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Copy pathescape_peaks_subset.R
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p <- data.frame(rgcs@meta.data$GOBP_MAINTENANCE_OF_SYNAPSE_STRUCTURE[rgcs$stage == 'Adult'])
pvis <- ggplot(p, aes(x=rgcs.meta.data.GOBP_MAINTENANCE_OF_SYNAPSE_STRUCTURE.rgcs.stage....)) + geom_density()
ggplotly(pvis)
cellidspos <- names(which(rgcs$stage == 'Adult' & rgcs$GOBP_MAINTENANCE_OF_SYNAPSE_STRUCTURE >= 0.45))
cellidsneg <- names(which(rgcs$stage == 'Adult' & rgcs$GOBP_MAINTENANCE_OF_SYNAPSE_STRUCTURE < 0.45))
rgcs <- SetIdent(rgcs, cells = cellidspos, value = '1')
rgcs <- SetIdent(rgcs, cells = cellidsneg, value = '0')
#Same for FD125,FD82,FD59
rgcs$maint_syn_str <- rgcs@active.ident
cellidspos <- names(which(rgcs$stage == 'Adult' & rgcs$GOBP_MAINTENANCE_OF_PRESYNAPTIC_ACTIVE_ZONE_STRUCTURE >= 0.714))
cellidsneg <- names(which(rgcs$stage == 'Adult' & rgcs$GOBP_MAINTENANCE_OF_PRESYNAPTIC_ACTIVE_ZONE_STRUCTURE < 0.471))
cellidsmid <- names(which(rgcs$stage == 'Adult' & rgcs$GOBP_MAINTENANCE_OF_PRESYNAPTIC_ACTIVE_ZONE_STRUCTURE >= 0.471 &
rgcs$GOBP_MAINTENANCE_OF_PRESYNAPTIC_ACTIVE_ZONE_STRUCTURE < 0.714))
rgcs <- SetIdent(rgcs, cells = cellidspos, value = '1')
rgcs <- SetIdent(rgcs, cells = cellidsneg, value = '0')
rgcs <- SetIdent(rgcs, cells = cellidsmid, value = '0.5')
rgcsfd59 <- subset(rgcs, subset = stage == 'FD59')
ProcessSeu <- function(Seurat){
Seurat <- NormalizeData(Seurat)
Seurat <- FindVariableFeatures(Seurat, selection.method = "vst", nfeatures = 2000)
Seurat <- ScaleData(Seurat)
Seurat <- RunPCA(Seurat)
Seurat <- FindNeighbors(Seurat, dims = 1:25)
Seurat <- FindClusters(Seurat, resolution = 2)
Seurat <- RunUMAP(Seurat, dims = 1:25)
Seurat <- RunTSNE(Seurat, dims.use = 1:10 )
DimPlot(object = Seurat, reduction = "umap")
return (Seurat)
}
rgcsfd59 <- ProcessSeu(rgcsfd59)
FeaturePlot(rgcsfd59, reduction = 'umap', features = c('POU4F2','RBPMS'))
DimPlot(rgcsfd59, reduction = 'umap', label = TRUE, label.box = TRUE)
DimPlot(rgcsfd59, reduction = 'umap', label = TRUE, label.box = TRUE, split.by = 'presyn_act_zone_org') + ggtitle('FD59')
#maint_syn_str maint_presyn_act_zone_str maint_postsyn_spec_str pos_reg_syn_plast postsyn_act_cytosk_org presyn_act_zone_org
rgcsfd59@active.ident <- rgcsfd59$maint_syn_str
markers <- FindAllMarkers(rgcsfd59, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
markers %>%
group_by(cluster) %>%
slice_max(n = 2, order_by = avg_log2FC)
markers %>%
group_by(cluster) %>%
top_n(n = 20, wt = avg_log2FC) -> top20
DoHeatmap(rgcsfd59, features = top20$gene) + NoLegend()
