Cell Line L7 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 L7 from Merged Object
# Assuming All_samples_Merged is already loaded
L7 <- subset(All_samples_Merged, subset = cell_line == "L7")
L7An object of class Seurat
62900 features across 5331 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 14140169 755.2 20798778 1110.8 20798778 1110.8
Vcells 167368011 1277.0 1233954972 9414.4 1424574304 10868.73. QC
# Set identity classes to an existing column in meta data
Idents(object = L7) <- "cell_line"
L7[["percent.rb"]] <- PercentageFeatureSet(L7, pattern = "^RP[SL]")
VlnPlot(L7, 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(L7, feature1 = "nCount_RNA", feature2 = "percent.mt") + geom_smooth(method = "lm")
FeatureScatter(L7, 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 18407 by 5331
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 110 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 18407 genes
Computing corrected count matrix for 18407 genes
Calculating gene attributes
Wall clock passed: Time difference of 49.1344 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
L7 <- SCTransform(L7, 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 18407 by 5331
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 110 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 18407 genes
Computing corrected count matrix for 18407 genes
Calculating gene attributes
Wall clock passed: Time difference of 46.30487 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 <- L7@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
L7 <- RunPCA(L7,
features = Variables_genes_after_exclusion,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 15,
npcs = 50)PC_ 1
Positive: NPM1, HSP90AB1, HSPD1, HSPE1, RPL35A, HMGA1, SRM, NME2, HSP90AA1, PPP1R14B
RPL7, NME1, RPL23, CHCHD10, RPS2, HSPA9, RPL37A, RPL27, C1QBP, NCL
RPL36, ATP5F1B, RPL22L1, RPL9, ATP5MC3, RANBP1, RPS17, FCER2, CYC1, PABPC1
Negative: S100A4, IL32, S100A6, SH3BGRL3, S100A11, LAPTM5, CD52, ARHGDIB, B2M, IFITM2
CRIP1, FXYD5, TMSB4X, LGALS3, LGALS1, EMP3, LCK, GMFG, COTL1, CORO1A
RAC2, ITM2B, LSP1, S1PR4, IFITM1, TMSB10, TSC22D3, CNN2, ACTB, EVL
PC_ 2
Positive: TUBA1B, UBE2S, JPT1, H2AFZ, PRELID1, HMGB1, TUBB4B, CYCS, PTMA, ATP5MC3
TXN, HSPE1, TUBA4A, NDUFAB1, MRPS12, HSP90AA1, PPIB, CALM1, RPL35, HMGN2
FTL, EIF1, RANBP1, PTTG1, DYNLL1, EIF5A, CYC1, EMP3, HNRNPAB, EBP
Negative: MBNL1, DENND4A, PDE4D, GRAMD1B, RABGAP1L, ELMO1, MACROD2, WWOX, NCALD, MSC-AS1
SPOCK1, RAD51B, AHR, CDKAL1, LRBA, TRIO, PDE7A, PVT1, EXT1, ARHGAP15
CAMK4, PTPRJ, ATP8B4, MACF1, NEAT1, INPP4B, FTX, HUWE1, DOCK2, CASK
PC_ 3
Positive: IQCG, TNFSF9, IL2RG, CSF2, PHLDA1, SRGN, IFNG, PGK1, IL32, CD2
BNIP3, CCL3, PLIN2, CD40LG, PKM, PGAM1, GADD45G, CCL1, RGCC, FAM162A
CD82, CCL4, LDHA, C4orf3, PIM3, LINC01480, KLF6, SLC16A3, ENO1, ZFP36L1
Negative: RRM2, TYMS, HIST1H4C, STMN1, HIST1H1E, SMC4, TUBB, ATAD2, MKI67, HIST1H1C
TUBA1B, HIST1H1D, H2AFZ, LMNB1, HMGB2, PCLAF, TOP2A, NUSAP1, PKMYT1, TK1
PCNA, KIFC1, H2AFX, DHFR, DUT, HIST2H2AC, CDCA2, ASF1B, NEIL3, ZWINT
PC_ 4
Positive: CCR7, CD74, KRT7, MT2A, EBI3, DNAJC5B, CYP1B1, CD44, AC099552.1, BIRC3
AC097518.2, NFKB2, AC011990.1, PRDX1, KCNMA1, IGFBP4, CSF2, AC114977.1, BATF3, LINC02341
ITPR1, BLK, AKR1A1, CBR3, NFKBIA, CCL1, CCL5, LTA, CD82, TNFSF9
Negative: BNIP3, FAM162A, LDHA, PLIN2, PGK1, ENO1, GPI, UBALD2, TPI1, BNIP3L
GAPDH, HILPDA, PTPRR, MIF, PFKFB4, SLC2A3, MXI1, NMU, CYTIP, INSIG2
ALDOA, AK4, RIMKLA, KIF2A, PLAAT3, PGAM1, SLC16A3, C4orf3, PHF19, P4HA1
PC_ 5
Positive: CDC20, DANCR, CCNB1, UBE2S, RNF213, MRPS12, YWHAZ, SFPQ, NOP16, GALNTL6
EFHD2, DYNLL1, MAT2A, SYNCRIP, HNRNPAB, HSPH1, PLK1, TENM3, CCT6A, NIFK
CCT2, BAK1, IQGAP2, ABCE1, PTTG1, CCDC86, INPP4A, CAPN2, PTPRG, ARL6IP1
Negative: CD74, GAPDH, FAM162A, LDHA, BNIP3, PKM, HIST1H4C, MIF, PGK1, ENO1
HILPDA, BNIP3L, HIST1H1E, MT2A, KIF2A, PGAM1, CCR7, CD70, HIST1H1C, TPI1
PFKFB4, CYTIP, ARID5A, TRAF1, GPI, PLIN2, ALDOC, RRM2, PMAIP1, HIST1H1B # determine dimensionality of the data
ElbowPlot(L7, 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 L7$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(L7$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 <- L7[["pca"]]@stdev / sum(L7[["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] 18# TEST-2
# get significant PCs
stdv <- L7[["pca"]]@stdev
sum.stdv <- sum(L7[["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] 18# 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
L7 <- FindNeighbors(L7,
dims = 1:min.pc,
verbose = FALSE)
# understanding resolution
L7 <- FindClusters(L7,
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9297
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8981
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8753
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8528
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8317
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8175
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8045
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7919
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7792
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7667
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7550
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: 5331
Number of edges: 189013
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7445
Number of communities: 13
Elapsed time: 0 seconds
# non-linear dimensionality reduction --------------
L7 <- RunUMAP(L7,
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(L7,group.by = "cell_line",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.0.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.0.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.0.3",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.0.4",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.0.5",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.0.6",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.0.7",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.0.8",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.0.9",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.1.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L7,
group.by = "SCT_snn_res.1.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
7. clusTree
library(clustree)
clustree(L7, prefix = "SCT_snn_res.")
8. Save the Seurat object as an RDS-L7
saveRDS(L7, file = "../0-RDS_Cell_lines/L7_clustered.rds")
---
title: "Cell Line L7 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 L7 from Merged Object
```{r}

# Assuming All_samples_Merged is already loaded
L7 <- subset(All_samples_Merged, subset = cell_line == "L7")

L7

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 = L7) <- "cell_line"

L7[["percent.rb"]] <- PercentageFeatureSet(L7, pattern = "^RP[SL]")
VlnPlot(L7, features = c("nFeature_RNA", "nCount_RNA", "percent.mt", "percent.rb"),
        pt.size = 0.1, ncol = 4) & theme(plot.title = element_text(size = 10))

FeatureScatter(L7, feature1 = "nCount_RNA", feature2 = "percent.mt") + geom_smooth(method = "lm")
FeatureScatter(L7, 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)
L7 <- SCTransform(L7, 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


L7 <- CellCycleScoring(L7, s.features = s.genes, 
                           g2m.features = g2m.genes, 
                           set.ident = TRUE)

DefaultAssay(L7) <- "RNA"

L7$CC.Difference <- L7$S.Score - L7$G2M.Score

