Cell Line L1 Analysis
Nasir Mahmood Abbasi
2024-07-08
1. load libraries
2. Load Seurat Object
#Load Seurat Object L1
load("../Documents/1-SS-STeps/4-Analysis_and_Robj_Marie/analyse juillet 2023/ObjetsR/L1.Robj")
L13. QC
# Set identity classes to an existing column in meta data
Idents(object = L1) <- "cell_line"
L1[["percent.rb"]] <- PercentageFeatureSet(L1, pattern = "^RP[SL]")
VlnPlot(L1, features = c("nFeature_RNA",
"nCount_RNA",
"percent.mito",
"percent.rb"),
ncol = 4, pt.size = 0.1) &
theme(plot.title = element_text(size=10))
FeatureScatter(L1, feature1 = "percent.mito",
feature2 = "percent.rb")
VlnPlot(L1, features = c("nFeature_RNA",
"nCount_RNA",
"percent.mito"),
ncol = 3)
FeatureScatter(L1,
feature1 = "percent.mito",
feature2 = "percent.rb") +
geom_smooth(method = 'lm')
FeatureScatter(L1,
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(L1,
feature1 = "nCount_RNA",
feature2 = "percent.mito")+
geom_smooth(method = 'lm')
FeatureScatter(L1,
feature1 = "nCount_RNA",
feature2 = "nFeature_RNA")+
geom_smooth(method = 'lm')
Assign Cell-Cycle Scores
Running SCTransform on assay: RNA
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 17646 by 5825
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
Found 99 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 17646 genes
Computing corrected count matrix for 17646 genes
Calculating gene attributes
Wall clock passed: Time difference of 19.01677 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
L1 <- SCTransform(L1, 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 17646 by 5825
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
Found 99 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 17646 genes
Computing corrected count matrix for 17646 genes
Calculating gene attributes
Wall clock passed: Time difference of 18.97784 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 <- L1@assays$SCT@var.features
# Exclude genes starting with "HLA-" AND "Xist" AND "TRBV, TRAV"
Variables_genes_after_exclusion <- Variables_genes[!grepl("^HLA-|^XIST|^TRBV|^TRAV", Variables_genes)]
# These are now standard steps in the Seurat workflow for visualization and clustering
L1 <- RunPCA(L1,
features = Variables_genes_after_exclusion,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 15,
npcs = 50)PC_ 1
Positive: TUBA1B, H2AFZ, HMGB1, STMN1, DUT, TUBB, HMGB2, MCM7, HMGN2, HIST1H4C
UBE2C, TYMS, PCLAF, PTMA, TUBB4B, TK1, DEK, RRM2, MCM3, CDT1
CKS2, PKMYT1, SIVA1, TOP2A, DNMT1, CENPM, UBE2S, ALYREF, RANBP1, HIST1H1A
Negative: CCND2, LTB, RNF213, BTG2, CD44, SLFN5, CD48, NFATC3, CTSC, DDIT4
GAS5, CSF1, INPP4B, SEMA4A, CD74, CALD1, IKZF3, TGFBR3, BTG1, ZYX
KDM5B, EGLN3, DLEU1, PNRC1, PTGIS, PTPRC, VIM, KDM7A, IL4, RALGAPA2
PC_ 2
Positive: ACTB, TMSB10, PFN1, NME1, MIF, SRRT, CLIC1, ATP5MC3, EIF4A1, PRDX1
MRPL4, CFL1, PRELID1, HSP90AB1, PPIB, ATP5MF, POMP, TIMM13, LDHA, SSBP1
NDUFS5, MRPL16, PARK7, BCAP31, PSMB3, CORO1A, PSMA7, PSME2, ACTG1, GADD45GIP1
Negative: MALAT1, ASPM, CENPF, MKI67, TOP2A, HIST1H1E, HIST1H1B, TUBB, PCLAF, NUSAP1
CAMK4, RRM2, TYMS, ATAD2, MBNL1, DHFR, SYNE2, RCSD1, HIST1H1C, STMN1
RABGAP1L, PDE3B, KIF11, IKZF2, PTPRC, MAL, NEIL3, NEK7, NCAPG2, BRCA1
PC_ 3
Positive: RPS28, RPL32, RPL29, RPL35A, RPS15, RPL36, RPL14, GZMA, CEBPD, HSP90AB1
LSR, SLCO3A1, ADGRE5, OAZ1, FXYD5, EEF2, AC002069.2, BSG, RPSA, MLLT3
FAM107B, AC069410.1, PDGFD, LEF1, P2RY14, FOXP1, SPINT2, H1FX, PRDX2, TRBC2
Negative: GAPDH, HPGDS, PKM, NKG7, KRT1, FABP5, LY6E, S100A4, NPDC1, EEF1A1
VIM, CD48, NQO1, S100P, C12orf75, CFH, FSCN1, SLC25A5, LMNA, SH3BGRL3
SH2D2A, CYP1B1, PRDX1, SIX3, ALOX5AP, ACTG1, P4HA2, TMSB4X, BACE2, TPST2
PC_ 4
Positive: RPL10, MRPL16, ID3, SEPTIN11, HTATSF1, LAGE3, TCN1, GIMAP7, BCAP31, RIPOR2
PON2, ARHGEF6, FYB1, NRXN3, KLF2, LINC00892, TMSB10, AIFM1, STK26, DKC1
TCF7, DSC1, ZP3, CD81, MKRN1, TAFA2, IDH3G, COA1, COBL, IKZF2
Negative: RPL14, RPL32, RPL29, IL32, RPS27, CYBA, CTSC, CISH, RPSA, CDKN1A
RHOC, RPL35A, CACYBP, ACTB, GAS5, IFITM1, ATP1B1, SOCS1, FTH1, FXYD5
CXCR3, CEBPD, LAT, S100A11, H3F3A, LTB, GZMM, ITGB7, PIM1, B2M
PC_ 5
Positive: KLF2, HNRNPA1, S100A10, IMPDH2, GIMAP7, NUCKS1, SH3BP5, MAL, MT-CYB, TLE5
VIM, ETS1, S1PR4, SELPLG, KLF3, CD55, IL7R, PTMA, RPSA, HDGF
ITGB1, CCR7, ATP5MC2, NOSIP, HSD17B11, CDKN2B, CACYBP, FGFBP2, HMGB2, RPL28
Negative: HIST1H1A, SKAP1, HIST1H1B, TUBA4A, HIST1H1E, HIST1H2AH, HIST1H4C, HIST1H1D, HIST1H1C, SRRT
CTSC, HIST1H3B, MRPL16, TUBB, SSR4, EIF4G3, ARHGAP4, RPL10, HIST1H3D, CD96