#After merging labels:
rgcsfd59 <- RenameIdents(rgcsfd59, 'Maintenance of syn structure - Maintenance of postsyn spec structure - Maintenance of presyn act zone structure -
Pos reg syn plast +- Postsyn act cytosk org +- Presyn act zone org -' = '1', 'Maintenance of syn structure + Maintenance of postsyn spec structure -
Maintenance of presyn act zone structure + Pos reg syn plast - Postsyn act cytosk org - Presyn act zone org +' = '3', 'Maintenance of syn structure
+ Maintenance of postsyn spec structure - Maintenance of presyn act zone structure +- Pos reg syn plast + Postsyn act cytosk org +- Presyn act zone org +-' = '3.5')
#Repetative part for DimPlots + markers
rgcsfd125$stat <- paste(rgcsfd125$maint_syn_str, rgcsfd125$maint_postsyn_spec_str, rgcsfd125$maint_presyn_act_zone_str, rgcsfd125$pos_reg_syn_plast, rgcsfd125$postsyn_act_cytosk_org, rgcsfd125$presyn_act_zone_org)
DimPlot(rgcsfd125, reduction = 'umap', label = TRUE, label.box = TRUE, split.by = 'maint_syn_str') + ggtitle('FD125')
DimPlot(rgcsfd125, reduction = 'umap', label = TRUE, label.box = TRUE, split.by = 'maint_postsyn_spec_str') + ggtitle('FD125')
DimPlot(rgcsfd125, reduction = 'umap', label = TRUE, label.box = TRUE, split.by = 'pos_reg_syn_plast') + ggtitle('FD125')
DimPlot(rgcsfd125, reduction = 'umap', label = TRUE, label.box = TRUE, split.by = 'postsyn_act_cytosk_org') + ggtitle('FD125')
DimPlot(rgcsfd125, reduction = 'umap', label = TRUE, label.box = TRUE, split.by = 'presyn_act_zone_org') + ggtitle('FD125')
DimPlot(rgcsfd125, reduction = 'umap', label = TRUE, label.box = TRUE, split.by = 'maint_presyn_act_zone_str') + ggtitle('FD125')
#active.ident line + markers
rgcsfd125@active.ident <- rgcsfd125$maint_syn_str
rgcsfd125@active.ident <- rgcsfd125$maint_presyn_act_zone_str
rgcsfd125@active.ident <- rgcsfd125$maint_postsyn_spec_str
rgcsfd125@active.ident <- rgcsfd125$pos_reg_syn_plast
rgcsfd125@active.ident <- rgcsfd125$postsyn_act_cytosk_org
rgcsfd125@active.ident <- rgcsfd125$presyn_act_zone_org
markers <- FindAllMarkers(rgcsfd125, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
markers %>%
group_by(cluster) %>%
slice_max(n = 2, order_by = avg_log2FC)
markers %>%
group_by(cluster) %>%
top_n(n = 20, wt = avg_log2FC) -> top20
write.csv(markers, 'C://Users/Emil/10X/scretina/markers.csv')
gene.sets1 <- getGeneSets(library = "C5", gene.sets = c('GOBP_MAINTENANCE_OF_SYNAPSE_STRUCTURE','GOBP_PRESYNAPTIC_ACTIVE_ZONE_ORGANIZATION',
'GOBP_POSITIVE_REGULATION_OF_SYNAPTIC_PLASTICITY','GOBP_MAINTENANCE_OF_PRESYNAPTIC_ACTIVE_ZONE_STRUCTURE',
