size = .01,
label.size = 5,
raster=FALSE,
shuffle=T,
label = F) &
scale_color_manual(values = as.vector(pals::alphabet())) & NoAxes()
FeaturePlot(merFISH_integrated, features = c("nCount_RNA", "nFeature_RNA"),
pt.size = .01,
raster=FALSE) & NoAxes() &
scale_color_gradientn(colours = c("grey95", "blue"))
saveRDS(merFISH_integrated, file = "./RDS/merFISH_integrated_L1.seurat.rds")
merFISH_integrated = readRDS("./RDS/merFISH_integrated_L1.seurat.rds")
#
merFISH_integrated.markers = FindAllMarkers(merFISH_integrated,
logfc.threshold = 1,
min.pct = 0.5,
only.pos = T)
merFISH_integrated.markers %>%
group_by(cluster) %>%
slice_max(n = 3, order_by = avg_log2FC)
DotPlot(object = merFISH_integrated, features = c("Gjb6", "Acsbg1", "Cldn5", "Flt1",
"Grin2a", "Grin2b", "Gad1", "Gad2" ,
"Ctss", "C1qb", "Mog", "Ermn", "Pdgfra", "Cacng4"),
col.min = -2, col.max = 2) +
theme(axis.text.x = element_text(angle = 30, hjust=1)) +
scale_y_discrete(limits=rev) + xlab("") + ylab("") +
scale_color_gradientn(colours = pals::coolwarm(10))
# all cell
plot_cell_location(merFISH_integrated, "L3_cluster", color = L3_color , "../data/20211208_PFCL6", 1:500)
# ext cell
plot_cell_location(merFISH_integrated,
"L1_cluster", color = c(rep("gray90", 2), L1_color[3:7], rep("gray90", 10)),
"../data/20211208_PFCL6/feature_metadata.csv", 1:500)
# inh cell
plot_cell_location(merFISH_integrated,
"L1_cluster", color = c(rep("gray90", 7), L1_color[8:9], rep("gray90", 10)),
"../data/20211208_PFCL6/feature_metadata.csv", 1:500)
# non neuron
plot_cell_location(merFISH_integrated,
"L1_cluster", color = c(L1_color[1:2], rep("gray90", 7), L1_color[10:13]),
"../data/20211208_PFCL6/feature_metadata.csv", 1:500)
SpatialDimPlot(merFISH_integrated,
cells.highlight = CellsByIdentities(object = merFISH_integrated),
images = "image.3",
stroke=0,
pt.size.factor = 2,
ncol = 7,
cols.highlight = c("#DE2D26", "grey80"),
facet.highlight = TRUE) & theme(aspect.ratio = 1.5) & coord_flip() & scale_y_reverse()
######### sub types
# ext
merFISH_integrated_ext = subset(merFISH_integrated, idents = "Excitatory") # 256190 samples
nCount_RNA_quantile = quantile(merFISH_integrated_ext$nCount_RNA, probs = c(0.02, 0.98))
merFISH_integrated_ext = subset(merFISH_integrated_ext,
subset = nCount_RNA >= 50 & nCount_RNA < nCount_RNA_quantile[2]) # 232157
merFISH_integrated_ext = ScaleData(merFISH_integrated_ext, vars.to.regress = c("abs_volume"))
merFISH_integrated_ext = RunPCA(merFISH_integrated_ext, npcs = 50, verbose = FALSE)
ElbowPlot(merFISH_integrated_ext, ndims=50)
determinePCA(merFISH_integrated_ext) # 34
# normalize batch effects
# harmony
require(harmony)
merFISH_integrated_ext <- RunHarmony(
object = merFISH_integrated_ext,
group.by.vars = 'orig.ident',
assay.use = "RNA",
dims.use = 1:34,
theta = 3,
lambda = 0.05,
plot_convergence = TRUE
)
merFISH_integrated_ext <- RunUMAP(merFISH_integrated_ext, dims = 1:34,
reduction = 'harmony')
DimPlot(merFISH_integrated_ext, group.by = "orig.ident")
FeaturePlot(merFISH_integrated_ext, features = "nCount_RNA",
pt.size = .01,
raster=FALSE) & NoAxes() &
scale_color_gradientn(colours = c("blue", "grey95", "red"))
FeaturePlot(merFISH_integrated_ext, features = "abs_volume") &
scale_color_gradientn(colours = rainbow(10)) & NoAxes()
