Cell Line L1 Analysis-Reclustering
Nasir Mahmood Abbasi
2025-07-14
1. load libraries
2. load seurat object
All_samples_Merged <- readRDS("/home/nabbasi/isilon/PHD_3rd_YEAR_Analysis/0-Seurat_RDS_OBJECT_FINAL/All_samples_Merged_Harmony_integrated_Cell_line_renamed_03-07-2025.rds")
Subset L1 from Merged Object
# Assuming All_samples_Merged is already loaded
L1 <- subset(All_samples_Merged, subset = cell_line == "L1")
L1An object of class Seurat
62900 features across 5825 samples within 6 assays
Active assay: SCT (26176 features, 3000 variable features)
3 layers present: counts, data, scale.data
5 other assays present: RNA, ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3
5 dimensional reductions calculated: integrated_dr, ref.umap, pca, umap, harmonyrm(All_samples_Merged)
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 14328350 765.3 25809192 1378.4 23408137 1250.2
Vcells 176011755 1342.9 1256390711 9585.6 1472463884 11234.13. QC
# Set identity classes to an existing column in meta data
Idents(object = L1) <- "cell_line"
L1[["percent.rb"]] <- PercentageFeatureSet(L1, pattern = "^RP[SL]")
VlnPlot(L1, features = c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.rb"),
pt.size = 0.1, ncol = 4) & theme(plot.title = element_text(size = 10))Avis : The `slot` argument of `FetchData()` is deprecated as of SeuratObject 5.0.0.
Please use the `layer` argument instead.Avis : `PackageCheck()` was deprecated in SeuratObject 5.0.0.
Please use `rlang::check_installed()` instead.
FeatureScatter(L1, feature1 = "nCount_RNA", feature2 = "percent.mt") + geom_smooth(method = "lm")
FeatureScatter(L1, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") + geom_smooth(method = "lm")
Assign Cell-Cycle Scores
Running SCTransform on assay: RNA
Avis : The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.
Please use the `layer` argument instead.vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 17646 by 5825
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Found 99 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 17646 genes
Computing corrected count matrix for 17646 genes
Calculating gene attributes
Wall clock passed: Time difference of 48.46975 secs
Determine variable features
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Place corrected count matrix in counts slot
Avis : The `slot` argument of `SetAssayData()` is deprecated as of SeuratObject 5.0.0.
Please use the `layer` argument instead.Avis : Different cells and/or features from existing assay SCTSet default assay to SCT
Avis : The following features are not present in the object: MLF1IP, not searching for symbol synonymsAvis : The following features are not present in the object: FAM64A, HN1, not searching for symbol synonyms4. Normalize data
# Apply SCTransform
L1 <- SCTransform(L1, vars.to.regress = c("percent.rb","percent.mt", "CC.Difference"),
do.scale=TRUE,
do.center=TRUE,
verbose = TRUE)Running SCTransform on assay: RNA
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 17646 by 5825
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Avis : useNames = NA is deprecated. Instead, specify either useNames = TRUE or useNames = FALSE.Found 99 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 17646 genes
Computing corrected count matrix for 17646 genes
Calculating gene attributes
Wall clock passed: Time difference of 46.92 secs
Determine variable features
Regressing out percent.rb, percent.mt, CC.Difference
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Centering and scaling data matrix
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Place corrected count matrix in counts slot
