Merged All samples with PBMC_10x and removed non CD4 T cells from Control apply SCT

1. load libraries

Loading required package: SeuratObject

Loading required package: sp

Attaching package: 'SeuratObject'

The following objects are masked from 'package:base':

    intersect, t

── Installed datasets ──────────────────────────────── SeuratData v0.2.2.9001 ──

✔ pbmcref 1.0.0                         ✔ pbmcsca 3.0.0

────────────────────────────────────── Key ─────────────────────────────────────

✔ Dataset loaded successfully
❯ Dataset built with a newer version of Seurat than installed
❓ Unknown version of Seurat installed

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from '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 conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Attaching package: 'magrittr'


The following object is masked from 'package:purrr':

    set_names


The following object is masked from 'package:tidyr':

    extract



Attaching package: 'dbplyr'


The following objects are masked from 'package:dplyr':

    ident, sql


Registered S3 method overwritten by 'SeuratDisk':
  method            from  
  as.sparse.H5Group Seurat



Attaching shinyBS

Loading required package: ggraph


Attaching package: 'ggraph'


The following object is masked from 'package:sp':

    geometry

2. Load Seurat Object

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("0-imp_Robj/SS_CD4_Tcells_Azimuth_Annotated_PBMC10x_excluding_nonCD4_cells_from_Control.robj")

All_samples_Merged <- seurat_filtered

Summarizing Seurat Object

# Load necessary libraries
library(Seurat)

# Display basic metadata summary
head(All_samples_Merged@meta.data)

# Check if columns such as `orig.ident`, `nCount_RNA`, `nFeature_RNA`, `nUMI`, `ngene`, and any other necessary columns exist
required_columns <- c("orig.ident", "nCount_RNA", "nFeature_RNA", "nUMI", "ngene")
missing_columns <- setdiff(required_columns, colnames(All_samples_Merged@meta.data))

if (length(missing_columns) > 0) {
    cat("Missing columns:", paste(missing_columns, collapse = ", "), "\n")
} else {
    cat("All required columns are present.\n")
}

All required columns are present.

# Check cell counts and features
cat("Number of cells:", ncol(All_samples_Merged), "\n")

Number of cells: 49515 

cat("Number of features:", nrow(All_samples_Merged), "\n")

Number of features: 36601 

# Verify that each `orig.ident` label has the correct number of cells
cat("Cell counts per group:\n")

Cell counts per group:

print(table(All_samples_Merged$orig.ident))

     L1      L2      L3      L4      L5      L6      L7    PBMC PBMC10x 
   5825    5935    6428    6150    6022    5148    5331    5171    3505 

# Check that the cell IDs are unique (which ensures no issues from merging)
if (any(duplicated(colnames(All_samples_Merged)))) {
    cat("Warning: There are duplicated cell IDs.\n")
} else {
    cat("Cell IDs are unique.\n")
}

Cell IDs are unique.

# Check the assay consistency for RNA
DefaultAssay(All_samples_Merged) <- "RNA"

# Check dimensions of the RNA counts layer using the new method
cat("Dimensions of the RNA counts layer:", dim(GetAssayData(All_samples_Merged, layer = "counts")), "\n")

Dimensions of the RNA counts layer: 36601 49515 

cat("Dimensions of the RNA data layer:", dim(GetAssayData(All_samples_Merged, layer = "data")), "\n")

Dimensions of the RNA data layer: 36601 49515 

# Check the ADT assay (optional)
if ("ADT" %in% names(All_samples_Merged@assays)) {
    cat("ADT assay is present.\n")
    cat("Dimensions of the ADT counts layer:", dim(GetAssayData(All_samples_Merged, assay = "ADT", layer = "counts")), "\n")
} else {
    cat("ADT assay is not present.\n")
}

ADT assay is present.
Dimensions of the ADT counts layer: 56 49515 

Azimuth 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 <- 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')

`geom_smooth()` using formula = 'y ~ x'

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

`geom_smooth()` using formula = 'y ~ x'