#
KNN_K = determineK(merFISH_integrated_ext, 2, 4, reduction="harmony", dims=1:34)
ggsave("../figure_new//merFISH_integrated_ext_determineK.pdf", width = 4, height = 4)
# clust
merFISH_integrated_ext <- FindNeighbors(merFISH_integrated_ext, k.param = 50,
reduction = "harmony", dims = 1:34)
merFISH_integrated_ext <- FindClusters(merFISH_integrated_ext,
resolution = 2,
method = "igraph",
algorithm = 4,
group.singletons = F,
verbose = FALSE)
# remove cell cluster that contains less than 10 cells
cluster_select = table(merFISH_integrated_ext$RNA_snn_res.2)
cluster_select = cluster_select[cluster_select > 10]
# remove singleton
cluster_select = cluster_select[names(cluster_select) != "singleton"]
merFISH_integrated_ext = subset(merFISH_integrated_ext, subset = RNA_snn_res.2 %in% names(cluster_select)) #226310
DimPlot(merFISH_integrated_ext, label = T, raster=FALSE)
# remove doublets, check the makers
merFISH_integrated_ext.markers = FindAllMarkers(merFISH_integrated_ext,
logfc.threshold = 1,
min.pct = 0.2,
only.pos = T)
merFISH_integrated_ext.markers_top <-
merFISH_integrated_ext.markers %>%
group_by(cluster) %>%
slice_max(n = 3, order_by = avg_log2FC)
FeaturePlot(merFISH_integrated_ext, features = "Mog")
FeaturePlot(merFISH_integrated_ext, features = c("Gad1","Gad2"))
FeaturePlot(merFISH_integrated_ext, features = c("C1qb"))
FeaturePlot(merFISH_integrated_ext, features = c("Cldn5"))
FeaturePlot(merFISH_integrated, features = c("Moxd1", "Kcnab1", "Clic5"))
FeaturePlot(merFISH_integrated_ext, features = c("Lhx6", "Ndst4")) &
scale_color_gradientn(colours = rainbow(10))
SpatialPlot(subset(merFISH_integrated_ext, idents = c(5,6,17)),
images = "image.4")
#
FindMarkers(merFISH_integrated_ext,
ident.1 = 19,
only.pos = T)
table(merFISH_integrated_ext$orig.ident, merFISH_integrated_ext$seurat_clusters)[, names(cluster_select)]
merFISH_integrated_ext = subset(merFISH_integrated_ext, idents = c(19:24,26:33), invert = T) # 193740 samples
KNN_K = determineK(merFISH_integrated_ext, 2, 4, reduction="harmony", dims=1:34)
ggsave("../figure_new//merFISH_integrated_ext_determineK.pdf", width = 4, height = 4)
saveRDS(merFISH_integrated_ext, "./RDS/merFISH_integrated_ext_0708.rds")
# re-clusting
#merFISH_integrated_ext = ScaleData(merFISH_integrated_ext, vars.to.regress = c("abs_volume"))
#merFISH_integrated_ext = RunPCA(merFISH_integrated_ext, npcs = 30, verbose = FALSE)
#merFISH_integrated_ext <- RunHarmony(
# object = merFISH_integrated_ext,
# group.by.vars = 'orig.ident',
# assay.use = "RNA",
# plot_convergence = TRUE
#)
merFISH_integrated_ext <- RunUMAP(merFISH_integrated_ext, dims = 1:34, n.neighbors = 50, #min.dist = 0.1,
reduction = 'harmony')
Idents(merFISH_integrated_ext) = "RNA_snn_res.2"
DimPlot(merFISH_integrated_ext, label = T, raster=FALSE)
scale_color_manual(values = as.vector(pals::alphabet(18))) & NoAxes()
#source("stable_cluster.R")
#stable_c = stable_cluster(merFISH_integrated_ext, 2, "RNA_snn_res.2", 1, reduction="harmony", dims = 1:30)
#stable_c = one_bootstrap(merFISH_integrated_ext, 2, "RNA_snn_res.2", 1,reduction="harmony", dims=1:30)
#merFISH_integrated_ext = subset(merFISH_integrated_ext, idents = na.omit(stable_c[stable_c$percent >= 0.5, 1])) # 136331
