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Compare_with_other_regions.Rmd
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---
title: "Compare with other Data-sets"
author: "Mohamed Nadhir Djekidel"
date: "October 16, 2018"
output: html_document
---
# Load data
```{r}
load("Excitatory/PFC_excitatory_sobj.RData")
```
## Visual cortex
```{r}
vc_expr <- read.csv("../../../Public/AllanBrain/mouse_VISp_gene_expression_matrices_2018-06-14/mouse_VISp_2018-06-14_exon-matrix.csv")
rownames(vc_expr) <- vc_expr$X
vc_expr$X=NULL
vc_meta <- read.csv("../../../Public/AllanBrain/mouse_VISp_gene_expression_matrices_2018-06-14/mouse_VISp_2018-06-14_samples-columns.csv")
rownames(vc_meta) <- vc_meta$sample_name
vc_sobj <- CreateSeuratObject(raw.data = Matrix(data.matrix(vc_expr)),
project = "Allen_VC",
normalization.method = "LogNormalize",
min.genes = 1500,
min.cells = 3,
scale.factor = 1e-6,
meta.data = vc_meta)
rs = rowSums(vc_sobj@data)
vc_sobj@data =vc_sobj@data[rs>0,]
vc_sobj <- ScaleData(vc_sobj,vars.to.regress = c("total_reads","percent_exon_reads","seq_batch","percent_mt_exon_reads","nUMI"))
toUse <- rownames(subset([email protected], subclass %in% c("L2/3 IT", "L4","L5 IT","L5 PT","L6 CT","L6 IT","L6b")))
vc_neurons_sobj <- SubsetData(vc_sobj, cells.use = toUse,subset.raw = T)
vc_neurons_sobj
```
# Do CCA and compare with PFC
```{r}
PFC_excitatory_saline_sobj = ScaleData(PFC_excitatory_saline_sobj, vars.to.regress=c("nUMI","percent.mito","Sample"))
toUse <-grep("ALM",[email protected]$cluster,invert=T)
vc_neurons_sobj = SubsetData(vc_neurons_sobj, cells.use = [email protected][toUse], subset.raw=T)
vc_neurons_sobj = NormalizeData(vc_neurons_sobj)
vc_neurons_sobj = ScaleData(vc_neurons_sobj,vars.to.regress = c("total_reads","percent_mt_exon_reads","complexity_cg"))
[email protected]$DevStage = "mPFC"
[email protected]$DevStage= "VISp"
hvg.pfc <- rownames(x = head(x = [email protected], n = 2000))
hvg.vc <- rownames(x = head(x = [email protected], n = 2000))
hvg.union <- union(hvg.pfc, hvg.vc)
mPFC_VISp_cca_sobj <- RunCCA(PFC_excitatory_saline_sobj, vc_neurons_sobj,genes.use = hvg.union)
DimHeatmap(mPFC_VISp_cca_sobj,dim.use = 1:20,reduction.type = "cca",do.balanced = T,cells.use = 500)
mPFC_VISp_cca_sobj = AlignSubspace(mPFC_VISp_cca_sobj,
reduction.type = "cca",
grouping.var = "DevStage",
dims.align = 1:20)
p_PFC = MetageneBicorPlot(mPFC_VISp_cca_sobj, grouping.var = "DevStage",
dims.eval = 1:20, display.progress = TRUE,return.mat = T)
tmp =p_PFC %>% filter(bicor >= 0.15)
toUse = as.numeric( names(table(tmp$cc))[table(tmp$cc)==2])
toUse
[email protected]$L2_clusters = as.character([email protected]$L2_clusters)
pos <- which(is.na([email protected]$L2_clusters))
[email protected]$L2_clusters[pos] = as.character([email protected]$subclass[pos])
system.time( celltype_PFC_VC_L1 <- MetaNeighborUS_CCA(dat = t(mPFC_VISp_cca_sobj@[email protected]),
study_id = [email protected]$DevStage,
cell_type = [email protected]$L2_clusters)
)
tmp = as.matrix(t(mPFC_VISp_cca_sobj@[email protected]))
top_hits = topHits.my(cell_NV = celltype_PFC_VC_L1,
dat = tmp,
study_id = [email protected]$DevStage,
cell_type = [email protected]$L2_clusters,
