Merged All samples with PBMC_10x and SCT analysis on annotated Object-Rserver
Nasir Mahmood Abbasi
2024-10-25
1. load libraries
Le chargement a nécessité le package : SeuratObject
Le chargement a nécessité le package : sp
Attachement du package : ‘SeuratObject’
Les objets suivants sont masqués depuis ‘package:base’:
intersect, t
Registered S3 methods overwritten by 'htmltools':
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print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
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── Installed datasets ─────────────────────────────────────────────────────────────────────────────── SeuratData v0.2.2.9001 ──
✔ pbmc3k 3.1.4 ✔ pbmcsca 3.0.0
───────────────────────────────────────────────────────────── Key ─────────────────────────────────────────────────────────────
✔ Dataset loaded successfully
❯ Dataset built with a newer version of Seurat than installed
❓ Unknown version of Seurat installed
Attachement du package : ‘dplyr’
Les objets suivants sont masqués depuis ‘package:stats’:
filter, lag
Les objets suivants sont masqués depuis ‘package:base’:
intersect, setdiff, setequal, union
── Attaching core tidyverse packages ─────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ forcats 1.0.0 ✔ readr 2.1.5
✔ ggplot2 3.5.1 ✔ stringr 1.5.1
✔ lubridate 1.9.3 ✔ tibble 3.2.1
✔ purrr 1.0.2 ✔ tidyr 1.3.1── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
Attachement du package : ‘magrittr’
L'objet suivant est masqué depuis ‘package:purrr’:
set_names
L'objet suivant est masqué depuis ‘package:tidyr’:
extract
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
Attachement du package : ‘dbplyr’
Les objets suivants sont masqués depuis ‘package:dplyr’:
ident, sql
Registered S3 method overwritten by 'rmarkdown':
method from
print.paged_df
Registered S3 method overwritten by 'SeuratDisk':
method from
as.sparse.H5Group Seurat
Attaching shinyBS
Le chargement a nécessité le package : SummarizedExperiment
Le chargement a nécessité le package : MatrixGenerics
Le chargement a nécessité le package : matrixStats
Attachement du package : 'matrixStats'
L'objet suivant est masqué depuis 'package:dplyr':
count
Attachement du package : 'MatrixGenerics'
Les objets suivants sont masqués depuis 'package:matrixStats':
colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods,
colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2,
colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians,
colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts,
rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs,
rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans,
rowWeightedMedians, rowWeightedSds, rowWeightedVars
Le chargement a nécessité le package : GenomicRanges
Le chargement a nécessité le package : stats4
Le chargement a nécessité le package : BiocGenerics
Attachement du package : 'BiocGenerics'
Les objets suivants sont masqués depuis 'package:lubridate':
intersect, setdiff, union
Les objets suivants sont masqués depuis 'package:dplyr':
combine, intersect, setdiff, union
L'objet suivant est masqué depuis 'package:SeuratObject':
intersect
Les objets suivants sont masqués depuis 'package:stats':
IQR, mad, sd, var, xtabs
Les objets suivants sont masqués depuis 'package:base':
anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval,
evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order,
paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, table,
tapply, union, unique, unsplit, which.max, which.min
Le chargement a nécessité le package : S4Vectors
Attachement du package : 'S4Vectors'
Les objets suivants sont masqués depuis 'package:lubridate':
second, second<-
L'objet suivant est masqué depuis 'package:tidyr':
expand
Les objets suivants sont masqués depuis 'package:dplyr':
first, rename
Les objets suivants sont masqués depuis 'package:base':
expand.grid, I, unname
Le chargement a nécessité le package : IRanges
