readRDS(file = "~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/rdata/barcodes.post.demultiplexing.brown.rds")
x=readRDS(file = "~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/rdata/barcodes.post.demultiplexing.brown.rds")
load(file = "~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/rdata/barcodes.post.demultiplexing.brown.rds")
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
# Load libraries
library(Seurat)
# Load UMI-count-matrix for genes in scRNA-seq preadipocyte data
# Subset to remove empty and doublet barcodes as annotated after demultiplexing
load(file = "rdata/umi.count.preadipocytes.rds")
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
# Load libraries
library(Seurat)
# Load UMI-count-matrix for genes in scRNA-seq preadipocyte data
# Subset to remove empty and doublet barcodes as annotated after demultiplexing
load(file = "rdata/umi.count.preadipocytes.rds")
load(file="rdata/barcodes.post.demultiplexing.white.rds")
load(file="rdata/barcodes.post.demultiplexing.brown.rds")
mat_genes=mat_genes[,c(WAT.barcodes,BAT.barcodes)]
library(Seurat)
library(ggplot2)
dataset <- ReadH5AD(file ="/Users/anushka/Desktop/nuclei_v_cells/adata/preadipocytes_post_demultiplexing.h5ad")
dim(dataset)
dim(mat_genes)
test=dataset
dataset=CreateSeuratObject(mat_genes,min.features = 200)
dim(dataset)
colnames(dataset)
colnames(dataset)[1]
colnames(test)[1]
identical(colnames(dataset),colnames(test))
View(test)
length(intersect(colnames(dataset),colnames(test)))
library(Seurat)
library(ggplot2)
dataset <- ReadH5AD(file ="/Users/anushka/Desktop/nuclei_v_cells/adata/preadipocytes_post_demultiplexing.h5ad")
latent <- read.csv("/Users/anushka/Desktop/nuclei_v_cells/latent_space/preadipocytes_post_demultiplexing.csv", row.names=1)
latent=as.matrix(latent)
rownames(latent)=rownames(dataset@meta.data)
dataset[["scVI"]] <- CreateDimReducObject(embeddings =latent,key = "scVI_")
dataset=FindNeighbors(dataset,reduction="scVI",graph.name="RNA_snn_scVI",dims=1:20)
dataset=RunUMAP(dataset,reduction="scVI",dims = 1:20)
UMAPPlot(dataset,group.by='type',label=TRUE,label.size=6)+theme_bw(base_size = 20)+theme(legend.position = "none")+scale_color_manual(values=c("brown", "grey"))
library(RColorBrewer)
dataset=FindClusters(dataset,graph.name = "RNA_snn_scVI",resolution = .4,algorithm = 1)
UMAPPlot(dataset,label=TRUE,cols=brewer.pal(n = 4,name = "Dark2"))+theme_bw(base_size = 20)
dataset=NormalizeData(dataset,verbose = FALSE)
markers.3.SCT=FindMarkers(object = dataset,ident.1 = 3,assay = "RNA",slot = "data",logfc.threshold = .5,verbose = FALSE,only.pos = TRUE)
markers.3.SCT=subset.data.frame(markers.3.SCT,subset = p_val_adj<.05)
dataset <- PercentageFeatureSet(dataset, pattern = "^MT-", col.name = "percent.mt")
dataset@meta.data %>% group_by(seurat_clusters) %>% summarise(mean(percent.mt))
library(dplyr)
dataset@meta.data %>% group_by(seurat_clusters) %>% summarise(mean(percent.mt))
View(markers.3.SCT)
View(dataset)
dataset.subset=SubsetData(dataset, cells =rownames(dataset@meta.data)[dataset@meta.data$seurat_clusters!=3])
table(dataset.subset$type)
table(dataset.subset$seurat_clusters)
