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Neurodiff_scRNA_Seurat_analysis_2021.Rmd
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---
title: "Neuro Differentiation Single Cell RNAseq"
Authors: Ankush Sharma & Martin Falck
output:
html_document:
df_print: paged
---
```{r setup, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r setup Import packages, message=FALSE, warning=FALSE,include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
invisible(utils::memory.limit(64000))
reqPkg = c("markdown","Seurat","SingleCellExperiment","SummarizedExperiment","EnsDb.Hsapiens.v86","BSgenome.Hsapiens.UCSC.hg38","AnnotationHub","clustree","cowplot","ensembldb","ggthemes","ggplot2","ggplotify","gridGraphics","Matrix","org.Hs.eg.db","Matrix.utils","patchwork","reticulate","reshape2","RCurl","scales","scater","chromVAR","tidyverse","edgeR","plotly","robustbase","knitr","ggpubr","clustree","dplyr","kableExtra", "VISION","JASPAR2018","TFBSTools","Signac")
lapply(reqPkg, library, character.only = TRUE)
# Memory
options(future.globals.maxSize = 4000 * 1024^2) #Set a higher maximum memory size
# Seed (set for reproducibility)
set.seed(1234)
# Increase threshold before R starts showing scientific notation
options(scipen = 10L)
```
<!-- #################################### -->
<!-- #loading data and Quality Control -->
<!-- #################################### -->
```{r importing files }
# Loading the the Differentiation Single cell data after aggregating replicates of each day and then performing Merge
# Settings for the current run
currentjob <- "NeuroDiff_unfilt"
# Path and name
dirname1<- "~/Dropbox (UiO)/Paracetamol_shared/SINGLE_CELL_ANALYSIS/AGGREGATED_ANALYSIS/DIFFERENTIATION/INPUT_FILES/aggr_d0_ctrl/outs/filtered_feature_bc_matrix/"
# scRNAseq - Initialize the Seurat object with the raw (non-normalized data).
diffscRNA.data <- Read10X(data.dir = dirname1)
Day0 <- CreateSeuratObject(counts = diffscRNA.data,
min.cells = 3,
min.features = 200,
project = "D0",
assay = "RNA",
names.delim = "-")
###
dirname2 <- "~/Dropbox (UiO)/Paracetamol_shared/SINGLE_CELL_ANALYSIS/AGGREGATED_ANALYSIS/DIFFERENTIATION/INPUT_FILES/aggr_d7_ctrl/outs/filtered_feature_bc_matrix/"
diffscRNA.data <- Read10X(data.dir = dirname2)
Day7 <- CreateSeuratObject(counts = diffscRNA.data,
min.cells = 3,
min.features = 200,
project = "D7",
assay = "RNA",
names.delim = "-")
####
dirname3<- "~/Dropbox (UiO)/Paracetamol_shared/SINGLE_CELL_ANALYSIS/AGGREGATED_ANALYSIS/DIFFERENTIATION/INPUT_FILES/aggr_d13_ctrl/outs/filtered_feature_bc_matrix/"
diffscRNA.data <- Read10X(data.dir = dirname3)
Day13 <- CreateSeuratObject(counts = diffscRNA.data,
min.cells = 3,
min.features = 200,
project = "D13",
assay = "RNA",
names.delim = "-")
#####
dirname4<- "~/Dropbox (UiO)/Paracetamol_shared/SINGLE_CELL_ANALYSIS/AGGREGATED_ANALYSIS/DIFFERENTIATION/INPUT_FILES/aggr_d20_ctrl/outs/filtered_feature_bc_matrix/"
diffscRNA.data <- Read10X(data.dir = dirname4)
Day20 <- CreateSeuratObject(counts = diffscRNA.data,
min.cells = 3,
min.features = 200,
project = "D20",
assay = "RNA",
names.delim = "-")
differentiation.combined <- merge(Day0, y = c(Day7, Day13, Day20), add.cell.ids = c("Day 0", "Day 7","Day 13","Day 20"), project = "Merged_Differentiation")
differentiation.combined
head(colnames(differentiation.combined))
table(differentiation.combined$orig.ident)
diffscRNA <- differentiation.combined
```
<!-- #################################### -->
<!-- # Quality Control Metrics -->
<!-- #################################### -->
```{r QC metrics -violin plots1}
# scRNAseq
counts_per_cell <- Matrix::colSums(diffscRNA)
counts_per_gene <- Matrix::rowSums(diffscRNA)
genes_per_cell <- Matrix::colSums(diffscRNA)
# Plots
# Counts per cell
p1 <- qplot(log10(counts_per_cell+1), geom = "histogram", main="Counts per cell",
xlab="log10 Counts per cell",
fill=I('#CAB8CB'),
col=I("black"))
pdf(file = paste0("QCmetrics_counts_per_cell-",currentjob,".pdf"), width=8, height=6, paper='special')
p1 <- qplot(log10(counts_per_cell+1), geom = "histogram", main="Counts per cell",
xlab="log10 Counts per cell",
fill=I('#CAB8CB'),
col=I("black"))#+theme(text=element_text(size=16, family="Arial"))
dev.off()
# Genes per cell
p2 <- qplot(log10(genes_per_cell+1), geom = "histogram", main="Genes per cell",
xlab="log10 Genes per cell",
fill=I('#CAB8CB'),
col=I("black"))
pdf(file = paste0("QCmetrics_Genes_per_cell.pdf-",currentjob,".pdf"), width=8, height=6, paper='special')
p2 <- qplot(log10(genes_per_cell+1), geom = "histogram", main="Genes per cell",
xlab="log10 Genes per cell",
fill=I('#CAB8CB'),
col=I("black"))
dev.off()
# Counts per cell vs Genes per cell
p3 <- qplot(counts_per_cell, genes_per_cell, log = "xy", colour=I('#aa93ab'), main="Counts vs Genes per cell", ylab = "Genes", xlab="Counts")
pdf(file = paste0("QCmetrics_counts_vs_genespercell-",currentjob,".pdf"), width=8, height=6, paper='special')
p3 <- qplot(counts_per_cell, genes_per_cell, log = "xy", colour=I('#aa93ab'), main="Counts vs Genes per cell", ylab = "Genes", xlab="Counts")
dev.off()
# Histogram of Counts per gene in log10 scale
p4 <- qplot(log10(counts_per_gene+1), geom = "histogram", main="Counts per gene",
xlab="log10 Counts per gene",
fill=I('#CAB8CB'),
col=I("black"))
pdf(file = paste0("QCmetrics_counts_per_gene-",currentjob,".pdf"), width=8, height=6, paper='special')
p4 <- qplot(log10(counts_per_gene+1), geom = "histogram", main="Counts per gene",
xlab="log10 Counts per gene",
fill=I('#CAB8CB'),
col=I("black"))
dev.off()
(p1 & p2) / (p3 & p4)
dev.copy(pdf,"QCmetrics_panel_1.pdf")
dev.off()
```
```{r QC metrics -violin plots2}
# The [[ operator can add columns to object metadata. This is a great place to stash QC stats
diffscRNA[["percent.mt"]] <- PercentageFeatureSet(diffscRNA, pattern = "^MT-")
# Show QC metrics for the first 5 cells
head(diffscRNA@meta.data, 5)
# Visualize QC metrics as a violin plot
p1 <- VlnPlot(object = diffscRNA, features = c("nCount_RNA","nFeature_RNA","percent.mt"), ncol = 3, pt.size = 0.2)
p1
dev.copy(pdf,paste0("QCmetrics-",currentjob,".pdf"))
dev.off()
```
```{r feature scatter plots}
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot2 <- FeatureScatter(diffscRNA, feature1 = "nCount_RNA", feature2 = "percent.mt") + ggtitle("Counts vs Mitochondrial percentage") + theme(plot.title = element_text(size = 12, face = "bold")) + NoLegend() + theme(text = element_text(size = 10))#+theme(text=element_text(size=16, family="Arial"))
plot3 <- FeatureScatter(diffscRNA, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") + ggtitle("Counts vs Features") + theme(plot.title = element_text(size = 12, face = "bold")) + NoLegend() + theme(text = element_text(size = 10))
plot2 + plot3
dev.copy(pdf,paste0("Featurescatter-",currentjob,".pdf"))
dev.off()
```
```{r Percentage feature set, message=FALSE, warning=FALSE}
#Regression, but no filtering yet (QC metrics evaluated again later in script)!