Attachement du package : 'IRanges'
L'objet suivant est masqué depuis 'package:lubridate':
%within%
L'objet suivant est masqué depuis 'package:purrr':
reduce
Les objets suivants sont masqués depuis 'package:dplyr':
collapse, desc, slice
L'objet suivant est masqué depuis 'package:sp':
%over%
Le chargement a nécessité le package : GenomeInfoDb
Attachement du package : 'GenomicRanges'
L'objet suivant est masqué depuis 'package:magrittr':
subtract
Le chargement a nécessité le package : Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attachement du package : 'Biobase'
L'objet suivant est masqué depuis 'package:MatrixGenerics':
rowMedians
Les objets suivants sont masqués depuis 'package:matrixStats':
anyMissing, rowMedians
Attachement du package : 'SummarizedExperiment'
L'objet suivant est masqué depuis 'package:Seurat':
Assays
L'objet suivant est masqué depuis 'package:SeuratObject':
Assays
Attachement du package : 'celldex'
Les objets suivants sont masqués depuis 'package:SingleR':
BlueprintEncodeData, DatabaseImmuneCellExpressionData, HumanPrimaryCellAtlasData, ImmGenData,
MonacoImmuneData, MouseRNAseqData, NovershternHematopoieticData
Le chargement a nécessité le package : ggraph
Attachement du package : 'ggraph'
L'objet suivant est masqué depuis 'package:sp':
geometry2. Load Seurat Object
All_samples_Merged
An object of class Seurat
64169 features across 59355 samples within 6 assays
Active assay: SCT (27417 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
3 dimensional reductions calculated: integrated_dr, ref.umap, pcaAzimuth Annotation
# InstallData("pbmcref")
#
# # The RunAzimuth function can take a Seurat object as input
# All_samples_Merged <- RunAzimuth(All_samples_Merged, reference = "pbmcref")
3. QC
# Remove the percent.mito column
All_samples_Merged$percent.mito <- NULLAvis : Cannot find cell-level meta data named percent.mito# Set identity classes to an existing column in meta data
Idents(object = All_samples_Merged) <- "cell_line"
All_samples_Merged[["percent.rb"]] <- PercentageFeatureSet(All_samples_Merged,
pattern = "^RP[SL]")
# Convert 'percent.mt' to numeric, replacing "NaN" with 0
All_samples_Merged$percent.rb <- replace(as.numeric(All_samples_Merged$percent.rb), is.na(All_samples_Merged$percent.rb), 0)
# The [[ operator can add columns to object metadata. This is a great place to stash QC stats
All_samples_Merged[["percent.mt"]] <- PercentageFeatureSet(All_samples_Merged, pattern = "^MT-")
# Convert 'percent.mt' to numeric, replacing "NaN" with 0
All_samples_Merged$percent.mt <- replace(as.numeric(All_samples_Merged$percent.mt), is.na(All_samples_Merged$percent.mt), 0)
VlnPlot(All_samples_Merged, features = c("nFeature_RNA",
"nCount_RNA",
"percent.mt",
"percent.rb"),
ncol = 4, pt.size = 0.1) &
theme(plot.title = element_text(size=10))
FeatureScatter(All_samples_Merged, feature1 = "percent.mt",
feature2 = "percent.rb")
VlnPlot(All_samples_Merged, features = c("nFeature_RNA",
"nCount_RNA",
"percent.mt"),
ncol = 3)
FeatureScatter(All_samples_Merged,
feature1 = "percent.mt",
feature2 = "percent.rb") +
geom_smooth(method = 'lm')
FeatureScatter(All_samples_Merged,
feature1 = "nCount_RNA",
feature2 = "nFeature_RNA") +
geom_smooth(method = 'lm')
##FeatureScatter is typically used to visualize feature-feature relationships ##for anything calculated by the object, ##i.e. columns in object metadata, PC scores etc.
FeatureScatter(All_samples_Merged,
feature1 = "nCount_RNA",
feature2 = "percent.mt")+
geom_smooth(method = 'lm')
FeatureScatter(All_samples_Merged,
feature1 = "nCount_RNA",
feature2 = "nFeature_RNA")+
geom_smooth(method = 'lm')
Assign Cell-Cycle Scores
Running SCTransform on assay: RNA
Running SCTransform on layer: counts
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 27417 by 59355
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 453 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 27417 genes