'GOBP_MAINTENANCE_OF_POSTSYNAPTIC_SPECIALIZATION_STRUCTURE',
'GOBP_POSTSYNAPTIC_ACTIN_CYTOSKELETON_ORGANIZATION'), species = "Mus musculus")
ES <- enrichIt(obj = mousefetalRGC,
gene.sets = gene.sets1,
groups = 1000, cores = 4,
min.size = NULL)
mousefetalRGC <- AddMetaData(mousefetalRGC, ES)
ES2 <- data.frame(mousefetalRGC [[]], Idents(mousefetalRGC ))
colnames(ES2)[ncol(ES2)] <- "cluster"
#after it visualisation from the 1 line
mousefetalRGC$maint_syn_str <- as.numeric(as.character(mousefetalRGC$maint_syn_str))
mousefetalRGC$presyn_act_zone_org <- as.numeric(as.character(mousefetalRGC$presyn_act_zone_org))
mousefetalRGC$pos_reg_syn_plast <- as.numeric(as.character(mousefetalRGC$pos_reg_syn_plast))
mousefetalRGC$maint_presyn_act_zone_str <- as.numeric(as.character(mousefetalRGC$maint_presyn_act_zone_str))
mousefetalRGC$maint_postsyn_spec_str <- as.numeric(as.character(mousefetalRGC$maint_postsyn_spec_str))
mousefetalRGC$postsyn_act_cytosk_org <- as.numeric(as.character(mousefetalRGC$postsyn_act_cytosk_or))
mousefetalRGC$postsyn_act_cytosk_org <- as.numeric(as.character(mousefetalRGC$postsyn_act_cytosk_org))
mousefetalRGC$stat <- mousefetalRGC$presyn_act_zone_org + mousefetalRGC$maint_syn_str +
mousefetalRGC$pos_reg_syn_plast + mousefetalRGC$maint_presyn_act_zone_str +
mousefetalRGC$maint_postsyn_spec_str + mousefetalRGC$postsyn_act_cytosk_org
mousefetalRGC <- SetIdent(mousefetalRGC, value = mousefetalRGC$stat)
mousefetalRGC <- RenameIdents(mousefetalRGC, '0' = 'low','0.5'='low','1'='low','1.5'='mid','2'='mid','2.5'='mid','3'='mid','3.5'='mid','4'='mid','4.5'='mid','5'='high','5.5'='high','6'='high')
table(mousefetalRGC$stat[mousefetalRGC$stage == 'E14'])
ggplot(my_data1, aes(x = Day, y = Percentage, fill = Amount)) + geom_bar(position="stack", stat="identity")
#for mouse
gene_list <- c('ADORA1', 'ADRA2A', 'ADRB2', 'AGTR2', 'AHSG', 'AKT1', 'AKT2', 'ALK', 'AR', 'AREG', 'ARNT', 'AXL', 'BDKRB2',
'CEACAM1', 'BLK', 'BRAF', 'DDR1', 'RUNX2', 'CD4', 'CD7', 'CD8A', 'CD63', 'CDH3', 'CDH13', 'CHN1', 'CHRNA3',
'AP3S1', 'CSF1R', 'CSPG4', 'CTNNB1', 'DOK1', 'EFNA1', 'EFNA2', 'EFNA3', 'EFNA4', 'EFNA5', 'EFNB1', 'EFNB2',
'EFNB3', 'EGFR', 'EPHA2', 'EPHA1', 'EPHA3', 'EPHA4', 'EPHA5', 'EPHA7', 'EPHA8', 'EPHB1', 'EPHB2', 'EPHB3', 'EPHB4',
'EPHB6', 'ERBB2', 'ERBB3', 'ERBB4', 'EFEMP1', 'FER', 'FES', 'FGFR1', 'FGFR3', 'FGFR2', 'FGFR4', 'FGR', 'FLT1', 'FLT3',
'FLT4', 'FRK', 'FUT7', 'GATA3', 'GFRA1', 'GFRA2', 'GFRA3', 'GHR', 'GHRHR', 'GHSR', 'GPR21', 'GPER1', 'FFAR3', 'GRB2',
'GRB7', 'GRB10', 'GRB14', 'HCK', 'NRG1', 'NDST1', 'IGF1R', 'IGF2R', 'IGFBP1', 'IGFBP2', 'IGFBP3', 'IGFBP4', 'IGFBP5',