```

# 4. Normalize data
```{r}


# Apply SCTransform
L7 <- SCTransform(L7, 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 <- L7@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
L7 <- RunPCA(L7,
             features = Variables_genes_after_exclusion,
             do.print = TRUE, 
             pcs.print = 1:5, 
             genes.print = 15,
             npcs = 50)

# determine dimensionality of the data
ElbowPlot(L7, 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 L7$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(L7$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 <- L7[["pca"]]@stdev / sum(L7[["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 <- L7[["pca"]]@stdev
sum.stdv <- sum(L7[["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}
L7 <- FindNeighbors(L7, 
                    dims = 1:min.pc, 
                    verbose = FALSE)

# understanding resolution
L7 <- FindClusters(L7, 
                  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 --------------
L7 <- RunUMAP(L7, 
              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(L7,group.by = "cell_line", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)


DimPlot(L7,
        group.by = "SCT_snn_res.0.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L7,
        group.by = "SCT_snn_res.0.2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L7,
        group.by = "SCT_snn_res.0.3", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L7,
        group.by = "SCT_snn_res.0.4", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)


DimPlot(L7,
        group.by = "SCT_snn_res.0.5", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L7,
        group.by = "SCT_snn_res.0.6", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L7,
        group.by = "SCT_snn_res.0.7", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L7,
        group.by = "SCT_snn_res.0.8", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L7,
        group.by = "SCT_snn_res.0.9", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L7,
        group.by = "SCT_snn_res.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L7,
        group.by = "SCT_snn_res.1.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L7,
        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(L7, prefix = "SCT_snn_res.")

```

# 8. Save the Seurat object as an RDS-L7
```{r saveROBJ, echo=TRUE}

saveRDS(L7, file = "../0-RDS_Cell_lines/L7_clustered.rds")


```