write.table(x = colnames(dataset),file = "/Users/anushka/Desktop/order.txt",quote = FALSE,sep = "\n",eol = "\n",row.names = FALSE,col.names = FALSE)
# Create Seurat object
dataset <- CreateSeuratObject(mat_genes,min.features = 200)
dataset$type=factor(x = "white",levels = c("white","brown"))
dataset@meta.data[BAT.barcodes,"type"]="brown"
# Add latent space inferred using scVI. FInd latent space in additional_files --> latent_space folder
latent <- read.csv("additional_files/latent_space/preadipocytes_post_demultiplexing.csv", row.names=1)
View(latent)
latent=as.matrix(latent)
latent=latent[colnames(dataset),]
dataset[["scVI"]] <- CreateDimReducObject(embeddings =latent,key = "scVI_")
dataset=FindNeighbors(dataset,reduction="scVI",graph.name="RNA_snn_scVI",dims=1:20)
dataset=RunUMAP(dataset,reduction="scVI",dims = 1:20)
dataset=RunUMAP(dataset,reduction="scVI",dims = 1:20)
UMAPPlot(dataset,group.by='type',label=TRUE,label.size=6)+theme_bw(base_size = 20)+theme(legend.position = "none")+scale_color_manual(values=c("brown", "grey"))
UMAPPlot(dataset,group.by='type',label=TRUE,label.size=6)+theme_bw(base_size = 20)+theme(legend.position = "none")+scale_color_manual(values=c("grey", "brown"))
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
# Load libraries
library(Seurat)
library(ggplot2)
library(RColorBrewer)
# We demultiplexed scRNA-seq preadipocyte dataset to annotate barcodes as singlets, doublets or empty in demultiplexing.htos.Rmd notebook. Singlets were further annotated as white or brown in demultiplexing.htos.Rmd notebook. These barcodes can be found in rdata folder as .rds files
# The gene count matrix for singlet barcodes only was used as input to scVI package to infer the latent space. While using scVI, singlet barcodes with < 200 genes were removed.
# We used this inferred latent space as input in this notebook, along with the anndata object used for analysis in scVI
# Find latent space in additional_files --> latent_space folder
# Find anndata object in additional_files --> adata
dataset <- ReadH5AD(file ="additional_files/adata/preadipocytes_post_demultiplexing.h5ad")
# Add latent space inferred using scVI to Seurat object.
latent <- read.csv("additional_files/latent_space/preadipocytes_post_demultiplexing.csv", row.names=1)
latent=as.matrix(latent)
rownames(latent)=rownames(dataset@meta.data)
dataset[["scVI"]] <- CreateDimReducObject(embeddings =latent,key = "scVI_")
dataset=FindNeighbors(dataset,reduction="scVI",graph.name="RNA_snn_scVI",dims=1:20)
dataset=RunUMAP(dataset,reduction="scVI",dims = 1:20)
UMAPPlot(dataset,group.by='type',label=TRUE,label.size=6)+theme_bw(base_size = 20)+theme(legend.position = "none")+scale_color_manual(values=c("brown", "grey"))
# Find Clusters
dataset=FindClusters(dataset,graph.name = "RNA_snn_scVI",resolution = .4,algorithm = 1)
UMAPPlot(dataset,label=TRUE,cols=brewer.pal(n = 4,name = "Dark2"))+theme_bw(base_size = 20)
#### Perform QC on clusters to remove low quality clusters
dataset=NormalizeData(dataset,verbose = FALSE)
dataset <- PercentageFeatureSet(dataset, pattern = "^MT-", col.name = "percent.mt")
dataset@meta.data %>% group_by(seurat_clusters) %>% summarise(mean(percent.mt))
library(dplyr)
dataset@meta.data %>% group_by(seurat_clusters) %>% summarise(mean(percent.mt))
