Cell Line L4 Analysis
1. load libraries
2. Load Seurat Object
#Load Seurat Object L4
load("../Documents/1-SS-STeps/4-Analysis_and_Robj_Marie/analyse juillet 2023/ObjetsR/L4_B.Robj")
L4 <- L4_B
L4## An object of class Seurat
## 36629 features across 6150 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 = L4) <- "cell_line"
L4[["percent.rb"]] <- PercentageFeatureSet(L4, pattern = "^RP[SL]")
VlnPlot(L4, features = c("nFeature_RNA",
"nCount_RNA",
"percent.mito",
"percent.rb"),
ncol = 4, pt.size = 0.1) &
theme(plot.title = element_text(size=10))
## `geom_smooth()` using formula = 'y ~ x'
## `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.
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x' ## 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 19823 by 6150## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells## Found 142 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 19823 genes## Computing corrected count matrix for 19823 genes## Calculating gene attributes## Wall clock passed: Time difference of 24.04605 secs## Determine variable features## 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 synonyms## Warning: The following features are not present in the object: FAM64A, HN1, not
## searching for symbol synonyms4. Normalize data
# Apply SCTransform
L4 <- SCTransform(L4, 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 19823 by 6150## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells## Found 142 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 19823 genes## Computing corrected count matrix for 19823 genes## Calculating gene attributes## Wall clock passed: Time difference of 20.77539 secs## Determine variable features## Regressing out percent.rb, percent.mito, CC.Difference## Centering and scaling data matrix## Place corrected count matrix in counts slot## Set default assay to SCT5. Perform PCA
Variables_genes <- L4@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
L4 <- RunPCA(L4,
features = Variables_genes_after_exclusion,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 15,
npcs = 50)## PC_ 1
## Positive: HSPD1, HSP90AB1, SRM, NPM1, NCL, SERBP1, HSPA9, CCT6A, RPS17, HNRNPAB
## HSPE1, SET, C1QBP, YBX1, RANBP1, ATP5MC1, RBM17, ODC1, MTDH, RPL23
## PRKDC, BATF3, CCT8, NME2, PAICS, NME1, VDAC1, TXNL4A, RAN, TRAP1
## Negative: LGALS1, S100A6, S100A4, S100A11, B2M, LSP1, S100A10, SH3BGRL3, TMSB10, CRIP1
## CYBA, MYL6, LAPTM5, IL32, CAPG, PRDX5, TAGLN2, ANXA2, FXYD5, IFITM2
## TMSB4X, TIMP1, IFITM1, ANXA1, RHOC, VIM, CD63, IL9R, CARS, TNFRSF18
## PC_ 2
## Positive: RABGAP1L, FN1, ATP8A1, DENND4A, CAMK4, CABLES1, RORA, RNGTT, TNFRSF11A, APP
## LINC01934, FNBP1, PCBP3, MAPK8, TMEM178B, ATP8B4, MCTP2, ZNF292, CCR7, CAMK1D
## PDE4D, THEMIS, NEDD4L, OSBPL10, CDC42BPA, MACF1, AKAP13, RERE, MALAT1, ELMO1
## Negative: CORO1A, CYCS, RPL35, RAN, DYNLL1, SNRPD1, UBE2S, RPL17, PSMB8, PA2G4
## CDC20, CLIC1, PRELID1, HSPE1, NOP16, NPM1, TXN, ARHGDIB, SSBP1, PTTG1
## ODC1, CCDC85B, MT1E, CCT2, PDCD5, IFITM2, TOMM40, HNRNPAB, CALM1, NOL7
## PC_ 3
## Positive: KCNQ5, AHNAK, RASGRP2, MKI67, PRUNE2, RRM2, LMNB1, ARHGEF6, SLC1A5, PXYLP1
## CORO1A, DYNC1H1, RPS6KA5, HIST1H1B, FLNA, MYH9, ANTXR2, AAK1, KLF2, TPM4
## ATAD2, MAD2L2, SMC4, GNAQ, CTDSPL, SPATA5, NSD2, UBAC1, UBASH3B, TUBA1B
## Negative: RPL19, NME2, CA2, CCR7, DEGS1, TOMM20, MATN4, RPL27, SNRPE, CD74
## SNHG16, SLC2A3, MINDY3, ARF1, ARID5A, JPT1, ANKRD37, PHB, PNRC1, PSMB3
## NENF, TNFRSF11A, SEC14L1, RGS1, NME1, PKM, RPL22, MT-ND4, RPL38, RPS3A
## PC_ 4
## Positive: HSPA8, DDX21, CDC20, RAB11FIP1, CELF2, NCL, HSPA4, CD37, HSPH1, NAMPT
## AK4, SLC2A3, HSPA5, JUN, GSPT1, NOP58, NOLC1, SERBP1, GTPBP4, ATP12A
## NUDCD1, DDX17, PRR13, CORO1B, NOP16, SNHG3, DBN1, RSL1D1, DCUN1D5, CAPRIN1
## Negative: HIST1H4C, TUBB, H2AFZ, TUBA1B, RRM2, RPL19, NME2, PPIA, GGH, H3F3A
## STMN1, NUSAP1, NENF, TOP2A, EIF1, PCLAF, H1FX, HIST1H1D, HIST1H1C, HIST1H1B
## EIF4EBP1, CDK1, CENPU, ZWINT, TYMS, PSAT1, H3F3B, ATAD2, BATF3, HIST1H1E
## PC_ 5
## Positive: TRAF3IP3, LIMD2, DANCR, RPL23, FRMPD4, JPT1, MT-ND1, BATF3, ARL6IP5, ZNF804A
## CD52, SNRPE, AGBL1, ALAS1, NLGN1, TNFSF10, SLFN5, CD47, AC004687.1, LINC00271
## ALOX5AP, TSHZ2, WFDC1, SH3BGRL3, GRIA4, CISD3, SLC20A2, RNGTT, RPL19, NDUFC2
## Negative: GAPDH, LDHA, ENO1, MIF, TPI1, SLC2A3, DDIT4, PLIN2, ANKRD37, RGS1
## PPP1R15A, BNIP3, HILPDA, BNIP3L, MXI1, DUSP4, DNAJB1, HSPA1B, MT-ND4, KLF6
## C12orf75, HIST1H1E, RPL41, P4HA1, BHLHE40, N4BP3, TNFAIP3, JUN, AK4, GPI
# 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 L4$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(L4$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 <- L4[["pca"]]@stdev / sum(L4[["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] 14# TEST-2
# get significant PCs
stdv <- L4[["pca"]]@stdev
sum.stdv <- sum(L4[["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] 14# 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
L4 <- FindNeighbors(L4,
dims = 1:min.pc,
verbose = FALSE)
# understanding resolution
L4 <- FindClusters(L4,
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: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9423
