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Acell_NucleiLabelTransfer_Fig5.Rmd
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Acell_NucleiLabelTransfer_Fig5.Rmd
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---
title: "R Notebook"
output: html_notebook
---
title: "A cell Label Transfer"
author: "SAR"
date: "20200827"
output: html_notebook
editor_options:
chunk_output_type: inline
---
```{r setup, include=T}
knitr::opts_knit$set(root.dir = '/Plots')
```
#Load packages
```{r}
#devtools::install_github(repo = "satijalab/seurat", ref = "develop")
library(Seurat)
library(ggplot2)
library(dplyr)
library(viridis)
#load Seurat objects:
nucleus.integrated <- readRDS("sNucSeq.rds")
DefaultAssay(nucleus.integrated) <- "SCT"
Idents(nucleus.integrated) <- "integrated_snn_res.2"
experiment.WC <- readRDS("scSeq.rds")
```
##Subset SVZ A cell clusters
```{r}
DimPlot(nucleus.integrated, label=T)
table(nucleus.integrated@active.ident)
nucleus.lineage <- subset(nucleus.integrated, idents =c(12,29))
table(nucleus.lineage@active.ident)
DimPlot(nucleus.lineage, label=T)
ggsave(paste("NucSeqIntegrated_Acells_DimPlot_",format(Sys.time(), "%Y-%m-%d_%H-%M"), ".png", sep = ""), width = 12, height = 8, units = "in", dpi=300)
experiment.A <- subset(experiment.WC, idents = c(4,6,0,1,15) )
DimPlot(experiment.A, label=T)
ggsave(paste("WC_Acells_DimPlot_",format(Sys.time(), "%Y-%m-%d_%H-%M"), ".png", sep = ""), width = 12, height = 8, units = "in", dpi=300)
```
##SCTransform Whole cell A cells
```{r}
experiment.A <- SCTransform(experiment.A)
```
#Split nuclei A cells by region
```{r}
nucleus.lineage
nucleus.cellsList <- SplitObject(nucleus.lineage, split.by = "Method")
nucleus.cellsList
```
##Feature 25-50th percentile cutoff per region
```{r}
#AV
AV_Q<-quantile(nucleus.cellsList[[1]]$nFeature_SCT)
AV_Q
AV_Q[[2]]
AV_Q[[4]]
table(nucleus.cellsList[[1]]@active.ident)
nucleus.cellsList[[1]] <- subset(nucleus.cellsList[[1]], subset = `nFeature_SCT` > AV_Q[[2]])
table(nucleus.cellsList[[1]]@active.ident)
nucleus.cellsList[[1]] <- subset(nucleus.cellsList[[1]], subset = `nFeature_SCT` < AV_Q[[4]])
table(nucleus.cellsList[[1]]@active.ident)
#AD
AD_Q<-quantile(nucleus.cellsList[[2]]$nFeature_SCT)
AD_Q
AD_Q[[2]]
AD_Q[[4]]
table(nucleus.cellsList[[2]]@active.ident)
nucleus.cellsList[[2]] <- subset(nucleus.cellsList[[2]], subset = `nFeature_SCT` > AD_Q[[2]])
table(nucleus.cellsList[[2]]@active.ident)
nucleus.cellsList[[2]] <- subset(nucleus.cellsList[[2]], subset = `nFeature_SCT` < AD_Q[[4]])
table(nucleus.cellsList[[2]]@active.ident)
#PD
PD_Q<-quantile(nucleus.cellsList[[3]]$nFeature_SCT)
PD_Q
PD_Q[[2]]
PD_Q[[4]]
table(nucleus.cellsList[[3]]@active.ident)
nucleus.cellsList[[3]] <- subset(nucleus.cellsList[[3]], subset = `nFeature_SCT` > PD_Q[[2]])
table(nucleus.cellsList[[3]]@active.ident)
nucleus.cellsList[[3]] <- subset(nucleus.cellsList[[3]], subset = `nFeature_SCT` < PD_Q[[4]])
table(nucleus.cellsList[[3]]@active.ident)
#PV
PV_Q<-quantile(nucleus.cellsList[[4]]$nFeature_SCT)
PV_Q
PV_Q[[2]]
PV_Q[[4]]
table(nucleus.cellsList[[4]]@active.ident)
nucleus.cellsList[[4]] <- subset(nucleus.cellsList[[4]], subset = `nFeature_SCT` > PV_Q[[2]])
