Cell Line L3 Analysis
Nasir Mahmood Abbasi
2024-07-08
1. load libraries
2. Load Seurat Object
#Load Seurat Object L2
load("../Documents/1-SS-STeps/4-Analysis_and_Robj_Marie/analyse juillet 2023/ObjetsR/L2.Robj")
L2An object of class Seurat
36629 features across 5935 samples within 2 assays
Active assay: RNA (36601 features, 0 variable features)
2 layers present: counts, data
1 other assay present: ADT3. QC
# Set identity classes to an existing column in meta data
Idents(object = L2) <- "cell_line"
L2[["percent.rb"]] <- PercentageFeatureSet(L2, pattern = "^RP[SL]")
VlnPlot(L2, features = c("nFeature_RNA",
"nCount_RNA",
"percent.mito",
"percent.rb"),
ncol = 4, pt.size = 0.1) &
theme(plot.title = element_text(size=10))
FeatureScatter(L2, feature1 = "percent.mito",
feature2 = "percent.rb")
VlnPlot(L2, features = c("nFeature_RNA",
"nCount_RNA",
"percent.mito"),
ncol = 3)
FeatureScatter(L2,
feature1 = "percent.mito",
feature2 = "percent.rb") +
geom_smooth(method = 'lm')
FeatureScatter(L2,
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(L2,
feature1 = "nCount_RNA",
feature2 = "percent.mito")+
geom_smooth(method = 'lm')
FeatureScatter(L2,
feature1 = "nCount_RNA",
feature2 = "nFeature_RNA")+
geom_smooth(method = 'lm')
Assign Cell-Cycle Scores
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 18709 by 5935
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
Found 111 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 18709 genes
Computing corrected count matrix for 18709 genes
Calculating gene attributes
Wall clock passed: Time difference of 22.73244 secs
Determine variable features
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Place corrected count matrix in counts slot
Set default assay to SCT
Warning: The following features are not present in the object: MLF1IP, not searching for symbol synonymsWarning: The following features are not present in the object: FAM64A, HN1, not searching for symbol synonyms4. Normalize data
# Apply SCTransform
L2 <- SCTransform(L2, vars.to.regress = c("percent.rb","percent.mito", "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 18709 by 5935
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
Found 111 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 18709 genes
Computing corrected count matrix for 18709 genes
Calculating gene attributes
Wall clock passed: Time difference of 19.31813 secs
Determine variable features
Regressing out percent.rb, percent.mito, 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 <- L2@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
L2 <- RunPCA(L2,
features = Variables_genes_after_exclusion,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 15,
npcs = 50)PC_ 1
Positive: ESYT2, MALAT1, ARHGAP15, PDE3B, IKZF2, SYNE2, KCNQ5, NEAT1, CAMK4, DOCK10
EXOC4, WWOX, MT-ND6, TOX, RCSD1, TOP2A, SOS1, PTPRC, RAD51B, LRBA
ELMO1, OSBPL3, SPTBN1, ATXN1, MACF1, PRKCH, ADD3, RABGAP1L, UTRN, MBNL1
Negative: ACTB, HSPE1, NME1, RAN, CLIC1, GAPDH, PRELID1, CYCS, RPL35, ENO1
XCL1, UBE2S, ACTG1, HSPD1, LDHA, PPP1R14B, SRM, NPM1, MRPL41, SNRPG
NDUFAB1, PFDN2, KRT86, TMSB4X, TUBA1B, CHCHD2, JPT1, KRT81, TPI1, SNRPD1
PC_ 2
Positive: TMSB10, S100A10, S100A6, B2M, EMP3, CRIP1, S100A4, S100A11, KLF2, TLE5
CD52, LGALS1, LTB, PFN1, CYBA, PLP2, ANXA5, AHNAK, GABARAP, ANXA2
RAP1GAP2, CALM1, PIM1, CD9, PLSCR3, CNN2, IFITM1, RIPOR2, SIGIRR, SLC9A3R1
Negative: XCL1, XCL2, CST7, CRIM1, KSR2, HSPD1, PRKCH, PDE7B, SRGN, DPF3
HDAC9, ADAMTS17, GRK3, ASB2, NPM1, SRM, CD3D, PEBP1, AHI1, KCNQ5
PLPP1, RERE, GFOD1, MT-ND2, CD96, UBE2F, NME1, SRRT, MIR181A1HG, GCSH
PC_ 3
Positive: HIST1H4C, DUT, PCLAF, WDR34, HIST1H1A, HIST1H1D, HIST1H3D, STMN1, HIST1H1C, TYMS
H2AFX, SIVA1, HIST1H1E, TK1, HIST1H1B, H2AFZ, HIST1H2AH, HIST2H2AC, RRM2, TUBB
GAPDH, KLRC1, GABARAP, NUSAP1, GPX4, WWOX, HIST1H3B, GSTP1, COX5B, CARHSP1
Negative: LTB, EIF4G2, CALR, HSPA8, CCND2, CDC20, HSP90AB1, EMP3, ARL6IP1, FLNA
HNRNPU, CCT5, S100A6, CCNB1, TMSB10, YWHAZ, FLT3LG, B2M, MYH9, VIM
HSPA5, EIF3B, HNRNPA2B1, HDGF, YWHAG, PLEC, HNRNPA3, HNRNPH1, CAPRIN1, RIPOR2
PC_ 4
Positive: C1QBP, EIF5A, PSMB6, PFN1, YWHAE, RPL26, RNASEK, SNHG29, RPA1, RPL31
GLOD4, UBB, PRPF8, EIF4A1, NAA38, GABARAP, UBE2G1, CAPG, TXNDC17, ACADVL
GSTP1, TRAPPC1, EMC6, ITGAE, ARRB2, RABEP1, PITPNA, ACAP1, PAFAH1B1, RANGRF
Negative: HIST1H4C, TUBB, HIST1H1E, RPS4X, HIST1H1B, HIST1H1C, RRM2, HIST1H1D, H2AFX, MKI67
HIST1H2AH, HIST1H3D, HIST1H1A, RNF212, CXCR3, MAL, DUT, HIST1H3B, CORO1B, TYMS
HIST2H2AC, MRPS5, HIST1H2AL, TUBA1B, UBE2C, UBC, TOP2A, CD74, HIST1H3C, ALYREF
PC_ 5
Positive: ARL6IP1, RPS4X, PTTG1, SEPTIN6, CORO1B, IL2RG, MIF, SOD1, MT1E, SYT1
CD3D, RNF212, PTMA, OAZ1, PDE7B, TXN, PPIA, CHCHD2, MT1G, ATP5MF
DAD1, BTG1, IL32, ATP5MC3, FOXP2, LITAF, CDC20, HMGB2, SDCBP, MYL6
Negative: TSR1, C1QBP, PFN1, HIST1H1E, PRPF8, MYBBP1A, HIST1H1B, RPL26, HNRNPU, NCL
YWHAE, HIST1H1C, EIF4A1, RPL31, MYH9, EIF5A, HIST1H2AH, SMC1A, TXNDC17, ACAP1
PELP1, CTDNEP1, EIF2S3, EMC6, HSPA9, RPA1, HIST1H4C, IPO7, COPS3, LRPPRC # determine dimensionality of the data
ElbowPlot(L2, 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 L2$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(L2$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 <- L2[["pca"]]@stdev / sum(L2[["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] 12# TEST-2