'IGFBP6', 'RBPJ', 'INSR', 'INSRR', 'IRS1', 'ITGA1', 'ITGA5', 'ITGB3', 'JAK2', 'JAK3', 'KDR', 'KIT', 'STMN1', 'LCK', 'LEP',
'LRP1', 'LTK', 'LYN', 'MET', 'FOXO4', 'MST1R', 'MUSK', 'NDN', 'NEDD9', 'NGFR', 'NKX3-1', 'NTRK1', 'NTRK2', 'NTRK3', 'ROR1',
'ROR2', 'DDR2', 'PAK1', 'PAK2', 'PAK3', 'PDGFRA', 'PDGFRL', 'PDGFRB', 'PIGR', 'PIK3C2A', 'PIK3CA', 'PIK3CB', 'PIK3CD', 'PIK3R1',
'PIK3R2', 'PLAUR', 'PRLR', 'PSEN1', 'PTGIR', 'PTPN1', 'PTPN2', 'PTPN3', 'PTPN11', 'PTPN12', 'PTPRA', 'PTPRE', 'PTPRG', 'PTPRJ',
'PTPRR', 'RAC1', 'RAF1', 'RARRES2', 'RET', 'ROBO1', 'ROS1', 'RYK', 'SORT1', 'SORL1', 'SOS1', 'SOX9', 'SRC', 'SREBF1', 'SRMS',
'STAT3', 'STAT5A', 'STAT5B', 'STAT6', 'TEK', 'TIAM1', 'TIE1', 'TYRO3', 'VTN', 'WNT1', 'WNT5A', 'YES1', 'FZD4', 'PIK3R3',
'SOCS1', 'IRS2', 'TNK1', 'NRP2', 'NRP1', 'SOCS2', 'HAP1', 'MPZL1', 'SOCS3', 'HIP1R', 'GPRC5A', 'FIBP', 'REPS2', 'NOG', 'KL', 'MVP',
'NR1H4', 'FGFBP1', 'NAMPT', 'TNK2', 'SPRY3', 'SPRY1', 'SPRY2', 'CNKSR1', 'STUB1', 'EFS', 'MERTK', 'SH2B2', 'TXNIP', 'RGS14', 'NRG3',
'FRS3', 'FRS2', 'NEU3', 'EMILIN1', 'PTPRT', 'PTP4A3', 'LMTK2', 'SIRT2', 'SETX', 'SIK2', 'SIRT1', 'LEPROTL1', 'FLRT3', 'FLRT2',
'FLRT1', 'SHC2', 'DSTYK', 'NGEF', 'PRKD2', 'ADGRA2', 'GREM1', 'CYFIP2', 'NPTN', 'SNX5', 'DLL1', 'ADIPOR1', 'CRIM1',
'GHRL', 'FGFRL1', 'ERRFI1', 'WNT4', 'LEPROT', 'STYK1', 'SMPD3', 'SULF2', 'RTN4', 'SEMA6A', 'NCOA5', 'SNX6', 'SMOC2',
'GFRA4', 'HHIP', 'GIGYF1', 'GKAP1', 'NDEL1', 'MVB12B', 'OSBPL8', 'RASGRP4', 'NUS1', 'CLNK', 'ZFYVE27', 'SOCS4', 'OTOL1', 'IL31RA',
'SAMD10', 'SOGA1', 'FGFBP3', 'SESN3', 'DAB2IP', 'CLEC14A', 'CADM4', 'MUC20', 'DOK6', 'FAM83B', 'STXBP4', 'EPHA10', 'EPHA6', 'LRIT3',
'GFRAL', 'ADRB2','ANGPT1','ANGPT4','CHRNA3','DGKQ','EFNA5','EGF','GREM1','NRG1','NRG3','PDGFC','PRLR','TAL1', 'AGT','AGTR2',
'AKT1S1','BCAR1','BDNF','CASP3','CORO1A','CYFIP1','CYFIP2','DDIT4','DOK5','GFRA1','HAP1','KIDINS220','MAGI2','NDN','NGF','NTF3',
'NTRK1','NTRK2','NTRK3','PPP2R5B','PTPN11','RAF1','RAP1A','RAPGEF1','RAPGEF2','SORT1','SOS1','SPRY1','SPRY2','SRC','TMEM108','WASF1',
'ZDHHC17','ZFYVE27', 'ADAM17','AGR2','APP','AREG','ARF4','ARTN','ATXN2','BTC','CADM4','CBLC','CCDC88A','CD2AP','CD300LF','CDH5',
'CNTF','CSF2','CSF3','DAB2IP','ECM1','EFEMP1','EGF','EPGN','ERAP1','ERBB4','EREG','ERN1','ESM1','FAM83B','FER','FGF1','FGF10','FGF16',
'FGF17','FGF18','FGF2','FGF20','FGF21','FGF22','FGF23','FGF3','FGF4','FGF5','FGF6','FGF7','FGF8','FGF9','FLRT2','FLRT3','FRS2',
'FRS3','FYN','GATA3','GDNF','GLMN','GRB2','GREM1','HBEGF','HIP1','IL10','IL11','IL12A','IL12B','IL12RB1','IL1A','IL1B','IL1F10','IL1R1',