table(nucleus.cellsList[[4]]@active.ident)
nucleus.cellsList[[4]] <- subset(nucleus.cellsList[[4]], subset = `nFeature_SCT` < PV_Q[[4]])
table(nucleus.cellsList[[4]]@active.ident)
nucleus.cellsList
VlnMerge <- merge(nucleus.cellsList[[1]], y= c(nucleus.cellsList[[2]], nucleus.cellsList[[3]], nucleus.cellsList[[4]]), merge.data = T)
VlnPlot(VlnMerge, features=c("nFeature_SCT", "nCount_SCT"),split.by = "Method", split.plot=T,pt.size = 0, combine=T)
ggsave(paste("NucSeq_Acell_FeatQuantFilt_nFeatnCount_VlnPlot_",format(Sys.time(), "%Y-%m-%d_%H-%M"), ".png", sep = ""), width = 12, height = 8, units = "in", dpi=300)
```
##Region Downsample to Least Cell Number
```{r}
#downsample each to match region with least cells
nCells=128
nucleus.cellsList[[1]] = subset(nucleus.cellsList[[1]], cells = sample(Cells(nucleus.cellsList[[1]]), nCells))
nucleus.cellsList[[2]] = subset(nucleus.cellsList[[2]], cells = sample(Cells(nucleus.cellsList[[2]]), nCells))
nucleus.cellsList[[3]] = subset(nucleus.cellsList[[3]], cells = sample(Cells(nucleus.cellsList[[3]]), nCells))
nucleus.cellsList[[4]] = subset(nucleus.cellsList[[4]], cells = sample(Cells(nucleus.cellsList[[4]]), nCells))
#Add D/V metadata
nucleus.cellsList[[1]]$DV <- "Ventral"
nucleus.cellsList[[2]]$DV <- "Dorsal"
nucleus.cellsList[[3]]$DV <- "Dorsal"
nucleus.cellsList[[4]]$DV <- "Ventral"
#merge back to one object
nucleus.Sampled <- merge(nucleus.cellsList[[1]], y= c(nucleus.cellsList[[2]], nucleus.cellsList[[3]], nucleus.cellsList[[4]]), merge.data = T)
table(nucleus.Sampled$DV)
```
##Find Anchors
```{r}
nucleus.Sampled <- SCTransform(nucleus.Sampled)
nucleus.Sampled
transfer.anchors <- FindTransferAnchors(reference = nucleus.Sampled, reference.assay = "SCT", query = experiment.A, query.assay = "SCT", normalization.method = "SCT", npcs=30, project.query=T, dims = 1:30)
```
#Dorsal Ventral Transfer Data
```{r}
predictionsDV <- TransferData(anchorset = transfer.anchors, refdata = nucleus.Sampled$DV, dims = 1:30)
experiment.A <- AddMetaData(experiment.A, metadata = predictionsDV)
```
##Save Predictions Metadata
```{r}
saveRDS(predictionsDV,file=paste("WC_AcellTransferLabels_DVNucMetaData_DownSampledNuclei_",format(Sys.time(), "%Y-%m-%d_%H-%M"), ".rds", sep = ""))
```
##Plot Using Scores
```{r}
#Make color palette with premade ColorBrewer palette PiYG
colsDV <- brewer_pal(11, "PiYG", direction = -1)
#Ventral Scores:
FeaturePlot(experiment.A,features = "Ventral_Score_Acells", cols = colsDV(11)[6:11], pt.size = 0.2, order=F) + NoAxes() + coord_fixed() + xlim(-4.5, 3.5) + ylim(-14, -2)
ggsave(paste("Fig5_ptSmall_Acell_VentralScore_FeaturePlot_",format(Sys.time(), "%Y-%m-%d_%H-%M"), ".png", sep = ""), width = 4, height = 4, units = "in", dpi=300)
#Dorsal Scores:
colsDV <- brewer_pal(11, "PiYG", direction = 1)
FeaturePlot(experiment.A,features = "Dorsal_Score_Acells", cols = colsDV(11)[6:11], pt.size = 0.2, order=F) + NoAxes() + coord_fixed() + xlim(-4.5, 3.5) + ylim(-14, -2)
ggsave(paste("Fig5_ptSmall_Acell_DorsalScore_FeaturePlot_",format(Sys.time(), "%Y-%m-%d_%H-%M"), ".png", sep = ""), width = 4, height = 4, units = "in", dpi=300)
```