Setting up the directory and loading necessary libraries

library(Seurat)

library(sctransform)
library(MAST)

library(tidyverse)

library(rcna)
library(homologene)
library(viridis)

setwd("~/Pirozzi")
singlecell <- readRDS('Pirozzi_integrated_data_doublet_cells_removed.Robj')

Formatting the Data

Idents(singlecell) <- 'library'
singlecell <- RenameIdents(object = singlecell, `288-2-L-AG` = "AG188", `288-90-R-AG` = "AG188", `290-10-L-Veh` = "Vehicle", `290-91-R-Veh` = "Vehicle")
singlecell@meta.data$library = Idents(singlecell)
singlecell@meta.data$orig.ident = Idents(singlecell)
Idents(singlecell) <- 'seurat_clusters'
singlecell$orig.ident <- factor(singlecell$orig.ident, levels = c("Vehicle", "AG188"))
singlecell$library <- factor(singlecell$library, levels = c("Vehicle", "AG188"))

Looking at the Data at the Highest Level

DimPlot(object = singlecell, reduction = "umap", label = TRUE, pt.size = 1, label.size = 4)

DimPlot(object = singlecell, reduction = "umap",split.by = "orig.ident", label = TRUE, pt.size = 1, label.size = 4,ncol = 2)

#Annotating the Data ##Identifying Tumor Data

pal <- plasma(n = 10, direction = -1)
FeaturePlot(object = singlecell, cols = pal, order = T, features = "Egfr")

FeaturePlot(object = singlecell, cols = pal, order = T, features = "Sox2")

FeaturePlot(object = singlecell, cols = pal, order = T, features = "Ptprz1")

Based on these clusters it seem that 6, 16, 9, and 3 are the tumor fields: For the rest of the clusters the following pipeline reveals what the other fields might be based on human reference atlases along with relative confidence scores:

##Clustering the Data

lapply(c("dplyr","Seurat","HGNChelper","openxlsx"), library, character.only = T)

[[1]]
 [1] "viridis"              "viridisLite"          "homologene"          
 [4] "rcna"                 "forcats"              "stringr"             
 [7] "dplyr"                "purrr"                "readr"               
[10] "tidyr"                "tibble"               "ggplot2"             
[13] "tidyverse"            "MAST"                 "SingleCellExperiment"
[16] "SummarizedExperiment" "Biobase"              "GenomicRanges"       
[19] "GenomeInfoDb"         "IRanges"              "S4Vectors"           
[22] "BiocGenerics"         "stats4"               "MatrixGenerics"      
[25] "matrixStats"          "sctransform"          "SeuratObject"        
[28] "Seurat"               "stats"                "graphics"            
[31] "grDevices"            "utils"                "datasets"            
[34] "methods"              "base"                

[[2]]
 [1] "viridis"              "viridisLite"          "homologene"          
 [4] "rcna"                 "forcats"              "stringr"             
 [7] "dplyr"                "purrr"                "readr"               
[10] "tidyr"                "tibble"               "ggplot2"             
[13] "tidyverse"            "MAST"                 "SingleCellExperiment"
[16] "SummarizedExperiment" "Biobase"              "GenomicRanges"       
[19] "GenomeInfoDb"         "IRanges"              "S4Vectors"           
[22] "BiocGenerics"         "stats4"               "MatrixGenerics"      
[25] "matrixStats"          "sctransform"          "SeuratObject"        
[28] "Seurat"               "stats"                "graphics"            
[31] "grDevices"            "utils"                "datasets"            
[34] "methods"              "base"                