# get significant PCs
stdv <- L2[["pca"]]@stdev
sum.stdv <- sum(L2[["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] 12# 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
L2 <- FindNeighbors(L2,
dims = 1:min.pc,
verbose = FALSE)
# understanding resolution
L2 <- FindClusters(L2,
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: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9000
Number of communities: 1
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8363
Number of communities: 2
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7966
Number of communities: 3
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7689
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: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7498
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7296
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: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7145
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7002
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6857
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6744
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: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6628
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: 5935
Number of edges: 180434
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.6522
Number of communities: 12
Elapsed time: 0 seconds
# non-linear dimensionality reduction --------------
L2 <- RunUMAP(L2,
dims = 1:min.pc,
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(L2,group.by = "cell_line",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.0.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.0.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.0.3",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.0.4",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.0.5",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.0.6",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.0.7",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.0.8",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.0.9",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.1.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2,
group.by = "SCT_snn_res.1.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
7. Azimuth Annotation
InstallData("pbmcref")Warning: The following packages are already installed and will not be reinstalled: pbmcref# The RunAzimuth function can take a Seurat object as input
L2 <- RunAzimuth(L2, reference = "pbmcref")Warning: Overwriting miscellanous data for modelWarning: Adding a dimensional reduction (refUMAP) without the associated assay being presentWarning: Adding a dimensional reduction (refUMAP) without the associated assay being presentdetected inputs from HUMAN with id type Gene.name
reference rownames detected HUMAN with id type Gene.name
Normalizing query using reference SCT model
Warning: 113 features of the features specified were not present in both the reference query assays.
Continuing with remaining 4887 features.Projecting cell embeddings
Finding query neighbors
Finding neighborhoods
Finding anchors
Found 383 anchors
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
Predicting cell labels
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')Predicting cell labels
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
| | 0 % ~calculating
Integrating dataset 2 with reference dataset
Finding integration vectors
Integrating data
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from integrated_dr_ to integrateddr_Computing nearest neighbors
Running UMAP projection
Warning: Number of neighbors between query and reference is not equal to the number of neighbors within reference16:53:40 Read 5935 rows
16:53:40 Processing block 1 of 1
16:53:40 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 20
16:53:40 Initializing by weighted average of neighbor coordinates using 1 thread
16:53:40 Commencing optimization for 67 epochs, with 118700 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
16:53:40 Finished
Warning: No assay specified, setting assay as RNA by default.Projecting reference PCA onto query
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Projecting back the query cells into original PCA space
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Computing scores:
Finding neighbors of original query cells
Finding neighbors of transformed query cells
Computing query SNN
Determining bandwidth and computing transition probabilities
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Total elapsed time: 2.22579503059387UMAPPlot(L2, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
UMAPPlot(L2, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = T)
8. Cell type annotation using ProjectTils
#Load reference atlas and query data
ref <- readRDS(file = "CD4T_human_ref_v1.rds")
#Run Projection algorithm
query.projected <- Run.ProjecTILs(L2, ref = ref)
|
| | 0%[1] "Using assay SCT for query"Pre-filtering cells with scGate...