'IL1RAP','IL1RN','IL2','IL21','IL23R','IL27RA','IL3','IL4','IL5','IL6','IL6ST','IL7','IL9',
'IRAK4','ITGA5','ITGB3','JAK2','KL','KLB','LINGO1','LYN','MS4A1','MYD88','NCSTN','NPTN','NRTN','PDCL3','PDGFA','PDGFB','PDGFC','PDGFD',
'PDGFRA','PDGFRB','PGF','PIBF1','PLSCR1','PSEN1','PSPN','PTPRJ','PYCARD','RNF126','RNF41','SDCBP','SHC1','SLC9A3R1','SNX1','SNX2','SNX4',
'SOCS5','SRC','TGFA','TIMM50','TLR5','TLR9','TNK2','TOLLIP','TRIP6','TSLP','VAV2','VAV3','VEGFA','VEGFB','VEGFC', 'AGT','AGTR2',
'AKT1S1','BCAR1','BDNF','CASP3','CORO1A','CYFIP1','CYFIP2','DDIT4','DOK5','GFRA1','HAP1','KIDINS220','MAGI2','NDN','NGF','NTF3',
'NTRK1','NTRK2','NTRK3','PPP2R5B','PTPN11','RAF1','RAP1A','RAPGEF1','RAPGEF2','SORT1','SOS1','SPRY1','SPRY2','SRC','TMEM108','WASF1',
'ZDHHC17','ZFYVE27', 'ARF6', 'RAB3A', 'CTBP2', 'ERC1', 'PCLO', 'DBN1', 'DBNL', 'INA', 'RAB3A','CBLN2','CTBP2','DBN1','ARF6','TAGLN3',
'ERC1','CTBP2','PCLO','MALAT1', 'DNAJC6','STAT3','RALBP1',
#from here after cellchat
'Ncam1', 'L1cam', 'Nrxn1', 'Nlgn1', 'Mpzl1', 'Efna5', 'Nrxn3', 'Nrxn2', 'Ephb2', 'Cntn2', 'Cntnap2', 'Mstn', 'Tgfbr1', 'Acvr2b',
#from here after individual pathway DEGs
'Rab3a','Sdf4','Sncg','Tubb2a','Mapt','Meg3','Ly6h','Uchl1','Stmn3','Pcsk1n','Ina','Stmn4','Syt4','Nefl','Snrpn','Aplp1',
'S100a10','Rab6b','Tppp3','Apbb2','Rbpms','Celf4','Rac3','Map1lc3a','Islr2','Nsg1','Calb2','Snap25','Runx1t1','Mgst3','Pou4f1','Gng3','Gadd45a',
'Erc1','Ly6h','Meg3','Sncg','Tubb2a','Snhg11','Mapt','Pcsk1n','Snrpn','Nell2','Rab6b','Syt4','Celf4','Rbpms','Islr2','Apbb2','Tppp3','Map1lc3a','Calb2',
'Dbn1','Cplx2','Islr2','Ap2m1','Oaz2','Snrpn','Mtch1','Prelid1','Fscn1','Map1lc3a','Uchl1','Arhgdia','Rab3a','Cotl1','Ctxn1',
'Erc1','Erc2','Ly6h','Mapt','Sncg','Tubb2a','Snhg11','Meg3','Pcsk1n','Snrpn','Rab6b','Islr2','Syt4','Rbpms','Tppp3','Apbb2','Map1lc3a','Celf4','Calb2',
'Arf6','Snhg11','Nell2','Ly6h','Snrpn','Rab3a','Meg3','Pcsk1n','Calb2',
'Dbnl','Actg1','Dbn1','Ina','Rab3a','Islr2','Snrpn','Uchl1','Tubb2a','Thra','Sncg','Mapt','Pcsk1n','Meg3','Map1lc3a','Tppp3','Ly6h','Apbb2','Calb2',
'Snhg11','Gadd45a','Mdk','Prdx1','Gm10260','Hes6','H2afv','Dlx2','Hes5','Btg2','Mfng','Btbd17',
'Rassf4','Ccnd1','Itm2a','Hmgn3','Tead2','Alyref','Sfrp2','Fos')
#for human
gene_list <- c('ADORA1', 'ADRA2A', 'ADRB2', 'AGTR2', 'AHSG', 'AKT1', 'AKT2', 'ALK', 'FASLG', 'AR', 'AREG', 'ARNT', 'AXL', 'BDKRB2',
'CEACAM1', 'BLK', 'BRAF', 'DDR1', 'RUNX2', 'CD4', 'CD7', 'CD8A', 'CD8B', 'CD63', 'CDH3', 'CDH13', 'CHN1', 'CHRNA3',
'AP3S1', 'CSF1R', 'CSPG4', 'CTNNB1', 'DOK1', 'EFNA1', 'EFNA2', 'EFNA3', 'EFNA4', 'EFNA5', 'EFNB1', 'EFNB2',