Computing corrected count matrix for 27417 genes
Calculating gene attributes
Wall clock passed: Time difference of 8.810053 mins
Determine variable features
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Getting residuals for block 1(of 12) for counts dataset
Getting residuals for block 2(of 12) for counts dataset
Getting residuals for block 3(of 12) for counts dataset
Getting residuals for block 4(of 12) for counts dataset
Getting residuals for block 5(of 12) for counts dataset
Getting residuals for block 6(of 12) for counts dataset
Getting residuals for block 7(of 12) for counts dataset
Getting residuals for block 8(of 12) for counts dataset
Getting residuals for block 9(of 12) for counts dataset
Getting residuals for block 10(of 12) for counts dataset
Getting residuals for block 11(of 12) for counts dataset
Getting residuals for block 12(of 12) for counts dataset
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Finished calculating residuals for counts
Set 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
DefaultAssay(All_samples_Merged) <- "RNA"
# Apply SCTransform
All_samples_Merged <- SCTransform(All_samples_Merged,
vars.to.regress = c("percent.rb","percent.mt", "CC.Difference"),
do.scale=TRUE,
do.center=TRUE,
verbose = TRUE)Running SCTransform on assay: RNA
Running SCTransform on layer: counts
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 27417 by 59355
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 453 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 27417 genes
Computing corrected count matrix for 27417 genes
Calculating gene attributes
Wall clock passed: Time difference of 9.458052 mins
Determine variable features
Regressing out percent.rb, percent.mt, CC.Difference
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Getting residuals for block 4(of 12) for counts dataset
Getting residuals for block 5(of 12) for counts dataset
Getting residuals for block 6(of 12) for counts dataset
Getting residuals for block 7(of 12) for counts dataset
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Getting residuals for block 9(of 12) for counts dataset
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Getting residuals for block 12(of 12) for counts dataset
Regressing out percent.rb, percent.mt, CC.Difference
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Finished calculating residuals for counts
Set default assay to SCT 5. Perform PCA
Variables_genes <- All_samples_Merged@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
All_samples_Merged <- RunPCA(All_samples_Merged,
features = Variables_genes_after_exclusion,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 15,
npcs = 50)
# determine dimensionality of the data
ElbowPlot(All_samples_Merged, ndims = 50)
6. Perform 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 All_samples_Merged$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(All_samples_Merged$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 <- All_samples_Merged[["pca"]]@stdev / sum(All_samples_Merged[["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] 22# TEST-2
# get significant PCs
stdv <- All_samples_Merged[["pca"]]@stdev
sum.stdv <- sum(All_samples_Merged[["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] 22# 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
NA7. Clustering
All_samples_Merged <- FindNeighbors(All_samples_Merged,
dims = 1:22,
verbose = FALSE)
# understanding resolution
All_samples_Merged <- FindClusters(All_samples_Merged,
resolution = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7,0.8, 0.9, 1, 1.2, 1.5,2))Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9888
Number of communities: 14
Elapsed time: 21 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9804
Number of communities: 17
Elapsed time: 39 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9723
Number of communities: 18
Elapsed time: 20 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9643