# re-label
Ext_subcluster <- c(1:length(levels(merFISH_integrated_ext)))
names(Ext_subcluster) <- levels(merFISH_integrated_ext)
merFISH_integrated_ext <- RenameIdents(merFISH_integrated_ext, Ext_subcluster)
merFISH_integrated_ext@meta.data$Ext_subcluster = Idents(merFISH_integrated_ext)
DimPlot(merFISH_integrated_ext,
group.by = "Ext_subcluster",
pt.size = .01,
label = T,
label.size = 5,
raster=FALSE) &
scale_color_manual(values = as.vector(pals::polychrome())[-c(1:2)]) & NoAxes()
saveRDS(merFISH_integrated_ext, "./RDS/merFISH_integrated_ext_0711.rds")
merFISH_integrated_ext = readRDS( "./RDS/merFISH_integrated_ext_0711.rds")
SpatialDimPlot(merFISH_integrated_ext,
images = "image.3",
crop = T,
stroke=0.1,
pt.size.factor = 1.6) &
theme(aspect.ratio = 2) &
scale_fill_manual(values = as.vector(pals::polychrome())[-c(1:2)]) & NoAxes()
SpatialDimPlot(merFISH_integrated_ext,
cells.highlight = CellsByIdentities(object = merFISH_integrated_ext),
images = "image.3",
crop = T,
stroke=0,
pt.size.factor = 2,
ncol = 5,
cols.highlight = c("#DE2D26", "grey80"),
facet.highlight = TRUE) &
theme(aspect.ratio = 2)
merFISH_integrated$Ext_subcluster = as.character(merFISH_integrated_ext$Ext_subcluster[colnames(merFISH_integrated)])
merFISH_integrated$Ext_subcluster[is.na(merFISH_integrated$Ext_subcluster)] <- "Other"
merFISH_integrated$Ext_subcluster = factor(merFISH_integrated$Ext_subcluster, levels = c(levels(merFISH_integrated_ext$Ext_subcluster), "Other"))
plot_cell_location(merFISH_integrated, "Ext_subcluster", color = c(as.vector(pals::polychrome())[3:21], "gray90") , "../data/20211208_PFCL6/feature_metadata.csv", 1:500)
merFISH_integrated_ext.markers = FindAllMarkers(merFISH_integrated_ext,
logfc.threshold = 0.5,
min.pct = 0.3,
only.pos = T)
write.csv(merFISH_integrated_ext.markers, "./DEGs/merFISH_integrated_ext.markers0711.csv", quote = F)
merFISH_integrated_ext.markers$pct_diff = merFISH_integrated_ext.markers$pct.1 - merFISH_integrated_ext.markers$pct.2
merFISH_integrated_ext.markers_top <-
merFISH_integrated_ext.markers %>%
group_by(cluster) %>%
slice_max(n = 2, order_by = avg_log2FC)
DotPlot(object = merFISH_integrated_ext, features = unique(merFISH_integrated_ext.markers_top$gene)) +
theme(axis.text.x = element_text(angle = 30, hjust=1)) +
scale_y_discrete(limits=rev) + xlab("") + ylab("") +
scale_color_gradientn(colours = c(pals::coolwarm(10)[3:10]))
FeaturePlot(merFISH_integrated_ext, features = c("Otof", "Cux2", "Rorb", "Hrh3", "Nnat", "Fezf2", "Tshz2", "Syt6"),
min.cutoff = 0.5, ncol = 4, pt.size = 1.5) &
scale_color_gradientn(colours = c( "grey95", "pink", "red", "red4")) & NoAxes()
##
# inh
merFISH_integrated_inh = subset(merFISH_integrated, idents = levels(merFISH_integrated)[4]) # 49857 samples
merFISH_integrated_inh = ScaleData(merFISH_integrated_inh, vars.to.regress = c("abs_volume")) # regress out abs_volume
merFISH_integrated_inh = RunPCA(merFISH_integrated_inh, npcs = 50, verbose = FALSE)
ElbowPlot(merFISH_integrated_inh, ndims=50)
determinePCA(merFISH_integrated_inh) # 31
DimPlot(merFISH_integrated_inh)
# normalize batch effects
# harmony
require(harmony)
merFISH_integrated_inh <- RunHarmony(
object = merFISH_integrated_inh,
group.by.vars = 'orig.ident',