'EFNB3', 'EGFR', 'EPHA2', 'EPHA1', 'EPHA3', 'EPHA4', 'EPHA5', 'EPHA7', 'EPHA8', 'EPHB1', 'EPHB2', 'EPHB3', 'EPHB4',
'EPHB6', 'ERBB2', 'ERBB3', 'ERBB4', 'EFEMP1', 'FER', 'FES', 'FGFR1', 'FGFR3', 'FGFR2', 'FGFR4', 'FGR', 'FLT1', 'FLT3',
'FLT4', 'FRK', 'FUT7', 'GATA3', 'GFRA1', 'GFRA2', 'GFRA3', 'GHR', 'GHRHR', 'GHSR', 'GPR21', 'GPER1', 'FFAR3', 'GRB2',
'GRB7', 'GRB10', 'GRB14', 'HCK', 'NRG1', 'NDST1', 'IGF1R', 'IGF2R', 'IGFBP1', 'IGFBP2', 'IGFBP3', 'IGFBP4', 'IGFBP5',
'IGFBP6', 'RBPJ', 'INSR', 'INSRR', 'IRS1', 'ITGA1', 'ITGA5', 'ITGB3', 'JAK2', 'JAK3', 'KDR', 'KIT', 'STMN1', 'LCK', 'LEP',
'LRP1', 'LTK', 'LYN', 'MET', 'FOXO4', 'MST1R', 'MUSK', 'NDN', 'NEDD9', 'NGFR', 'NKX3-1', 'NTRK1', 'NTRK2', 'NTRK3', 'ROR1',
'ROR2', 'DDR2', 'PAK1', 'PAK2', 'PAK3', 'PDGFRA', 'PDGFRL', 'PDGFRB', 'PIGR', 'PIK3C2A', 'PIK3CA', 'PIK3CB', 'PIK3CD', 'PIK3R1',
'PIK3R2', 'PLAUR', 'PRLR', 'PSEN1', 'PTGIR', 'PTPN1', 'PTPN2', 'PTPN3', 'PTPN11', 'PTPN12', 'PTPRA', 'PTPRE', 'PTPRG', 'PTPRJ',
'PTPRR', 'RAC1', 'RAF1', 'RARRES2', 'RET', 'ROBO1', 'ROS1', 'RYK', 'SORT1', 'SORL1', 'SOS1', 'SOX9', 'SRC', 'SREBF1', 'SRMS',
'STAT3', 'STAT5A', 'STAT5B', 'STAT6', 'TEK', 'TIAM1', 'TIE1', 'TYRO3', 'VTN', 'WNT1', 'WNT5A', 'YES1', 'RAB7A', 'FZD4', 'PIK3R3',
'SOCS1', 'IRS2', 'TNK1', 'NRP2', 'NRP1', 'SOCS2', 'HAP1', 'MPZL1', 'SOCS3', 'HIP1R', 'GPRC5A', 'FIBP', 'REPS2', 'NOG', 'KL', 'MVP',
'NR1H4', 'FGFBP1', 'NAMPT', 'TNK2', 'SPRY3', 'SPRY1', 'SPRY2', 'CNKSR1', 'STUB1', 'EFS', 'MERTK', 'SH2B2', 'TXNIP', 'RGS14', 'NRG3',
'FRS3', 'FRS2', 'NEU3', 'EMILIN1', 'PTPRT', 'PTP4A3', 'LMTK2', 'SIRT2', 'SETX', 'SIK2', 'ANKS1A', 'SIRT1', 'LEPROTL1', 'FLRT3', 'FLRT2',
'FLRT1', 'SHC2', 'DSTYK', 'NGEF', 'PRKD2', 'ADGRA2', 'GREM1', 'CYFIP2', 'DNAI1', 'NPTN', 'SNX5', 'DLL1', 'PILRB', 'ADIPOR1', 'CRIM1',
'GHRL', 'FGFRL1', 'ERRFI1', 'WNT4', 'LEPROT', 'STYK1', 'SMPD3', 'SULF2', 'RTN4', 'SEMA6A', 'NCOA5', 'SNX6', 'SMOC2',
'GFRA4', 'HHIP', 'GIGYF1', 'GKAP1', 'NDEL1', 'MVB12B', 'OSBPL8', 'RASGRP4', 'NUS1', 'CLNK', 'ZFYVE27', 'SOCS4', 'OTOL1', 'IL31RA',
'SAMD10', 'SOGA1', 'FGFBP3', 'SESN3', 'DAB2IP', 'CLEC14A', 'CADM4', 'MUC20', 'DOK6', 'FAM83B', 'STXBP4', 'EPHA10', 'EPHA6', 'LRIT3',
'GFRAL', 'ADRB2','ANGPT1','ANGPT4','CHRNA3','DGKQ','EFNA5','EGF','GREM1','NRG1','NRG3','PDGFC','PILRB','PRLR','TAL1', 'AGT','AGTR2',
'AKT1S1','BCAR1','BDNF','CASP3','CORO1A','CYFIP1','CYFIP2','DDIT4','DOK5','GFRA1','HAP1','KIDINS220','MAGI2','NDN','NGF','NTF3','NTF4',
'NTRK1','NTRK2','NTRK3','PPP2R5B','PTPN11','RAF1','RAP1A','RAPGEF1','RAPGEF2','SORT1','SOS1','SPRY1','SPRY2','SRC','TMEM108','WASF1',