[[3]]
 [1] "HGNChelper"           "viridis"              "viridisLite"         
 [4] "homologene"           "rcna"                 "forcats"             
 [7] "stringr"              "dplyr"                "purrr"               
[10] "readr"                "tidyr"                "tibble"              
[13] "ggplot2"              "tidyverse"            "MAST"                
[16] "SingleCellExperiment" "SummarizedExperiment" "Biobase"             
[19] "GenomicRanges"        "GenomeInfoDb"         "IRanges"             
[22] "S4Vectors"            "BiocGenerics"         "stats4"              
[25] "MatrixGenerics"       "matrixStats"          "sctransform"         
[28] "SeuratObject"         "Seurat"               "stats"               
[31] "graphics"             "grDevices"            "utils"               
[34] "datasets"             "methods"              "base"                

[[4]]
 [1] "openxlsx"             "HGNChelper"           "viridis"             
 [4] "viridisLite"          "homologene"           "rcna"                
 [7] "forcats"              "stringr"              "dplyr"               
[10] "purrr"                "readr"                "tidyr"               
[13] "tibble"               "ggplot2"              "tidyverse"           
[16] "MAST"                 "SingleCellExperiment" "SummarizedExperiment"
[19] "Biobase"              "GenomicRanges"        "GenomeInfoDb"        
[22] "IRanges"              "S4Vectors"            "BiocGenerics"        
[25] "stats4"               "MatrixGenerics"       "matrixStats"         
[28] "sctransform"          "SeuratObject"         "Seurat"              
[31] "stats"                "graphics"             "grDevices"           
[34] "utils"                "datasets"             "methods"             
[37] "base"

source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R"); source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/sctype_score_.R")

# get cell-type-specific gene sets from our in-built database (DB)
gs_list = gene_sets_prepare("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/ScTypeDB_short.xlsx", "Brain") # e.g. Immune system, Liver, Pancreas, Kidney, Eye, Brain

Warning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbolsWarning: x contains non-approved gene symbols

es.max =  sctype_score(scRNAseqData = singlecell[["integrated"]]@scale.data, scaled = TRUE, gs = gs_list$gs_positive, gs2 = gs_list$gs_negative)

cL_resutls = do.call("rbind", lapply(unique(singlecell@meta.data$seurat_clusters), function(cl){
    es.max.cl = sort(rowSums(es.max[ ,rownames(singlecell@meta.data[singlecell@meta.data$seurat_clusters==cl, ])]), decreasing = !0)
    head(data.frame(cluster = cl, type = names(es.max.cl), scores = es.max.cl, ncells = sum(singlecell@meta.data$seurat_clusters==cl)), 10)}))
sctype_scores = cL_resutls %>% group_by(cluster) %>% top_n(n = 1, wt = scores) 
sctype_scores$type[as.numeric(as.character(sctype_scores$scores)) < sctype_scores$ncells/4] = "Unknown"
print(sctype_scores[,1:3])

Relabeling the Objects in The Main Object Based on ID’s assigned

Idents(singlecell)<-'seurat_clusters'
singlecell<-RenameIdents(singlecell,'0'='Oligodendrocytes c1','1'='Oligodendrocytes c2','2'='Oligodendrocytes c3','3'='Tumor c1','4'='Oligodendrocytes c7','5'='Endothelial cells','6'='Tumor c3','7'='Oligodendrocytes c4','8'='Neural stem cells','9'='Tumor c2','10'='Microglial cells c1','11'='Microglial cells c2','12'='Oligodendrocytes c6', '13'='Oligodendrocytes c5','14'='Oligodendrocyte precursor cells c2','15'='Oligodendrocytes c8','16'='Tumor c4','17'='Oligodendrocytes c7','18'='Neuroblasts','19'='Microglial cells c3','20'='Neuroepithelial cells','21'='Immature neurons','22'='Oligodendrocyte precursor cells','23'='Dopaminergic neurons','24'='Endothelial cells c1','25'='Tanycytes c1','26'='Endothelial cells c2','27'='Endothelial cells c3','28'='Endothelial cells c3','29'='Microglial cells c4','30'='Immune system cells c2','31'='Immune system cells c1','32'='Tanycytes c2','33'='Microglial cells c5')
singlecell$clusterlabels<-Idents(singlecell)
Idents(singlecell)<-'clusterlabels'