Set default assay to SCT 5. Perform PCA
Variables_genes <- L1@assays$SCT@var.features
# Exclude genes starting with "HLA-" AND "Xist" AND "TRBV, TRAV"
Variables_genes_after_exclusion <- Variables_genes[!grepl("^HLA-|^XIST|^TRBV|^TRAV", Variables_genes)]
# These are now standard steps in the Seurat workflow for visualization and clustering
L1 <- RunPCA(L1,
features = Variables_genes_after_exclusion,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 15,
npcs = 50)PC_ 1
Positive: TUBA1B, H2AFZ, HMGB1, STMN1, DUT, HMGB2, TUBB, MCM7, HMGN2, PTMA
UBE2C, HIST1H4C, PCLAF, TYMS, DEK, MCM3, TK1, TUBB4B, CDT1, RRM2
CKS2, DNMT1, SIVA1, PKMYT1, TOP2A, CENPM, UBE2S, PRDX2, ALYREF, RANBP1
Negative: CCND2, RNF213, LTB, CD48, NFATC3, CTSC, SLFN5, BTG2, CD44, CSF1
INPP4B, GAS5, CALD1, DDIT4, SEMA4A, CD74, IKZF3, TGFBR3, KDM5B, BTG1
DLEU1, S100P, NKG7, VIM, ZYX, IL4, PBX4, PNRC1, PTGIS, CYP1B1
PC_ 2
Positive: MALAT1, ASPM, CENPF, TOP2A, MKI67, TUBB, HIST1H1E, HIST1H1B, PCLAF, NUSAP1
RRM2, ATAD2, TYMS, DHFR, CAMK4, STMN1, MBNL1, HIST1H1C, SYNE2, RCSD1
MAL, HIST1H4C, SMC4, KIF11, NEIL3, PDE3B, BRCA1, NCAPG2, BRIP1, MCM4
Negative: PFN1, ACTB, TMSB10, NME1, MIF, SRRT, CLIC1, ATP5MC3, EIF4A1, PRDX1
CFL1, ACTG1, LDHA, ATP5MF, PRELID1, MRPL4, POMP, PPIB, SSBP1, CORO1A
NDUFS5, PSME2, PSMA7, PSMB3, PARK7, HSP90AB1, TIMM13, NDUFB9, HSPE1, SEM1
PC_ 3
Positive: RPS28, RPL32, RPL29, RPL35A, RPS15, RPL36, RPL14, GZMA, CEBPD, HSP90AB1
SLCO3A1, LSR, OAZ1, ADGRE5, EEF2, FXYD5, AC002069.2, AC069410.1, BSG, MLLT3
PDGFD, RPSA, FAM107B, CD96, LEF1, SPINT2, MAML2, TRBC2, GZMM, P2RY14
Negative: GAPDH, HPGDS, PKM, KRT1, FABP5, LY6E, S100A4, NKG7, NPDC1, EEF1A1
VIM, C12orf75, NQO1, CD48, SLC25A5, CFH, S100P, FSCN1, LMNA, SIX3
BACE2, S100A10, SH3BGRL3, CYP1B1, TPST2, SH2D2A, P4HA2, ALOX5AP, TMSB4X, ACTG1
PC_ 4
Positive: RPL10, ID3, GIMAP7, KLF2, SEPTIN11, MRPL16, HTATSF1, RIPOR2, FYB1, LAGE3
ARHGEF6, TMSB10, BCAP31, TCN1, DSC1, TCF7, IKZF2, CAMK4, IL7R, S1PR1
ETS1, MKRN1, PON2, S1PR4, CD28, NRXN3, ESYT2, KLF3, AIFM1, PLCL1
Negative: CYBA, RPL32, RPL14, RPL29, RHOC, IL32, CTSC, CISH, SOCS1, CDKN1A
RPS27, ATP1B1, IFITM1, FXYD5, RPL35A, TUBB, GAS5, GZMM, ACTB, LAT
RPSA, CXCR3, FTH1, LTB, S100A11, DDIT4, ITGB7, TUBA1C, PTPN7, PIM1
PC_ 5
Positive: HNRNPA1, IMPDH2, NUCKS1, HDGF, MT-CYB, CACYBP, RPSA, SH3BP5, RPL14, ASPM
KLF2, S100A10, ACTG1, CENPF, HNRNPA2B1, ANP32E, PTMA, HMGA1, CDC20, CCR7
RPS27, LBR, NPM1, CNN2, MAL, EIF4A1, ETS1, TPR, VIM, ATP5MC2
Negative: RPL10, HIST1H1A, MRPL16, HIST1H1B, HIST1H4C, SKAP1, RPS4X, TUBA4A, SSR4, MT-CO2
RPLP1, RPL34, RPS3A, LAGE3, RPL11, RPS12, TCN1, ARHGAP4, SEPTIN11, PON2
HIST1H1D, HIST1H2AH, HIST1H1E, RPS18, HIST1H1C, MALAT1, MATK, RPS3, HTATSF1, HIST1H3B # determine dimensionality of the data
ElbowPlot(L1, ndims =50)
NA
NAPerform PCA TEST
library(ggplot2)
library(RColorBrewer)
# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")
# Assuming L1$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(L1$cell_line))
colnames(data) <- c("cell_line", "nUMI") # Change column name to nUMI
ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) +
geom_col() +
theme_classic() +
geom_text(aes(label = nUMI),
position = position_dodge(width = 0.9),
vjust = -0.25) +
scale_fill_manual(values = cell_line_colors) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)) + # Adjust the title position
ggtitle("Filtered cells per sample") +
xlab("Cell lines") + # Adjust x-axis label
ylab("Frequency") # Adjust y-axis label
print(ncells)
# TEST-1
# given that the output of RunPCA is "pca"
# replace "so" by the name of your seurat object
pct <- L1[["pca"]]@stdev / sum(L1[["pca"]]@stdev) * 100