# Cluster 3 is the only cluster which has > 5% MT-content.
# Check if DE also identifies MT genes
markers.3=FindMarkers(object = dataset,ident.1 = 3,assay = "RNA",slot = "data",logfc.threshold = .5,verbose = FALSE,only.pos = TRUE)
markers.3=subset.data.frame(markers.3,subset = p_val_adj<.05)
# Only 21 marker genes with top DE genes for MT genes
# Therefore, remove cluster 3 from downstream analyses
dataset.subset=subset(dataset, cells =rownames(dataset@meta.data)[dataset@meta.data$seurat_clusters!=3])
UMAPPlot(dataset.subset,group.by='type',pt.size=1.5)+theme_void()+theme(legend.position = "none")+scale_color_manual(values=c("grey", "brown"))
UMAPPlot(dataset.subset,group.by='seurat_clusters',label=TRUE,label.size=6,cols=brewer.pal(n = 3,name = "Dark2"))+theme_bw(base_size = 20)+theme(legend.position = "none")
View(dataset.subset)
dataset.subset=SCTransform(dataset.subset,verbose = FALSE)
View(dataset.subset)
Idents(dataset.subset)="type"
markers.white=FindMarkers(object = dataset.subset,ident.1 = 1,assay = "SCT",slot = "data",logfc.threshold = .5,verbose = FALSE,only.pos = TRUE)
markers.white=subset.data.frame(markers.white,subset = p_val_adj<.05)
View(markers.white)
load("~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/rdata/seurat.preadipocytes.post.demultiplexingAndQC.RData")
Idents(dataset.subset)="type"
markers.white=FindMarkers(object = dataset.subset,ident.1 = 1,assay = "SCT",slot = "data",logfc.threshold = .5,verbose = FALSE,only.pos = TRUE)
markers.white=subset.data.frame(markers.white,subset = p_val_adj<.05)
View(markers.white)
View(dataset.subset)
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
# load libraries
library(Seurat)
# Before analysis of scRNA-seq data for Figure 1, we performed demultiplexing and QC which can be followed in notebooks in pre-processing folder
# load seurat object after demultiplexing and QC
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
# load libraries
library(Seurat)
# Read cellenone seurat data
load(file = "rdata/seurat.cellenone.RData")
scellenone=seurat_singlet_object
rm(seurat_singlet_object)
table(scellenone$Type)
# We initially ran the experiment for both white and brown adipocytes
# This Seurat object has brown adipocytes as well. However, doublets and negatives were removed based on the images of cells acquired during spotting.
# Therefore, sub-sample to white cells and extract the count and metadata for white cells
# Then, initiate a new Seurat object
scellenone=subset(scellenone,Type=="Deep")
count=scellenone@assays$RNA@counts
meta=scellenone@meta.data
scellenone=CreateSeuratObject(counts = count,meta.data = meta)
scellenone=NormalizeData(scellenone)
scellenone=FindVariableFeatures(scellenone)
scellenone=ScaleData(scellenone)
scellenone=RunPCA(scellenone)
ElbowPlot(scellenone)
# Clustering
scellenone=RunUMAP(scellenone,dims = 1:20)
UMAPPlot(scellenone)
FeaturePlot(scellenone,"PLIN1")
FeaturePlot(scellenone,"ADIPOQ")
FeaturePlot(scellenone,"SEMA5A")
FeaturePlot(scellenone,"S100A4")
FeaturePlot(scellenone,"PPARG")
FeaturePlot(scellenone,"S100A4")
FeaturePlot(scellenone,"SEMA5A")
FeaturePlot(scellenone,"NPPB")
FeaturePlot(scellenone,"MEST")
FeaturePlot(scellenone,"FST")
`non-adipo` <- read.table("~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/de_genes/1.vs.2.cells.txt", quote="\"", comment.char="")