##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')

`geom_smooth()` using formula = 'y ~ x'

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

`geom_smooth()` using formula = 'y ~ x'

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 26195 by 49515

Model formula is y ~ log_umi

Get Negative Binomial regression parameters per gene

Using 2000 genes, 5000 cells

Found 526 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 26195 genes

Computing corrected count matrix for 26195 genes

Calculating gene attributes

Wall clock passed: Time difference of 7.363764 mins

Determine variable features

Getting residuals for block 1(of 10) for counts dataset

Getting residuals for block 2(of 10) for counts dataset

Getting residuals for block 3(of 10) for counts dataset

Getting residuals for block 4(of 10) for counts dataset

Getting residuals for block 5(of 10) for counts dataset

Getting residuals for block 6(of 10) for counts dataset

Getting residuals for block 7(of 10) for counts dataset

Getting residuals for block 8(of 10) for counts dataset

Getting residuals for block 9(of 10) for counts dataset

Getting residuals for block 10(of 10) for counts dataset

Finished calculating residuals for counts

Set default assay to SCT

Warning: The following features are not present in the object: MLF1IP, not
searching for symbol synonyms

Warning: The following features are not present in the object: FAM64A, HN1, not
searching for symbol synonyms

4. Normalize data

# 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 26195 by 49515

Model formula is y ~ log_umi

Get Negative Binomial regression parameters per gene

Using 2000 genes, 5000 cells

Found 526 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 26195 genes

Computing corrected count matrix for 26195 genes

Calculating gene attributes

Wall clock passed: Time difference of 7.497466 mins

Determine variable features

Regressing out percent.rb, percent.mt, CC.Difference

Centering and scaling data matrix

Getting residuals for block 1(of 10) for counts dataset

Getting residuals for block 2(of 10) for counts dataset

Getting residuals for block 3(of 10) for counts dataset

Getting residuals for block 4(of 10) for counts dataset

Getting residuals for block 5(of 10) for counts dataset

Getting residuals for block 6(of 10) for counts dataset

Getting residuals for block 7(of 10) for counts dataset

Getting residuals for block 8(of 10) for counts dataset

Getting residuals for block 9(of 10) for counts dataset

Getting residuals for block 10(of 10) for counts dataset

Regressing out percent.rb, percent.mt, CC.Difference

Centering and scaling data matrix

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)