threshold = 0.7)
```
Plot results
```{r}
load("../Compate_with_VC/celltype_PFC_VC_L1.RData")
load("../Compate_with_VC/top_hits_0.7.RData")
top_hits_0.7 = top_hits_0.8
lbls = celltype_PFC_VC_L1*0
colnames(top_hits_0.7)[4] ="Match_type"
for(i in 1:nrow(top_hits_0.7)){
if(top_hits_0.7$Match_type[i] == "Reciprocal_top_hit"){
c1 = as.character(top_hits_0.7$`Study_ID|Celltype_1`[i])
c2 = as.character(top_hits_0.7$`Study_ID|Celltype_2`[i])
lbls[c1,c2] = "*"
lbls[c2,c1] = "*"
}else{
c1 = as.character(top_hits_0.7$`Study_ID|Celltype_1`[i])
c2 = as.character(top_hits_0.7$`Study_ID|Celltype_2`[i])
#lbls[c1,c2] = "+"
}
}
lbls[lbls==0] = ""
ph= pheatmap(celltype_PFC_VC_L1,clustering_method = "average",display_numbers = lbls,number_color = "black",fontsize_number = 12)
tmp = circlize::colorRamp2(c(0, 0.5, 0.7,1), c("grey80", "royalblue", "yellow","red"))
paletteLength <- 50
myColor <- colorRampPalette(c("#F9F9F9FF", "royalblue","#B71B1BFF"))(paletteLength)
# length(breaks) == length(paletteLength) + 1
# use floor and ceiling to deal with even/odd length pallettelengths
myBreaks <- c(seq(min(celltype_PFC_VC_L1-0.9), 0, length.out=ceiling(paletteLength/2) + 1),
seq(max(celltype_PFC_VC_L1-0.9)/paletteLength, max(celltype_PFC_VC_L1-0.9), length.out=floor(paletteLength/2)))
gaps= c(7,15,19,21,22,24,27,30,34,37,41,45,46,50,51)
tmp = celltype_PFC_VC_L1[ph$tree_row$order,ph$tree_col$order]-0.9
tmp[lower.tri(tmp)] = NA
pheatmap(tmp,cluster_cols = F,cluster_rows = F,
gaps_col = gaps,
gaps_row = gaps,
show_colnames = T,
display_numbers = lbls[ph$tree_row$order,ph$tree_col$order],
number_color = "black",
fontsize_number = 12,
color = myColor,
breaks = myBreaks)
```
## ALM (Anterior Lateral Motor Area)
```{r}
ALM_expr <- read.csv("../../../Public/AllanBrain/mouse_ALM_gene_expression_matrices_2018-06-14/mouse_ALM_2018-06-14_exon-matrix.csv")
rownames(ALM_expr) <- ALM_expr$X
ALM_expr$X=NULL
ALM_meta <- read.csv("../../../Public/AllanBrain/mouse_ALM_gene_expression_matrices_2018-06-14/mouse_ALM_2018-06-14_samples-columns.csv")
rownames(ALM_meta) <- ALM_meta$sample_name
ALM_meta$sample_name = NULL
ALM_sobj <- CreateSeuratObject(raw.data = Matrix(data.matrix(ALM_expr)),
project = "Allen_ALM",
normalization.method = "LogNormalize",
min.genes = 1000,
min.cells = 3,
scale.factor = 1e-6,
meta.data = ALM_meta)
ALM_neuron_sobj = SubsetData(ALM_sobj,subset.name ="class",accept.value = "Glutamatergic",subset.raw = T)
```
# Save results
```{r}
dir.create("AllenBrain_objects",showWarnings = F)
save(vc_neurons_sobj, file="AllenBrain_objects/vc_neurons_sobj.RData")
save(ALM_neuron_sobj, file="AllenBrain_objects/ALM_neuron_sobj.RData")
gnames <- read.csv("../../../Public/AllanBrain/mouse_ALM_gene_expression_matrices_2018-06-14/mouse_ALM_2018-06-14_genes-rows.csv")
```
```{r}
ALM_neuron_sobj = NormalizeData(ALM_neuron_sobj, scale.factor=1e6)
ALM_neuron_sobj <- ScaleData(ALM_neuron_sobj,
vars.to.regress = c("total_reads","percent_exon_reads","seq_batch","percent_mt_exon_reads"),
do.scale = TRUE,
do.center = TRUE)
pos <- match(rownames([email protected]), gnames$gene_entrez_id)
rownames([email protected]) = gnames$gene_symbol[pos]
pos <- match(rownames(ALM_neuron_sobj@data), gnames$gene_entrez_id)
