---
title: "dot plot fine clustering"
author: "Luise A. Seeker"
date: "29/10/2021"
output: html_document
---
```{r}
library(Seurat)
library(dplyr)
library(ggsci)
library(here)
library(ggplot2)
```
```{r}
seur <- readRDS(here("data",
"single_nuc_data",
"all_cell_types",
"srt_fine_anno_01.RDS"))
seur <- readRDS(here("data",
"single_nuc_data",
"data_for_publication",
"HCA_complete_dataset.RDS"))
FeaturePlot(nad_ol, features = c("LAMA2", "MBP"))
```
```{r}
levels(as.factor(seur$Fine_cluster))
```
```{r}
order<- c("Neur",
"RELN_4",
"RELN_3",
"RELN_2",
"RELN_1",
"Ex_4",
"Ex_3",
"Ex_2",
"Ex_1",
"In_9",
"In_8",
"In_7",
"In_6",
"In_5",
"In_4",
"In_3",
"In_2",
"In_1",
"vSMC",
"Mural_vein_1",
"Mural_cap_2",
"Mural_cap_1",
"EC_art_3",
"EC_art_2",
"EC_art_1",
"EC_cap_5",
"EC_cap_4",
"EC_cap_3",
"EC_cap_2",
"EC_cap_1",
"Immune",
"BAM",
"Microglia_5",
"Microglia_4",
"Microglia_3",
"Microglia_2",
"Microglia_1",
"AS_12",
"AS_11",
"AS_10",
"AS_9",
"AS_8",
"AS_7",
"AS_6",
"AS_5",
"AS_4",
"AS_3",
"AS_2",
"AS_1",
"Oligo_F",
"Oligo_E",
"Oligo_D",
"Oligo_C",
"Oligo_B",
"Oligo_A",
"COP_C",
"COP_B",
"COP_A",
"OPC_B",
"OPC_A")
invert_order <- rev(order)
seur$Fine_cluster <- factor(seur$Fine_cluster, levels = invert_order)
```
```{r}
Idents(seur) <- "Fine_cluster"
DotPlot(seur, features = c("SNAP25", "RBFOX3", "PLP1"), group.by = "Tissue")
```
```{r}
genes <- c("GAPDH",
"PDGFRA",
"PAX3",
"SLC22A3",
"SEMA3D",
"NELL1",
"L3MBTL4",
"LINC01965",
"GAP43",
"TRIO",
"ATRNL1",
"GPC5",
"SPARCL1",
"TRPM3",
"GRIA1",
"GPR17",
"PRICKLE1",
"SGK1",
"BCAN",
"PLP1",
"MAG",
"MOG",
"MYRF",
"OPALIN",
"PLXDC2",
"LAMA2",
"PALM2",
"HHIP",
"RBFOX1",
"RASGRF1",
"FMN1",
"AFF3",
"LGALS1",
"SPARC",
"DHCR24",
"HCN2",
"TUBA1A",
"TUBB2B",
"VAMP3",
"PMP2",
#Astrocytes
"GJA1",
"GFAP",
#AS_1
"MDGA2",
"EDNRB",
"PPFIA2",
"KCNJ16",
# AS_2
"APLNR",
"CDC42EP4",
"PAMR1",
#AS_3
"LINC01094",
"PRKAG2",
"BHLHE40",
#AS_4
"NTNG2",
"ATP10B",
#AS_5
"LINC00609",
"EMID1",
"CTSH",
#AS_6
"GABRA2",
"EPHA6",
"EPHB1",
#AS_7
"SLC6A11",
"PAK3",
"GRM5",
"VAV3",
"PTPRT",
#AS_8
"ST18",
"SLC24A2",
"RNF220",
"ELMO1",
"NKAIN2",
#AS_9
"CFAP43",
"SPAG17",
"DNAH11",
#AS_10
"SPOCK1",
"SPSB1",
"SAMD4A",
"CLIP1",
"MYO1E",
#AS_11,
"S100B",
"TMSB4X",
"FTL",
"MTURN",
#AS_12
"YWHAG",
"ATP1B1",
"GNAS",
"NEFM",
#Microglia
"CD74",
"P2RY12",
#MI_1
"P2RY12",
"RASGEF1C",
#MI_2
"GPNMB",
"MITF",
"APOC1",
"CPM",
#MI_3
"PCDH9",
"EDIL3",
"PTPRD",
#MI_4
"HIF1A",
"GNA13",
"RGS1",
"RANBP2",
"FAM110B",
#MI_5
"NRG3",
"RNF219-AS1",
"SORBS1",
"CTNND2",
#BAM
"F13A1",
"CD163",
"LYVE1",
"MRC1",
"PID1",
# Immune
"HLA-A",
"PTPRC",
#vascular cells
"CLDN5",
"NOTCH3",
#EC_cap_1
"ATP10A",
"SPOCK3",
"SLC39A10",
#EC_cap_2
"JCAD",
"ITM2A",
"NRXN1",
"SLC26A3",