PC_ 1 
Positive:  MALAT1, RPS27, PTPRC, SARAF, RPS4Y1, TCF7, RPL39, RPS29, RPL34, FYB1 
       ITM2B, GIMAP7, PNRC1, LEF1, BTG1, B2M, FOXP1, GIMAP5, IL7R, EVL 
       CD52, RIPOR2, LINC00861, SELL, YPEL3, CD3E, PIK3IP1, ITK, ZFP36L2, ANKRD12 
Negative:  PPIA, NPM1, RAN, NME2, GAPDH, PRDX1, PRELID1, RPS2, TUBA1B, HSP90AB1 
       HSPD1, RPLP0, TPI1, HMGA1, VDAC1, H2AFZ, SRM, TXN, ATP5F1B, CYC1 
       UBE2S, ENO1, PPP1R14B, NME1, RANBP1, CCT8, JPT1, SNRPD1, MIF, HNRNPAB 
PC_ 2 
Positive:  KIR3DL1, CST7, EPCAM, TRGV2, KIR2DL3, RPL27A, MATK, DAD1, KIR3DL2, XCL1 
       C1QBP, KLRC1, KIR2DL4, PFN1, MYO1E, RAB25, GZMM, CXCR3, CD7, NDUFA4 
       EIF4A1, ESYT2, ZBTB16, KLRK1, XCL2, KRT86, RCBTB2, SPINT2, PTPN6, TRGV4 
Negative:  RPL30, FAM107B, SEC11C, ELL2, CD74, LRRFIP1, MTHFD2, YBX3, VIM, RAD21 
       MT-ND3, SMAP2, CFLAR, TAP1, CD2, ANXA1, TMEM173, HDGFL3, IL2RA, CD70 
       AHNAK, HTATIP2, BATF3, MTLN, EEF1A1, CD58, C3orf14, RPL35A, RPL39, CCR7 
PC_ 3 
Positive:  TNFRSF4, C12orf75, HACD1, EGFL6, TIGIT, BACE2, ARPC2, SYT4, NET1, PXYLP1 
       LY6E, GRIA4, GGH, PARK7, MAP1B, UBE2D2, CCL17, KRT7, PON2, ADGRB3 
       PTP4A3, ACTN1, SCCPDH, MCTP2, CYBA, EEF1A2, PLEKHH2, SMIM3, MSC, HDGFL3 
Negative:  PAGE5, NDUFV2, RPL35A, RBPMS, CDKN2A, RPL22L1, CD74, STMN1, KIF2A, LMNA 
       TENM3, PSMB2, PSMB9, ANXA2, GPX4, FAM50B, PPP2R2B, GAPDH, ANXA5, IFI27L2 
       RPL11, VAMP5, FAM241A, NEURL1, PPBP, SH3KBP1, RPS3A, PLD1, HEBP2, TYMS 
PC_ 4 
Positive:  RPS4X, EGLN3, GAS5, WFDC1, KRT1, LINC02752, TTC29, TBX4, RPLP1, IL32 
       IFNGR1, AC069410.1, PLCB1, S100A11, TNS4, SP5, RPL13, FAM9C, SEMA4A, S100A4 
       IL4, NKG7, S100A6, LINC00469, VIM, PTGIS, HSPB1, CEBPD, VIPR2, NPTX1 
Negative:  EIF5A, HSPE1, ATP5MC3, ODC1, CHCHD10, PPID, MT-ND3, CYCS, PPBP, CYC1 
       RPL34, GCSH, FCER2, RPS4Y1, DNAJC12, FKBP4, FAM162A, GSTP1, CD7, FKBP11 
       HSPD1, RPS29, MTDH, RPL39, HSP90B1, TCF7, SET, PSMB6, SLC7A11-AS1, C1QBP 
PC_ 5 
Positive:  TMSB4X, TMSB10, LGALS1, S100A11, COTL1, S100A4, TP73, LSP1, IFITM2, S100A6 
       TMEM163, GPAT3, TAGLN2, HOXC9, LIME1, GPAT2, DUSP4, GAS2L1, EEF1A2, TNFRSF18 
       IFITM1, QPRT, MIIP, RBM38, LAPTM5, CEP135, PGAM1, CRIP1, PXYLP1, PRDX5 
Negative:  CCL17, MIR155HG, MAP4K4, MYO1D, RXFP1, LRBA, CA10, CFI, CA2, RUNX1 
       PRKCA, THY1, FRMD4A, AL590550.1, EZH2, IGHE, IMMP2L, HS3ST1, NFIB, LTA 
       SNTB1, RANBP17, MGST3, AKAP12, SLC35F3, AC100801.1, DOCK10, CCL5, EPB41L2, ONECUT2 

# 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] 16

# 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] 16

# 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

All_samples_Merged <- FindNeighbors(All_samples_Merged, 
                                dims = 1:16, 
                                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: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9871
Number of communities: 12
Elapsed time: 21 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9767
Number of communities: 12
Elapsed time: 15 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9665
Number of communities: 13
Elapsed time: 17 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9568
Number of communities: 15
Elapsed time: 15 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9476
Number of communities: 17
Elapsed time: 14 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9384
Number of communities: 17
Elapsed time: 17 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9305
Number of communities: 19
Elapsed time: 25 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9225
Number of communities: 19
Elapsed time: 16 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9155
Number of communities: 21
Elapsed time: 16 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9086
Number of communities: 24
Elapsed time: 15 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8978
Number of communities: 28
Elapsed time: 14 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8835
Number of communities: 31
Elapsed time: 15 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 49515
Number of edges: 1638661

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8632
Number of communities: 37
Elapsed time: 19 seconds