Number of communities: 20
Elapsed time: 21 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9567
Number of communities: 22
Elapsed time: 39 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9497
Number of communities: 25
Elapsed time: 35 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9426
Number of communities: 25
Elapsed time: 21 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9359
Number of communities: 28
Elapsed time: 20 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9296
Number of communities: 29
Elapsed time: 24 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9235
Number of communities: 31
Elapsed time: 23 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9127
Number of communities: 35
Elapsed time: 20 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9001
Number of communities: 41
Elapsed time: 22 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 59355
Number of edges: 1966818
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8810
Number of communities: 45
Elapsed time: 20 seconds
# non-linear dimensionality reduction --------------
All_samples_Merged <- RunUMAP(All_samples_Merged,
dims = 1:22,
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(All_samples_Merged,group.by = "cell_line",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.3",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.4",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.5",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.6",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.7",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.8",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.9",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.1.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.1.5",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# Set identity classes to an existing column in meta data
Idents(object = All_samples_Merged) <- "SCT_snn_res.0.7"
cluster_table <- table(Idents(All_samples_Merged))
barplot(cluster_table, main = "Number of Cells in Each Cluster",
xlab = "Cluster",
ylab = "Number of Cells",
col = rainbow(length(cluster_table)))
print(cluster_table)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
6391 5923 5827 5582 5516 4948 4627 4054 3329 3235 1978 1878 1364 945 927 822 431 394 316 309
20 21 22 23 24
252 122 90 60 35 table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$SCT_snn_res.0.7)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
ASDC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
B intermediate 0 0 0 1 0 0 0 1 0 6 0 0 488 1 177 17
B memory 9 0 0 0 0 80 1 31 0 1 117 4 195 0 69 4
B naive 0 0 0 0 0 0 0 0 0 13 0 0 499 0 677 0
CD14 Mono 0 0 0 2 0 0 0 4 0 3034 7 0 0 0 0 758
CD16 Mono 0 0 0 0 0 0 0 0 0 8 0 0 1 0 0 2
CD4 CTL 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
CD4 Naive 0 0 0 518 0 0 1480 0 0 1 0 0 7 0 0 0
CD4 Proliferating 5449 2849 2462 0 5345 3889 4 3213 2809 0 1318 1464 2 1 0 0
CD4 TCM 870 267 3322 4201 146 535 1831 109 26 20 462 44 89 11 2 35
CD4 TEM 0 0 1 61 0 0 16 0 0 0 0 0 0 9 0 0
CD8 Naive 0 0 0 341 0 0 1002 0 0 2 0 0 2 1 0 1
CD8 Proliferating 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0
CD8 TCM 0 16 1 225 0 0 144 0 0 0 0 0 2 29 0 0
CD8 TEM 0 6 1 29 0 3 23 2 0 1 1 0 1 171 0 0
cDC1 0 0 0 0 0 4 0 2 0 13 0 0 0 0 0 0
cDC2 0 0 0 0 0 3 0 9 0 123 36 0 0 0 0 1
dnT 0 0 1 22 0 1 16 3 0 1 3 0 6 0 0 2
gdT 0 0 0 1 0 0 14 0 0 0 0 0 0 52 0 0
HSPC 55 0 0 0 1 203 0 670 483 0 6 365 38 0 0 0
ILC 0 0 0 0 0 0 0 0 0 0 0 0 2 3 0 0
MAIT 0 0 0 0 0 0 3 0 0 2 0 0 0 220 0 0
NK 0 0 0 0 0 0 0 0 0 8 0 0 0 432 1 1
NK Proliferating 6 2785 38 0 23 227 1 10 11 0 27 1 1 2 0 0
NK_CD56bright 0 0 0 0 0 0 0 0 0 0 0 0 0 13 0 0
pDC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Plasmablast 0 0 0 0 0 0 0 0 0 0 0 0 18 0 1 0
Platelet 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Treg 2 0 1 181 1 2 92 0 0 1 0 0 13 0 0 0
16 17 18 19 20 21 22 23 24