"INO80D",
#EC_cap3
"SLC9A9",
"HDAC9",
"RBM47",
#EC_cap_4
"PCDH9",
"TF",
"IL1RAPL1",
"ARL15",
#EC_cap_5
"PTPRC",
"SKAP1",
"ARHGAP15",
"CD247",
#EC_art_1
"PELI1",
"ARL15",
"RALGAPA2",
"BACE2",
"IL1R1",
#EC_art_2
"ACKR1",
"AQP1",
#EC_art_3
"S100A6",
"TIMP1",
"CTSL",
"TFPI2",
"MGP",
#Mural_cap_1 abd Mural_cap_2
"GPC5",
"GRM3",
"GRM8",
"SLC38A11",
"SLC20A2",
"FRMD3",
#Mural_vein_1
"CEMIP",
"FLRT2",
"BICC1",
"MIR99AHG",
"NTRK3",
#vSMC
"ACTA2",
"MYH11",
"TAGLN",
"ZFHX3",
"SLIT3",
#neurons
"SNAP25",
"RBFOX3",
#inhibitory
"GAD1",
"GAD2",
#IN_1
"GPC5",
"PCDH15",
"HTR2C",
#IN_2
"SCG2",
"PEG3",
"INPP5F",
"SLC22A17",
"VGF",
#IN_3
"NEFH",
"NEFM",
"SPP1",
"INA",
"VAMP1",
"PVALB",
#IN_4
"SOX6",
"NXPH1",
"KCNC2",
"GRIK1",
"SST",
#IN_5
"CCK",
"NPAS3",
"ADARB2",
"DLX6-AS1",
"PRELID2",
"CNR1",
"FBXL7",
#IN_6
"INPP4B",
"UNC5C",
"PHACTR2",
"VCAN",
"SYN3",
#IN_7
"NEAT1",
"LHFPL3",
"NTNG1",
"FHIT",
#IN_8
"TSHZ2",
"EBF2",
"EBF1",
"KIRREL3",
#IN_9
"LHX6",
"SERPINI1",
"RAB3B",
"TAC1",
"NOS1",
#excitatory
"SLC17A7",
#EX_1
"ATRNL1",
"NECAB1",
"KCTD16",
#EX_2
"HS3ST4",
"MGAT4C",
"LMO3",
"SATB2",
"THSD7B",
#EX_3
"ENC1",
"SLC17A7",
"ARPP19",
#RELN
"RELN",
"TIAM1",
"CADPS2",
"MSRA",
"ZNF385D",
#RELN_1
"SNAP25-AS1",
#RELN_2
"ERVMER61-1",
#RELN_3
"RPL3",
"RPS27A",
"RPL37A",
"CHGB",
#RELN_4
"SST",
"CTNNA3",
"QKI",
"SCD",
#Neur
"C1QL1",
"CRH",
"GPR88",
"POU4F1",
"CALB1",
"CALB2" )
invert_genes <- rev(genes)
DotPlot(seur, features = unique(genes))+ theme(axis.text.x = element_text(angle = 90))
DotPlot(seur, features = unique(invert_genes))+
theme(axis.text.x=element_text(angle=90,hjust=0.9,vjust=0.2)) + coord_flip()
```
```{r}
seur@meta.data$Fine_cluster_rename <- ifelse(seur@meta.data$Fine_cluster ==
"AS_9", "AS_9_ep",
paste(seur@meta.data$Fine_cluster))
new_order<- c("Neur",
"RELN_4",
"RELN_3",
"RELN_2",
"RELN_1",
"Ex_4",
"Ex_3",
"Ex_2",
"Ex_1",
"In_9",
"In_8",
"In_7",
"In_6",
"In_5",
"In_4",
"In_3",
"In_2",
"In_1",
"vSMC",
"Mural_vein_1",
"Mural_cap_2",
"Mural_cap_1",
"EC_art_3",
"EC_art_2",
"EC_art_1",
"EC_cap_5",
"EC_cap_4",
"EC_cap_3",
"EC_cap_2",
"EC_cap_1",
"Immune",
"BAM",
"Microglia_5",
"Microglia_4",
"Microglia_3",
"Microglia_2",
"Microglia_1",
"AS_12",
"AS_11",
"AS_10",
"AS_9_ep",
"AS_8",
"AS_7",
"AS_6",
"AS_5",
"AS_4",
"AS_3",
"AS_2",
"AS_1",
"Oligo_F",
"Oligo_E",
"Oligo_D",
"Oligo_C",
"Oligo_B",
"Oligo_A",
"COP_C",
"COP_B",
"COP_A",
"OPC_B",
"OPC_A")
new_invert_order <- rev(new_order)
seur$Fine_cluster_rename <- factor(seur$Fine_cluster_rename, levels = new_invert_order)
Idents(seur) <- "Fine_cluster_rename"
DotPlot(seur, features = unique(invert_genes))+
theme(axis.text.x=element_text(size = 10, angle=90,hjust=0.9,vjust=0.2)) +
coord_flip()
```