rownames(ALM_neuron_sobj@data) = gnames$gene_symbol[pos]
pos <- match(rownames([email protected]), gnames$gene_entrez_id)
rownames([email protected]) = gnames$gene_symbol[pos]
# remove chromosome MT genes
chomM_genes <- as.character(subset(gnames, chromosome == "MT")$gene_symbol)
ALM_neuron_sobj@data = ALM_neuron_sobj@data[! rownames(ALM_neuron_sobj@data) %in% chomM_genes, ]
ALM_neuron_sobj@data = ALM_neuron_sobj@data[grep("Gm",rownames(ALM_neuron_sobj@data), invert=TRUE),]
ALM_neuron_sobj= FindVariableGenes(ALM_neuron_sobj,y.cutoff = 1.5)
```
```{r}
vc_neurons_sobj = NormalizeData(vc_neurons_sobj, scale.factor=1e6)
vc_neurons_sobj <- ScaleData(vc_neurons_sobj,
vars.to.regress = c("total_reads","percent_exon_reads","seq_batch","percent_mt_exon_reads"),
do.scale = TRUE,
do.center = TRUE)
# pos <- match(rownames([email protected]), gnames$gene_entrez_id)
# rownames([email protected]) = gnames$gene_symbol[pos]
#
#
# pos <- match(rownames(vc_neurons_sobj@data), gnames$gene_entrez_id)
# rownames(vc_neurons_sobj@data) = gnames$gene_symbol[pos]
# remove chromosome MT genes
chomM_genes <- as.character(subset(gnames, chromosome == "MT")$gene_symbol)
vc_neurons_sobj@data = vc_neurons_sobj@data[! rownames(vc_neurons_sobj@data) %in% chomM_genes, ]
vc_neurons_sobj@data = vc_neurons_sobj@data[grep("Gm",rownames(vc_neurons_sobj@data), invert=TRUE),]
[email protected] = [email protected][rownames(vc_neurons_sobj@data), colnames(vc_neurons_sobj@data)]
vc_neurons_sobj= FindVariableGenes(vc_neurons_sobj,y.cutoff = 1.5)
```
```{r}
PFC_excitatory_sobj <- FindVariableGenes(PFC_excitatory_sobj,y.cutoff = 1.5)
common_varaible <[email protected]
common_varaible <- intersect(common_varaible, [email protected])
common_varaible <- intersect(common_varaible, [email protected])
length(common_varaible)
```
# Find the markers of the different datasets
```{r}
load("Excitatory/PFC_excitatory_sobj.RData")
load("Excitatory/mrks_26clus.RData")
load("AllenBrain_objects/alm_markers.RData")
load("AllenBrain_objects/vc_markers.RData")
pos <- match(vc_markers$gene, gnames$gene_entrez_id)
vc_markers$gene_name = gnames$gene_symbol[pos]
PFC_excitatory_saline_sobj = SubsetData(PFC_excitatory_sobj,
subset.raw=T,
subset.name="treatment",
accept.value="Saline")
PFC_excitatory_saline_sobj = ScaleData(PFC_excitatory_saline_sobj,
vars.to.regress=c("nUMI","percent.mito","Sample"),
do.scale=T,
do.center=T)
markers <- list()
#markers[[1]] <- FindAllMarkers(PFC_excitatory_sobj,test.use = "bimod")
#markers[[2]] <- FindAllMarkers(vc_neurons_sobj,test.use = "bimod")
#markers[[3]] <- FindAllMarkers(ALM_neuron_sobj,test.use = "bimod")
mrks_26clus$gene_name = mrks_26clus$gene # just to have the same column names
markers[["PFC"]] <- mrks_26clus
markers[["VC"]] <- vc_markers
markers[["ALM"]] <- alm_markers
sig_markers <- lapply(markers, function(x){
subset(x, p_val_adj < 1e-3 & avg_logFC > log(2) )
})
sapply(sig_markers, nrow)
```
# Get common markers
```{r}
common_markers <- c()
for(i in 1:length(sig_markers)){
if(i==1){
common_markers = sig_markers[[1]]$gene_name
}else{
common_markers = intersect(common_markers,sig_markers[[i]]$gene_name)
}
}
length(common_markers)
```
