UMAP of T cells without other PBMC cells using clusters and PC-1:50
I removed B cells and other cells and we will apply script for UMAP just on T cells
1. load libraries
## Loading required package: SeuratObject## Loading required package: sp##
## Attaching package: 'SeuratObject'## The following objects are masked from 'package:base':
##
## intersect, t## ── Installed datasets ──────────────────────────────── SeuratData v0.2.2.9001 ──## ✔ pbmcref 1.0.0 ✔ pbmcsca 3.0.0## ────────────────────────────────────── Key ─────────────────────────────────────## ✔ Dataset loaded successfully
## ❯ Dataset built with a newer version of Seurat than installed
## ❓ Unknown version of Seurat installed##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ ggplot2 3.5.1 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
##
## Attaching package: 'magrittr'
##
##
## The following object is masked from 'package:purrr':
##
## set_names
##
##
## The following object is masked from 'package:tidyr':
##
## extract
##
##
##
## Attaching package: 'dbplyr'
##
##
## The following objects are masked from 'package:dplyr':
##
## ident, sql
##
##
## Registered S3 method overwritten by 'SeuratDisk':
## method from
## as.sparse.H5Group Seurat
##
##
##
## Attaching shinyBS
##
## Loading required package: SummarizedExperiment
##
## Loading required package: MatrixGenerics
##
## Loading required package: matrixStats
##
##
## Attaching package: 'matrixStats'
##
##
## The following object is masked from 'package:dplyr':
##
## count
##
##
##
## Attaching package: 'MatrixGenerics'
##
##
## The following objects are masked from 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars
##
##
## Loading required package: GenomicRanges
##
## Loading required package: stats4
##
## Loading required package: BiocGenerics
##
##
## Attaching package: 'BiocGenerics'
##
##
## The following objects are masked from 'package:lubridate':
##
## intersect, setdiff, union
##
##
## The following objects are masked from 'package:dplyr':
##
## combine, intersect, setdiff, union
##
##
## The following object is masked from 'package:SeuratObject':
##
## intersect
##
##
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
##
##
## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
## table, tapply, union, unique, unsplit, which.max, which.min
##
##
## Loading required package: S4Vectors
##
##
## Attaching package: 'S4Vectors'
##
##
## The following objects are masked from 'package:lubridate':
##
## second, second<-
##
##
## The following object is masked from 'package:tidyr':
##
## expand
##
##
## The following objects are masked from 'package:dplyr':
##
## first, rename
##
##
## The following object is masked from 'package:utils':
##
## findMatches
##
##
## The following objects are masked from 'package:base':
##
## expand.grid, I, unname
##
##
## Loading required package: IRanges
##
##
## Attaching package: 'IRanges'
##
##
## The following object is masked from 'package:lubridate':
##
## %within%
##
##
## The following object is masked from 'package:purrr':
##
## reduce
##
##
## The following objects are masked from 'package:dplyr':
##
## collapse, desc, slice
##
##
## The following object is masked from 'package:sp':
##
## %over%
##
##
## Loading required package: GenomeInfoDb
##
##
## Attaching package: 'GenomicRanges'
##
##
## The following object is masked from 'package:magrittr':
##
## subtract
##
##
## Loading required package: Biobase
##
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