View(`non-adipo`)
adipo <- read.table("~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/de_genes/2.vs.1.cells.txt", quote="\"", comment.char="")
View(adipo)
# Create vision signature
sig.non.adipo=vector(mode = "double",length = length(`non-adipo`))
`non-adipo`
`non-adipo`$V1
sig.non.adipo=vector(mode = "double",length = length(`non-adipo`))
names(sig.non.adipo)=`non-adipo`$V1
sig.non.adipo=vector(mode = "double",length = length(`non-adipo`$V1))
names(sig.non.adipo)=`non-adipo`$V1
sig.adipo=vector(mode = "double",length = length(adipo))
names(sig.adipo)=adipo$V1
sig.adipo=vector(mode = "double",length = length(adipo$V1))
names(sig.adipo)=adipo$V1
sig.non.adipo=vector(mode = "double",length = length(`non-adipo`$V1))
names(sig.non.adipo)=`non-adipo`$V1
sig.adipo=vector(mode = "double",length = length(adipo$V1))
names(sig.adipo)=adipo$V1
sig.non.adipo <- createGeneSignature(name = "sig.non.adipo", sigData = sig.non.adipo)
library(VISION)
sig.non.adipo <- createGeneSignature(name = "sig.non.adipo", sigData = sig.non.adipo)
sig.adipo <- createGeneSignature(name = "sig.adipo", sigData = sig.adipo)
sig=c(sig.non.adipo,sig.adipo,"~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
source(file = "~/Downloads/custom_functions.R")
vis=vision(s.object = scellenone,sig = sig)
library(Matrix)
vis=vision(s.object = scellenone,sig = sig)
# Scale counts within a sample
n.umi <- colSums(count)
scaled_counts <- t(t(count) / n.umi) * median(n.umi)
# Read in meta data (Cells x Vars)
meta = scellenone@meta.data
# Adding UMAP
projection <- scellenone@reductions$umap@cell.embeddings
vis <- Vision(scaled_counts,signatures = sig,meta = meta,)
vis <- addProjection(vis, "UMAP", projection)
vis <- analyze(vis)
sig
# Create vision signature
sig.non.adipo=vector(mode = "double",length = length(`non-adipo`$V1))
names(sig.non.adipo)=`non-adipo`$V1
sig.adipo=vector(mode = "double",length = length(adipo$V1))
names(sig.adipo)=adipo$V1
sig.non.adipo <- createGeneSignature(name = "sig.non.adipo", sigData = sig.non.adipo)
sig.adipo <- createGeneSignature(name = "sig.adipo", sigData = sig.adipo)
#sig=c(sig.non.adipo,sig.adipo,"~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
sig=c(sig.non.adipo)
library(Matrix)
# Scale counts within a sample
n.umi <- colSums(count)
scaled_counts <- t(t(count) / n.umi) * median(n.umi)
# Adding UMAP
projection <- scellenone@reductions$umap@cell.embeddings
# Read in meta data (Cells x Vars)
meta = scellenone@meta.data
vis <- Vision(scaled_counts,signatures = sig,meta = meta,)
vis <- addProjection(vis, "UMAP", projection)
vis <- analyze(vis)
# Create vision signature
sig.non.adipo=vector(mode = "double",length = length(`non-adipo`$V1))
names(sig.non.adipo)=`non-adipo`$V1
sig.adipo=vector(mode = "double",length = length(adipo$V1))
names(sig.adipo)=adipo$V1
sig.non.adipo <- createGeneSignature(name = "sig.non.adipo", sigData = sig.non.adipo)
sig.adipo <- createGeneSignature(name = "sig.adipo", sigData = sig.adipo)
#sig=c(sig.non.adipo,sig.adipo,"~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
sig=c(sig.adipo)
library(Matrix)
# Scale counts within a sample
n.umi <- colSums(count)
scaled_counts <- t(t(count) / n.umi) * median(n.umi)