assay.use = "RNA",
dims.use = 1:31,
theta = 3,
lambda = 0.05,
plot_convergence = TRUE
)
merFISH_integrated_inh <- RunUMAP(merFISH_integrated_inh, dims = 1:31,
reduction = 'harmony')
#
KNN_K = determineK(merFISH_integrated_inh, 2, 4, reduction="harmony", dims=1:31)
ggsave("../figure_new//merFISH_integrated_inh_determineK.pdf", width = 4, height = 4)
# clust
merFISH_integrated_inh <- FindNeighbors(merFISH_integrated_inh, k.param = 20,
reduction = "harmony", dims = 1:31)
merFISH_integrated_inh <- FindClusters(merFISH_integrated_inh,
resolution = 2,
method = "igraph",
algorithm = 4,
group.singletons = F,
verbose = FALSE)
DimPlot(merFISH_integrated_inh, group.by = "RNA_snn_res.2")
# remove cell cluster that contains less than 10 cells
cluster_select = table(merFISH_integrated_inh$RNA_snn_res.2)
cluster_select = cluster_select[cluster_select > 10]
# remove singleton
cluster_select = cluster_select[names(cluster_select) != "singleton"]
merFISH_integrated_inh = subset(merFISH_integrated_inh, subset = RNA_snn_res.2 %in% names(cluster_select)) #49847
DimPlot(merFISH_integrated_inh, label = T)
#source("stable_cluster.R")
#stable_c = stable_cluster(merFISH_integrated_inh, 2, "RNA_snn_res.2", 1, reduction="harmony", dims = 1:25)
#stable_c = one_bootstrap(merFISH_integrated_inh, 2, "RNA_snn_res.2", 1,reduction="harmony", dims=1:29)
#merFISH_integrated_inh = subset(merFISH_integrated_inh, idents = stable_c[stable_c$percent >= 0.4, 1] ) # 41732 samples
# remove doublets, check the makers
merFISH_integrated_inh.markers = FindAllMarkers(merFISH_integrated_inh,
logfc.threshold = 0.5,
min.pct = 0.2,
only.pos = T)
merFISH_integrated_inh.markers_top <-
merFISH_integrated_inh.markers %>%
group_by(cluster) %>%
slice_max(n = 3, order_by = avg_log2FC)
FindMarkers(merFISH_integrated_inh,
ident.1 = 8,
logfc.threshold = 0.3,
min.pct = 0.1,
only.pos = T)
FeaturePlot(merFISH_integrated_inh, features = c("Mog", "Gjb6"))
FeaturePlot(merFISH_integrated_inh, features = c("Gad1", "Gad2"))
DimPlot(subset(merFISH_integrated_inh, RNA_snn_res.2 %in% 20:24),
label = T) &
scale_color_manual(values = as.vector(pals::alphabet())) & NoAxes()
merFISH_integrated_inh = subset(merFISH_integrated_inh, idents = c(8, 17, 20, 21, 23, 24), invert = T) # 42907 samples
cluster_sample = as.matrix(table(merFISH_integrated_inh$orig.ident, merFISH_integrated_inh$RNA_snn_res.2))
pheatmap::pheatmap(log(cluster_sample[,colSums(cluster_sample)!=0]), scale = "row")
DimPlot(merFISH_integrated_inh, label = T, group.by = "RNA_snn_res.2",
label.size = 5) &
scale_color_manual(values = as.vector(pals::alphabet())) & NoAxes()
# re-label
Inh_subcluster <- c(1:length(levels(merFISH_integrated_inh)))
names(Inh_subcluster) <- levels(merFISH_integrated_inh)
merFISH_integrated_inh <- RenameIdents(merFISH_integrated_inh, Inh_subcluster)
merFISH_integrated_inh@meta.data$Inh_subcluster = Idents(merFISH_integrated_inh)
merFISH_integrated_inh <- RunUMAP(merFISH_integrated_inh, dims = 1:31,
reduction = 'harmony')
DimPlot(merFISH_integrated_inh, label = T,
label.size = 5) &
scale_color_manual(values = as.vector(pals::alphabet2())) & NoAxes()
FeaturePlot(merFISH_integrated_inh, features = c("Pvalb", "Kcnc2", "Grin3a", "Lamp5", "Pdyn", "Th", "Penk", "Cnr1"),