# non-linear dimensionality reduction --------------
All_samples_Merged <- RunUMAP(All_samples_Merged, 
                          dims = 1:16,
                          verbose = FALSE)

Warning: 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 
6255 5929 5421 4932 4674 3798 3343 3338 3299 2021 1895 1796  880  520  512  478 
  16   17   18 
 231  121   72 

table(All_samples_Merged$predicted.celltype.l2, All_samples_Merged$SCT_snn_res.0.2)

                   
                       0    1    2    3    4    5    6    7    8    9   10   11
  B intermediate       0    0    0    0    0    3    3    1    0    0   53    0
  B memory             9    2    0    0  118   86    1   30    0    3   36    0
  B naive              0    0    0    0    0    0    0    0    0    0   41    0
  CD14 Mono            0    2    0    0    6    0    0    5    0    0   13    0
  CD4 CTL              0    0    0    0    0    0   11    0    0    0    2    0
  CD4 Naive            0    0    0    0    0    0  523    0 1480    0    9   30
  CD4 Proliferating 5451 5386 2852 2461 4124 4061    1 3225    5 1436    9    0
  CD4 TCM            879  523  267 3317  478  590 4548  109 1838   48  142   91
  CD4 TEM              0    0    0    1    0    0   62    0   21    0    0    0
  CD8 Proliferating    0    0    0    0    1    1    0    0    0    0    0    0
  CD8 TCM              0    0   16    1    0    0    0    0    0    0    0    0
  CD8 TEM              0    0    8    1    1    3    0    2    0    0    0    0
  cDC1                 0    0    0    0    0    6    0    2    0    0    0    0
  cDC2                 0    2    0    0   35    3    0   10    0    1    2    0
  dnT                  0    1    1    1    3    4    1    2    0    0    2    0
  HSPC                57    1    0    0  489  213    0  672    0  363   10    0
  ILC                  0    0    0    0    0    0    0    0    0    0    1    0
  NK                   0    0    0    0    0    0    0    0    0    0    1    0
  NK Proliferating     7   23 2785   38   34  263    0   10    0    1    0    0
  Treg                15    1    0    1    0   14    2    0    0    0   12    0

8. 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.0.2)

                   
                       0    1    2    3    4    5    6    7    8    9   10   11
  B intermediate       0    0    0    0    0    3    3    1    0    0   53    0
  B memory             9    2    0    0  118   86    1   30    0    3   36    0
  B naive              0    0    0    0    0    0    0    0    0    0   41    0
  CD14 Mono            0    2    0    0    6    0    0    5    0    0   13    0
  CD4 CTL              0    0    0    0    0    0   11    0    0    0    2    0
  CD4 Naive            0    0    0    0    0    0  523    0 1480    0    9   30
  CD4 Proliferating 5451 5386 2852 2461 4124 4061    1 3225    5 1436    9    0
  CD4 TCM            879  523  267 3317  478  590 4548  109 1838   48  142   91
  CD4 TEM              0    0    0    1    0    0   62    0   21    0    0    0
  CD8 Proliferating    0    0    0    0    1    1    0    0    0    0    0    0
  CD8 TCM              0    0   16    1    0    0    0    0    0    0    0    0
  CD8 TEM              0    0    8    1    1    3    0    2    0    0    0    0
  cDC1                 0    0    0    0    0    6    0    2    0    0    0    0
  cDC2                 0    2    0    0   35    3    0   10    0    1    2    0
  dnT                  0    1    1    1    3    4    1    2    0    0    2    0
  HSPC                57    1    0    0  489  213    0  672    0  363   10    0
  ILC                  0    0    0    0    0    0    0    0    0    0    1    0
  NK                   0    0    0    0    0    0    0    0    0    0    1    0
  NK Proliferating     7   23 2785   38   34  263    0   10    0    1    0    0
  Treg                15    1    0    1    0   14    2    0    0    0   12    0