# Add mitochondria gene % information
diffscRNA <- PercentageFeatureSet(diffscRNA, pattern = "^MT-", col.name = "percent.mt")
# Plot and save QC metrics for dataset (unfiltered)
VlnPlot(diffscRNA, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, combine = TRUE)
dev.copy(pdf,paste0(currentjob,"_1.nFeat-nCount-perMT.pdf"), width=8, height=10, paper='special')
dev.off()
```
```{r CC Scoring, message=FALSE, warning=FALSE}
# Import cell cycle (cc) genes stored in Seurat then do cc scoring on dataset
# As an alternative to completely removing cell-cycle signal this workflow regress out the difference between the G2M and S-phase scores. Seurat recommends this for differentiating cells and it was best in tests.
s.genes <- cc.genes.updated.2019$s.genes
g2m.genes <- cc.genes.updated.2019$g2m.genes
diffscRNA <- CellCycleScoring(diffscRNA, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
diffscRNA$CC.Difference <- diffscRNA$S.Score - diffscRNA$G2M.Score
# Visualize the distribution of cell cycle markers across
RidgePlot(diffscRNA, features = c("PCNA", "TOP2A", "MCM6", "MKI67"), ncol = 2)
dev.copy(pdf,paste0(currentjob,"_2.CC-markers-distribution.pdf"), height=5, width=8)
dev.off()
```
```{r SCTransform normalization, message=FALSE, warning=FALSE, include=FALSE}
# Normalization and scaling with SCTransform (see Seurat webpage for information)
diffscRNA <- SCTransform(diffscRNA, vars.to.regress = c("percent.mt","CC.Difference"))
```
```{r PCA, message=FALSE, warning=FALSE}
# Run PCA
diffscRNA <- RunPCA(diffscRNA, verbose = FALSE)
```
<!-- #################################### -->
<!-- #Clustree branchpoint analysis -->
<!-- #################################### -->
```{r Clustree, message=FALSE, warning=FALSE}
# This code can be used to evaluate what resolution can be used when running clustering algorithm (louvain)
# It is only an approximation of how many clusters are reasonable.
# Run the FindClusters with a vector of different values then run clustree and see where datsset has stable branching then use that resolution for further analysis.
diffscRNA <- FindNeighbors(diffscRNA, dims = 1:30, verbose = FALSE)
diffscRNA <- FindClusters(diffscRNA, resolution = c(
0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.7,0.8,1),
verbose = FALSE)
diffscRNA <- RunUMAP(diffscRNA, dims = 1:30, verbose = FALSE)
# Clustree plots
clustree(diffscRNA, prefix="SCT_snn_res.", use_core_edges=TRUE, label=T)
dev.copy(pdf,paste0(currentjob,"_3.Clustree.pdf"), height=5, width=8)
dev.off()
clustree_overlay(diffscRNA, x_value = "umap1", y_value = "umap2", red_dim = "umap", prefix="SCT_snn_res.")
dev.copy(pdf,paste0(currentjob,"_3.2.ClustreeOverlay.pdf"), height=5, width=8)
dev.off()
# Once decided on a resolution, rerun FindClusters with the picked value (in this case 0.5)
diffscRNA <- FindClusters(diffscRNA, resolution = 0.5, verbose = FALSE)
```
```{Clustering and dimensions reduction (UMAP), message=FALSE, warning=FALSE}
# Pick appropriate dims from Elbowplot (when it flattens enough).
# Use Jackstraw for more accuracy if needed
# Tweak FindNeigbors in Clustree chunk if you change dims!
ElbowPlot(diffscRNA, ndims = 50)
dev.copy(pdf,paste0(currentjob,"_4.Elbowplot.pdf"), height=5, width=8)
dev.off()