min.cutoff = 0.5, ncol = 4, pt.size = 0.1) &
scale_color_gradientn(colours = c( "grey95", "pink", "red", "red4")) & NoAxes()
saveRDS(merFISH_integrated_inh, file = "./RDS/merFISH_integrated_inh.seurat_0712.rds")
merFISH_integrated_inh = readRDS(file = "./RDS/merFISH_integrated_inh.seurat.rds")
merFISH_integrated$Inh_subcluster = as.character(merFISH_integrated_inh$Inh_subcluster[colnames(merFISH_integrated)])
merFISH_integrated$Inh_subcluster[is.na(merFISH_integrated$Inh_subcluster)] <- "Other"
merFISH_integrated$Inh_subcluster = factor(merFISH_integrated$Inh_subcluster, levels = c(levels(merFISH_integrated_inh$Inh_subcluster), "Other"))
plot_cell_location(merFISH_integrated, "Inh_subcluster", color = c(as.vector(pals::alphabet2(20)), "gray90") , "../data/20211208_PFCL6/feature_metadata.csv", 1:500)
plot_cell_location(merFISH_integrated, "Inh_subcluster", color = c(as.vector(pals::alphabet2(20)), "gray90") , "../data/20220513_PFCL1_Results/feature_metadata.csv", 1:500)
SpatialDimPlot(merFISH_integrated_inh,
cells.highlight = CellsByIdentities(object = merFISH_integrated_inh),
images = "image.3",
crop = T,
stroke=0,
pt.size.factor = 3,
ncol = 10,
cols.highlight = c("#DE2D26", "grey80"),
facet.highlight = TRUE) &
theme(aspect.ratio = 2)
VlnPlot(merFISH_integrated, features = "nCount_RNA", pt.size = -1, group.by = "orig.ident")
#
merFISH_integrated_inh.markers = FindAllMarkers(merFISH_integrated_inh,
logfc.threshold = 0.5,
min.pct = 0.3,
only.pos = T)
write.csv(merFISH_integrated_inh.markers, "./DEGs/merFISH_integrated_inh.markers_0712.csv", quote = F)
merFISH_integrated_inh.markers_top <-
merFISH_integrated_inh.markers %>%
group_by(cluster) %>%
slice_max(n = 2, order_by = avg_log2FC)
DotPlot(object = merFISH_integrated_inh, features = unique(merFISH_integrated_inh.markers_top$gene),
scale.by = "radius", col.min = -2, col.max = 2) +
theme(axis.text.x = element_text(angle = 30, hjust=1)) +
scale_y_discrete(limits=rev) + xlab("") + ylab("") +
scale_color_gradientn(colours = c(pals::coolwarm(10)[2:10]))
SpatialPlot(merFISH_integrated_inh,
images = paste0("image.", c(1:3,17:18)),
stroke=0.1,
ncol = 3,
pt.size.factor = 2) & theme(aspect.ratio = 1.5) &
scale_fill_manual(values = as.vector(pals::alphabet2())) & NoAxes()&
theme(legend.position = "none")
### non-neuron
merFISH_integrated_non = subset(merFISH_integrated, idents = levels(merFISH_integrated)[3:4], invert = T) # 168399
merFISH_integrated_non = ScaleData(merFISH_integrated_non, vars.to.regress = c("abs_volume")) # regress out abs_volume
merFISH_integrated_non = RunPCA(merFISH_integrated_non, npcs = 50, verbose = FALSE)
ElbowPlot(merFISH_integrated_non, ndims=50)
determinePCA(merFISH_integrated_non) # 26
DimPlot(merFISH_integrated_non)
# normalize batch effects
# harmony
require(harmony)
merFISH_integrated_non <- RunHarmony(
object = merFISH_integrated_non,
group.by.vars = 'orig.ident',
assay.use = "RNA",
dims.use = 1:26,
plot_convergence = TRUE
)
merFISH_integrated_non <- RunUMAP(merFISH_integrated_non, dims = 1:26,
reduction = 'harmony')
KNN_K = determineK(merFISH_integrated_non, 2, 4, reduction="harmony", dims=1:26)
ggsave("../figure_new//merFISH_integrated_non_determineK.pdf", width = 4, height = 4)