# Calculate the correlation with the visual corext
```{r}
vc_neurons_sobj <- SetIdent(vc_neurons_sobj, ident.use = [email protected]$cluster)
ae_vc_layer = AverageExpression(vc_neurons_sobj,use.scale = F)
ae_vc_layer = log2(ae_vc_layer+1)
pos = match(rownames(ae_vc_layer), gnames$gene_entrez_id)
rownames(ae_vc_layer) = gnames$gene_symbol[pos]
PFC_excitatory_sobj <- SetIdent(PFC_excitatory_sobj,ident.use = [email protected]$L2_clusters)
ae_pfc_layer = AverageExpression(PFC_excitatory_sobj, use.scale=F)
ae_pfc_layer = log2(ae_pfc_layer+1)
cor_pfc_vc <- matrix(0, nrow=ncol(ae_pfc_layer), ncol=ncol(ae_vc_layer))
rownames(cor_pfc_vc) <- colnames(ae_pfc_layer)
colnames(cor_pfc_vc) <- colnames(ae_vc_layer)
#common_varaible <- c(common_varaible,"Cux2","Etv1","Calb1","Pcp4","Bcl11b","Foxp2")
#common_varaible = unique(common_varaible)
common_varaible = intersect(sig_markers$PFC$gene_name, sig_markers$VC$gene_name)
#common_varaible = c("Cux2","Etv1","Calb1","Pcp4","Bcl11b","Foxp2")
for(cls1 in colnames(ae_pfc_layer)){
for(cls2 in colnames(ae_vc_layer)){
cor_pfc_vc[cls1,cls2] <- cor(ae_pfc_layer[common_varaible,cls1], ae_vc_layer[common_varaible, cls2])
}
}
pheatmap(t(cor_pfc_vc), display_numbers = T,number_color = "black",clustering_method = "ward.D")
```
# Use metNeighbour to compare data
## Compare with Visual Cortex
```{r}
load("PFC_excitatory_sobj.RData")
load("Compate_with_VC/vc_neurons_sobj.RData")
PFC_excitatory_saline_sobj = SubsetData(PFC_excitatory_sobj, subset.name="treatment",accept.value="Saline",subset.raw=T)
PFC_excitatory_saline_sobj = NormalizeData(PFC_excitatory_saline_sobj)
gene_names = read.csv("mouse_ALM_2018-06-14_genes-rows.csv")
gene_names$gene_entrez_id = as.character(gene_names$gene_entrez_id)
pos <- match(rownames([email protected]), gene_names$gene_entrez_id )
rownames([email protected]) = gene_names$gene_symbol[pos]
vc_neurons_sobj = NormalizeData(vc_neurons_sobj)
PFC_excitatory_saline_sobj = FindVariableGenes(PFC_excitatory_saline_sobj)
vc_neurons_sobj = FindVariableGenes(vc_neurons_sobj)
[email protected]$Source ="mPFC"
[email protected]$Source = "VisualCortex"
# Set clusters (fine scale clusters)
[email protected]$Clusters_L2 = PFC_excitatory_saline_sobj@ident
[email protected]$Clusters_L2 = as.character([email protected]$cluster)
# Set clusters (Boad scale clusters)
[email protected]$Clusters_L1 = as.character([email protected]$L1_cluster)
[email protected]$Clusters_L1 = as.character([email protected]$subclass)
# Compare with layers
[email protected]$Clusters_L3 = PFC_excitatory_saline_sobj@ident
[email protected]$Clusters_L3 = as.character([email protected]$subclass)
# Merge Objects
PFC_excitatory_visualCortex_sobj <- MergeSeurat(PFC_excitatory_saline_sobj,vc_neurons_sobj)
[email protected]$Clusters_L3 = [email protected]$Clusters_L1
pos <- which([email protected]$Source == "mPFC")
[email protected]$Clusters_L3[pos] = [email protected]$Clusters_L2[pos]
var_genes = unique(c([email protected], [email protected]))
#se = SummarizedExperiment(assays = list(counts = as.matrix(PFC_excitatory_visualCortex_sobj@data[var_genes,])))
exprs = as.matrix(PFC_excitatory_visualCortex_sobj@data[var_genes,])