# Standard Seurat3, remember the resolution from Clustree above!
diffscRNA <- RunUMAP(diffscRNA, dims = 1:30, verbose = FALSE)
diffscRNA <- FindNeighbors(diffscRNA, dims = 1:30, verbose = FALSE)
diffscRNA <- FindClusters(diffscRNA, resolution = 0.5, verbose = FALSE)
DimPlot(diffscRNA, label = TRUE, label.size = 5)
dev.copy(pdf,paste0(currentjob,"_5.UMAP.un-labelled-clusters.pdf"), height=5, width=8)
dev.off()
```
```{r Find cluster markers, message=FALSE, warning=FALSE}
# Run normalization and scaledata on "RNA" slot as recommended by Seurat/Satijahlab
DefaultAssay(diffscRNA) <- "RNA"
diffscRNA <- NormalizeData(diffscRNA, assay="RNA")
diffscRNA <- FindVariableFeatures(diffscRNA, selection.method = "vst", nfeatures = 2000, assay="RNA")
top10 <- head(VariableFeatures(diffscRNA), 10)
diffscRNA <- ScaleData(diffscRNA, vars.to.regress = c("percent.mt","CC.Difference"))
diffscRNA.markers <- FindAllMarkers(diffscRNA, only.pos = F, min.pct = 0.1, logfc.threshold = 0.2)
top500 <- diffscRNA.markers %>% group_by(cluster) %>% top_n(n = 500, wt = avg_log2FC)
# Write markers to file
write.table(top500, paste0(currentjob,"_Unfilt.Top500-cluster-markers.csv"))
```
```{r Heatmap: Top10 genes , message=FALSE, warning=FALSE}
# setting slim.col.label to TRUE will print just the cluster IDS instead of
# every cell name
top10 <- diffscRNA.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
DoHeatmap(diffscRNA, features = top10$gene, label = FALSE)+ scale_fill_gradientn(colors = c("#8CC2CA","#8175AA","#FFC966"))
pdf(file = paste0(currentjob,"_6.heatmap.Top10-cluster-markers.pdf"), width=12, height=14, paper='special')
DoHeatmap(diffscRNA, features = top10$gene, label = FALSE)
dev.off()
```
```{r UMAP PLOTS, message=FALSE, warning=FALSE}
# Change back to "SCT" assay for dims/clusters
DefaultAssay(diffscRNA) <- "SCT"
# Make UMAP with labels from scCatch
plot9 <- DimPlot(diffscRNA, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
HoverLocator(plot = plot9, information = FetchData(diffscRNA, vars = c("ident", "PC_1", "nFeature_RNA")))
pdf(file = paste0(currentjob,"_7.UMAP-scCatch.unFilt.pdf"), width=8, height=5, paper='special')
DimPlot(diffscRNA, reduction = "umap", label = TRUE, pt.size = 0.5)
dev.off()
DimPlot(diffscRNA, label = TRUE, label.size = 5, reduction = 'umap', group.by = 'orig.ident')
#ASSIGNING COLORS
DimPlot(diffscRNA, label = TRUE, label.size = 5, reduction = 'umap', group.by = 'orig.ident', cols =c('D0' = '#FF9888', 'D7' = '#B60A1C','D13' = '#C3CE3D','D20' = '#C0D6E4'))
dev.copy(pdf,paste0(currentjob,"_5.UMAP.BYorigin.pdf"), height=5, width=8)
dev.off()
```
#Save RDS for end of pipe file, first run (no filtering)
```{r Save end-RDS nr 1 of 2, message=FALSE, warning=FALSE}
# Save final RDS and countfile (files 1 of 2, unfiltered)
saveRDS(diffscRNA, file = paste0(currentjob,"_FirstIteration.rds"))
```
```{r Unfiltered dataset, warning=FALSE}
#Evaluating Clusters
#Tweaking QC, filtering using isOutlier() from scater for QC metrics across clusters and annotation and the filter cells
# Add the active ident (assigned cell types by scCatch) to metadata as 'cell_type'
currentjob <- "NeuroDiff"
names(diffscRNA@meta.data)
diffscRNA <- AddMetaData(diffscRNA, metadata =diffscRNA@active.ident ,col.name = 'cell_type')
# Print table with n cells and fraction per cluster/cell-type
table(Idents(diffscRNA))
prop.table(table(Idents(diffscRNA)))
# VlnPlot counts, features and % mit grouped by cell-type
VlnPlot(diffscRNA, c("nCount_RNA"), pt.size = 0, ncol = 1, group.by = "cell_type") + NoLegend()
dev.copy(pdf,paste0(currentjob,"_8.ReEval.nCount-cell-type.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA, c("nFeature_RNA"), pt.size = 0, ncol = 1, group.by = "cell_type") + NoLegend()
dev.copy(pdf,paste0(currentjob,"_8.ReEval.nFeature-cell-type.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA, c("percent.mt"), pt.size = 0, ncol = 1, group.by = "cell_type") + NoLegend()
dev.copy(pdf,paste0(currentjob,"_8.ReEval.percent.mt-cell-type.pdf"), width=8, height=6, paper='special')
dev.off()
# Scatterplot comparing counts to % mit
FeatureScatter(diffscRNA, feature1 = "nCount_RNA", feature2 = "percent.mt") + ggtitle(label = "Scatter: count vs % mt | Unfiltered data")
dev.copy(pdf,paste0(currentjob,"_8.ReEval.scatter.count.vs.mt.pdf"), width=8, height=5, paper='special')
dev.off()
FeatureScatter(diffscRNA, feature1 = "nCount_RNA", feature2 = "percent.mt") + ggtitle(label = "Scatter: count vs % mt | Unfiltered data")+ scale_x_continuous(name="nCount_RNA", limits=c(0, 10000))
dev.copy(pdf,paste0(currentjob,"_8.ReEval.scatter.count.vs.mt.ZOOM.pdf"), width=8, height=5, paper='special')
dev.off()
```
<!-- #################################### -->
<!-- #ReEvaluation of Dataset -->
<!-- #################################### -->
```{r Scater analysis, echo=TRUE, warning=FALSE}
#Creating New Object diffscRNA.qc
diffscRNA.qc <- diffscRNA
# Change back to the raw counts
DefaultAssay(diffscRNA.qc) <- "RNA"
DefaultAssay(diffscRNA.qc) #Confirm
# Rerun PercentageFeatureSet
diffscRNA.qc <- PercentageFeatureSet(diffscRNA.qc, pattern = "^MT-", col.name = "percent.mt")
# Scater detection of outliers, isOutlier()
# nmads= 3 is used here to set a proper filter level to identify the cells that can be removed with this threshold
.
discard.mito <- isOutlier(diffscRNA.qc$percent.mt, type="higher", nmads=3)
# Summmary gives a certain amount of cells that will be removed.
summary(discard.mito)
# Plot counts vs mit % and add horizontal line
plot(diffscRNA.qc$nCount_RNA, diffscRNA.qc$percent.mt, log="x",
xlab="Total count", ylab='Mitochondrial %')
abline(h=attr(discard.mito, "thresholds")["higher"], col="red")
dev.copy(pdf,paste0(currentjob,"_10.FILT.qc_countsVSmitpercent.pdf"), width=8, height=6, paper='special')
dev.off()
lost <- calculateAverage(diffscRNA.qc@assays$RNA@counts[,!discard.mito])
kept <- calculateAverage(diffscRNA.qc@assays$RNA@counts[,discard.mito])
#If discarded pool is enriched for a certain cell type, we should observe increased expression of the corresponding marker genes.