## 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: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9061
## 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: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8818
## 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: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8615
## 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: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8458
## 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: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8312
## 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: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8157
## 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: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8017
## 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: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7899
## Number of communities: 14
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7812
## Number of communities: 14
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7720
## Number of communities: 14
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 6150
## Number of edges: 209562
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7631
## Number of communities: 15
## Elapsed time: 0 seconds# non-linear dimensionality reduction --------------
L4 <- RunUMAP(L4,
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(L4,group.by = "cell_line",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.0.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.0.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.0.3",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.0.4",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.0.5",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.0.6",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.0.7",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.0.8",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.0.9",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.1.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4,
group.by = "SCT_snn_res.1.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
7. Azimuth Annotation
## Warning: The following packages are already installed and will not be
## reinstalled: pbmcref# The RunAzimuth function can take a Seurat object as input
L4 <- RunAzimuth(L4, reference = "pbmcref")## Warning: Overwriting miscellanous data for model## Warning: Adding a dimensional reduction (refUMAP) without the associated assay
## being present
## Warning: Adding a dimensional reduction (refUMAP) without the associated assay
## being present## detected 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 553 anchors## Finding integration vectors## Finding integration vector weights## 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
## ('-')##
## Integrating dataset 2 with reference dataset## Finding integration vectors## Integrating data## 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 in RunUMAP.default(object = neighborlist, reduction.model =
## reduction.model, : Number of neighbors between query and reference is not equal
## to the number of neighbors within reference## 15:28:29 Read 6150 rows## 15:28:29 Processing block 1 of 1## 15:28:29 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 20
## 15:28:29 Initializing by weighted average of neighbor coordinates using 1 thread
## 15:28:29 Commencing optimization for 67 epochs, with 123000 positive edges
## 15:28:29 Finished## Warning: No assay specified, setting assay as RNA by default.## Projecting reference PCA onto query
## Finding integration vector weights
## Projecting back the query cells into original PCA space
## Finding integration vector weights
## Computing scores:
## Finding neighbors of original query cells
## Finding neighbors of transformed query cells
## Computing query SNN
## Determining bandwidth and computing transition probabilities
## Total elapsed time: 2.35772180557251DimPlot(L4, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot (L4, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = T)## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
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(L4, ref = ref)## | | | 0%[1] "Using assay SCT for query"## Pre-filtering cells with scGate...##
## ### Detected a total of 4321 pure 'Target' cells (70.26% of total)## [1] "1829 out of 6150 ( 30% ) non-pure cells removed. Use filter.cells=FALSE to avoid pre-filtering"
## [1] "Aligning query to reference map for batch-correction..."## Warning: Layer counts isn't present in the assay object[[assay]]; returning
## NULL## Preparing PCA embeddings for objects...## Warning: Number of dimensions changing from 50 to 20##
## Projecting corrected query onto Reference PCA space
##
## Projecting corrected query onto Reference UMAP space## Warning: Not all features provided are in this Assay object, removing the
## following feature(s): GZMK, CCL20, MT1G, CD177, CGA, G0S2, IL17A, CCL4L2, XCL1,
## IGFL2, GZMH, TRDC, XCL2, IL1RL1, ACTG2, RASD1, KLRD1, ZBED2, CXCR6, TASL, CPA5,
## TMIGD2, H1-4, IL26, H2AZ1, GPR25, ELAPOR1, CCL3L3, POLR1F, EGR3, KIR3DL2, AHSP,
## CDKN2B, LIMS2, H1-2, CDKN2A, H1-0, WARS1, H1-3, ASCL2, DTHD1, H2BC11, GPX1,
## H2AC6, IRAG2, H1-10, MYO7A, FASLG, SCML1, CLEC7A, H3C10, NAP1L2, HS3ST3B1,
## FBLN7, FGFBP2, IL22, SLC28A3, PDLIM4, ZNF683, ECEL1, ARC, NLRP3, GFPT2, H4C3,
## RORC, GSTM2## | |======================================================================| 100%## Creating slots functional.cluster and functional.cluster.conf in query object
#Plot the predicted composition of the query in terms of reference T cell subtypes
plot.statepred.composition(ref, query.projected, metric = "Percent")