Performing CNA Analysis to Find Treatment Effects At Population Level

singlecell<-FindNeighbors(singlecell)

singlecell@meta.data$ConditionNum <- as.numeric(factor(singlecell@meta.data$library, c('Vehicle', 'AG188')))
library(glue)
library(ggthemes)

singlecell2<-association.Seurat(
  seurat_object = singlecell, 
  test_var = 'ConditionNum', 
  samplem_key = 'old.ident', 
  graph_use = 'integrated_snn', 
  verbose = TRUE,
  batches = NULL, ## no batch variables to include
  covs = NULL, ## no covariates to include 
)

FeaturePlot(singlecell2, features = c('cna_ncorrs'))[[1]] + scale_color_gradient2_tableau() + labs(title = 'CNA by Condition', color = 'Correlation',subtitle = sprintf('global p=%0.3f', singlecell2@reductions$cna@misc$p))

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

FeaturePlot(singlecell2, features = c('cna_ncorrs_fdr10'))[[1]] + scale_color_gradient2_tableau() + labs(title = 'CNA by Condition', subtitle = 'Filtered for FDR<0.10', color = 'Correlation')

Warning: All cells have the same value (0) of cna_ncorrs_fdr10.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

DimPlot(singlecell2, group.by = 'library')[[1]] + scale_color_tableau() + labs(title = 'Condition')

Here we can see which populations are associated with treatment. Blue cells are associated with AG188 treatment wereas red are negatively associated with treatment. You can see that in the tumor population, AG188 relatively changes proportions of cells.

#Subsetting the Tumor Clusters and Reclustering

tumorsinglecells <- FindVariableFeatures(tumorsinglecells, selection.method = "vst", nfeatures = 2000, verbose = FALSE)

Warning: Not all features provided are in this Assay object, removing the following feature(s): Wfdc21, Mmp8, Cd3g, Slc22a6, Sln, Dlk1, Trbc2, Fam180a, Sct, Ly6c2, 1200007C13Rik, Avp, Mmp12, Cd3d, Cd177, Ms4a4b, Mirt1, Eomes, Sell, Clec4e, Tmem130, Penk, Slc5a7, Cxcl13, Gm36266, Fpr2, Gm35021, Lyve1, Fcer2a, Prokr2, Gm37963, Htr3a, Glra3, Atp8b4, Cyp4b1, Col6a6, Vipr2, Ecel1, Ssu2, Slc17a7, Zfp607a, Il2ra, Myo1g, C1ql2, Tbr1, Gm15551, Gm49975, 0610039K10Rik, Retn, Gm28050, Gm44751, Kcng4, Cyp2e1, Dhrs9, 4932435O22Rik, C530044C16Rik, Ltb4r1, Gm50130, Ifi211, Gm42700, Necab2, Frk, Gm13536, Mgst2, Jchain, Cplx3, Gm26620, Mgarp, Nefm, Ifi208, mt-Nd4l, 4933406B17Rik, Plch2, mt-Atp6, Fam78a, Gm10800, AI662270, Tmem267, Sema3f, Chodl, Fut4, Tspan32os, Fanci, Tsix, Gm39556

tumorsinglecells <- ScaleData(tumorsinglecells, verbose = FALSE)
tumorsinglecells<-RunPCA(tumorsinglecells)