# clust
merFISH_integrated_non <- FindNeighbors(merFISH_integrated_non, k.param = 15,
reduction = "harmony", dims = 1:26)
merFISH_integrated_non <- FindClusters(merFISH_integrated_non,
resolution = 2,
method = "igraph",
algorithm = 4,
group.singletons = F,
verbose = FALSE)
DimPlot(merFISH_integrated_non)
FeaturePlot(merFISH_integrated_non,
features = c("nCount_RNA", "abs_volume"),
min.cutoff = 0.5, pt.size = 0.1,raster=FALSE) &
scale_color_gradientn(colours = c( "grey95", "pink", "red", "red4")) & NoAxes()
FeaturePlot(merFISH_integrated_non, features = c("Gjb6", "Cldn5", "C1qb", "Mog", "Pdgfra", "Serpinf1", "Gad1", "Grin2a"),
min.cutoff = 0.5, ncol = 4, pt.size = 0.1) &
scale_color_gradientn(colours = c( "grey95", "pink", "red", "red4")) & NoAxes()
FeaturePlot(merFISH_integrated_non, features = c("Enpp6"),
min.cutoff = 0.5, pt.size = 0.1) &
scale_color_gradientn(colours = c( "grey95", "pink", "red", "red4")) & NoAxes()
# remove cell cluster that contains less than 10 cells
cluster_select = table(merFISH_integrated_non$RNA_snn_res.2)
cluster_select = cluster_select[cluster_select > 10]
# remove singleton
cluster_select = cluster_select[names(cluster_select) != "singleton"]
merFISH_integrated_non = subset(merFISH_integrated_non, subset = RNA_snn_res.2 %in% names(cluster_select)) #168397
Idents(merFISH_integrated_non) = "RNA_snn_res.2"
DimPlot(merFISH_integrated_non, label = T, group.by = "L1_cluster")
#source("stable_cluster.R")
#stable_c = stable_cluster(merFISH_integrated_non, 2, "RNA_snn_res.2", 1, reduction="harmony", dims = 1:23)
#stable_c = one_bootstrap(merFISH_integrated_non, 2, "RNA_snn_res.2", 1,reduction="harmony", dims=1:23)
#merFISH_integrated_non = subset(merFISH_integrated_non, idents = stable_c[stable_c$percent >= 0.4, 1] ) #
#DimPlot(merFISH_integrated_non, label = T)
# remove doublets
merFISH_integrated_non.markers = FindAllMarkers(merFISH_integrated_non,
logfc.threshold = 0.5,
min.pct = 0.2,
only.pos = T)
merFISH_integrated_non.markers_top <-
merFISH_integrated_non.markers %>%
group_by(cluster) %>%
slice_max(n = 3, order_by = avg_log2FC)
merFISH_integrated_non = subset(merFISH_integrated_non, subset = RNA_snn_res.2 %in% c(24,25,20,13,6,26,19,21,22,17), invert = T) # 139823 samples
# re-label
Non_subcluster <- c("Oligo 1", "Endo 5", "Endo 2", "Astro 3", "Endo 3", "OPC 2", "Microglia 2", "Endo 3", "Astro 1", "Endo 4",
"Astro 2", "Endo 1", "Microglia 1", "OPC 1", "VLMC", "Oligo 2")
names(Non_subcluster) <- levels(merFISH_integrated_non)
SpatialDimPlot(merFISH_integrated_non,
cells.highlight = CellsByIdentities(object = merFISH_integrated_non),
images = "image.18",
crop = T,
stroke=0,
pt.size.factor = 2,
ncol = 8,
cols.highlight = c("#DE2D26", "grey80"),
facet.highlight = TRUE) &
theme(aspect.ratio = 2)
merFISH_integrated_non <- RenameIdents(merFISH_integrated_non, Non_subcluster)
merFISH_integrated_non@meta.data$Non_subcluster = Idents(merFISH_integrated_non)
table(merFISH_integrated_non$orig.ident, merFISH_integrated_non$Non_subcluster)
Idents(merFISH_integrated_non) = factor(Idents(merFISH_integrated_non), levels = sort(levels(merFISH_integrated_non)))
merFISH_integrated_non@meta.data$Non_subcluster = Idents(merFISH_integrated_non)