### Detected a total of 5466 pure 'Target' cells (92.10% of total)[1] "469 out of 5935 ( 8% ) non-pure cells removed. Use filter.cells=FALSE to avoid pre-filtering"Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|[1] "Aligning query to reference map for batch-correction..."Warning: Layer counts isn't present in the assay object[[assay]]; returning NULLPreparing PCA embeddings for objects...
Warning: Number of dimensions changing from 50 to 20
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
| | 0 % ~calculating
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s
Projecting corrected query onto Reference PCA space
Projecting corrected query onto Reference UMAP spaceWarning: Not all features provided are in this Assay object, removing the following feature(s): CXCL13, GNLY, CD177, CCL22, CGA, SOX4, G0S2, IL17A, IGFL2, PMCH, TRDC, AREG, IL21, HLA-DQA1, IL1RL1, IL1R2, EBI3, DIRAS3, NPPC, RGS2, CAVIN3, CD200, MS4A6A, IL2, BEX3, BASP1, PVALB, MRC1, CTLA4, TASL, CPM, LHFPL6, H1-4, IL26, HLA-DPA1, HLA-DPB1, H2AZ1, TENT5A, MSC, SH2D1A, RCAN2, GPR25, TNS3, ELAPOR1, IGFBP3, CCL3L3, POLR1F, BTLA, PTPN13, FCRL3, FANK1, PLK2, CRLF2, F5, IL1R1, AHSP, CCR2, TSHZ2, CSF2RB, CDCA7L, HTRA1, PPARG, H1-2, H1-0, LMCD1, WARS1, H1-3, H2BC11, GPX1, STAC, H2AC6, IRAG2, CYP7B1, H1-10, MYO7A, PTGIR, H3C10, NAP1L2, FAS, IL22, CHGB, SLC28A3, PDLIM4, ECEL1, HES1, HSD11B1, CPE, CD40, TNFSF11, CD86, RNF144B, H4C3, NEBL, HS3ST1
|
|==========================================================================================================================| 100%Creating slots functional.cluster and functional.cluster.conf in query object#reference atlas
DimPlot(ref, label = T)
#Visualize projection
plot.projection(ref, query.projected, linesize = 0.5, pointsize = 0.5)
#Plot the predicted composition of the query in terms of reference T cell subtypes
plot.statepred.composition(ref, query.projected, metric = "Percent")
L2 <- ProjecTILs.classifier(query = L2, ref = ref)
|
| | 0%[1] "Using assay SCT for query"Pre-filtering cells with scGate...
### Detected a total of 5466 pure 'Target' cells (92.10% of total)[1] "469 out of 5935 ( 8% ) non-pure cells removed. Use filter.cells=FALSE to avoid pre-filtering"Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|[1] "Aligning query to reference map for batch-correction..."Warning: Layer counts isn't present in the assay object[[assay]]; returning NULLPreparing PCA embeddings for objects...
Warning: Number of dimensions changing from 50 to 20
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
| | 0 % ~calculating
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s
Projecting corrected query onto Reference PCA space
Projecting corrected query onto Reference UMAP spaceWarning: Not all features provided are in this Assay object, removing the following feature(s): CXCL13, GNLY, CD177, CCL22, CGA, SOX4, G0S2, IL17A, IGFL2, PMCH, TRDC, AREG, IL21, HLA-DQA1, IL1RL1, IL1R2, EBI3, DIRAS3, NPPC, RGS2, CAVIN3, CD200, MS4A6A, IL2, BEX3, BASP1, PVALB, MRC1, CTLA4, TASL, CPM, LHFPL6, H1-4, IL26, HLA-DPA1, HLA-DPB1, H2AZ1, TENT5A, MSC, SH2D1A, RCAN2, GPR25, TNS3, ELAPOR1, IGFBP3, CCL3L3, POLR1F, BTLA, PTPN13, FCRL3, FANK1, PLK2, CRLF2, F5, IL1R1, AHSP, CCR2, TSHZ2, CSF2RB, CDCA7L, HTRA1, PPARG, H1-2, H1-0, LMCD1, WARS1, H1-3, H2BC11, GPX1, STAC, H2AC6, IRAG2, CYP7B1, H1-10, MYO7A, PTGIR, H3C10, NAP1L2, FAS, IL22, CHGB, SLC28A3, PDLIM4, ECEL1, HES1, HSD11B1, CPE, CD40, TNFSF11, CD86, RNF144B, H4C3, NEBL, HS3ST1
|
|==========================================================================================================================| 100%Creating slots functional.cluster and functional.cluster.conf in query objectUMAPPlot(L2, group.by = "functional.cluster",
reduction = "umap",
label.size = 3,
repel = T,
label = T)
NA
NA9.Cell type annotation using SingleR
# Monaco Annotations
DimPlot(L2, group.by = "monaco.main",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L2, group.by = "monaco.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2, group.by = "monaco.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L2, group.by = "monaco.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# HPCA Annotations
DimPlot(L2, group.by = "hpca.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2, group.by = "hpca.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L2, group.by = "hpca.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# DICE Annotations
DimPlot(L2, group.by = "dice.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2, group.by = "dice.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L2, group.by = "dice.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# BPE Annotations
DimPlot(L2, group.by = "bpe.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L2, group.by = "bpe.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L2, group.by = "bpe.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
NA
NA10. clusTree
library(clustree)Loading required package: ggraph
Attaching package: 'ggraph'
The following object is masked from 'package:sp':
geometryclustree(L2, prefix = "SCT_snn_res.")
11.Save the Seurat object as an Robj file
---
title: "Cell Line L3 Analysis"
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, include=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)

```


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

#Load Seurat Object L3
load("../Documents/1-SS-STeps/4-Analysis_and_Robj_Marie/analyse juillet 2023/ObjetsR/L3.Robj")

L3
```