PC_ 1 
Positive:  Cfap299, Gm48050, Sncg, 3300002A11Rik, Gm38414, P2rx1, Dmkn, Rsph4a, Ptgis, Actg2 
       Aoc3, Olfr558, Tbx18, Cbr2, Krt15, Tmem212, Iqca, Cnn1, Got1l1, Dnali1 
       Des, Spag6l, 1700001C02Rik, Calml4, 2010001K21Rik, Ccdc153, Col18a1, 1700012B09Rik, Rsph1, Zp3 
Negative:  Hic1, Ces2e, Ace2, Myct1, Cyyr1, Pla1a, Thbd, Zfp366, Bcl2a1d, Gimap6 
       Alox5ap, Gm6377, Adcy4, Esm1, Ctla2b, Tbxa2r, Pcdh12, Abcc9, Clec14a, Plaur 
       Casp4, Gpr182, Il6, Nos2, She, Sebox, Saa2, Steap4, Ndufa4l2, Adh1 
PC_ 2 
Positive:  Cst3, Sbpl, Foxs1, Mt3, Gpr37l1, Plpp3, Aldoc, Clu, Mt1, Apoe 
       Cacna1s, Atp1a2, Slc1a3, Htra1, Col3a1, Gja1, S1pr1, Cldn10, F3, Mfge8 
       Ldhb, Ptprz1, Mt2, Sparcl1, Cbr2, Chchd10, Scg3, Prdx6, Cxcl14, Pla2g7 
Negative:  Plp1, Cnp, Mal, Cryab, Stmn4, Ermn, Trf, Mag, Fth1, Cldn11 
       Gatm, Ppp1r14a, App, Mog, Qdpr, Sept4, Tubb4a, Aplp1, Dbndd2, Car2 
       Tspan2, Opalin, Stmn1, Aspa, Ptgds, Bin1, Edil3, Enpp2, Ttll7, Apod 
PC_ 3 
Positive:  Agt, Slc6a11, Itih3, Sparc, Col3a1, Slc4a4, Ptch1, Sbpl, Igsf1, Lrig1 
       Trpm3, Gpld1, Fry, Cbr2, Gria1, Tbx18, Ttll3, Atp13a4, Foxs1, Ace 
       Etnppl, Ntrk3, Sparcl1, Gm13861, Selenop, Nnat, Spon1, Itpkb, Slc6a1, Cit 
Negative:  Prdx6, Ptn, Gstm5, Cspg5, Pantr1, Mt3, Tspan7, Lhx2, Rgcc, Id4 
       Kcnk1, Fjx1, Scg3, Mt1, Mgll, Lix1, Ddah1, Chst2, Slc1a2, Cxcl14 
       Mfge8, Htra1, Hmmr, S100a1, Aldoc, Glul, Atp1b1, Pimreg, Luzp2, Dclk1 
PC_ 4 
Positive:  Cst3, Agt, Apoe, Sparc, Ckb, Dbi, Sparcl1, Slc6a11, Nkain4, Ramp1 
       Selenop, Itih3, Nnat, Marcks, Fabp7, Cldn10, Ednrb, Atp1b2, Mdk, Itm2c 
       Ldhb, Aldoc, Mgst1, Igsf1, Slc4a4, Ctsl, Pla2g7, Hmmr, Ckap2l, Actb 
Negative:  Dclk1, Gm26917, Vegfa, Gria2, Slc1a2, Syne1, Neat1, Nrxn1, Appl2, Dtna 
       Grin2c, Nwd1, Macf1, Gm3764, Dio2, Frmd4a, Msi2, Mertk, Ntm, Slco1c1 
       Col11a2, AC149090.1, Mir100hg, Rapgef3, Caskin1, Adgrl1, Phkg1, Swap70, Col3a1, Tbx18 
PC_ 5 
Positive:  Meg3, Chgb, Scg2, Ly6h, Resp18, Map1b, Nsg2, Snhg11, Pafah1b3, Celf4 
       Gng2, Bex2, Stmn3, Rtn1, Cd24a, Cdkn1c, Plac8, Gad2, Igfbp5, Jaml 
       Spint2, Ifi203, Stmn2, Slamf7, Atp1a3, Caly, Fxyd6, Mia, Tmsb10, Basp1 
Negative:  Nxph4, Crabp1, Ccr2, Asb4, 2310001H17Rik, Atp1a2, F13a1, Lilrb4a, Cpz, Cd69 
       Slc4a4, Atp1b2, Neurl3, Gpr37l1, Runx3, Slc6a11, Tril, Plpp3, Mgl2, Sparcl1 
       Slc7a10, Gja1, Ntsr2, Ptprz1, Slc6a1, Fgfr3, Agt, Gjb6, F3, Slc39a12

tumorsinglecells<-FindNeighbors(tumorsinglecells)

Computing nearest neighbor graph
Computing SNN

tumorsinglecells<-FindClusters(tumorsinglecells,resolution=0.4)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1322
Number of edges: 40632

Running Louvain algorithm...