merFISH_integrated_non <- RunUMAP(merFISH_integrated_non, dims = 1:26,
reduction = 'harmony')
DimPlot(merFISH_integrated_non, raster=FALSE) &
scale_color_manual(values = as.vector(pals::kelly()[-c(1:2)])) & NoAxes()
saveRDS(merFISH_integrated_non, file = "./RDS/merFISH_integrated_non.seurat_0715.rds")
merFISH_integrated_non.markers = FindAllMarkers(merFISH_integrated_non,
logfc.threshold = 0.5,
min.pct = 0.2,
only.pos = T)
merFISH_integrated_non.markers_top <-
merFISH_integrated_non.markers %>%
group_by(cluster) %>%
slice_max(n = 3, order_by = avg_log2FC)
DotPlot(object = merFISH_integrated_non, features = unique(merFISH_integrated_non.markers_top$gene),
scale.by = "radius", col.min = -2, col.max = 2) +
theme(axis.text.x = element_text(angle = 30, hjust=1)) +
scale_y_discrete(limits=rev) + xlab("") + ylab("") +
scale_color_gradientn(colours = c(pals::coolwarm(10)[2:10]))
SpatialDimPlot(merFISH_integrated_non,
images = "image.18", pt.size.factor = 1) &
theme(aspect.ratio = 1.5) &
scale_fill_manual(values = as.vector(pals::kelly()[-c(1:2)])) & NoAxes()
SpatialDimPlot(merFISH_integrated_non,
cells.highlight = CellsByIdentities(object = merFISH_integrated_non),
images = "image.3",
crop = T,
stroke=0,
pt.size.factor = 3,
ncol = 8,
cols.highlight = c("#DE2D26", "grey80"),
facet.highlight = TRUE) &
theme(aspect.ratio = 1.5)
merFISH_integrated$Non_subcluster = as.character(merFISH_integrated_non$Non_subcluster[colnames(merFISH_integrated)])
merFISH_integrated$Non_subcluster[is.na(merFISH_integrated$Non_subcluster)] <- "Other"
merFISH_integrated$Non_subcluster = factor(merFISH_integrated$Non_subcluster, levels = c(levels(merFISH_integrated_non$Non_subcluster), "Other"))
plot_cell_location(merFISH_integrated, "Non_subcluster", color = c(as.vector(pals::kelly()[3:17]), "gray90") , "../data/20211208_PFCL6/feature_metadata.csv", 1:500)
plot_cell_location(merFISH_integrated, "Non_subcluster", color = c(as.vector(pals::kelly()[3:17]), "gray90") , "../data/20220513_PFCL1_Results/feature_metadata.csv", 1:500)
# merge
merFISH_integrated_L2 = merFISH_integrated[, c(colnames(merFISH_integrated_ext),
colnames(merFISH_integrated_inh),
colnames(merFISH_integrated_non))] # 376470
merFISH_integrated_L2$L2_cluster = as.character(merFISH_integrated_L2$L1_cluster)
merFISH_integrated_L2$L2_cluster[colnames(merFISH_integrated_ext)] <- paste0("Ext ", merFISH_integrated_ext$Ext_subcluster)
merFISH_integrated_L2$L2_cluster[colnames(merFISH_integrated_inh)] <- paste0("Inh ", merFISH_integrated_inh$Inh_subcluster)
merFISH_integrated_L2$L2_cluster[colnames(merFISH_integrated_non)] <- as.character(merFISH_integrated_non$Non_subcluster)
DimPlot(merFISH_integrated_L2, group.by = "L2_cluster", raster=FALSE)
merFISH_integrated_L2$L2_cluster = factor(merFISH_integrated_L2$L2_cluster,
levels = c(paste0("Ext ", levels(merFISH_integrated_ext$Ext_subcluster)),
paste0("Inh ", levels(merFISH_integrated_inh$Inh_subcluster)),
levels(merFISH_integrated_non$Non_subcluster)))
Idents(merFISH_integrated_L2) = "L2_cluster"
merFISH_integrated_L2$CellType = stringr::str_replace(merFISH_integrated_L2$L2_cluster, " .+", "")
merFISH_integrated_L2$CellType = factor(merFISH_integrated_L2$CellType,
levels = unique(merFISH_integrated_L2$CellType)[c(1,2,6,4,7,5,8,3)])