system.time( celltype_PFC_VC_L2 <- MetaNeighborUS_parallel(var_genes = var_genes,
dat = exprs,
study_id = [email protected]$Source,
cell_type = [email protected]$Clusters_L2)
)
save(celltype_PFC_VC_L2,file="celltype_PFC_VC_L2.RData")
system.time( celltype_PFC_VC_L1 <- MetaNeighborUS_parallel(var_genes = var_genes,
dat = exprs,
study_id = [email protected]$Source,
cell_type = [email protected]$Clusters_L1)
)
system.time( celltype_PFC_VC_L3 <- MetaNeighborUS_parallel(var_genes = var_genes,
dat = exprs,
study_id = [email protected]$Source,
cell_type = [email protected]$Clusters_L3)
)
save(celltype_PFC_VC_L1,file="celltype_PFC_VC_L1.RData")
```
# Differential expression between the different similar groups in VISp
```{r}
require(scran)
# Do differential gene expression
dir.create("Compate_with_VC/DEG_by_Group",showWarnings = F)
load("FinalObjects/vc_groups.RData")
#vc_neurons_sobj = NormalizeData(vc_neurons_sobj)
PFC_VC_DEG <- list()
#hcols = viridis::plasma(20)
hcols= colorRampPalette(rev(brewer.pal(n = 11, name ="RdBu")))(12)
for(grp in 1:length(vc_groups)){
message(paste("***",grp,"***"))
mPFC_clus = vc_groups[[grp]]$PFC
visp_clus = vc_groups[[grp]]$VISp
if(length(mPFC_clus)>0 & length(visp_clus)>0){
pos1 = rownames(subset([email protected], L2_clusters %in% mPFC_clus))
pos2 = rownames(subset([email protected], cluster %in% visp_clus))
sobj1 = SubsetData(PFC_excitatory_saline_sobj,cells.use = pos1, subset.raw=T)
sobj2 = SubsetData(vc_neurons_sobj,cells.use = pos2, subset.raw=T)
common_genes = intersect(rownames(sobj1@data), rownames(sobj2@data))
sce1 = Convert(sobj1, to="sce")
sce2 = Convert(sobj2, to="sce")
sce1 = sce1[common_genes,]
sce2 = sce2[common_genes,]
out = scran::multiBatchNorm(sce1, sce2)
rm(sce1,sce2)
sobj_mrg = MergeSeurat(sobj1, sobj2, do.normalize = T,do.scale = F)
rm(sobj1,sobj2)
sobj_mrg@data = cbind(assay(out[[1]],2),assay(out[[2]],2))
newIdent = c(rep("mPFC",length(pos1)), rep("VISp",length(pos2)))
sobj_mrg = SetIdent(sobj_mrg, ident.use = newIdent)
sobj_mrg = ScaleData(sobj_mrg)
PFC_VC_DEG[[grp]] = FindMarkers(sobj_mrg,test.use = "bimod",ident.1 = "mPFC")
# Plot the results
fname = paste0("Compate_with_VC/DEG_by_Group/group_",grp,"_DEG.pdf")
de_sig = subset(PFC_VC_DEG[[grp]], abs(pct.1-pct.2)/max(pct.1,pct.2) > 0.3 & p_val_adj < 1e-5 & abs(avg_logFC) > log(2) )
de_sig = de_sig[order(de_sig$avg_logFC,decreasing = T),]
tmp = [email protected][rownames(de_sig),]
q99 = quantile(tmp,0.99)
q1 = quantile(tmp,0.01)
tmp = Seurat:::MinMax(tmp,min = -2,max = 2)
tmp = as.matrix(tmp)
pdf(fname,width = 2,height = 9)
deDirect = ifelse(de_sig$avg_logFC>0,"1","2")
h=Heatmap(tmp,show_row_names = F,show_column_names = F,show_column_dend = F,show_row_dend = F,cluster_rows = F,
col = hcols, #PurpleAndYellow(),
name="expression",split = deDirect)
draw(h)
dev.off()
}
}
save(PFC_VC_DEG,file = "Compate_with_VC/PFC_VC_DEG.RData")
```
#
```{r}
PFC_VC_DEG_sig = list()
for(grp in 1:length(vc_groups)){
message(paste("***",grp,"***"))
mPFC_clus = vc_groups[[grp]]$PFC
visp_clus = vc_groups[[grp]]$VISp
if(length(mPFC_clus)>0 & length(visp_clus)>0){
PFC_VC_DEG_sig[[grp]] = subset(PFC_VC_DEG[[grp]], abs(pct.1-pct.2)/max(pct.1,pct.2) > 0.2 & p_val_adj < 1e-5 & abs(avg_logFC) > log(2) )