0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Maximum modularity in 10 random starts: 0.8601
Number of communities: 6
Elapsed time: 0 seconds

tumorsinglecells<-RunUMAP(tumorsinglecells,dims=1:30)

01:18:23 UMAP embedding parameters a = 0.9922 b = 1.112
01:18:23 Read 1322 rows and found 30 numeric columns
01:18:23 Using Annoy for neighbor search, n_neighbors = 30
01:18:23 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:18:24 Writing NN index file to temp file /tmp/Rtmp0g1Qq1/file1e085c314d8d63
01:18:24 Searching Annoy index using 1 thread, search_k = 3000
01:18:24 Annoy recall = 100%
01:18:25 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
01:18:27 Initializing from normalized Laplacian + noise (using irlba)
01:18:27 Commencing optimization for 500 epochs, with 58204 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:18:29 Optimization finished

DimPlot(object = tumorsinglecells, reduction = "umap", label = TRUE, pt.size = 1, label.size = 4)

DimPlot(object = tumorsinglecells, reduction = "umap",split.by = "orig.ident", label = TRUE, pt.size = 1, label.size = 4,ncol = 2)

#Differential Gene Expression Between Treatments

tumorsinglecells <- ScaleData(
  tumorsinglecells,
  features = NULL,
  vars.to.regress = NULL,
  split.by = NULL,
  model.use = "linear",
  use.umi = FALSE,
  do.scale = TRUE,
  do.center = TRUE,
  scale.max = 10,
  block.size = 1000,
  min.cells.to.block = 3000,
  verbose = TRUE)

Centering and scaling data matrix

  |                                                                                                                      
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  |========================================================                                                        |  50%
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Idents(tumorsinglecells) <- 'library'

Subset_Tumor1 <- FindMarkers(tumorsinglecells,ident.1 = c("Vehicle"), ident.2 = c("AG188"), verbose = TRUE, assay = "RNA", slot = "data", min.pct = 0.1, test.use = "MAST", logfc.threshold = 0.01)


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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...

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Subset_Tumor1$gene <- rownames(Subset_Tumor1)
Subset_Tumor1$Expression <- rownames(Subset_Tumor1)
Subset_Tumor1 <- Subset_Tumor1 %>% 
  mutate(
    Expression = case_when(avg_log2FC >= 0.1 & p_val_adj <= 0.05 ~ "Up-regulated",
                           avg_log2FC <= -0.1 & p_val_adj <= 0.05 ~ "Down-regulated",
                           TRUE ~ "Unchanged"))

top <- 25
top_genes <- bind_rows(
  Subset_Tumor1 %>% 
    filter(Expression == 'Up-regulated') %>% 
    arrange(p_val_adj, desc(abs(avg_log2FC))) %>% 
    head(top),
  Subset_Tumor1 %>% 
    filter(Expression == 'Down-regulated') %>% 
    arrange(p_val_adj, desc(abs(avg_log2FC))) %>% 
    head(top)
)

library(ggrepel)
options(ggrepel.max.overlaps = Inf)
library(cowplot) 
theme_set(theme_cowplot())
theme(plot.title = element_text(hjust = 0.5,face = "plain"))