# 3. QC
```{r QC, fig.height=6, fig.width=10}
# Set identity classes to an existing column in meta data
Idents(object = L3) <- "cell_line"

L3[["percent.rb"]] <- PercentageFeatureSet(L3, pattern = "^RP[SL]")

VlnPlot(L3, features = c("nFeature_RNA", 
                                         "nCount_RNA", 
                                         "percent.mito",
                                         "percent.rb"), 
                            ncol = 4, pt.size = 0.1) & 
              theme(plot.title = element_text(size=10))

FeatureScatter(L3, feature1 = "percent.mito", 
                                  feature2 = "percent.rb")

VlnPlot(L3, features = c("nFeature_RNA", 
                                    "nCount_RNA", 
                                    "percent.mito"), 
                                      ncol = 3)

FeatureScatter(L3, 
               feature1 = "percent.mito", 
               feature2 = "percent.rb") +
        geom_smooth(method = 'lm')

FeatureScatter(L3, 
               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(L3, 
               feature1 = "nCount_RNA", 
               feature2 = "percent.mito")+
  geom_smooth(method = 'lm')

FeatureScatter(L3, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA")+
  geom_smooth(method = 'lm')

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

L3 <- SCTransform(L3, 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


L3 <- CellCycleScoring(L3, s.features = s.genes, 
                           g2m.features = g2m.genes, 
                           set.ident = TRUE)

DefaultAssay(L3) <- "RNA"
L3$CC.Difference <- L3$S.Score - L3$G2M.Score