PFC_VC_DEG_sig[[grp]] = PFC_VC_DEG_sig[[grp]][order(PFC_VC_DEG_sig[[grp]]$avg_logFC,decreasing = T),]
PFC_VC_DEG_sig[[grp]]$direction = ifelse(PFC_VC_DEG_sig[[grp]]$avg_logFC>0,"Up","Down")
fname = paste0("Compate_with_VC/DEG_by_Group/PFC_VC_DEG_group",grp,".xlsx")
write.xlsx(PFC_VC_DEG_sig[[grp]],file = fname)
#write.csv(PFC_VC_DEG_sig[[grp]],file = fname)
}
}
#sapply(PFC_VC_DEG_sig, function(x) (27.5 *table(x$direction))/125 )
```
```{r}
require(readxl)
PFC_VC_DEG_sig = list()
nb_DEG_vc = data.frame()
for(grp in 1:length(vc_groups)){
fname = paste0("Compate_with_VC/DEG_by_Group/PFC_VC_DEG_group",grp,".xlsx")
if(file.exists(fname)){
PFC_VC_DEG_sig[[grp]] = read_xlsx(fname)
df = data.frame(group = grp, nbDEG = nrow(PFC_VC_DEG_sig[[grp]]))
nb_DEG_vc = rbind(nb_DEG_vc,df)
}
}
nb_DEG_vc
```
# Differential gene expression for ALM
```{r}
load("Compare_with_ALM/ALM_neuron_sobj.RData")
load("FinalObjects/alm_groups.RData")
[email protected]$Clusters_L2 = as.character([email protected]$cluster)
#ALM_neuron_sobj = NormalizeData(ALM_neuron_sobj)
PFC_ALM_DEG <- list()
dir.create("Compare_with_ALM/DEG_by_Group",showWarnings = F)
#hcols = colorRampPalette(rev(brewer.pal(n = 7, name ="RdYlBu")))(50)
hcols = colorRampPalette(rev(brewer.pal(n = 11, name ="RdBu")))(12)
for(grp in 1:length(alm_groups)){
message(paste("***",grp,"***"))
mPFC_clus = alm_groups[[grp]]$PFC
ALM_clus = alm_groups[[grp]]$ALM
if(length(mPFC_clus)>0 & length(ALM_clus)>0){
pos1 = rownames(subset([email protected], L2_clusters %in% mPFC_clus))
pos2 = rownames(subset([email protected], cluster %in% ALM_clus))
sobj1 = SubsetData(PFC_excitatory_saline_sobj,cells.use = pos1, subset.raw=T)
sobj2 = SubsetData(ALM_neuron_sobj,cells.use = pos2, subset.raw=T)
common_genes = intersect(rownames(sobj1@data), rownames(sobj2@data))
sce1 = Convert(sobj1, to="sce")
sce2 = Convert(sobj2, to="sce")
sce1 = sce1[common_genes,]
sce2 = sce2[common_genes,]
out = multiBatchNorm(sce1, sce2)
rm(sce1,sce2)
sobj_mrg = MergeSeurat(sobj1, sobj2, do.normalize = T,do.scale = T)
rm(sobj1,sobj2)
sobj_mrg@data = cbind(assay(out[[1]],2),assay(out[[2]],2))
sobj_mrg = ScaleData(sobj_mrg)
newIdent = c(rep("mPFC",length(pos1)), rep("ALM",length(pos2)))
sobj_mrg = SetIdent(sobj_mrg, ident.use = newIdent)
PFC_ALM_DEG[[grp]] = FindMarkers(sobj_mrg,test.use = "bimod",ident.1 = "mPFC")
fname = paste0("Compare_with_ALM/DEG_by_Group/group_",grp,"_DEG.pdf")
de_sig = subset(PFC_ALM_DEG[[grp]], abs(pct.1-pct.2)/max(pct.1,pct.2) > 0.3 & p_val_adj < 1e-5 & abs(avg_logFC) > log(2) )
de_sig = de_sig[order(de_sig$avg_logFC,decreasing = T),]
tmp = [email protected][rownames(de_sig),]
q99 = quantile(tmp,0.98)
q1 = quantile(tmp,0.01)
tmp = Seurat:::MinMax(tmp,min = -2,max = 2)
tmp = as.matrix(tmp)
deDirect = ifelse(de_sig$avg_logFC>0,"1","2")
# pdf(fname,width = 3,height = 9)
# h=Heatmap(tmp,show_row_names = F,show_column_names = F,show_column_dend = F,show_row_dend = F,cluster_rows = F,
# col = hcols, #PurpleAndYellow(),
# name="expression",split = deDirect)
# draw(h)
# dev.off()
}
}
save(PFC_ALM_DEG,file = "Compare_with_ALM/PFC_ALM_DEG.RData")
```
```{r}
require(xlsx)
PFC_ALM_DEG_sig = list()
for(grp in 1:length(alm_groups)){