List of 1
 $ plot.title:List of 11
  ..$ family       : NULL
  ..$ face         : chr "plain"
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : num 0.5
  ..$ vjust        : NULL
  ..$ angle        : NULL
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi FALSE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi FALSE
 - attr(*, "validate")= logi TRUE

library(forcats)

p=
  ggplot(Subset_Tumor1, aes(x=avg_log2FC, y=-log10(p_val_adj))) +
  geom_point(aes(color = Expression), size = 1, alpha=0.8)+
  xlab("Log 2 Fold Change") +
  ylab("-Log10 Adjusted P-Value")+
  scale_color_manual(values = c("dodgerblue3", "gray50", "sienna2")) +
  guides(colour = guide_legend(override.aes = list(size=1.5)))+
  geom_hline(yintercept=1.3, linetype="dashed",
             color = "black", size=0.5)+
  geom_vline(xintercept = -0.1, linetype="dotdash",
             color = "black", size=0.5)+
  geom_vline(xintercept = 0.1, linetype="dotdash",
             color = "black", size=0.5)+
  theme(plot.title = element_text(hjust = 0.5,face = "plain"))+
  theme(legend.position = "none")+
  geom_label_repel(data = top_genes,
                   mapping = aes(avg_log2FC, -log10(p_val_adj), label = gene),
                   size = 3)

p

---
title: "AG-188 ScRNA Seq Analysis For Dr. Pirozzi"
output: html_notebook
---


Setting up the directory and loading necessary libraries

```{r,results='hide'}
library(Seurat)
library(sctransform)
library(MAST)
library(tidyverse)
library(rcna)
library(homologene)
library(viridis)
setwd("~/Pirozzi")
singlecell <- readRDS('Pirozzi_integrated_data_doublet_cells_removed.Robj')
```
Formatting the Data
```{r}
Idents(singlecell) <- 'library'
singlecell <- RenameIdents(object = singlecell, `288-2-L-AG` = "AG188", `288-90-R-AG` = "AG188", `290-10-L-Veh` = "Vehicle", `290-91-R-Veh` = "Vehicle")
singlecell@meta.data$library = Idents(singlecell)
singlecell@meta.data$orig.ident = Idents(singlecell)
Idents(singlecell) <- 'seurat_clusters'
singlecell$orig.ident <- factor(singlecell$orig.ident, levels = c("Vehicle", "AG188"))
singlecell$library <- factor(singlecell$library, levels = c("Vehicle", "AG188"))
```
# Looking at the Data at the Highest Level
```{r}
DimPlot(object = singlecell, reduction = "umap", label = TRUE, pt.size = 1, label.size = 4)
DimPlot(object = singlecell, reduction = "umap",split.by = "orig.ident", label = TRUE, pt.size = 1, label.size = 4,ncol = 2)
```
#Annotating the Data
##Identifying Tumor Data
```{r}
pal <- plasma(n = 10, direction = -1)
FeaturePlot(object = singlecell, cols = pal, order = T, features = "Egfr")
FeaturePlot(object = singlecell, cols = pal, order = T, features = "Sox2")
FeaturePlot(object = singlecell, cols = pal, order = T, features = "Ptprz1")
```
Based on these clusters it seem that 6, 16, 9, and 3 are the tumor fields:
For the rest of the clusters the following pipeline reveals what the other fields might be based on human reference atlases along with relative confidence scores:

##Clustering the Data
```{r}
lapply(c("dplyr","Seurat","HGNChelper","openxlsx"), library, character.only = T)
source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R"); source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/sctype_score_.R")

# get cell-type-specific gene sets from our in-built database (DB)
gs_list = gene_sets_prepare("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/ScTypeDB_short.xlsx", "Brain") # e.g. Immune system, Liver, Pancreas, Kidney, Eye, Brain

es.max =  sctype_score(scRNAseqData = singlecell[["integrated"]]@scale.data, scaled = TRUE, gs = gs_list$gs_positive, gs2 = gs_list$gs_negative)

cL_resutls = do.call("rbind", lapply(unique(singlecell@meta.data$seurat_clusters), function(cl){
    es.max.cl = sort(rowSums(es.max[ ,rownames(singlecell@meta.data[singlecell@meta.data$seurat_clusters==cl, ])]), decreasing = !0)
    head(data.frame(cluster = cl, type = names(es.max.cl), scores = es.max.cl, ncells = sum(singlecell@meta.data$seurat_clusters==cl)), 10)}))
sctype_scores = cL_resutls %>% group_by(cluster) %>% top_n(n = 1, wt = scores) 
sctype_scores$type[as.numeric(as.character(sctype_scores$scores)) < sctype_scores$ncells/4] = "Unknown"
print(sctype_scores[,1:3])
```