# Adding UMAP
projection <- scellenone@reductions$umap@cell.embeddings
# Read in meta data (Cells x Vars)
meta = scellenone@meta.data
vis <- Vision(scaled_counts,signatures = sig,meta = meta,)
vis <- addProjection(vis, "UMAP", projection)
vis <- analyze(vis)
# Create vision signature
sig.non.adipo=vector(mode = "double",length = length(`non-adipo`$V1))
names(sig.non.adipo)=`non-adipo`$V1
sig.adipo=vector(mode = "double",length = length(adipo$V1))
names(sig.adipo)=adipo$V1
sig.non.adipo <- createGeneSignature(name = "sig.non.adipo", sigData = sig.non.adipo)
sig.adipo <- createGeneSignature(name = "sig.adipo", sigData = sig.adipo)
#sig=c(sig.non.adipo,sig.adipo,"~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
sig=c("~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
library(Matrix)
# Scale counts within a sample
n.umi <- colSums(count)
scaled_counts <- t(t(count) / n.umi) * median(n.umi)
# Adding UMAP
projection <- scellenone@reductions$umap@cell.embeddings
# Read in meta data (Cells x Vars)
meta = scellenone@meta.data
vis <- Vision(scaled_counts,signatures = sig,meta = meta,)
vis <- addProjection(vis, "UMAP", projection)
vis <- analyze(vis)
intersect(adipo$V1,rownames(scellenone))
# Create vision signature
sig.non.adipo=vector(mode = "double",length = length(intersect(rownames(scellenone),`non-adipo`$V1)))
names(sig.non.adipo)=intersect(rownames(scellenone),`non-adipo`$V1)
sig.adipo=vector(mode = "double",length = length(intersect(rownames(scellenone),adipo$V1)))
names(sig.adipo)=intersect(rownames(scellenone),adipo$V1)
sig.non.adipo <- createGeneSignature(name = "sig.non.adipo", sigData = sig.non.adipo)
sig.adipo <- createGeneSignature(name = "sig.adipo", sigData = sig.adipo)
sig=c(sig.non.adipo,sig.adipo,"~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
#sig=c("~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
library(Matrix)
# Scale counts within a sample
n.umi <- colSums(count)
scaled_counts <- t(t(count) / n.umi) * median(n.umi)
# Adding UMAP
projection <- scellenone@reductions$umap@cell.embeddings
# Read in meta data (Cells x Vars)
meta = scellenone@meta.data
vis <- Vision(scaled_counts,signatures = sig,meta = meta,)
vis <- addProjection(vis, "UMAP", projection)
vis <- analyze(vis)
sig.adipo
# Create vision signature
sig.non.adipo=vector(mode = "double",length = length(intersect(rownames(scellenone),`non-adipo`$V1)))
names(sig.non.adipo)=intersect(rownames(scellenone),`non-adipo`$V1)
sig.adipo=vector(mode = "double",length = length(intersect(rownames(scellenone),adipo$V1)))
names(sig.adipo)=intersect(rownames(scellenone),adipo$V1)
sig.non.adipo <- createGeneSignature(name = "sig.non.adipo", sigData = sig.non.adipo)
sig.adipo <- createGeneSignature(name = "sig.adipo", sigData = sig.adipo)
#sig=c(sig.non.adipo,sig.adipo,"~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
sig=c(sig.non.adipo)
library(Matrix)
# Scale counts within a sample
n.umi <- colSums(count)
scaled_counts <- t(t(count) / n.umi) * median(n.umi)
# Adding UMAP
projection <- scellenone@reductions$umap@cell.embeddings
# Read in meta data (Cells x Vars)
meta = scellenone@meta.data
vis <- Vision(scaled_counts,signatures = sig,meta = meta,)
vis <- addProjection(vis, "UMAP", projection)
vis <- analyze(vis)
sig.non.adipo