logged <- cpm(cbind(lost, kept), log=TRUE, prior.count=2)
logFC <- logged[,1] - logged[,2]
abundance <- rowMeans(logged)
is.mito <- grep("^MT-",rownames(diffscRNA.qc@assays$RNA),ignore.case = TRUE)
plot(abundance, logFC, xlab="Average count", ylab="Log-FC (lost/kept)", pch=16)
points(abundance[is.mito], logFC[is.mito], col="dodgerblue", pch=16)
dev.copy(pdf,paste0(currentjob,"_10.FILT.qc_logFC-abundance-in-Discarded-Cells.pdf"), width=8, height=6, paper='special')
dev.off()
# Top 200 gene set of positive log-fold changes, remove unwanted cells and create .hESC
# Analyze this list to know what nmads= threshold is suitable for keeping cells/features that are interesting
coefs <- predFC(cbind(lost, kept), design=cbind(1, c(1, 0)))[,2]
info <- data.frame(logFC=coefs, Lost=lost, Kept=kept,
row.names=rownames(diffscRNA.qc))
top200_info <- head(info[order(info$logFC, decreasing=TRUE),], 200)
```
```{r SCATER outlier filter on determined nmads, echo=TRUE}
# Using that nmads value=5 and comparing it with diagnostic plots for comparison with other nmads threshold e.g 3,4,6
#Optimal nmads =
discard.mito <- isOutlier(diffscRNA.qc$percent.mt, type="higher", nmads=5)
summary(discard.mito)
plot(diffscRNA.qc$nCount_RNA, diffscRNA.qc$percent.mt, log="x",
xlab="Total count", ylab='Mitochondrial %')
abline(h=attr(discard.mito, "thresholds")["higher"], col="red")
dev.copy(pdf,paste0(currentjob,"_10.2.FILT.mads5.qc_countsVSmitpercent.pdf"), width=8, height=6, paper='special')
dev.off()
lost <- calculateAverage(diffscRNA.qc@assays$RNA@counts[,!discard.mito])
kept <- calculateAverage(diffscRNA.qc@assays$RNA@counts[,discard.mito])
logged <- cpm(cbind(lost, kept), log=TRUE, prior.count=2)
logFC <- logged[,1] - logged[,2]
abundance <- rowMeans(logged)
is.mito <- grep("^MT-",rownames(diffscRNA.qc@assays$RNA),ignore.case = TRUE)
plot(abundance, logFC, xlab="Average count", ylab="Log-FC (lost/kept)", pch=16)
points(abundance[is.mito], logFC[is.mito], col="dodgerblue", pch=16)
dev.copy(pdf,paste0(currentjob,"_10.2.FILT.mads5.qc_logFC-abundance-in-Discarded-Cells.pdf"), width=8, height=6, paper='special')
dev.off()
coefs <- predFC(cbind(lost, kept), design=cbind(1, c(1, 0)))[,2]
info.nmads5 <- data.frame(logFC=coefs, Lost=lost, Kept=kept,
row.names=rownames(diffscRNA.qc))
top200_nmads5.info <- head(info.nmads5[order(info.nmads5$logFC, decreasing=TRUE),], 200)
top200_nmads5.info
# Save the top200 list for documentation
write.csv(top200_nmads5.info, paste0(currentjob,"_F.top200.nmads5.logFC-in-DiscardedPool.csv"))
```
<!-- #################################### -->
<!-- #ReAnalyzing with Filtered data after Quality control-->
<!-- #nmads for filtering data = 5 -->
<!-- #################################### -->
```{r Continue with filtered dataset using all steps in first run!, message=FALSE, warning=FALSE, include=FALSE}
# Remove outliers from dataset
# Remove above 50k reads and under 2000
diffscRNA.hESC <- diffscRNA.qc[,!discard.mito]
diffscRNA.hESC <- subset(diffscRNA.hESC, nCount_RNA >= 2000 & nCount_RNA <= 50000)
# Rerun pipeline with .hESC dataset
# CC-regression, SCTransform, PCA, etc
s.genes <- cc.genes.updated.2019$s.genes
g2m.genes <- cc.genes.updated.2019$g2m.genes
diffscRNA.hESC <- CellCycleScoring(diffscRNA.hESC, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
diffscRNA.hESC$CC.Difference <- diffscRNA.hESC$S.Score - diffscRNA.hESC$G2M.Score
diffscRNA.hESC <- SCTransform(diffscRNA.hESC, vars.to.regress = c("percent.mt","CC.Difference"))
diffscRNA.hESC <- RunPCA(diffscRNA.hESC)
diffscRNA.hESC <- FindNeighbors(diffscRNA.hESC,dims = 1:30, verbose = FALSE)
diffscRNA.hESC <- RunTSNE(diffscRNA.hESC, dims = 1:30)
diffscRNA.hESC <- RunUMAP(diffscRNA.hESC, dims = 1:30)
```
```{r Clustree analysis on filtered data , echo=TRUE, warning=FALSE}
# cluster analysis on filtered data to obtain optimal threshold for cluster detection
# Run the FindClusters with a vector of different values analysis.
diffscRNA.hESC <- FindNeighbors(diffscRNA.hESC, dims = 1:30, verbose = FALSE)
diffscRNA.hESC <- FindClusters(diffscRNA.hESC, resolution = c(
0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.7,0.8,0.9,1),
verbose = FALSE)
diffscRNA.hESC <- RunUMAP(diffscRNA.hESC, dims = 1:30, verbose = FALSE)
# Clustree II plots
clustree(diffscRNA.hESC, prefix="SCT_snn_res.", use_core_edges=TRUE, label=T)
dev.copy(pdf,paste0(currentjob,"_11.FILT.Clustree.pdf"), height=5, width=8)
dev.off()
clustree_overlay(diffscRNA.hESC, x_value = "umap1", y_value = "umap2", red_dim = "umap", prefix="SCT_snn_res.")