# Relabeling the Objects in The Main Object Based on ID's assigned

```{r}
Idents(singlecell)<-'seurat_clusters'
singlecell<-RenameIdents(singlecell,'0'='Oligodendrocytes c1','1'='Oligodendrocytes c2','2'='Oligodendrocytes c3','3'='Tumor c1','4'='Oligodendrocytes c7','5'='Endothelial cells','6'='Tumor c3','7'='Oligodendrocytes c4','8'='Neural stem cells','9'='Tumor c2','10'='Microglial cells c1','11'='Microglial cells c2','12'='Oligodendrocytes c6', '13'='Oligodendrocytes c5','14'='Oligodendrocyte precursor cells c2','15'='Oligodendrocytes c8','16'='Tumor c4','17'='Oligodendrocytes c7','18'='Neuroblasts','19'='Microglial cells c3','20'='Neuroepithelial cells','21'='Immature neurons','22'='Oligodendrocyte precursor cells','23'='Dopaminergic neurons','24'='Endothelial cells c1','25'='Tanycytes c1','26'='Endothelial cells c2','27'='Endothelial cells c3','28'='Endothelial cells c3','29'='Microglial cells c4','30'='Immune system cells c2','31'='Immune system cells c1','32'='Tanycytes c2','33'='Microglial cells c5')
singlecell$clusterlabels<-Idents(singlecell)
Idents(singlecell)<-'clusterlabels'

```

```{r}
DimPlot(object = singlecell, reduction = "umap",split.by = "orig.ident", label = TRUE, pt.size = 1, label.size = 4,ncol = 2)
```
# Performing CNA Analysis to Find Treatment Effects At Population Level
```{r}
singlecell<-FindNeighbors(singlecell)

singlecell@meta.data$ConditionNum <- as.numeric(factor(singlecell@meta.data$library, c('Vehicle', 'AG188')))
library(glue)
library(ggthemes)

singlecell2<-association.Seurat(
  seurat_object = singlecell, 
  test_var = 'ConditionNum', 
  samplem_key = 'old.ident', 
  graph_use = 'integrated_snn', 
  verbose = TRUE,
  batches = NULL, ## no batch variables to include
  covs = NULL, ## no covariates to include 
)
```
```{r}
FeaturePlot(singlecell2, features = c('cna_ncorrs'))[[1]] + scale_color_gradient2_tableau() + labs(title = 'CNA by Condition', color = 'Correlation',subtitle = sprintf('global p=%0.3f', singlecell2@reductions$cna@misc$p))
FeaturePlot(singlecell2, features = c('cna_ncorrs_fdr10'))[[1]] + scale_color_gradient2_tableau() + labs(title = 'CNA by Condition', subtitle = 'Filtered for FDR<0.10', color = 'Correlation')
DimPlot(singlecell2, group.by = 'library')[[1]] + scale_color_tableau() + labs(title = 'Condition')

```
Here we can see which populations are associated with treatment. Blue cells are associated with AG188 treatment wereas red are negatively associated with treatment. You can see that in the tumor population, AG188 relatively changes proportions of cells.