DimPlot(merFISH_integrated_L2, group.by = "CellType",label = T)
saveRDS(merFISH_integrated_L2, file = "./RDS/merFISH_integrated_L2.seurat_0715.rds")
merFISH_integrated_L2 = readRDS( file = "./RDS/merFISH_integrated_L2.seurat_0715.rds")
# Remove Ext 16, which is out of PFC
merFISH_integrated_L2 = subset(merFISH_integrated_L2, Ext_subcluster != 16)
merFISH_integrated_L2 = RunUMAP(merFISH_integrated_L2, dims = 1:39,
reduction = "harmony", #min.dist = 0.1,
umap.method = "umap-learn"
)
DimPlot(merFISH_integrated_L2,
group.by = "L1_cluster",
label.size = 5,
pt.size = 0.1,
shuffle = T,
label = T,
raster=FALSE) &
scale_color_manual(values = L1_color) & NoAxes()
FeaturePlot(merFISH_integrated_L2, features = c("Gjb6", "Acsbg1", "Gad1", "Gad2"))
#
subcluster <- c("L6 CT 1", "L4/5 IT 1", "L5/6 NP", "L6 CT 2", "L2/3 IT 2",
"L2/3 IT 1", "L5 IT 1", "L6 CT 3", "L6 IT 1", "L5 IT 2",
"L4/5 IT 2", "L6 CT 4", "L6 IT 2", "L5 IT 3", "L2/3 IT 3",
"L2/3 IT 4", "L5 ET 1", "L5 ET 2", "Pvalb 1",
"Lamp5 1", "Sncg 1", "Sncg 2", "Pvalb 2", "Sst 1",
"Cdca7", "Pvalb 3", "Sst 2", "Vip 1", "Pvalb 4",
"Sst 3", "Lamp5 2", "Pvalb 5", "Pvalb 6", "Sst 4",
"Vip 2", "Sst 5", "Sst 6", "Lamp5 3", levels(merFISH_integrated_L2)[-c(1:38)])
names(subcluster) <- levels(merFISH_integrated_L2)
merFISH_integrated_L2 <- RenameIdents(merFISH_integrated_L2, subcluster)
Idents(merFISH_integrated_L2) = factor(Idents(merFISH_integrated_L2),
levels = c(sort(subcluster[1:18]), sort(subcluster[19:38]), levels(merFISH_integrated_L2)[-c(1:38)]))
merFISH_integrated_L2@meta.data$subcluster = Idents(merFISH_integrated_L2)
#
merFISH_integrated_L2@meta.data$L3_cluster = stringr::str_replace(Idents(merFISH_integrated_L2), " [0-9]", "")
merFISH_integrated_L2@meta.data$L3_cluster = factor(merFISH_integrated_L2@meta.data$L3_cluster,
levels = c("L2/3 IT", "L4/5 IT", "L5 IT", "L6 IT",
"L5 ET", "L5/6 NP", "L6 CT",
"Lamp5", "Pvalb", "Sncg", "Sst", "Vip", "Cdca7",
"Astro", "Endo", "Microglia", "Oligo", "OPC", "VLMC"))
L3_color = c( pals::parula(7), as.vector(pals::plasma(6)),
"#AA0DFE","#993F00","#F99379","#604E97", "maroon2", "yellow3")
DimPlot(merFISH_integrated_L2,
group.by = "L3_cluster",
label.size = 4,
pt.size = 0.1,
label = T,
shuffle = T,
raster=FALSE) &
scale_color_manual(values = L3_color) & NoAxes()
saveRDS(merFISH_integrated_L2, file = "./RDS/merFISH_integrated_L2.seurat_0716.rds")
merFISH_integrated_L2 = readRDS(file = "./RDS/merFISH_integrated_L2.seurat_0716.rds")
# remove Cdca7 cluster
merFISH_integrated_L3 = subset(merFISH_integrated_L2, L3_cluster != "Cdca7")
saveRDS(merFISH_integrated_L3, file = "./RDS/merFISH_integrated_L3.seurat_0716.rds")
merFISH_integrated_L3 = readRDS(file = "./RDS/merFISH_integrated_L3.seurat_0716.rds")
DimPlot(merFISH_integrated_L3,
group.by = "L1_cluster",
label.size = 4,
pt.size = 0.01,
shuffle = T,
label = T,
raster=FALSE) &
scale_color_manual(values = L1_color) & NoAxes()
L3_color = c( pals::parula(7), as.vector(pals::plasma(5)),
"#AA0DFE","#993F00","#F99379","#604E97", "maroon2", "yellow3")
DimPlot(merFISH_integrated_L3,
group.by = "L3_cluster",
label.size = 4,
pt.size = 0.01,
shuffle = T,
label = T,
raster=FALSE) &
scale_color_manual(values = L3_color) & NoAxes()