cumu <- cumsum(pct) # Calculate cumulative percents for each PC
# Determine the difference between variation of PC and subsequent PC
co2 <- sort(which((pct[-length(pct)] - pct[-1]) > 0.1), decreasing = T)[1] + 1
# last point where change of % of variation is more than 0.1%. -> co2
co2[1] 15# TEST-2
# get significant PCs
stdv <- L1[["pca"]]@stdev
sum.stdv <- sum(L1[["pca"]]@stdev)
percent.stdv <- (stdv / sum.stdv) * 100
cumulative <- cumsum(percent.stdv)
co1 <- which(cumulative > 90 & percent.stdv < 5)[1]
co2 <- sort(which((percent.stdv[1:length(percent.stdv) - 1] -
percent.stdv[2:length(percent.stdv)]) > 0.1),
decreasing = T)[1] + 1
min.pc <- min(co1, co2)
min.pc[1] 15# Create a dataframe with values
plot_df <- data.frame(pct = percent.stdv,
cumu = cumulative,
rank = 1:length(percent.stdv))
# Elbow plot to visualize
ggplot(plot_df, aes(cumulative, percent.stdv, label = rank, color = rank > min.pc)) +
geom_text() +
geom_vline(xintercept = 90, color = "grey") +
geom_hline(yintercept = min(percent.stdv[percent.stdv > 5]), color = "grey") +
theme_bw()
NA
NA
NA6. Clustering
L1 <- FindNeighbors(L1,
dims = 1:min.pc,
verbose = FALSE)
# understanding resolution
L1 <- FindClusters(L1,
resolution = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6,
0.7,0.8, 0.9, 1, 1.1, 1.2))Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9184
Number of communities: 3
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8874
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8658
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8451
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8270
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8131
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8004
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7885
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7770
Number of communities: 11
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7663
Number of communities: 12
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7561
Number of communities: 12
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 199665
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7467
Number of communities: 14
Elapsed time: 0 seconds
# non-linear dimensionality reduction --------------
L1 <- RunUMAP(L1,
dims = 1:min.pc,
verbose = FALSE)Avis : The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
# note that you can set `label = TRUE` or use the Label Clusters function to help label
# individual clusters
DimPlot(L1,group.by = "cell_line",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.3",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.4",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.5",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.6",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.7",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.8",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.9",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.1.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.1.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
7. clusTree
library(clustree)Le chargement a nécessité le package : ggraph
Attachement du package : 'ggraph'
L'objet suivant est masqué depuis 'package:sp':
geometryclustree(L1, prefix = "SCT_snn_res.")
8. Save the Seurat object as an RDS-L1
saveRDS(L1, file = "../0-RDS_Cell_lines/L1_clustered.rds")
---
title: "Cell Line L1 Analysis-Reclustering"
author: Nasir Mahmood Abbasi
date: "`r Sys.Date()`"
output:
  # pdf_document: default
  # word_document: default
  # html_document: default
  #rmdformats::readthedown
  html_notebook:
    toc: true
    toc_float: true
    toc_collapsed: true
---