#Subsetting the Tumor Clusters and Reclustering
```{r}
tumorsinglecells <- subset(singlecell, subset = clusterlabels == c("Tumor c1","Tumor c2","Tumor c3","Tumor c4"))
tumorsinglecells <- FindVariableFeatures(tumorsinglecells, selection.method = "vst", nfeatures = 2000, verbose = FALSE)
tumorsinglecells <- ScaleData(tumorsinglecells, verbose = FALSE)
tumorsinglecells<-RunPCA(tumorsinglecells)
tumorsinglecells<-FindNeighbors(tumorsinglecells)
tumorsinglecells<-FindClusters(tumorsinglecells,resolution=0.4)
tumorsinglecells<-RunUMAP(tumorsinglecells,dims=1:30)
```
```{r}
DimPlot(object = tumorsinglecells, reduction = "umap", label = TRUE, pt.size = 1, label.size = 4)
DimPlot(object = tumorsinglecells, reduction = "umap",split.by = "orig.ident", label = TRUE, pt.size = 1, label.size = 4,ncol = 2)

```
#Differential Gene Expression Between Treatments

```{r}
tumorsinglecells <- ScaleData(
  tumorsinglecells,
  features = NULL,
  vars.to.regress = NULL,
  split.by = NULL,
  model.use = "linear",
  use.umi = FALSE,
  do.scale = TRUE,
  do.center = TRUE,
  scale.max = 10,
  block.size = 1000,
  min.cells.to.block = 3000,
  verbose = TRUE)

Idents(tumorsinglecells) <- 'library'

Subset_Tumor1 <- FindMarkers(tumorsinglecells,ident.1 = c("Vehicle"), ident.2 = c("AG188"), verbose = TRUE, assay = "RNA", slot = "data", min.pct = 0.1, test.use = "MAST", logfc.threshold = 0.01)

Subset_Tumor1$gene <- rownames(Subset_Tumor1)
Subset_Tumor1$Expression <- rownames(Subset_Tumor1)
Subset_Tumor1 <- Subset_Tumor1 %>% 
  mutate(
    Expression = case_when(avg_log2FC >= 0.1 & p_val_adj <= 0.05 ~ "Up-regulated",
                           avg_log2FC <= -0.1 & p_val_adj <= 0.05 ~ "Down-regulated",
                           TRUE ~ "Unchanged"))

top <- 25
top_genes <- bind_rows(
  Subset_Tumor1 %>% 
    filter(Expression == 'Up-regulated') %>% 
    arrange(p_val_adj, desc(abs(avg_log2FC))) %>% 
    head(top),
  Subset_Tumor1 %>% 
    filter(Expression == 'Down-regulated') %>% 
    arrange(p_val_adj, desc(abs(avg_log2FC))) %>% 
    head(top)
)

library(ggrepel)
options(ggrepel.max.overlaps = Inf)
library(cowplot) 
theme_set(theme_cowplot())
theme(plot.title = element_text(hjust = 0.5,face = "plain"))
library(forcats) 
```
```{r}
p=
  ggplot(Subset_Tumor1, aes(x=avg_log2FC, y=-log10(p_val_adj))) +
  geom_point(aes(color = Expression), size = 1, alpha=0.8)+
  xlab("Log 2 Fold Change") +
  ylab("-Log10 Adjusted P-Value")+
  scale_color_manual(values = c("dodgerblue3", "gray50", "sienna2")) +
  guides(colour = guide_legend(override.aes = list(size=1.5)))+
  geom_hline(yintercept=1.3, linetype="dashed",
             color = "black", size=0.5)+
  geom_vline(xintercept = -0.1, linetype="dotdash",
             color = "black", size=0.5)+
  geom_vline(xintercept = 0.1, linetype="dotdash",
             color = "black", size=0.5)+
  theme(plot.title = element_text(hjust = 0.5,face = "plain"))+
  theme(legend.position = "none")+
  geom_label_repel(data = top_genes,
                   mapping = aes(avg_log2FC, -log10(p_val_adj), label = gene),
                   size = 3)

p

```

AG-188 ScRNA Seq Analysis For Dr. Pirozzi

Looking at the Data at the Highest Level

Relabeling the Objects in The Main Object Based on ID’s assigned

Performing CNA Analysis to Find Treatment Effects At Population Level