dev.copy(pdf,paste0(currentjob,"_11.2.FILT.ClustreeOverlay.pdf"), height=5, width=8)
dev.off()
# Decided on e.g. 0.20 for the d20 sample
DefaultAssay(diffscRNA.hESC) <- "SCT"
diffscRNA.hESC <- FindClusters(diffscRNA.hESC, resolution = 0.55, verbose = FALSE)
diffscRNA.hESC <- RunTSNE(diffscRNA.hESC, dims = 1:30)
diffscRNA.hESC <- RunUMAP(diffscRNA.hESC, dims = 1:30)
```
```{r markers filtered}
currentjob <- "Diff_Merged_Final"
#number of genes expressed in each cluster
# Also check if only.pos, min.pct, and logfc.threshold can be changed to speed up with a better cell pool
# Run default normalization and scaleDATA on the "RNA" slot as recommended by Seurat/Satijalab
DefaultAssay(diffscRNA.hESC) <- "RNA"
diffscRNA.hESC <- NormalizeData(diffscRNA.hESC, assay="RNA")
diffscRNA.hESC <- FindVariableFeatures(diffscRNA.hESC, selection.method = "vst", nfeatures = 2000, assay="RNA")
top10 <- head(VariableFeatures(diffscRNA.hESC), 10)
diffscRNA.hESC <- ScaleData(diffscRNA.hESC, vars.to.regress = c("percent.mt","CC.Difference"))
#scCatch
DefaultAssay(diffscRNA.hESC) <- "RNA"
diffscRNA.hESC.markers <- FindAllMarkers(
diffscRNA.hESC,
only.pos = F,
min.pct = 0.1,
logfc.threshold = 0.25)
library(Seurat)
top500.diffscRNA.hESC <- diffscRNA.hESC.markers %>% group_by(cluster) %>% top_n(n = 500, wt = avg_log2FC)
top30.diffscRNA.hESC <- diffscRNA.hESC.markers %>% group_by(cluster) %>% top_n(n = 30, wt = avg_log2FC)
plotUMAP1 <- DimPlot(diffscRNA.hESC, reduction = "umap", label = TRUE, pt.size = 0.5) ##+theme(text=element_text(size=16, family="Arial"))
plotUMAP1
dev.copy(pdf,paste0(currentjob,"_5.UMAP_Unlabeled.pdf"), height=5, width=8)
dev.off()
write.table(top500.diffscRNA.hESC, paste0(currentjob,"_filt.Top500-cluster-markers.csv"))
write.table(top30.diffscRNA.hESC, paste0(currentjob,"_filt.Top30-cluster-markers.csv"))
```
<!-- #################################### -->
<!-- Plotting of data -->
<!-- #################################### -->
```{r markers filtered}
FeaturePlot(object = diffscRNA.hESC, features = c("OTP"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_1.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("ISL1"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_2.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("SIX3","SIX6"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_3.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("DLX1","DLX2"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_4.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("DLX5","DLX6"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_5.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("NKX2-1","LHX2"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_7.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("ASCL1","GNRH1"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_8.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("SP9","ARX"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_9.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("FANCI","LHX2"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_10.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("DCX","NES","VIM"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_11.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("TUBB3"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_12.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("SOX2","DHCR24","SOX21"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_13.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("PODXL","DHCR24","VCAN"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_14.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("FOXD3-AS1","TUBB4B","PRDX1"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_15.pdf"), height=5, width=8)
dev.off()
FeaturePlot(object = diffscRNA.hESC, features = c("SMC4","CD24","FOXG1","IGFBP5"), reduction = "umap")#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"featureplots_15.pdf"), height=5, width=8)
dev.off()
```
```{r Heatmap: Top10 genes, message=FALSE, warning=FALSE}
top10 <- diffscRNA.hESC.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
DoHeatmap(diffscRNA.hESC, features = top10$gene, label = FALSE) + scale_fill_gradientn(colors = c("#8CC2CA","#8175AA","#FFC966")) + theme(text=element_text(size=16, family="Arial"))
pdf(file = paste0(currentjob,"_6_FILT.heatmap.Top10-cluster-markers.pdf"), width=12, height=14, paper='special')
DoHeatmap(diffscRNA.hESC, features = top10$gene, label = FALSE) + scale_fill_gradientn(colors = c("#8CC2CA","#8175AA","#FFC966")) + theme(text=element_text(size=16, family="Arial"))
dev.off()
```
```{r UMAP by origin ,message=FALSE, warning=FALSE }
#UMAP by origin
library(dplyr)
Idents(diffscRNA.hESC) <- "orig.ident"
diffscRNA.hESC <- RenameIdents(diffscRNA.hESC,'d0-Ctrl' = 'D0', 'd7-Ctrl' = 'D7','d13-Ctrl'='D13','d20-Ctrl'='D20')
diffscRNA.hESC$ident <- Idents(diffscRNA.hESC)
diffscRNA.hESC$ident
DimPlot(diffscRNA.hESC, label = FALSE, label.size = 4, reduction = 'umap', group.by = 'ident',repel = TRUE, cols =c('D0' = '#FF9888', 'D7' = '#B60A1C','D13' = '#C3CE3D','D20' = '#C0D6E4')) + theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_5.UMAPbyOrigin.pdf"), width=8, height=6, paper='special')
dev.off()
plotUMAP2 <- DimPlot(diffscRNA.hESC, label = TRUE, label.size = 5, reduction = 'umap', group.by = 'orig.ident') +theme(text=element_text(size=16, family="Arial"))
plotUMAP2
HoverLocator(plot = plotUMAP2, information = FetchData(diffscRNA.hESC, vars = c("ident", "PC_1", "nFeature_RNA","percent.mt")))
```
```{r cellcyle Genes}
library(Seurat)
library(ggthemes)
library(ggplotify)
#UMAP_BY_FILT by origin
library(extrafontdb)
DimPlot(diffscRNA.hESC, label = FALSE, label.size = 4, reduction = 'umap', group.by = 'Phase',repel = TRUE, cols =c('S' = '#A72625', 'G2M' = '#8175AA','G1' = '#ffa500' )) +theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_5.Cell_Cycle_Origin.pdf"), width=8, height=6, paper='special')
dev.off()
plotUMAP3 <- DimPlot(diffscRNA.hESC, label = TRUE, label.size = 5, reduction = 'umap', group.by = 'ident') +theme(text=element_text(size=16, family="Arial"))
plotUMAP3
HoverLocator(plot = plotUMAP3, information = FetchData(diffscRNA.hESC, vars = c("ident", "PC_1", "nFeature_RNA","percent.mt")))
```
<!-- #################################### -->
<!-- #Renaming Cluster Names -->
<!-- #################################### -->
```{r renaming cluster names }