11.Harmony Integration

# Load required libraries
library(Seurat)
library(harmony)

Loading required package: Rcpp

library(ggplot2)

# Run Harmony, adjusting for batch effect using "cell_line" or another grouping variable
All_samples_Merged <- RunHarmony(
  object = All_samples_Merged,
  group.by.vars = "cell_line",  # Replace with the metadata column specifying batch or cell line
)

Transposing data matrix

Initializing state using k-means centroids initialization

Harmony 1/10

Harmony 2/10

Harmony converged after 2 iterations

# Check results in harmony embeddings
harmony_embeddings <- Embeddings(All_samples_Merged, reduction = "harmony")
head(harmony_embeddings)

                      harmony_1 harmony_2 harmony_3   harmony_4   harmony_5
L1_AAACCTGAGGGCTTCC-1 -4.320627 -2.317685 2.9430139  5.34050099  0.03130615
L1_AAACCTGGTGCAGGTA-1  8.343182  6.478844 2.6105995  9.39039981 -3.07004428
L1_AAACCTGGTTAAAGTG-1  3.603586 13.274618 0.3506875  5.78084998  0.89811646
L1_AAACCTGTCAGGTAAA-1 -5.976624  5.294591 0.9692598 -0.01281093  2.77149172
L1_AAACCTGTCCCTGACT-1 -1.853064 -4.962650 5.2762370  2.83288208 -1.51401130
L1_AAACCTGTCCTTCAAT-1  9.413513  9.068538 2.0306801  5.67023711 -1.72402761
                       harmony_6  harmony_7  harmony_8  harmony_9 harmony_10
L1_AAACCTGAGGGCTTCC-1 -1.0619370 -3.0401360  1.6617955  1.4779144  5.8455255
L1_AAACCTGGTGCAGGTA-1 -6.9019951 -0.1736879 -0.6861296 -1.4546126 -0.4235130
L1_AAACCTGGTTAAAGTG-1 -2.6929608 -2.5491623  3.1370845 -4.7034440 -3.2186842
L1_AAACCTGTCAGGTAAA-1 -0.2708270  2.4308115  0.3683388 -2.4538382 -2.8856956
L1_AAACCTGTCCCTGACT-1 -0.1350112 -1.8530740  0.8914700  0.8419062  2.6451692
L1_AAACCTGTCCTTCAAT-1 -3.7985693 -4.6277750  1.0286100 -2.3638671 -0.6485828
                      harmony_11  harmony_12  harmony_13 harmony_14  harmony_15
L1_AAACCTGAGGGCTTCC-1  1.0017413 -1.07240037 -0.09287815 -3.3461357  0.41327209
L1_AAACCTGGTGCAGGTA-1 -1.4560726 -0.50744218 -0.40539901  0.6890528 -0.29686610
L1_AAACCTGGTTAAAGTG-1  3.2061339  2.97949032  1.99578213  2.8535416 -4.17291661
L1_AAACCTGTCAGGTAAA-1  0.1240572  2.92741419  1.18206172  1.0893541  0.14280006
L1_AAACCTGTCCCTGACT-1  1.3883640 -0.09287043  0.22326928 -1.3124155  1.54353486
L1_AAACCTGTCCTTCAAT-1  1.0359249 -0.88996335 -0.38246480  0.6706358 -0.04157207
                       harmony_16 harmony_17 harmony_18 harmony_19 harmony_20
L1_AAACCTGAGGGCTTCC-1 -1.19337618  1.5215119  0.6279281   1.900453 -0.0883689
L1_AAACCTGGTGCAGGTA-1  1.45113148  0.7866671  0.7856182  -2.024082 -2.7355006
L1_AAACCTGGTTAAAGTG-1  0.42522022 -3.0031691 -0.1688935  -1.567725 -1.8483972
L1_AAACCTGTCAGGTAAA-1  0.33855377 -0.3991149  0.4769769  -1.389909  2.7205370
L1_AAACCTGTCCCTGACT-1 -4.35654249 -3.7322188 -1.3062391   2.082070  0.3145153
L1_AAACCTGTCCTTCAAT-1  0.01396619 -0.5907626  0.7888971  -1.779261 -0.3159930
                       harmony_21 harmony_22  harmony_23  harmony_24 harmony_25
L1_AAACCTGAGGGCTTCC-1  1.87568301  -5.111657  4.87214463 -0.08246990 -2.9138479
L1_AAACCTGGTGCAGGTA-1  0.58698305  -3.532982  0.09029822 -0.01787307 -0.9451554