message(paste("***",grp,"***"))
mPFC_clus = alm_groups[[grp]]$PFC
ALM_clus = alm_groups[[grp]]$ALM
if(length(mPFC_clus)>0 & length(ALM_clus)>0){
PFC_ALM_DEG_sig[[grp]] = subset(PFC_ALM_DEG[[grp]], abs(pct.1-pct.2)/max(pct.1,pct.2) > 0.2 & p_val_adj < 1e-5 & abs(avg_logFC) > log(2) )
PFC_ALM_DEG_sig[[grp]] = PFC_ALM_DEG_sig[[grp]][order(PFC_ALM_DEG_sig[[grp]]$avg_logFC,decreasing = T),]
PFC_ALM_DEG_sig[[grp]]$direction = ifelse(PFC_ALM_DEG_sig[[grp]]$avg_logFC>0,"Up","Down")
fname = paste0("Compare_with_ALM/DEG_by_Group/PFC_ALM_DEG_group",grp,".xlsx")
write.xlsx(PFC_ALM_DEG_sig[[grp]],file = fname)
}
}
sapply(PFC_ALM_DEG_sig, function(x) table(x$direction))
sapply(PFC_ALM_DEG_sig, function(x) (27.5 *table(x$direction))/149 )
```
```{r}
PFC_ALM_DEG_sig = list()
nb_DEG_ALM = data.frame()
for(grp in 1:length(alm_groups)){
fname = paste0("Compare_with_ALM/DEG_by_Group/PFC_ALM_DEG_group",grp,".xlsx")
if(file.exists(fname)){
PFC_ALM_DEG_sig[[grp]] = read_xlsx(fname)
df = data.frame(group = grp, nbDEG = nrow(PFC_ALM_DEG_sig[[grp]]))
nb_DEG_ALM = rbind(nb_DEG_ALM,df)
}
}
write.csv(nb_DEG_ALM,"Compare_with_ALM/DEG_by_Group/nbDEG_ALM.csv",row.names = T)
nb_DEG_ALM
```
## Count the number of DEG between the mPFC and ALM and VC
```{r}
PFC_VC_nbDEG <- data.frame()
for(grp in 1:length(PFC_VC_DEG)){
if(!is.null(PFC_VC_DEG[[grp]])){
de_sig = subset(PFC_VC_DEG[[grp]], abs(pct.1-pct.2)/max(pct.1,pct.2) > 0.2 & p_val_adj < 1e-5 & abs(avg_logFC) > log(2) )
nbUp = sum(de_sig$avg_logFC>0)
nbDown = sum(de_sig$avg_logFC<0)
#df = data.frame(group = grp, nbUp =nbUp, nbDown=nbDown)
df = data.frame(group=grp, direction = c("Up","Down"), nbDEG=c(nbUp,nbDown))
PFC_VC_nbDEG = rbind(PFC_VC_nbDEG, df)
}
}
PFC_VC_nbDEG$group = factor(PFC_VC_nbDEG$group,1:16)
ggplot(PFC_VC_nbDEG, aes(x=group,y=nbDEG, fill=direction)) + geom_bar(stat = "identity",position = position_dodge(),color="black") + ggsci::scale_fill_aaas() + theme_bw()
```
```{r}
PFC_ALM_nbDEG <- data.frame()
for(grp in 1:length(PFC_ALM_DEG)){
if(!is.null(PFC_ALM_DEG[[grp]])){
de_sig = subset(PFC_ALM_DEG[[grp]], abs(pct.1-pct.2)/max(pct.1,pct.2) > 0.2 & p_val_adj < 1e-5 & abs(avg_logFC) > log(2) )
nbUp = sum(de_sig$avg_logFC>0)
nbDown = sum(de_sig$avg_logFC<0)
#df = data.frame(group = grp, nbUp =nbUp, nbDown=nbDown)
df = data.frame(group=grp, direction = c("Up","Down"), nbDEG=c(nbUp,nbDown))
PFC_ALM_nbDEG = rbind(PFC_ALM_nbDEG, df)
}
}
PFC_ALM_nbDEG$group = factor(PFC_ALM_nbDEG$group,1:length(PFC_ALM_DEG))
ggplot(PFC_ALM_nbDEG, aes(x=group,y=nbDEG, fill=direction)) + geom_bar(stat = "identity",position = position_dodge(),color="black") + ggsci::scale_fill_aaas() + theme_bw()
```
# Find Markers specific to PFC specific clusters
```{r}
Clus7_14_ALM_DEG <- list()
Clus7_14_ALM_DEG_sig <- list()
for(grp in 1:length(alm_groups)){
message(paste("***",grp,"***"))
mPFC_clus = alm_groups[[grp]]$PFC
ALM_clus = "CR Lhx5"
if(length(mPFC_clus)>0 & length(ALM_clus)>0){
pos1 = rownames(subset([email protected], L2_clusters %in% mPFC_clus))
pos2 = rownames(subset([email protected], cluster %in% ALM_clus))
sobj1 = SubsetData(PFC_excitatory_saline_sobj,cells.use = pos1, subset.raw=T)
sobj2 = SubsetData(ALM_neuron_sobj,cells.use = pos2, subset.raw=T)