# Create vision signature
sig.non.adipo=vector(mode = "double",length = length(intersect(rownames(scellenone),`non-adipo`$V1)))
sig.non.adipo[]=1
names(sig.non.adipo)=intersect(rownames(scellenone),`non-adipo`$V1)
sig.adipo=vector(mode = "double",length = length(intersect(rownames(scellenone),adipo$V1)))
names(sig.adipo)=intersect(rownames(scellenone),adipo$V1)
sig.non.adipo <- createGeneSignature(name = "sig.non.adipo", sigData = sig.non.adipo)
sig.adipo <- createGeneSignature(name = "sig.adipo", sigData = sig.adipo)
#sig=c(sig.non.adipo,sig.adipo,"~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
sig=c(sig.non.adipo)
library(Matrix)
# Scale counts within a sample
n.umi <- colSums(count)
scaled_counts <- t(t(count) / n.umi) * median(n.umi)
# Adding UMAP
projection <- scellenone@reductions$umap@cell.embeddings
# Read in meta data (Cells x Vars)
meta = scellenone@meta.data
vis <- Vision(scaled_counts,signatures = sig,meta = meta,)
vis <- addProjection(vis, "UMAP", projection)
vis <- analyze(vis)
# Create vision signature
sig.non.adipo=vector(mode = "double",length = length(intersect(rownames(scellenone),`non-adipo`$V1)))
sig.non.adipo[]=1
names(sig.non.adipo)=intersect(rownames(scellenone),`non-adipo`$V1)
sig.adipo=vector(mode = "double",length = length(intersect(rownames(scellenone),adipo$V1)))
sig.adipo[]=1
names(sig.adipo)=intersect(rownames(scellenone),adipo$V1)
sig.non.adipo <- createGeneSignature(name = "sig.non.adipo", sigData = sig.non.adipo)
sig.adipo <- createGeneSignature(name = "sig.adipo", sigData = sig.adipo)
sig=c(sig.non.adipo,sig.adipo,"~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/additional_files/signatures/geneset.gmt")
#sig=c(sig.non.adipo)
library(Matrix)
# Scale counts within a sample
n.umi <- colSums(count)
scaled_counts <- t(t(count) / n.umi) * median(n.umi)
# Adding UMAP
projection <- scellenone@reductions$umap@cell.embeddings
# Read in meta data (Cells x Vars)
meta = scellenone@meta.data
vis <- Vision(scaled_counts,signatures = sig,meta = meta,)
vis <- addProjection(vis, "UMAP", projection)
vis <- analyze(vis)
viewResults(vis)
scellenone$adipo=vis@SigScores[,"sig.adipo"]
scellenone$non.adipo=vis@SigScores[,"sig.non.adipo"]
scellenone$hallmark=vis@SigScores[,"HALLMARK_ADIPOGENESIS"]
FeaturePlot(scellenone,"HALLMARK_ADIPOGENESIS")
scellenone$adipo=vis@SigScores[,"sig.adipo"]
scellenone$non.adipo=vis@SigScores[,"sig.non.adipo"]
scellenone$hallmark=vis@SigScores[,"HALLMARK_ADIPOGENESIS"]
FeaturePlot(scellenone,"hallmark")
library(ggplot2)
FeaturePlot(scellenone,"hallmark")
FeaturePlot(scellenone,"adipo")
library(ggplot2)
FeaturePlot(scellenone,"hallmark")
FeaturePlot(scellenone,"adipo")
FeaturePlot(scellenone,"non.adipo")
nrow(scellenone@meta.data)
df=data.frame(score=c(scellenone$adipo,scellenone$non.adipo,scellenone$hallmark),label=c(replicate(n = 162,"adipo"),replicate(n = 162,"non.adipo"),replicate(n = 162,"hallmark")))
ggplot(df,aes(x=score,color=label))+geom_histogram()
ggplot(df,aes(x=score,fill=label))+geom_histogram()
ggplot(scellenone@meta.data,aes(x=adipo,y=hallmark))+geom_point()
ggplot(scellenone@meta.data,aes(x=non.adipoadipo,y=hallmark))+geom_point()
ggplot(scellenone@meta.data,aes(x=non.adipo,y=hallmark))+geom_point()
cor.test(x = scellenone$ad,y=scellenone$hallmark)