ASDC 0 0 0 0 0 0 0 3 0
B intermediate 0 0 3 0 0 0 2 0 0
B memory 2 0 7 0 0 0 3 0 0
B naive 0 0 0 1 0 2 0 0 0
CD14 Mono 2 0 0 0 0 4 7 1 4
CD16 Mono 0 0 0 0 0 115 0 0 0
CD4 CTL 0 16 0 0 0 0 0 0 0
CD4 Naive 0 0 0 4 33 0 0 0 0
CD4 Proliferating 46 0 157 0 3 0 0 0 0
CD4 TCM 377 33 71 256 169 0 2 0 0
CD4 TEM 0 7 0 0 0 0 0 0 0
CD8 Naive 0 0 0 10 14 0 0 0 0
CD8 Proliferating 0 0 0 0 0 0 0 0 0
CD8 TCM 0 56 0 2 2 0 0 0 0
CD8 TEM 0 150 0 0 3 0 0 0 0
cDC1 0 0 2 0 0 0 21 0 0
cDC2 2 0 1 0 0 1 53 0 0
dnT 1 0 3 10 13 0 0 0 0
gdT 0 26 0 0 0 0 0 0 0
HSPC 0 0 12 0 0 0 1 0 0
ILC 0 1 1 0 0 0 0 0 0
MAIT 0 14 0 1 2 0 0 0 0
NK 0 90 0 0 2 0 0 0 0
NK Proliferating 0 0 35 0 0 0 0 0 0
NK_CD56bright 0 1 0 0 2 0 0 0 0
pDC 0 0 0 0 0 0 0 56 0
Plasmablast 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 31
Treg 1 0 24 25 9 0 1 0 08. clusTree
clustree(All_samples_Merged, prefix = "SCT_snn_res.")
9. Azimuth Annotation
# InstallData("pbmcref")
#
# # The RunAzimuth function can take a Seurat object as input
# All_samples_Merged <- RunAzimuth(All_samples_Merged, reference = "pbmcref")
10. Azimuth Visualization
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l1",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$SCT_snn_res.0.7)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
ASDC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
B intermediate 0 0 0 1 0 0 0 1 0 6 0 0 488 1 177 17
B memory 9 0 0 0 0 80 1 31 0 1 117 4 195 0 69 4
B naive 0 0 0 0 0 0 0 0 0 13 0 0 499 0 677 0
CD14 Mono 0 0 0 2 0 0 0 4 0 3034 7 0 0 0 0 758
CD16 Mono 0 0 0 0 0 0 0 0 0 8 0 0 1 0 0 2
CD4 CTL 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
CD4 Naive 0 0 0 518 0 0 1480 0 0 1 0 0 7 0 0 0
CD4 Proliferating 5449 2849 2462 0 5345 3889 4 3213 2809 0 1318 1464 2 1 0 0
CD4 TCM 870 267 3322 4201 146 535 1831 109 26 20 462 44 89 11 2 35
CD4 TEM 0 0 1 61 0 0 16 0 0 0 0 0 0 9 0 0
CD8 Naive 0 0 0 341 0 0 1002 0 0 2 0 0 2 1 0 1
CD8 Proliferating 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0
CD8 TCM 0 16 1 225 0 0 144 0 0 0 0 0 2 29 0 0
CD8 TEM 0 6 1 29 0 3 23 2 0 1 1 0 1 171 0 0
cDC1 0 0 0 0 0 4 0 2 0 13 0 0 0 0 0 0
cDC2 0 0 0 0 0 3 0 9 0 123 36 0 0 0 0 1
dnT 0 0 1 22 0 1 16 3 0 1 3 0 6 0 0 2
gdT 0 0 0 1 0 0 14 0 0 0 0 0 0 52 0 0
HSPC 55 0 0 0 1 203 0 670 483 0 6 365 38 0 0 0
ILC 0 0 0 0 0 0 0 0 0 0 0 0 2 3 0 0
MAIT 0 0 0 0 0 0 3 0 0 2 0 0 0 220 0 0
NK 0 0 0 0 0 0 0 0 0 8 0 0 0 432 1 1
NK Proliferating 6 2785 38 0 23 227 1 10 11 0 27 1 1 2 0 0
NK_CD56bright 0 0 0 0 0 0 0 0 0 0 0 0 0 13 0 0
pDC 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Plasmablast 0 0 0 0 0 0 0 0 0 0 0 0 18 0 1 0
Platelet 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Treg 2 0 1 181 1 2 92 0 0 1 0 0 13 0 0 0
16 17 18 19 20 21 22 23 24
ASDC 0 0 0 0 0 0 0 3 0
B intermediate 0 0 3 0 0 0 2 0 0
B memory 2 0 7 0 0 0 3 0 0
B naive 0 0 0 1 0 2 0 0 0
CD14 Mono 2 0 0 0 0 4 7 1 4
CD16 Mono 0 0 0 0 0 115 0 0 0
CD4 CTL 0 16 0 0 0 0 0 0 0
CD4 Naive 0 0 0 4 33 0 0 0 0
CD4 Proliferating 46 0 157 0 3 0 0 0 0
CD4 TCM 377 33 71 256 169 0 2 0 0
CD4 TEM 0 7 0 0 0 0 0 0 0
CD8 Naive 0 0 0 10 14 0 0 0 0
CD8 Proliferating 0 0 0 0 0 0 0 0 0
CD8 TCM 0 56 0 2 2 0 0 0 0
CD8 TEM 0 150 0 0 3 0 0 0 0
cDC1 0 0 2 0 0 0 21 0 0
cDC2 2 0 1 0 0 1 53 0 0
dnT 1 0 3 10 13 0 0 0 0
gdT 0 26 0 0 0 0 0 0 0
HSPC 0 0 12 0 0 0 1 0 0
ILC 0 1 1 0 0 0 0 0 0
MAIT 0 14 0 1 2 0 0 0 0
NK 0 90 0 0 2 0 0 0 0
NK Proliferating 0 0 35 0 0 0 0 0 0
NK_CD56bright 0 1 0 0 2 0 0 0 0
pDC 0 0 0 0 0 0 0 56 0
Plasmablast 0 0 0 0 0 0 0 0 0
Platelet 0 0 0 0 0 0 0 0 31
Treg 1 0 24 25 9 0 1 0 011.Save the Seurat object as an Robj file
save(All_samples_Merged, file = "/home/nabbasi/isilon/0-IMP-OBJECTS/All_Samples_Merged_with_10x_Rserver.robj")
---
title: "Merged All samples with PBMC_10x and SCT analysis on annotated Object-Rserver"
author: Nasir Mahmood Abbasi
date: "`r Sys.Date()`"
output:
  #rmdformats::readthedown
  html_notebook:
    toc: true
    toc_float: true
    toc_collapsed: true
---