SRM, MRPL41, HIST2H2AC, PTPN7, HIST1H3C, HIST1H4F, HIST1H2AL, SEPTIN11, TMC8, RALGAPA2 # determine dimensionality of the data
ElbowPlot(L1, ndims =50)
NA
NAPerform PCA TEST
library(ggplot2)
library(RColorBrewer)
# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")
# Assuming L1$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(L1$cell_line))
colnames(data) <- c("cell_line", "nUMI") # Change column name to nUMI
ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) +
geom_col() +
theme_classic() +
geom_text(aes(label = nUMI),
position = position_dodge(width = 0.9),
vjust = -0.25) +
scale_fill_manual(values = cell_line_colors) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)) + # Adjust the title position
ggtitle("Filtered cells per sample") +
xlab("Cell lines") + # Adjust x-axis label
ylab("Frequency") # Adjust y-axis label
print(ncells)
# TEST-1
# given that the output of RunPCA is "pca"
# replace "so" by the name of your seurat object
pct <- L1[["pca"]]@stdev / sum(L1[["pca"]]@stdev) * 100
cumu <- cumsum(pct) # Calculate cumulative percents for each PC
# Determine the difference between variation of PC and subsequent PC
co2 <- sort(which((pct[-length(pct)] - pct[-1]) > 0.1), decreasing = T)[1] + 1
# last point where change of % of variation is more than 0.1%. -> co2
co2[1] 15# TEST-2
# get significant PCs
stdv <- L1[["pca"]]@stdev
sum.stdv <- sum(L1[["pca"]]@stdev)
percent.stdv <- (stdv / sum.stdv) * 100
cumulative <- cumsum(percent.stdv)
co1 <- which(cumulative > 90 & percent.stdv < 5)[1]
co2 <- sort(which((percent.stdv[1:length(percent.stdv) - 1] -
percent.stdv[2:length(percent.stdv)]) > 0.1),
decreasing = T)[1] + 1
min.pc <- min(co1, co2)
min.pc[1] 15# Create a dataframe with values
plot_df <- data.frame(pct = percent.stdv,
cumu = cumulative,
rank = 1:length(percent.stdv))
# Elbow plot to visualize
ggplot(plot_df, aes(cumulative, percent.stdv, label = rank, color = rank > min.pc)) +
geom_text() +
geom_vline(xintercept = 90, color = "grey") +
geom_hline(yintercept = min(percent.stdv[percent.stdv > 5]), color = "grey") +
theme_bw()
NA
NA
NA6. Clustering
L1 <- FindNeighbors(L1,
dims = 1:min.pc,
verbose = FALSE)
# understanding resolution
L1 <- FindClusters(L1,
resolution = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6,
0.7,0.8, 0.9, 1, 1.1, 1.2))Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9215
Number of communities: 3
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8916
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8701
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8494
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8287
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8146
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8022
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7907
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7793
Number of communities: 11
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7681
Number of communities: 12
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7579
Number of communities: 13
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5825
Number of edges: 200767
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7486
Number of communities: 14
Elapsed time: 0 seconds
# non-linear dimensionality reduction --------------
L1 <- RunUMAP(L1,
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(L1,group.by = "cell_line",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.3",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.4",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.5",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.6",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.7",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.8",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.0.9",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.1.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1,
group.by = "SCT_snn_res.1.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
7. 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
L1 <- RunAzimuth(L1, 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 489 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 reference12:44:57 Read 5825 rows
12:44:57 Processing block 1 of 1
12:44:57 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 20
12:44:57 Initializing by weighted average of neighbor coordinates using 1 thread
12:44:57 Commencing optimization for 67 epochs, with 116500 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:44:58 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: 2.2985680103302UMAPPlot(L1, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