#Rearranging cluster names from default numbers assigned By Seurat, in order to facilitate constraining of Single cell ATAC seq clusters for optimal integration multi-omics data
diffscRNA.hESC$original_seurat_clusters<-diffscRNA.hESC$seurat_clusters
DimPlot(diffscRNA.hESC, reduction = "umap", label = TRUE,group.by = "original_seurat_clusters", pt.size = 0.5,cols = c('0' = '#D37295', '1' = '#FF9888', '2' = '#8175AA', '3' = '#DAB6AF','4' = '#FFC966','5' = '#C3CE3D', '6' = '#8CC2CA', '7' = '#59A14F', '8' = '#CCCCCC', '9' = '#008080', '10' = '#C0D6E4','11' = '#FFD700','12' = '#A3ACB9','13' = '#F28E2B','14' = '#B60A1C','15' = '#8A494D'))##+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_13.Original_clusternames_NUMBERS_FILT.UMAP_final.pdf"), width=8, height=6, paper='special')
dev.off()
# Assign new IDs based on scCatch annotation results - Using Seurat Markers
#renumbered clusters from 0to13
#fil.cluster.ids <- c("4","5","7","0","8","1","12","9","2","6","11","10","13","3")
fil.cluster.ids <- c("4_PAX6","5_FABP7","2_ID1","7_KIF15","1_NANOG","9_HES1","12_NTRK1","8_FGF17","11_CDKN1C","3_LRRC75A","6_BMP4","13_GNRH1","10_DLX5","15_PCNA","14_KPNA2")
Idents(diffscRNA.hESC) <- "seurat_clusters"
names(fil.cluster.ids) <- levels(diffscRNA.hESC)
diffscRNA.hESC <- RenameIdents(diffscRNA.hESC, fil.cluster.ids)
DimPlot(diffscRNA.hESC, reduction = "umap", label = TRUE,group.by = "ident", pt.size = 0.5,cols = c('5_FABP7' = '#FF9888', '1_NANOG' = '#C3CE3D','7_KIF15' = '#8175AA', '2_ID1' = '#DAB6AF', '10_DLX5' = '#FFD700', '3_LRRC75A' = '#023858', '4_PAX6' = '#D37295','9_HES1' = '#FFC966', '6_BMP4' ='#008080','11_CDKN1C' = '#59A14F', '8_FGF17' = '#B15928','12_NTRK1' ='#8CC2CA', '13_GNRH1' = '#C0D6E4', '14_KPNA2' = '#F28E2B', '15_PCNA' = '#A3ACB9'))#+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_13.NEW_CLUSTERNAME_NUMBERS_FILT.UMAP_final.pdf"), width=8, height=6, paper='special')
dev.off()
fil.cluster.ids <- c("4","5","2","7","1","9","12","8","11","3","6","13","10","15","14")
Idents(diffscRNA.hESC) <- "seurat_clusters"
names(fil.cluster.ids) <- levels(diffscRNA.hESC)
diffscRNA.hESC <- RenameIdents(diffscRNA.hESC, fil.cluster.ids)
DimPlot(diffscRNA.hESC, reduction = "umap", label = TRUE,group.by = "ident", pt.size = 0.5,cols = c('5' = '#FF9888', '1' = '#C3CE3D', '7' = '#8175AA', '2' = '#DAB6AF', '10' = '#FFD700', '3' = '#023858', '4' = '#D37295','9' = '#FFC966', '6' ='#008080','11' = '#59A14F', '8' = '#B15928','12' ='#8CC2CA', '13' = '#C0D6E4', '14' = '#F28E2B', '15' = '#A3ACB9'))#+theme(text=element_text(size=12, family="Arial"))
fil.cluster.ids <- c("R4","R5","R2","R7","R1","R9","R12","R8","R11","R3","R6","R13","R10","R15","R14")
Idents(diffscRNA.hESC) <- "seurat_clusters"
names(fil.cluster.ids) <- levels(diffscRNA.hESC)
diffscRNA.hESC <- RenameIdents(diffscRNA.hESC, fil.cluster.ids)
DimPlot(diffscRNA.hESC, reduction = "umap", label = TRUE,group.by = "ident", pt.size = 0.5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_13.NEW_RENAME_NUMBERS_FILT.UMAP_final.pdf"), width=8, height=6, paper='special')
dev.off()
diffscRNA.hESC$name_number_seurat_clusters<-diffscRNA.hESC$seurat_clusters
# Add in meta-data
diffscRNA.hESC <- AddMetaData(diffscRNA.hESC, metadata =diffscRNA.hESC@active.ident, col.name = 'cell_type')
```
```{r Single cell heatmap of feature expression3}
# Heatmap for the genes of interest
library(ggplot2)
#Selected140 genes
#Genes of interest
DoHeatmap(subset(diffscRNA.hESC, downsample = 100), features = c("POU5F1", "NANOG", "TDGF1", "GAL", "PODXL", "ID1", "MYC", "PAX6", "DNMT3B", "LIN28A", "FOXD3-AS1", "CD24", "EPCAM", "DPPA4", "FZD2", "NR6A1", "SOX21", "ZIC2", "SOX11", "HESX1", "MYLK", "IGFBP5", "FZD5", "FGF8", "SOX2", "RAX", "LIX1", "FEZF2", "POU3F1", "POU3F2", "NKX2-1", "ASCL1", "NEUROG1", "NEUROD1", "ISL1", "BSX", "HMX2", "OTP", "GSX1", "SF1", "GNRH1", "GRIA2", "L1CAM", "NHLH1", "ELAVL4", "SIX3", "SIX6", "OTX2", "TH", "INSM1", "ARX", "DBX1", "DCX", "DLL1", "DLX1", "DLX2", "DLX5", "DLX6", "GAD1", "GRIA1", "INA", "KIF19", "NEFL", "NEFM", "NEUROD4", "NSG2", "POU2F2", "RELN", "SCG2", "SIM1", "SIM2", "SNAP25", "SP9", "SST", "STMN2", "SYP", "SYT4", "TAC1", "TAGLN3", "TLX3", "NTRK1", "NTRK3", "PCSK2", "RET", "DLL3", "GAD2", "GAP43", "REST", "SOX3", "TUBB3", "HES1", "BMP4", "KLF15", "CDK1", "TYMS", "ANLN", "HES6", "LMO1", "ELAVL3", "PRRX1", "OTX2", "PCNA", "SPARCL1", "MAP6", "KRT18", "DDIT4", "PAMR1", "FOXH1", "FABP7", "RBFOX3", "SOX6", "CDH7", "CDH12", "PHC1", "PHC2", "YAF2", "CDH1", "CDH2", "GNG2", "EMX2", "CDK6", "TAGLN ", "GNG8", "KLF11", "LHX9", "DCT", "VEPH1", "GNG3", "GNG11", "TRIM71", "SOX3", "KRT19", "DKMN", "NSG1", "JHY"), size = 2) + scale_fill_gradientn(colors = c("#8CC2CA","#8175AA","#FFC966")) + theme(text = element_text(size = 7)) + NoLegend()
DoHeatmap(subset(diffscRNA.hESC, downsample = 100), features = c("POU5F1", "NANOG", "TDGF1", "GAL", "PODXL", "ID1", "MYC", "PAX6", "DNMT3B", "LIN28A", "FOXD3-AS1", "CD24", "EPCAM", "DPPA4", "FZD2", "NR6A1", "SOX21", "ZIC2", "SOX11", "HESX1", "MYLK", "IGFBP5", "FZD5", "FGF8", "SOX2", "RAX", "LIX1", "FEZF2", "POU3F1", "POU3F2", "NKX2-1", "ASCL1", "NEUROG1", "NEUROD1", "ISL1", "BSX", "HMX2", "OTP", "GSX1", "SF1", "GNRH1", "GRIA2", "L1CAM", "NHLH1", "ELAVL4", "SIX3", "SIX6", "OTX2", "TH", "INSM1", "ARX", "DBX1", "DCX", "DLL1", "DLX1", "DLX2", "DLX5", "DLX6", "GAD1", "GRIA1", "INA", "KIF19", "NEFL", "NEFM", "NEUROD4", "NSG2", "POU2F2", "RELN", "SCG2", "SIM1", "SIM2", "SNAP25", "SP9", "SST", "STMN2", "SYP", "SYT4", "TAC1", "TAGLN3", "TLX3", "NTRK1", "NTRK3", "PCSK2", "RET", "DLL3", "GAD2", "GAP43", "REST", "SOX3", "TUBB3", "HES1", "BMP4", "KLF15", "CDK1", "TYMS", "ANLN", "HES6", "LMO1", "ELAVL3", "PRRX1", "OTX2", "PCNA", "SPARCL1", "MAP6", "KRT18", "DDIT4", "PAMR1", "FOXH1", "FABP7", "RBFOX3", "SOX6", "CDH7", "CDH12", "PHC1", "PHC2", "YAF2", "CDH1", "CDH2", "GNG2", "EMX2", "CDK6", "TAGLN ", "GNG8", "KLF11", "LHX9", "DCT", "VEPH1", "GNG3", "GNG11", "TRIM71", "SOX3", "KRT19", "DKMN", "NSG1", "JHY"), size = 2) + scale_fill_gradientn(colors = c("#8CC2CA","#8175AA","#FFC966")) + theme(text = element_text(size = 7))
dev.copy(pdf,paste0(currentjob,"_14_Heatmap_140_GENES_OF_INTEREST.pdf"), width=10, height=14, paper='special')
dev.off()
dev.set(dev.next())
#SELECTED70 GENES