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

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

library(dplyr)
library(tidyverse)
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)

library(clustree)


```


# 2. Load Seurat Object 
```{r load_seurat}

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("/home/nabbasi/isilon/0-IMP-OBJECTS/All_Samples_Merged_with_10x_Azitmuth_Annotated.robj")

All_samples_Merged
 
```
## Azimuth Annotation
```{r azimuth_Annotation1, fig.height=6, fig.width=10}
# InstallData("pbmcref")
# 
# # The RunAzimuth function can take a Seurat object as input
# All_samples_Merged <- RunAzimuth(All_samples_Merged, reference = "pbmcref")

```

# 3. QC
```{r QC, fig.height=6, fig.width=10}

# Remove the percent.mito column
All_samples_Merged$percent.mito <- NULL


# Set identity classes to an existing column in meta data
Idents(object = All_samples_Merged) <- "cell_line"

All_samples_Merged[["percent.rb"]] <- PercentageFeatureSet(All_samples_Merged, 
                                                           pattern = "^RP[SL]")
# Convert 'percent.mt' to numeric, replacing "NaN" with 0
All_samples_Merged$percent.rb <- replace(as.numeric(All_samples_Merged$percent.rb), is.na(All_samples_Merged$percent.rb), 0)



# The [[ operator can add columns to object metadata. This is a great place to stash QC stats
All_samples_Merged[["percent.mt"]] <- PercentageFeatureSet(All_samples_Merged, pattern = "^MT-")

# Convert 'percent.mt' to numeric, replacing "NaN" with 0
All_samples_Merged$percent.mt <- replace(as.numeric(All_samples_Merged$percent.mt), is.na(All_samples_Merged$percent.mt), 0)





VlnPlot(All_samples_Merged, features = c("nFeature_RNA", 
                                         "nCount_RNA", 
                                         "percent.mt",
                                         "percent.rb"), 
                            ncol = 4, pt.size = 0.1) & 
              theme(plot.title = element_text(size=10))

FeatureScatter(All_samples_Merged, feature1 = "percent.mt", 
                                  feature2 = "percent.rb")

VlnPlot(All_samples_Merged, features = c("nFeature_RNA", 
                                    "nCount_RNA", 
                                    "percent.mt"), 
                                      ncol = 3)

FeatureScatter(All_samples_Merged, 
               feature1 = "percent.mt", 
               feature2 = "percent.rb") +
        geom_smooth(method = 'lm')

FeatureScatter(All_samples_Merged, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA") +
        geom_smooth(method = 'lm')

```

##FeatureScatter is typically used to visualize feature-feature relationships
##for anything calculated by the object, 
##i.e. columns in object metadata, PC scores etc.