'ZDHHC17','ZFYVE27', 'ADAM17','AGR2','APP','AREG','ARF4','ARTN','ATXN2','BTC','CADM4','CBLC','CCDC88A','CD2AP','CD300LF','CDH5',
'CNTF','CSF2','CSF3','DAB2IP','ECM1','EFEMP1','EGF','EPGN','ERAP1','ERBB4','EREG','ERN1','ESM1','FAM83B','FER','FGF1','FGF10','FGF16',
'FGF17','FGF18','FGF19','FGF2','FGF20','FGF21','FGF22','FGF23','FGF3','FGF4','FGF5','FGF6','FGF7','FGF8','FGF9','FLRT2','FLRT3','FRS2',
'FRS3','FYN','GATA3','GDNF','GLMN','GRB2','GREM1','HBEGF','HIP1','IL10','IL11','IL12A','IL12B','IL12RB1','IL1A','IL1B','IL1F10','IL1R1',
'IL1RAP','IL1RN','IL2','IL21','IL23R','IL27RA','IL3','IL36A','IL36B','IL36G','IL36RN','IL37','IL4','IL5','IL6','IL6R','IL6ST','IL7','IL9',
'IRAK4','ITGA5','ITGB3','JAK2','KL','KLB','LINGO1','LYN','MS4A1','MYD88','NCSTN','NPTN','NRTN','PDCL3','PDGFA','PDGFB','PDGFC','PDGFD',
'PDGFRA','PDGFRB','PGF','PIBF1','PLSCR1','PSEN1','PSPN','PTPRJ','PYCARD','RNF126','RNF41','SDCBP','SHC1','SLC9A3R1','SNX1','SNX2','SNX4',
'SOCS5','SRC','TGFA','TIMM50','TLR5','TLR9','TNK2','TOLLIP','TRIP6','TSLP','VAV2','VAV3','VEGFA','VEGFB','VEGFC', 'AGT','AGTR2',
'AKT1S1','BCAR1','BDNF','CASP3','CORO1A','CYFIP1','CYFIP2','DDIT4','DOK5','GFRA1','HAP1','KIDINS220','MAGI2','NDN','NGF','NTF3','NTF4',
'NTRK1','NTRK2','NTRK3','PPP2R5B','PTPN11','RAF1','RAP1A','RAPGEF1','RAPGEF2','SORT1','SOS1','SPRY1','SPRY2','SRC','TMEM108','WASF1',
'ZDHHC17','ZFYVE27', 'ARF6', 'RAB3A', 'CTBP2', 'ERC1', 'PCLO', 'DBN1', 'DBNL', 'INA', 'RAB3A','CBLN2','CTBP2','DBN1','ARF6','TAGLN3',
'ERC1','CTBP2','PCLO','MALAT1','MT-CYB', 'DNAJC6','SAMD4A','STAT3','RALBP1',
#from here after cellchat
'MDK', 'NCL', 'CADM1', 'NCAM1', 'PTN', 'PTPRZ1', 'ITGA6', 'ITGB1', 'ITGA4', 'L1CAM', 'SPP1', 'ITGA8', 'ITGA5', 'ITGA4', 'CNTN1', 'NRCAM',
'SEMA6A', 'PLXNA2',
'ITGAV', 'FGFR1', 'DLL3', 'ITGB5', 'IGF1', 'IGF1R', 'EPHB2', 'EFNB3',
#from here after individual pathway DEGs
'RAB3A','CBLN2','RPS4Y1',
'CPLX2','RPS4Y1','DBN1','CLEC2D','EIF5A','RPL17','AC009501.4','RPS10','TUBB3','NBEAL1','MIF','FKBP1A','ERC1','CTBP2','PCLO','RPS4Y1','ATP6V1G2','FKBP1B',
'ARF6', 'RPS4Y1','FARP1','MEG3','NEFL','APLP2','APP','INA','SNAP25','LY6H','ATP1B1','ARL6IP5','PRNP','ITM2C','SERPINE2','GABBR2','PCP4',
'REEP5','SPTBN1','PIK3R1','NEUROD1','FABP7','ERC1','CTBP2','PCLO','RPS4Y1','ATP6V1G2','FKBP1B')
)
gene_list <- as.list(gene_list)
gene_list <- lapply(gene_list , tolower) #or toupper and skip next two lines if human data
library(stringr)
gene_list <- str_to_title(gene_list)
gene_list <- unlist(gene_list)
gene_list <- unique(gene_list)
mousefetalRGC$score <- mousefetalRGC@active.ident