common_genes = intersect(rownames(sobj1@data), rownames(sobj2@data))
sce1 = Convert(sobj1, to="sce")
sce2 = Convert(sobj2, to="sce")
sce1 = sce1[common_genes,]
sce2 = sce2[common_genes,]
out = multiBatchNorm(sce1, sce2)
rm(sce1,sce2)
sobj_mrg = MergeSeurat(sobj1, sobj2, do.normalize = T,do.scale = T)
rm(sobj1,sobj2)
sobj_mrg@data = cbind(assay(out[[1]],2),assay(out[[2]],2))
sobj_mrg = ScaleData(sobj_mrg)
newIdent = c(rep("mPFC",length(pos1)), rep("ALM",length(pos2)))
sobj_mrg = SetIdent(sobj_mrg, ident.use = newIdent)
Clus7_14_ALM_DEG[[grp]] = FindMarkers(sobj_mrg,test.use = "bimod",ident.1 = "mPFC")
de_sig = subset(Clus7_14_ALM_DEG[[grp]], abs(pct.1-pct.2)/max(pct.1,pct.2) > 0.3 & p_val_adj < 1e-5 & abs(avg_logFC) > log(2) )
de_sig = de_sig[order(de_sig$avg_logFC,decreasing = T),]
Clus7_14_ALM_DEG_sig[[grp]] = de_sig
}
}
```
# Get genes always UP
```{r}
always_up <- NULL
for(i in 1:length(Clus7_14_ALM_DEG)){
if(!is.null(Clus7_14_ALM_DEG[[i]])){
up_genes <- rownames(subset(Clus7_14_ALM_DEG[[i]], p_val_adj < 0.2 & avg_logFC < -log(1.5) & pct.1 < 0.1 ))
if(is.null(always_up)){
always_up = up_genes
}else{
always_up = intersect(always_up,up_genes)
}
}
}
cat(always_up)
ALM_neuron_sobj = SetIdent(ALM_neuron_sobj,ident.use = [email protected]$cluster)
ae_ALM = AverageExpression(ALM_neuron_sobj)
ad_ALM = AverageExpression(ALM_neuron_sobj)
ae_ALM = ae_ALM[,grep("ALM",colnames(ae_ALM))]
m = apply(log2(ae_ALM[always_up,]+1),1,max)
```
```{r}
ClusGrp11_13_VISp_DEG <- list()
ClusGrp11_13_VISp_DEG_sig <- list()
for(grp in 1:length(vc_groups)){
message(paste("***",grp,"***"))
mPFC_clus = vc_groups[[grp]]$PFC
visp_clus = vc_groups[[15]]$VISp
if(length(mPFC_clus)>0 & length(visp_clus)>0){
pos1 = rownames(subset([email protected], L2_clusters %in% mPFC_clus))
pos2 = rownames(subset([email protected], cluster %in% visp_clus))
sobj1 = SubsetData(PFC_excitatory_saline_sobj,cells.use = pos1, subset.raw=T)
sobj2 = SubsetData(vc_neurons_sobj,cells.use = pos2, subset.raw=T)
common_genes = intersect(rownames(sobj1@data), rownames(sobj2@data))
sce1 = Convert(sobj1, to="sce")
sce2 = Convert(sobj2, to="sce")
sce1 = sce1[common_genes,]
sce2 = sce2[common_genes,]
out = multiBatchNorm(sce1, sce2)
rm(sce1,sce2)
sobj_mrg = MergeSeurat(sobj1, sobj2, do.normalize = T,do.scale = T)
rm(sobj1,sobj2)
sobj_mrg@data = cbind(assay(out[[1]],2),assay(out[[2]],2))
sobj_mrg = ScaleData(sobj_mrg)
newIdent = c(rep("mPFC",length(pos1)), rep("VISp",length(pos2)))
sobj_mrg = SetIdent(sobj_mrg, ident.use = newIdent)
ClusGrp11_13_VISp_DEG[[grp]] = FindMarkers(sobj_mrg,test.use = "bimod",ident.1 = "mPFC")
de_sig = subset(ClusGrp11_13_VISp_DEG[[grp]], abs(pct.1-pct.2)/max(pct.1,pct.2) > 0.3 & p_val_adj < 1e-5 & abs(avg_logFC) > log(2) )
de_sig = de_sig[order(de_sig$avg_logFC,decreasing = T),]
ClusGrp11_13_VISp_DEG_sig[[grp]] = de_sig
}
}
always_up_Visp <- NULL
for(i in 1:length(ClusGrp11_13_VISp_DEG)){
if(!is.null(ClusGrp11_13_VISp_DEG[[i]])){
up_genes <- rownames(subset(ClusGrp11_13_VISp_DEG[[i]], p_val_adj < 0.05 & avg_logFC > log(2) ))
if(is.null(always_up_Visp)){
always_up_Visp = up_genes
}else{
always_up_Visp = intersect(always_up_Visp,up_genes)
}
}
}
cat(t(always_up_Visp))
```