# 1. load libraries
```{r setup, include=FALSE}

library(Seurat)
library(SeuratObject)
library(SeuratData)
library(patchwork)

library(dplyr)
library(ggplot2)
library(RColorBrewer)
library(magrittr)
library(dbplyr)
library(rmarkdown)
library(knitr)
library(tinytex)
#Azimuth Annotation libraries
library(Azimuth)
#ProjecTils Annotation libraries
library(STACAS)
library(ProjecTILs)
#singleR Annotation libraries
library(SingleR)
library(celldex)
library(SingleCellExperiment)

```
# 2. load seurat object
```{r load_seurat}

All_samples_Merged <- readRDS("/home/nabbasi/isilon/PHD_3rd_YEAR_Analysis/0-Seurat_RDS_OBJECT_FINAL/All_samples_Merged_Harmony_integrated_Cell_line_renamed_03-07-2025.rds")



```

## Subset L1 from Merged Object
```{r}

# Assuming All_samples_Merged is already loaded
L1 <- subset(All_samples_Merged, subset = cell_line == "L1")

L1

rm(All_samples_Merged)
gc()
```


# 3. QC
```{r QC, fig.height=6, fig.width=10}
# Set identity classes to an existing column in meta data
Idents(object = L1) <- "cell_line"

L1[["percent.rb"]] <- PercentageFeatureSet(L1, pattern = "^RP[SL]")
VlnPlot(L1, features = c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.rb"),
        pt.size = 0.1, ncol = 4) & theme(plot.title = element_text(size = 10))

FeatureScatter(L1, feature1 = "nCount_RNA", feature2 = "percent.mt") + geom_smooth(method = "lm")
FeatureScatter(L1, feature1 = "nCount_RNA", feature2 = "nFeature_RNA") + geom_smooth(method = "lm")

```

## Assign Cell-Cycle Scores
```{r Regress, echo=FALSE, fig.height=6, fig.width=10}

#remotes::install_version("matrixStats", version="1.1.0") (if you are running R server use this first)
L1 <- SCTransform(L1, do.scale=FALSE, do.center=FALSE)


# A list of cell cycle markers, from Tirosh et al, 2015, is loaded with Seurat.  We can
# segregate this list into markers of G2/M phase and markers of S phase
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes


L1 <- CellCycleScoring(L1, s.features = s.genes, 
                           g2m.features = g2m.genes, 
                           set.ident = TRUE)

DefaultAssay(L1) <- "RNA"

L1$CC.Difference <- L1$S.Score - L1$G2M.Score

```

# 4. Normalize data
```{r}


# Apply SCTransform
L1 <- SCTransform(L1, vars.to.regress = c("percent.rb","percent.mt", "CC.Difference"), 
                  do.scale=TRUE, 
                  do.center=TRUE, 
                  verbose = TRUE)
                                      
```


# 5. Perform PCA
```{r PCA, fig.height=4, fig.width=6}

Variables_genes <- L1@assays$SCT@var.features

# Exclude genes starting with "HLA-" AND "Xist" AND "TRBV, TRAV"
Variables_genes_after_exclusion <- Variables_genes[!grepl("^HLA-|^XIST|^TRBV|^TRAV", Variables_genes)]


# These are now standard steps in the Seurat workflow for visualization and clustering
L1 <- RunPCA(L1,
             features = Variables_genes_after_exclusion,
             do.print = TRUE, 
             pcs.print = 1:5, 
             genes.print = 15,
             npcs = 50)

# determine dimensionality of the data
ElbowPlot(L1, ndims =50)