## | | | 0%[1] "Using assay SCT for query"## Pre-filtering cells with scGate...
##
## ### Detected a total of 4321 pure 'Target' cells (70.26% of total)## [1] "1829 out of 6150 ( 30% ) non-pure cells removed. Use filter.cells=FALSE to avoid pre-filtering"
## [1] "Aligning query to reference map for batch-correction..."## Warning: Layer counts isn't present in the assay object[[assay]]; returning
## NULL## Preparing PCA embeddings for objects...## Warning: Number of dimensions changing from 50 to 20##
## Projecting corrected query onto Reference PCA space
##
## Projecting corrected query onto Reference UMAP space## Warning: Not all features provided are in this Assay object, removing the
## following feature(s): GZMK, CCL20, MT1G, CD177, CGA, G0S2, IL17A, CCL4L2, XCL1,
## IGFL2, GZMH, TRDC, XCL2, IL1RL1, ACTG2, RASD1, KLRD1, ZBED2, CXCR6, TASL, CPA5,
## TMIGD2, H1-4, IL26, H2AZ1, GPR25, ELAPOR1, CCL3L3, POLR1F, EGR3, KIR3DL2, AHSP,
## CDKN2B, LIMS2, H1-2, CDKN2A, H1-0, WARS1, H1-3, ASCL2, DTHD1, H2BC11, GPX1,
## H2AC6, IRAG2, H1-10, MYO7A, FASLG, SCML1, CLEC7A, H3C10, NAP1L2, HS3ST3B1,
## FBLN7, FGFBP2, IL22, SLC28A3, PDLIM4, ZNF683, ECEL1, ARC, NLRP3, GFPT2, H4C3,
## RORC, GSTM2## | |======================================================================| 100%## Creating slots functional.cluster and functional.cluster.conf in query objectDimPlot(L4, group.by = "functional.cluster",
reduction = "umap",
label.size = 3,
repel = T,
label = T)
9.Cell type annotation using SingleR
## see ?celldex and browseVignettes('celldex') for documentation## loading from cache## see ?celldex and browseVignettes('celldex') for documentation## loading from cache## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache## see ?celldex and browseVignettes('celldex') for documentation## loading from cache## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache## see ?celldex and browseVignettes('celldex') for documentation## loading from cache## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache## see ?celldex and browseVignettes('celldex') for documentation## loading from cache#convert our Seurat object to single cell experiment (SCE)
sce <- as.SingleCellExperiment(DietSeurat(L4))
#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$pruned.labels)
#add the annotations to the Seurat object metadata
L4@meta.data$monaco.main <- monaco.main$pruned.labels
L4@meta.data$monaco.fine <- monaco.fine$pruned.labels
#
L4@meta.data$hpca.main <- hpca.main$pruned.labels
L4@meta.data$hpca.fine <- hpca.fine$pruned.labels
#
L4@meta.data$dice.main <- dice.main$pruned.labels
L4@meta.data$dice.fine <- dice.fine$pruned.labels
#
L4@meta.data$bpe.main <- bpe.main$pruned.labels
L4@meta.data$bpe.fine <- bpe.fine$pruned.labels
# Monaco Annotations
DimPlot(L4, group.by = "monaco.main",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(L4, group.by = "monaco.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4, group.by = "monaco.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# HPCA Annotations
DimPlot(L4, group.by = "hpca.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
DimPlot(L4, group.by = "hpca.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# DICE Annotations
DimPlot(L4, group.by = "dice.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4, group.by = "dice.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# BPE Annotations
DimPlot(L4, group.by = "bpe.main",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(L4, group.by = "bpe.fine",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
10. clusTree
## Loading required package: ggraph##
## Attaching package: 'ggraph'## The following object is masked from 'package:sp':
##
## geometry