cor.test(x = scellenone$adipo,y=scellenone$hallmark)
cor.test(x = scellenone$non.adipo,y=scellenone$hallmark)
cor.test(x = scellenone$non.adipo,y=scellenone$hallmark,method = "spearman")
cor.test(x = scellenone$adipo,y=scellenone$hallmark,method = "spearman")
ggplot(df,aes(x=score,fill=label))+geom_histogram()
FeaturePlot(scellenone,"hallmark")
FeaturePlot(scellenone,"PPARG")
FeaturePlot(scellenone,"hallmark")
ggplot(df,aes(x=score,fill=label))+geom_histogram()
FeaturePlot(scellenone,"hallmark")
FeaturePlot(scellenone,"adipo")
FeaturePlot(scellenone,"non.adipo")
ggplot(scellenone@meta.data,aes(x=non.adipo,y=adipo))+geom_point()
cor.test(x = scellenone$adipo,y=scellenone$non.adipo,method = "spearman")
viewResults(vis)
FeaturePlot(scellenone,"hallmark")+geom_point(size=2)
FeaturePlot(scellenone,"hallmark",pt.size = 2)#+geom_point(size=2)
library(viridis)
FeaturePlot(scellenone,"hallmark",pt.size = 2)+scale_color_viridis()
FeaturePlot(scellenone,"hallmark",pt.size = 2)+scale_color_viridis()+geom_void()
FeaturePlot(scellenone,"hallmark",pt.size = 2)+scale_color_viridis()+theme_void()
FeaturePlot(scellenone,"hallmark",pt.size = 2)+scale_color_viridis()+theme_void()
FeaturePlot(scellenone,"adipo",pt.size = 2)+scale_color_viridis()+theme_void()
FeaturePlot(scellenone,"adipo",pt.size = 2)+scale_color_viridis()+theme_void()
FeaturePlot(scellenone,"non.adipo",pt.size = 2)+scale_color_viridis()+theme_void()
FeaturePlot(scellenone,"non.adipo",pt.size = 2)+scale_color_viridis()+theme_void()
ggplot(df,aes(x=score,fill=label))+geom_histogram()
df=data.frame(score=c(scellenone$adipo,scellenone$non.adipo),label=c(replicate(n = 162,"adipo"),replicate(n = 162,"non.adipo")))
ggplot(df,aes(x=score,fill=label))+geom_histogram()
viewResults(vis)
ggplot(df,aes(x=score,fill=label))+geom_histogram(color=white)
ggplot(df,aes(x=score,fill=label))+geom_histogram(color="white")
ggplot(df,aes(x=score,color=label))+geom_histogram(fill="white")
ggplot(df,aes(x=score,color=label))+geom_histogram(aes(y=..density..),fill="white")+
geom_density(alpha=.2, fill="#FF6666")
ggplot(df,aes(x=score,color=label))+geom_boxplot()
ggplot(df,aes(x=score,color=label))+geom_boxplot()+theme_classic(base_size = 20)
ggplot(df,aes(x=score,color=label))+geom_boxplot()+theme_classic(base_size = 20)+theme(legend.position = "top")
ggplot(df,aes(x=score,color=label))+geom_boxplot()+theme_classic(base_size = 20)+theme(legend.position = "top")
ggplot(df,aes(x=label,y=score))+geom_boxplot()+theme_classic(base_size = 20)+theme(legend.position = "top")
ggplot(df,aes(x=label,y=score))+geom_boxplot()+theme_classic(base_size = 20)+theme(legend.position = "top")
df=data.frame(score=c(scellenone$adipo,scellenone$non.adipo),label=c(replicate(n = 162,"P2.score"),replicate(n = 162,"P1.score")))
ggplot(df,aes(x=label,y=score))+geom_boxplot()+theme_classic(base_size = 20)+theme(legend.position = "top")
ggplot(df,aes(x=label,y=score))+geom_boxplot()+theme_classic(base_size = 20)+theme(legend.position = "top")
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
# load libraries
library(Seurat)
# Read cellenone seurat data
load(file = "rdata/seurat.cellenone.RData")
scellenone=seurat_singlet_object
rm(seurat_singlet_object)
knitr::opts_knit$set(root.dir = '~/Desktop/scRNA-seq_snRNA-seq_adipocyte_lineages/')