```

# 4. Normalize data
```{r Normalize, fig.height=4, fig.width=6}


# Apply SCTransform
L3 <- SCTransform(L3, vars.to.regress = c("percent.rb","percent.mito", "CC.Difference"), 
                  do.scale=TRUE, 
                  do.center=TRUE, 
                  verbose = TRUE)
                                      
```


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

Variables_genes <- L3@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
L3 <- RunPCA(L3,
             features = Variables_genes_after_exclusion,
             do.print = TRUE, 
             pcs.print = 1:5, 
             genes.print = 15,
             npcs = 50)

# determine dimensionality of the data
ElbowPlot(L3, 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 L3$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(L3$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 <- L3[["pca"]]@stdev / sum(L3[["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 <- L3[["pca"]]@stdev
sum.stdv <- sum(L3[["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}
L3 <- FindNeighbors(L3, 
                    dims = 1:min.pc, 
                    verbose = FALSE)

# understanding resolution
L3 <- FindClusters(L3, 
                  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 --------------
L3 <- RunUMAP(L3, 
              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(L3,group.by = "cell_line", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)


DimPlot(L3,
        group.by = "SCT_snn_res.0.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3,
        group.by = "SCT_snn_res.0.2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3,
        group.by = "SCT_snn_res.0.3", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3,
        group.by = "SCT_snn_res.0.4", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)


DimPlot(L3,
        group.by = "SCT_snn_res.0.5", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3,
        group.by = "SCT_snn_res.0.6", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3,
        group.by = "SCT_snn_res.0.7", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3,
        group.by = "SCT_snn_res.0.8", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L3,
        group.by = "SCT_snn_res.0.9", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L3,
        group.by = "SCT_snn_res.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L3,
        group.by = "SCT_snn_res.1.1", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
DimPlot(L3,
        group.by = "SCT_snn_res.1.2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

```

# 7. Azimuth Annotation
```{r azimuth_Annotation, fig.height=4, fig.width=6}
InstallData("pbmcref")

# The RunAzimuth function can take a Seurat object as input
L3 <- RunAzimuth(L3, reference = "pbmcref")

UMAPPlot(L3, group.by = "predicted.celltype.L3", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)


UMAPPlot(L3, group.by = "predicted.celltype.L3", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)
```

# 8. Cell type annotation using ProjectTils
```{r ProjecTils, fig.height=4, fig.width=6}


#Load reference atlas and query data
ref <- readRDS(file = "CD4T_human_ref_v1.rds")

#Run Projection algorithm
query.projected <- Run.ProjecTILs(L3, ref = ref)