DoHeatmap(subset(diffscRNA.hESC, downsample = 100), features = c("SOX2", "IGFBP5", "RAX", "LIX1", "PAX6", "HESX1", "PAMR1", "FOXH1", "CDH1", "TDGF1", "DNMT3B", "EPCAM", "PODXL", "ID1", "POU5F1", "GAL", "FOXD3-AS1", "DPPA4", "NR6A1", "CD24", "ZIC2", "FGF8", "DLX6", "DLX5", "BMP4", "GRIA1", "SIX6", "NKX2-1", "NEUROG1", "DLL1", "ASCL1", "NHLH1", "NEUROD1", "FEZF2", "L1CAM", "INA", "GRIA2", "GNRH1", "INSM1", "ELAVL4", "ISL1", "TH", "DCX", "TAGLN3", "SCG2", "ELAVL3", "PCSK2", "MAP6", "HES6", "DLL3", "TLX3", "NEUROD4", "SYP", "STMN2", "POU2F2", "TUBB3", "SNAP25", "NSG2", "GAP43", "NEFM", "NEFL", "RET", "NTRK3", "RELN", "NTRK1", "DLX2", "DLX1", "GAD1", "ARX", "SOX3", "SYT4", "SPARCL1", "OTX2", "LMO1", "FABP7", "DCT", "VEPH1", "PRRX1", "CDK6", "GNG11", "HES1", "TYMS", "PCNA", "CDK1", "DLX6-AS1", "TUBB4B", "SELENOP", "ID2", "CRABP1", "APOE", "LDHA", "FZD5", "KRT18", "FGF17"), size = 2) + scale_fill_gradientn(colors = c("#8CC2CA","#8175AA","#FFC966")) + theme(text = element_text(size = 7))
DoHeatmap(subset(diffscRNA.hESC , downsample = 100), features = c("SOX2", "IGFBP5", "RAX", "LIX1", "PAX6", "HESX1", "PAMR1", "FOXH1", "CDH1", "TDGF1", "DNMT3B", "EPCAM", "PODXL", "ID1", "POU5F1", "GAL", "FOXD3-AS1", "DPPA4", "NR6A1", "CD24", "ZIC2", "FGF8", "DLX6", "DLX5", "BMP4", "GRIA1", "SIX6", "NKX2-1", "NEUROG1", "DLL1", "ASCL1", "NHLH1", "NEUROD1", "FEZF2", "L1CAM", "INA", "GRIA2", "GNRH1", "INSM1", "ELAVL4", "ISL1", "TH", "DCX", "TAGLN3", "SCG2", "ELAVL3", "PCSK2", "MAP6", "HES6", "DLL3", "TLX3", "NEUROD4", "SYP", "STMN2", "POU2F2", "TUBB3", "SNAP25", "NSG2", "GAP43", "NEFM", "NEFL", "RET", "NTRK3", "RELN", "NTRK1", "DLX2", "DLX1", "GAD1", "ARX", "SOX3", "SYT4", "SPARCL1", "OTX2", "LMO1", "FABP7", "DCT", "VEPH1", "PRRX1", "CDK6", "GNG11", "HES1", "TYMS", "PCNA", "CDK1", "DLX6-AS1", "TUBB4B", "SELENOP", "ID2", "CRABP1", "APOE", "LDHA", "FZD5", "KRT18", "FGF17"), size = 2) + scale_fill_gradientn(colors = c("#8CC2CA","#8175AA","#FFC966")) + theme(text = element_text(size = 7))
dev.copy(pdf,paste0(currentjob,"_14_Heatmap_70_GENES_OF_INTEREST.pdf"), width=10, height=16, paper='special')
dev.off()
dev.set(dev.next())
```
```{r Violin plot1}
VlnPlot(diffscRNA.hESC,features =c("PAX6"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5, cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_PAX6.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("RAX"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_RAX.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("LIX1"),ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_LIX1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("SOX2"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_SOX2.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("REST"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_REST.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("HES1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_HES1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("FABP7"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_FABP7.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("KIF15"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_KIF15.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("CDK1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_CDK1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("TYMS"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_TYMS.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("POU5F1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_POU5F1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("TDGF1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_TDGF1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("GAL"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))##+theme(text=element_text(size=16, family="Arial"))##+theme(text=element_text(size=16, family="Arial"))##+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_GAL.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("ID1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_ID1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("ANLN"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_ANLN.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("NANOG"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_NANOG.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("NTRK1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_NTRK1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("STMN2"),pt.size =0.1,ncol = 1,same.y.lims =6, y.max = 6,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_STMN2.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("ISL1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_ISL1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("L1CAM"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_L1CAM.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("NEUROG1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_NEUROG1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("NEUROD4"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_NEUROD4.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("NEUROD1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_NEUROD1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("HES6"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_HES6.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("GSX1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_GSX1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("LRRC75A"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_LRRC75A.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("BMP4"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_BMP4.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("DLX5"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_DLX5.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("GNRH1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_GNRH1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("GNG8"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_GNG8.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("DLX2"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_DLX2.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("GAD1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_GAD1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("GAD2"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_GAD2.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("ARX"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_ARX.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("DLX6"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_DLX6.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("GRIA1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_GRIA1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("PCNA"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_PCNA.