UMAPPlot(L1, 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(L1, ref = ref)
|
| | 0%[1] "Using assay SCT for query"Pre-filtering cells with scGate...
### Detected a total of 5396 pure 'Target' cells (92.64% of total)[1] "429 out of 5825 ( 7% ) 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% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s
| | 0 % ~calculating
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s
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, TNFRSF4, CD177, CCL22, LAG3, CGA, G0S2, IL17A, CCL4L2, TRDC, IL21, TNFRSF18, FOXP3, HLA-DQA1, KRT86, CAV1, IL10, CCL3, IL1R2, ACTG2, CCR8, EBI3, DIRAS3, KLRD1, THBS1, ZBED2, CAVIN3, GNG8, MS4A6A, TYROBP, PROK2, BEX3, BASP1, GLUL, PVALB, HES4, MRC1, CTLA4, TASL, IL1RN, LHFPL6, GBP5, MYADM, H1-4, KLF4, CTSH, H2AZ1, TENT5A, CD9, MSC, RCAN2, GPR25, CCR6, TNS3, ELAPOR1, NR4A3, IGFBP3, CCL3L3, PRRG4, POLR1F, SDC4, FCRL3, GPR55, PLK2, CRLF2, AHSP, FAIM2, CSF2RB, CDCA7L, HTRA1, H1-2, H1-0, ETV7, FLT1, LMCD1, WARS1, H1-3, ASCL2, CLNK, H2BC11, ADRB2, GPX1, STAC, H2AC6, IRAG2, GNA15, CD80, PLAAT3, H1-10, MYO7A, DAPK2, H3C10, NAP1L2, IL22, CHGB, KRT81, SLC28A3, PDLIM4, ZNF683, ECEL1, HES1, HSD11B1, CPE, CD40, CA2, NT5E, CD86, H4C3, RORC, CXXC5, NEBL, GSTM2
|
|==========================================================================================================================| 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")
L1 <- ProjecTILs.classifier(query = L1, ref = ref)
|
| | 0%[1] "Using assay SCT for query"Pre-filtering cells with scGate...
### Detected a total of 5396 pure 'Target' cells (92.64% of total)[1] "429 out of 5825 ( 7% ) 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% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s
| | 0 % ~calculating
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=13s
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, TNFRSF4, CD177, CCL22, LAG3, CGA, G0S2, IL17A, CCL4L2, TRDC, IL21, TNFRSF18, FOXP3, HLA-DQA1, KRT86, CAV1, IL10, CCL3, IL1R2, ACTG2, CCR8, EBI3, DIRAS3, KLRD1, THBS1, ZBED2, CAVIN3, GNG8, MS4A6A, TYROBP, PROK2, BEX3, BASP1, GLUL, PVALB, HES4, MRC1, CTLA4, TASL, IL1RN, LHFPL6, GBP5, MYADM, H1-4, KLF4, CTSH, H2AZ1, TENT5A, CD9, MSC, RCAN2, GPR25, CCR6, TNS3, ELAPOR1, NR4A3, IGFBP3, CCL3L3, PRRG4, POLR1F, SDC4, FCRL3, GPR55, PLK2, CRLF2, AHSP, FAIM2, CSF2RB, CDCA7L, HTRA1, H1-2, H1-0, ETV7, FLT1, LMCD1, WARS1, H1-3, ASCL2, CLNK, H2BC11, ADRB2, GPX1, STAC, H2AC6, IRAG2, GNA15, CD80, PLAAT3, H1-10, MYO7A, DAPK2, H3C10, NAP1L2, IL22, CHGB, KRT81, SLC28A3, PDLIM4, ZNF683, ECEL1, HES1, HSD11B1, CPE, CD40, CA2, NT5E, CD86, H4C3, RORC, CXXC5, NEBL, GSTM2
|
|===================================================================================================================================| 100%Creating slots functional.cluster and functional.cluster.conf in query objectUMAPPlot(L1, group.by = "functional.cluster",
reduction = "umap",
label.size = 3,
repel = T,
label = T)
NA
NA9.Cell type annotation using SingleR
# Monaco Annotations
DimPlot(L1, group.by = "monaco.main",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L1, group.by = "monaco.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1, group.by = "monaco.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L1, group.by = "monaco.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# HPCA Annotations
DimPlot(L1, group.by = "hpca.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1, group.by = "hpca.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L1, group.by = "hpca.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# DICE Annotations
DimPlot(L1, group.by = "dice.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1, group.by = "dice.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L1, group.by = "dice.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# BPE Annotations
DimPlot(L1, group.by = "bpe.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L1, group.by = "bpe.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L1, group.by = "bpe.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
NA
NA10. clusTree
library(clustree)
clustree(L1, prefix = "SCT_snn_res.")
11.Save the Seurat object as an Robj file
---
title: "Cell Line L1 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 L1
load("../Documents/1-SS-STeps/4-Analysis_and_Robj_Marie/analyse juillet 2023/ObjetsR/L1.Robj")