avexpr <- AverageExpression(mousefetalRGC, features = gene_list, assays = 'RNA', group.by = c('stage','score'))
avexprfun <- function(average_expression_table) {
average_expression_table <- as.data.frame(average_expression_table)
average_expression_table$barcode <- ''
for(i in 1:length(rownames(avexpr$RNA))) {
if(average_expression_table[i,1] > average_expression_table[i,2] &
average_expression_table[i,2] > average_expression_table[i,3] | average_expression_table[i,1]
< average_expression_table[i,2] & average_expression_table[i,2] < average_expression_table[i,3]) {average_expression_table$barcode[i] <- 'E14'}
if(average_expression_table[i,4] > average_expression_table[i,5] &
average_expression_table[i,5] > average_expression_table[i,6] | average_expression_table[i,4]
< average_expression_table[i,5] & average_expression_table[i,5] < average_expression_table[i,6]) {average_expression_table$barcode[i] <- paste(average_expression_table$barcode[i], 'E16', sep = ' ')}
if(average_expression_table[i,7] > average_expression_table[i,8] &
average_expression_table[i,8] > average_expression_table[i,9] | average_expression_table[i,7] <
average_expression_table[i,8] & average_expression_table[i,8] < average_expression_table[i,9]) {average_expression_table$barcode[i] <- paste(average_expression_table$barcode[i], 'E18', sep = ' ')}
}
return(average_expression_table)
}
test <- avexprfun(avtest)
table(test$barcode)
test$genes <- rownames(test)
barcodes <- test[, c("genes", "barcode")]
barcodes <- barcodes[!(is.na(barcodes$barcode) | barcodes$barcode==""), ]
install.packages('VennDiagram')
library('VennDiagram')
install.packages('venneuler')
library('venneuler')
ggplot(my_data, aes(x = barcode, y = SHIFT, label = GENE)) +
geom_text_repel(data=my_data, aes(label=GENE),position = position_jitter(seed = 1),
color = c('blue','blue','gray','gray','red','blue','blue','blue','gray','gray',
'blue','gray','blue','gray','gray','gray','blue','blue','gray','gray','gray',
'blue','blue','blue','blue','blue','blue','red','gray','red','red','red','gray','gray')) +
geom_jitter(position = position_jitter(seed = 1)) + geom_hline(yintercept=0) + geom_hline(yintercept=50, linetype="dashed", color = "red") +
geom_hline(yintercept=-50, linetype="dashed", color = "red") + theme_bw()
#to apply organoids filter
ggplot(my_data, aes(x = Sample, y = Expr, fill = humancorr, color = humancorr)) +
geom_text_repel(data = my_data, aes(label = GENE), max.overlaps = 60) + geom_jitter(width = 0.1) +
scale_y_continuous(trans='log2') + geom_line(aes(group = GENE)) + theme_bw() + facet_wrap(~GENE, scale = 'free')