L1_AAACCTGGTTAAAGTG-1  1.86667594  -3.074572  0.26092542 -1.67363661 -2.5888576
L1_AAACCTGTCAGGTAAA-1 -0.07876724   0.452983 -1.76033322  0.36133588  0.4935879
L1_AAACCTGTCCCTGACT-1  3.78124704  -4.791863  1.58294121 -0.60992989 -0.8401014
L1_AAACCTGTCCTTCAAT-1 -0.00866252  -4.251814  2.91232755  2.54476559  0.4380775
                       harmony_26 harmony_27 harmony_28 harmony_29 harmony_30
L1_AAACCTGAGGGCTTCC-1  0.49915898  1.1044102 -0.3714357 -0.7424044 0.48397303
L1_AAACCTGGTGCAGGTA-1  0.02498027  0.6448542  0.8595064  0.6553885 0.19538056
L1_AAACCTGGTTAAAGTG-1  0.92812115  0.4444257 -1.6033999 -2.6745387 0.63625048
L1_AAACCTGTCAGGTAAA-1  1.20343803  0.1554112  1.0715101  1.0798230 0.73986336
L1_AAACCTGTCCCTGACT-1 -0.74811611 -0.7705023 -1.0825354 -1.2346742 0.08136387
L1_AAACCTGTCCTTCAAT-1 -0.15308084  2.1517230 -0.2666182  1.3836759 0.63463441
                      harmony_31 harmony_32 harmony_33   harmony_34 harmony_35
L1_AAACCTGAGGGCTTCC-1  1.9925323  1.1201484 -1.3082249 -0.005557626 -2.2347211
L1_AAACCTGGTGCAGGTA-1 -1.7745965  0.4881785  2.6250817  1.325839660  1.7668570
L1_AAACCTGGTTAAAGTG-1  3.4189587 -1.1619459 -0.9131173 -0.738929237 -0.7944653
L1_AAACCTGTCAGGTAAA-1 -0.2905196  0.6616816  0.6616241 -0.417822466 -1.7207539
L1_AAACCTGTCCCTGACT-1  1.6030761 -0.6067572 -2.9550014 -1.073673601 -1.1409654
L1_AAACCTGTCCTTCAAT-1  0.8458875  1.0601501  2.1920442  0.453059896 -2.2320115
                       harmony_36 harmony_37 harmony_38 harmony_39 harmony_40
L1_AAACCTGAGGGCTTCC-1  0.58020487 -1.1289010  0.4495203  0.7372680 -0.3200434
L1_AAACCTGGTGCAGGTA-1 -0.69019882  0.3898066 -1.9040244  1.2530776 -2.1472602
L1_AAACCTGGTTAAAGTG-1  1.29037462  0.7052604 -0.3256223 -0.6045750  0.8934575
L1_AAACCTGTCAGGTAAA-1 -0.01755711 -0.6828708 -0.8218647 -0.5782875  1.0538171
L1_AAACCTGTCCCTGACT-1 -0.74725451 -0.8275788  2.0642014 -1.5769540  0.9883856
L1_AAACCTGTCCTTCAAT-1 -2.02453991 -0.7509340 -0.1231362  1.0223206 -0.9645708
                      harmony_41 harmony_42 harmony_43   harmony_44  harmony_45
L1_AAACCTGAGGGCTTCC-1  0.6400222 -0.5739799  0.4499493 -0.203359845  0.05030824
L1_AAACCTGGTGCAGGTA-1  0.7559842  0.9200416  1.7585739 -0.763331597  1.52800339
L1_AAACCTGGTTAAAGTG-1 -0.7195569  1.9110048 -0.9038872  0.007754539 -0.49006157
L1_AAACCTGTCAGGTAAA-1  0.7064127 -0.7832511 -1.2484201  0.917867543  1.81997842
L1_AAACCTGTCCCTGACT-1 -2.0747057 -0.4612828 -0.8220306 -1.367216381  0.10618932
L1_AAACCTGTCCTTCAAT-1 -1.1612844 -0.2023824 -3.0715510 -0.137506183  1.93822610
                        harmony_46 harmony_47 harmony_48  harmony_49 harmony_50
L1_AAACCTGAGGGCTTCC-1  1.586223770 -0.8058414  0.8400177  0.13174746  0.5703916
L1_AAACCTGGTGCAGGTA-1  0.004456556  0.8801568 -0.6638030 -0.73355571  0.7042793
L1_AAACCTGGTTAAAGTG-1 -1.649104678 -2.2764772 -2.1765942  0.79411393 -0.5709610
L1_AAACCTGTCAGGTAAA-1 -0.330346754  0.2369881 -1.2665325  0.39852531 -0.9456649
L1_AAACCTGTCCCTGACT-1  1.012225573 -0.5072353  1.0867109 -1.28707999 -0.1462727
L1_AAACCTGTCCTTCAAT-1  1.251540361 -1.4596204 -1.9589616  0.08050124 -0.4647281