L1
```


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

L1[["percent.rb"]] <- PercentageFeatureSet(L1, pattern = "^RP[SL]")

VlnPlot(L1, features = c("nFeature_RNA", 
                                         "nCount_RNA", 
                                         "percent.mito",
                                         "percent.rb"), 
                            ncol = 4, pt.size = 0.1) & 
              theme(plot.title = element_text(size=10))

FeatureScatter(L1, feature1 = "percent.mito", 
                                  feature2 = "percent.rb")

VlnPlot(L1, features = c("nFeature_RNA", 
                                    "nCount_RNA", 
                                    "percent.mito"), 
                                      ncol = 3)

FeatureScatter(L1, 
               feature1 = "percent.mito", 
               feature2 = "percent.rb") +
        geom_smooth(method = 'lm')

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

FeatureScatter(L1, 
               feature1 = "nCount_RNA", 
               feature2 = "nFeature_RNA")+
  geom_smooth(method = 'lm')

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

L1 <- SCTransform(L1, do.scale=FALSE, do.center=FALSE)


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


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

DefaultAssay(L1) <- "RNA"
L1$CC.Difference <- L1$S.Score - L1$G2M.Score

```

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


# Apply SCTransform
L1 <- SCTransform(L1, 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 <- L1@assays$SCT@var.features

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


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

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


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


library(ggplot2)
library(RColorBrewer)  

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

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

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

print(ncells)



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

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

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

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

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

  

```

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

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


```


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

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

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


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

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

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

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


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

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

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

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

```

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

# The RunAzimuth function can take a Seurat object as input
L1 <- RunAzimuth(L1, reference = "pbmcref")

UMAPPlot(L1, group.by = "predicted.celltype.l2", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)


UMAPPlot(L1, group.by = "predicted.celltype.l2", 
        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(L1, 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")


L1 <- ProjecTILs.classifier(query = L1, ref = ref)
UMAPPlot(L1, 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(L1))

#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
L1@meta.data$monaco.main <- monaco.main$pruned.labels
L1@meta.data$monaco.fine <- monaco.fine$pruned.labels
#
L1@meta.data$hpca.main   <- hpca.main$pruned.labels
L1@meta.data$hpca.fine   <- hpca.fine$pruned.labels
#  
L1@meta.data$dice.main   <- dice.main$pruned.labels
L1@meta.data$dice.fine   <- dice.fine$pruned.labels
# 
L1@meta.data$bpe.main   <- bpe.main$pruned.labels
L1@meta.data$bpe.fine   <- bpe.fine$pruned.labels

# Monaco Annotations
DimPlot(L1, group.by = "monaco.main", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

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

DimPlot(L1, group.by = "monaco.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

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

# HPCA Annotations
DimPlot(L1, group.by = "hpca.main", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L1, group.by = "hpca.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(L1, group.by = "hpca.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)
# DICE Annotations
DimPlot(L1, group.by = "dice.main", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L1, group.by = "dice.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

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

# BPE Annotations
DimPlot(L1, group.by = "bpe.main", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = T, label.box = T)

DimPlot(L1, group.by = "bpe.fine", 
        reduction = "umap",
        label.size = 3,
        repel = T,
        label = F)

DimPlot(L1, 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(L1, prefix = "SCT_snn_res.")
```

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

save(L1, file = "L1_Analysis.Robj")


```