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("REST"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_REST.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("CDH1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_CDH1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("CDH2"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_CDH2.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("PHC1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_PHC1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("PHC2"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_PHC2.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("LIN28A"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_LIN28A.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("FABP7"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_FABP7.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("GNG8"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_GNG8.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("CDKN1C"), pt.size =0.0,ncol = 1,same.y.lims =6, y.max = 6,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_CDKN1C.pdf"), width=8, height=8, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("LHX2"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_LHX2.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("FOXD3-AS1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_FOXD3-AS1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("FOXH1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_FOXH1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("FGF8"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_FGF8.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("NKX2-1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_NKX2-1.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("SOX4"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_SOX4.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("CDK6"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))#+theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_CDK6.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC,features =c("SOX9"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_SOX9.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(Diff_paracet_merged,features =c("ID1"),pt.size = 0,ncol = 1,same.y.lims =5, y.max = 5,cols = c('R5' = '#FF9888', 'R1' = '#C3CE3D', 'R7' = '#8175AA', 'R2' = '#DAB6AF', 'R10' = '#FFD700', 'R3' = '#023858', 'R4' = '#D37295','R9' = '#FFC966', 'R6' ='#008080','R11' = '#59A14F', 'R8' = '#B15928','R12' ='#8CC2CA', 'R13' = '#C0D6E4', 'R14' = '#F28E2B', 'R15' = '#A3ACB9'))+theme(text=element_text(size=12, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_15.FILT.VLNPLOT_FOXG1.pdf"), width=8, height=6, paper='special')
dev.off()
```
```{r Violin plot for cluster}
#plots with QC information on Cluster
table(Idents(diffscRNA.hESC))
prop.table(table(Idents(diffscRNA.hESC)))
library(ggthemes)
library(ggplot2)
VlnPlot(diffscRNA.hESC, c("nCount_RNA"), pt.size = 0.0, ncol = 1, group.by = "cell_type") + NoLegend()+ theme(text=element_text(size=16))
dev.copy(pdf,paste0(currentjob,"_13.FILT.nCount-cell-type.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC, c("nFeature_RNA"), pt.size = 0.0, ncol = 1, group.by = "cell_type") + NoLegend()+ theme(text=element_text(size=16))
dev.copy(pdf,paste0(currentjob,"_13.FILT.nFeature-cell-type.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC, c("percent.mt"), pt.size = 0.0, ncol = 1, group.by = "cell_type") + NoLegend()+ theme(text=element_text(size=16))
dev.copy(pdf,paste0(currentjob,"_13.FILT.percent.mt-cell-type.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC, c("nFeature_RNA"), pt.size = 0.0, ncol = 1, group.by = "orig.ident") + NoLegend()+ theme(text=element_text(size=16))
dev.copy(pdf,paste0(currentjob,"_13.FILT.nFeature-orig.ident.pdf"), width=8, height=6, paper='special')
dev.off()
VlnPlot(diffscRNA.hESC, c("nFeature_RNA"), pt.size = 0.0, ncol = 1, group.by = "Phase") + NoLegend()+ theme(text=element_text(size=16))
dev.copy(pdf,paste0(currentjob,"_13.FILT.nFeature-cellcycle.pdf"), width=8, height=6, paper='special')
dev.off()
# FILT scatter feature vs % mit
FeatureScatter(diffscRNA.hESC, "nCount_RNA", "nFeature_RNA") + ggtitle(label = "Scatter_ feature vs % mt | Filtered data")+ theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_13.FILT.scatter.feat.vs.mt.pdf"), width=8, height=5, paper='special')
dev.off()
# Calculate the average expression of all cells within a cluster
cluster.averages.FILT <- AverageExpression(diffscRNA.hESC, assays = "RNA")
cluster.averages.FILT
head(cluster.averages.FILT[["RNA"]][, 1:3])
# Need to replace spaces " " with "_" so that ggplot does not fail
cluster.averages.FILT.original <- levels(diffscRNA.hESC)
Idents(diffscRNA.hESC) <- gsub(pattern = " ", replacement = "_", x = Idents(diffscRNA.hESC))
cluster.averages.FILT.original <- gsub(pattern = " ", replacement = "_", x = cluster.averages.FILT.original)
levels(diffscRNA.hESC) <- cluster.averages.FILT.original
cluster.averages.FILT <- AverageExpression(diffscRNA.hESC, assays = "RNA", return.seurat = TRUE)
cluster.averages.FILT
#scatterplot
CellScatter(cluster.averages.FILT, cell1 = "R1", cell2 = "R2") + theme(text=element_text(size=16, family="Arial"))
# Heatmap Cluster Averages
DoHeatmap(cluster.averages.FILT, features = unlist(TopFeatures(diffscRNA.hESC[["pca"]], balanced = TRUE)), size = 3, draw.lines = FALSE)+ theme(text=element_text(size=16, family="Arial"))
dev.copy(pdf,paste0(currentjob,"_14.FILT.heatmap-TopFeat-cluster-averages.pdf"), width=8, height=10, paper='special')
dev.off()
dev.set(dev.next())
```
```{r Save final 2/2 RDS file and count matrix!, message=FALSE, warning=FALSE, include=FALSE}
# RDS For cytotrace
saveRDS(diffscRNA.hESC, paste0(currentjob,"_for_cytotrace_rnaobjects.qcFilt.rds"))
#Save SCT count to matrix
write.csv(diffscRNA.hESC@assays$SCT@counts, paste0(currentjob,"_endOfpipe_countMatrix-Filt.csv"))
```
```{r Cell-cycle genes!, echo=TRUE}
# proportions of cell cycle genes # Shiny cell is easy and better to use for this analysis
table(diffscRNA.hESC@meta.data$Phase) %>%
kable(caption = "Number of Cells in each Cell Cycle Stage", "markdown",col.names = c("Stage", "Count"), align = "c") %>% kable_styling()
table(diffscRNA.hESC@meta.data$cell_type) %>%
kable(caption = "Number of Cells in each Seurat clusters", "markdown",col.names = c("Stage", "Count"), align = "c") %>% kable_styling()