```
# Perform PCA TEST
```{r PCA-TEST, fig.height=6, fig.width=10}


library(ggplot2)
library(RColorBrewer)  

# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")

# Assuming L1$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(L1$cell_line))
colnames(data) <- c("cell_line", "nUMI")  # Change column name to nUMI

ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) + 
  geom_col() +
  theme_classic() +
  geom_text(aes(label = nUMI), 
            position = position_dodge(width = 0.9), 
            vjust = -0.25) +
  scale_fill_manual(values = cell_line_colors) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        plot.title = element_text(hjust = 0.5)) +  # Adjust the title position
  ggtitle("Filtered cells per sample") +
  xlab("Cell lines") +  # Adjust x-axis label
  ylab("Frequency")    # Adjust y-axis label

print(ncells)



# TEST-1
# given that the output of RunPCA is "pca"
# replace "so" by the name of your seurat object

pct <- L1[["pca"]]@stdev / sum(L1[["pca"]]@stdev) * 100
cumu <- cumsum(pct) # Calculate cumulative percents for each PC
# Determine the difference between variation of PC and subsequent PC
co2 <- sort(which((pct[-length(pct)] - pct[-1]) > 0.1), decreasing = T)[1] + 1
# last point where change of % of variation is more than 0.1%. -> co2
co2

# TEST-2
# get significant PCs
stdv <- L1[["pca"]]@stdev
sum.stdv <- sum(L1[["pca"]]@stdev)
percent.stdv <- (stdv / sum.stdv) * 100
cumulative <- cumsum(percent.stdv)
co1 <- which(cumulative > 90 & percent.stdv < 5)[1]
co2 <- sort(which((percent.stdv[1:length(percent.stdv) - 1] - 
                       percent.stdv[2:length(percent.stdv)]) > 0.1), 
              decreasing = T)[1] + 1
min.pc <- min(co1, co2)
min.pc

# Create a dataframe with values
plot_df <- data.frame(pct = percent.stdv, 
           cumu = cumulative, 
           rank = 1:length(percent.stdv))

# Elbow plot to visualize 
  ggplot(plot_df, aes(cumulative, percent.stdv, label = rank, color = rank > min.pc)) + 
  geom_text() + 
  geom_vline(xintercept = 90, color = "grey") + 
  geom_hline(yintercept = min(percent.stdv[percent.stdv > 5]), color = "grey") +
  theme_bw()

  

```

# 6. Clustering
```{r C1, fig.height=4, fig.width=6}
L1 <- FindNeighbors(L1, 
                    dims = 1:min.pc, 
                    verbose = FALSE)

# understanding resolution
L1 <- FindClusters(L1, 
                  resolution = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
                                0.7,0.8, 0.9, 1, 1.1, 1.2))


```


```{r C2, fig.height=4, fig.width=6}

# non-linear dimensionality reduction --------------
L1 <- RunUMAP(L1, 
              dims = 1:min.pc,
              verbose = FALSE)
                                  

# note that you can set `label = TRUE` or use the Label Clusters function to help label
# individual clusters
DimPlot(L1,group.by = "cell_line", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)


DimPlot(L1,
        group.by = "SCT_snn_res.0.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L1,
        group.by = "SCT_snn_res.0.2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L1,
        group.by = "SCT_snn_res.0.3", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L1,
        group.by = "SCT_snn_res.0.4", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)


DimPlot(L1,
        group.by = "SCT_snn_res.0.5", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L1,
        group.by = "SCT_snn_res.0.6", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L1,
        group.by = "SCT_snn_res.0.7", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L1,
        group.by = "SCT_snn_res.0.8", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L1,
        group.by = "SCT_snn_res.0.9", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L1,
        group.by = "SCT_snn_res.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L1,
        group.by = "SCT_snn_res.1.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L1,
        group.by = "SCT_snn_res.1.2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

```

# 7. clusTree
```{r clusTree, fig.height=12, fig.width=10}
library(clustree)
clustree(L1, prefix = "SCT_snn_res.")
```

# 8. Save the Seurat object as an RDS-L1
```{r saveROBJ, echo=TRUE}

saveRDS(L1, file = "../0-RDS_Cell_lines/L1_clustered.rds")


```