# Run UMAP on Harmony embeddings
All_samples_Merged <- RunUMAP(All_samples_Merged, reduction = "harmony", dims = 1:16)

17:07:42 UMAP embedding parameters a = 0.9922 b = 1.112

17:07:42 Read 49515 rows and found 16 numeric columns

17:07:42 Using Annoy for neighbor search, n_neighbors = 30

17:07:42 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
17:07:47 Writing NN index file to temp file /tmp/RtmpPISC9c/file217d72cb42c57
17:07:47 Searching Annoy index using 1 thread, search_k = 3000
17:08:05 Annoy recall = 100%
17:08:07 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
17:08:11 Initializing from normalized Laplacian + noise (using RSpectra)
17:08:14 Commencing optimization for 200 epochs, with 2100738 positive edges
17:09:16 Optimization finished

# Optionally, find neighbors and clusters (if you plan to do clustering analysis)
All_samples_Merged <- FindNeighbors(All_samples_Merged, reduction = "harmony", dims = 1:16)

Computing nearest neighbor graph
Computing SNN

All_samples_Merged <- FindClusters(All_samples_Merged, resolution = 0.5)  # Adjust resolution as needed

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

Number of nodes: 49515
Number of edges: 1517154

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8956
Number of communities: 14
Elapsed time: 18 seconds

# Visualize UMAP
DimPlot(All_samples_Merged, reduction = "umap", group.by = "cell_line", label = TRUE, pt.size = 0.5) +
    ggtitle("UMAP of Harmony-Integrated Data")

# Visualize UMAP with batch/cell line information
DimPlot(All_samples_Merged, reduction = "umap", group.by = "cell_line", label = TRUE, pt.size = 0.5) +
    ggtitle("UMAP - Colored by Cell Line (After Harmony Integration)")

# Visualize UMAP with clusters
DimPlot(All_samples_Merged, reduction = "umap", group.by = "seurat_clusters", label = TRUE, pt.size = 0.5) +
    ggtitle("UMAP - Clustered Data (After Harmony Integration)")

# Visualize specific cell types or other metadata
DimPlot(All_samples_Merged, reduction = "umap", group.by = "predicted.celltype.l2", label = TRUE, pt.size = 0.5) +
    ggtitle("UMAP - Cell Types After Harmony Integration")

12.Save the Seurat object as an Robj file

#save(All_samples_Merged, file = "../../../0-IMP-OBJECTS/All_Samples_Merged_with_10x_Azitmuth_Annotated_SCT_HPC_without_harmony_integration.robj")