#reference atlas
DimPlot(ref, label = T)

#Visualize projection
plot.projection(ref, query.projected, linesize = 0.5, pointsize = 0.5)

#Plot the predicted composition of the query in terms of reference T cell subtypes
plot.statepred.composition(ref, query.projected, metric = "Percent")


L3 <- ProjecTILs.classifier(query = L3, ref = ref)
UMAPPlot(L3, group.by = "functional.cluster", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T)


```

# 9.Cell type annotation using SingleR
```{r singleR, fig.height=4, fig.width=6}
#get reference datasets from celldex package

monaco.ref <- celldex::MonacoImmuneData()
hpca.ref <- celldex::HumanPrimaryCellAtlasData()
dice.ref <- celldex::DatabaseImmuneCellExpressionData()
bpe.ref <- celldex::BlueprintEncodeData()

#convert our Seurat object to single cell experiment (SCE)
sce <- as.SingleCellExperiment(DietSeurat(L3))

#using SingleR
monaco.main <- SingleR(test = sce,assay.type.test = 1,ref = monaco.ref,labels = monaco.ref$label.main)
monaco.fine <- SingleR(test = sce,assay.type.test = 1,ref = monaco.ref,labels = monaco.ref$label.fine)
hpca.main <- SingleR(test = sce,assay.type.test = 1,ref = hpca.ref,labels = hpca.ref$label.main)
hpca.fine <- SingleR(test = sce,assay.type.test = 1,ref = hpca.ref,labels = hpca.ref$label.fine)
dice.main <- SingleR(test = sce,assay.type.test = 1,ref = dice.ref,labels = dice.ref$label.main)
dice.fine <- SingleR(test = sce,assay.type.test = 1,ref = dice.ref,labels = dice.ref$label.fine)
bpe.main <- SingleR(test = sce,assay.type.test = 1,ref = bpe.ref,labels = bpe.ref$label.main)
bpe.fine <- SingleR(test = sce,assay.type.test = 1,ref = bpe.ref,labels = bpe.ref$label.fine)


#summary of general cell type annotations

#table(monaco.main$pruned.labels)
#table(hpca.main$pruned.labels)
#table(dice.main$pruned.labels)
#table(bpe.main$pruned.labels)

#The finer cell types annotations are you after, the harder they are to get reliably. 
#This is where comparing many databases, as well as using individual markers from literature, 
#would all be very valuable.

#table(monaco.fine$pruned.labels)
#table(hpca.fine$pruned.labels)
#table(dice.fine$pruned.labels)
#table(bpe.fine$pruned.labels)



#add the annotations to the Seurat object metadata
L3@meta.data$monaco.main <- monaco.main$pruned.labels
L3@meta.data$monaco.fine <- monaco.fine$pruned.labels
#
L3@meta.data$hpca.main   <- hpca.main$pruned.labels
L3@meta.data$hpca.fine   <- hpca.fine$pruned.labels
#  
L3@meta.data$dice.main   <- dice.main$pruned.labels
L3@meta.data$dice.fine   <- dice.fine$pruned.labels
# 
L3@meta.data$bpe.main   <- bpe.main$pruned.labels
L3@meta.data$bpe.fine   <- bpe.fine$pruned.labels

# Monaco Annotations
DimPlot(L3, group.by = "monaco.main", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(L3, group.by = "monaco.main", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3, group.by = "monaco.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(L3, group.by = "monaco.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

# HPCA Annotations
DimPlot(L3, group.by = "hpca.main", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3, group.by = "hpca.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(L3, group.by = "hpca.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
# DICE Annotations
DimPlot(L3, group.by = "dice.main", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3, group.by = "dice.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(L3, group.by = "dice.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

# BPE Annotations
DimPlot(L3, group.by = "bpe.main", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L3, group.by = "bpe.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(L3, group.by = "bpe.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)



```

# 10. clusTree
```{r clusTree, fig.height=12, fig.width=10}
library(clustree)
clustree(L3, prefix = "SCT_snn_res.")
```

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

save(L3, file = "L3_Analysis.Robj")


```