```{r FC, fig.height=6, fig.width=10}

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

FeatureScatter(All_samples_Merged, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA")+
  geom_smooth(method = 'lm')

```


## Assign Cell-Cycle Scores
```{r Regress, echo=FALSE, fig.height=6, fig.width=10}
options(future.globals.maxSize = 8000 * 1024^2)  # Set to 8000 MiB (about 8 GB)

All_samples_Merged <- SCTransform(All_samples_Merged, 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


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

DefaultAssay(All_samples_Merged) <- "RNA"
All_samples_Merged$CC.Difference <- All_samples_Merged$S.Score - All_samples_Merged$G2M.Score

```


# 4. Normalize data
```{r Normalize, include=TRUE}
DefaultAssay(All_samples_Merged) <- "RNA"

# Apply SCTransform
All_samples_Merged <- SCTransform(All_samples_Merged, 
                                  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=6, fig.width=10}

Variables_genes <- All_samples_Merged@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
All_samples_Merged <- RunPCA(All_samples_Merged,
                        features = Variables_genes_after_exclusion,
                        do.print = TRUE, 
                        pcs.print = 1:5, 
                        genes.print = 15,
                        npcs = 50)

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


```

# 6. 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 All_samples_Merged$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(All_samples_Merged$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 <- All_samples_Merged[["pca"]]@stdev / sum(All_samples_Merged[["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 <- All_samples_Merged[["pca"]]@stdev
sum.stdv <- sum(All_samples_Merged[["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()

  

```

# 7. Clustering
```{r C1, fig.height=6, fig.width=10}
All_samples_Merged <- FindNeighbors(All_samples_Merged, 
                                dims = 1:22, 
                                verbose = FALSE)

# understanding resolution
All_samples_Merged <- FindClusters(All_samples_Merged, 
                                    resolution = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7,0.8, 0.9, 1, 1.2, 1.5,2))


```


```{r C2, fig.height=6, fig.width=10}

# non-linear dimensionality reduction --------------
All_samples_Merged <- RunUMAP(All_samples_Merged, 
                          dims = 1:22,
                          verbose = FALSE)
                                  

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

DimPlot(All_samples_Merged,group.by = "predicted.celltype.l2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.1",
        reduction = "umap",
        label.size = 3,
        repel = T, 
        label = T, label.box = T)
DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.2",
        reduction = "umap",
        label.size = 3,
        repel = T, 
        label = T, label.box = T)
DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.3",
        reduction = "umap",
        label.size = 3,
        repel = T, 
        label = T, label.box = T)


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


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

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

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

DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.8", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.0.9", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.1.2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.1.5", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(All_samples_Merged,
        group.by = "SCT_snn_res.2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

# Set identity classes to an existing column in meta data
Idents(object = All_samples_Merged) <- "SCT_snn_res.0.7"

cluster_table <- table(Idents(All_samples_Merged))


barplot(cluster_table, main = "Number of Cells in Each Cluster", 
                      xlab = "Cluster", 
                      ylab = "Number of Cells", 
                      col = rainbow(length(cluster_table)))

print(cluster_table)

table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$SCT_snn_res.0.7)
```

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

# 9. Azimuth Annotation
```{r azimuth_Annotation2, fig.height=6, fig.width=10}
# InstallData("pbmcref")
# 
# # The RunAzimuth function can take a Seurat object as input
# All_samples_Merged <- RunAzimuth(All_samples_Merged, reference = "pbmcref")

```



# 10. Azimuth Visualization
```{r azimuth_Visualization, fig.height=6, fig.width=10}
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(All_samples_Merged, group.by = "predicted.celltype.l1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)


DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)



table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$SCT_snn_res.0.7)
```


# 11.Save the Seurat object as an Robj file
```{r saveROBJ, echo=TRUE}

save(All_samples_Merged, file = "/home/nabbasi/isilon/0-IMP-OBJECTS/All_Samples_Merged_with_10x_Rserver.robj")


```