##
##
##
## Attaching package: 'Biobase'
##
##
## The following object is masked from 'package:MatrixGenerics':
##
## rowMedians
##
##
## The following objects are masked from 'package:matrixStats':
##
## anyMissing, rowMedians
##
##
##
## Attaching package: 'SummarizedExperiment'
##
##
## The following object is masked from 'package:Seurat':
##
## Assays
##
##
## The following object is masked from 'package:SeuratObject':
##
## Assays
##
##
##
## Attaching package: 'celldex'
##
##
## The following objects are masked from 'package:SingleR':
##
## BlueprintEncodeData, DatabaseImmuneCellExpressionData,
## HumanPrimaryCellAtlasData, ImmGenData, MonacoImmuneData,
## MouseRNAseqData, NovershternHematopoieticData
##
##
## Loading required package: ggraph
##
##
## Attaching package: 'ggraph'
##
##
## The following object is masked from 'package:sp':
##
## geometry2. Load Seurat Object
#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("All_T_cells_Merged_filtered_Mono_using_clusters.Robj")
All_samples_Merged <- filtered_data
All_samples_Merged## An object of class Seurat
## 62626 features across 46976 samples within 6 assays
## Active assay: SCT (25902 features, 3000 variable features)
## 3 layers present: counts, data, scale.data
## 5 other assays present: RNA, ADT, prediction.score.celltype.l1, prediction.score.celltype.l2, prediction.score.celltype.l3
## 4 dimensional reductions calculated: pca, umap, integrated_dr, ref.umap3. Azimuth Annotation
4. QC
# Set identity classes to an existing column in meta data
Idents(object = All_samples_Merged) <- "cell_line"
All_samples_Merged[["percent.rb"]] <- PercentageFeatureSet(All_samples_Merged,
pattern = "^RP[SL]")
VlnPlot(All_samples_Merged, features = c("nFeature_RNA",
"nCount_RNA",
"percent.mt",
"percent.rb"),
ncol = 4, pt.size = 0.1) &
theme(plot.title = element_text(size=10))
FeatureScatter(All_samples_Merged,
feature1 = "percent.mito",
feature2 = "percent.rb") +
geom_smooth(method = 'lm')## `geom_smooth()` using formula = 'y ~ x'
FeatureScatter(All_samples_Merged,
feature1 = "nCount_RNA",
feature2 = "nFeature_RNA") +
geom_smooth(method = 'lm')## `geom_smooth()` using formula = 'y ~ x'
##FeatureScatter is typically used to visualize feature-feature relationships ##for anything calculated by the object, ##i.e. columns in object metadata, PC scores etc.
FeatureScatter(All_samples_Merged,
feature1 = "nCount_RNA",
feature2 = "percent.mito")+
geom_smooth(method = 'lm')## `geom_smooth()` using formula = 'y ~ x'
FeatureScatter(All_samples_Merged,
feature1 = "nCount_RNA",
feature2 = "nFeature_RNA")+
geom_smooth(method = 'lm')## `geom_smooth()` using formula = 'y ~ x'
Assign Cell-Cycle Scores
## Running SCTransform on assay: RNA## 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 25901 by 46976## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells## Found 484 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 25901 genes## Computing corrected count matrix for 25901 genes## Calculating gene attributes## Wall clock passed: Time difference of 2.299541 mins## Determine variable features## Place corrected count matrix in counts slot## Warning: Different cells and/or features from existing assay SCT## 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 synonyms5. Normalize data
# Apply SCTransform
All_samples_Merged <- SCTransform(All_samples_Merged,
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 25901 by 46976## Model formula is y ~ log_umi## Get Negative Binomial regression parameters per gene## Using 2000 genes, 5000 cells## Found 484 outliers - those will be ignored in fitting/regularization step## Second step: Get residuals using fitted parameters for 25901 genes## Computing corrected count matrix for 25901 genes## Calculating gene attributes## Wall clock passed: Time difference of 2.29561 mins## 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 SCT6. Perform PCA
Variables_genes <- All_samples_Merged@assays$SCT@var.features
# Exclude genes starting with "HLA-" AND "Xist" AND "TRBV, TRAV"
Variables_genes_after_exclusion <- Variables_genes[!grepl("^HLA-|^XIST|^TRBV|^TRAV", Variables_genes)]
# These are now standard steps in the Seurat workflow for visualization and clustering
All_samples_Merged <- RunPCA(All_samples_Merged,
features = Variables_genes_after_exclusion,
do.print = TRUE,
pcs.print = 1:5,
genes.print = 15,
npcs = 90)## PC_ 1
## Positive: CD7, KIR3DL1, SEPTIN9, KIR2DL3, PRKCH, CLEC2B, KIR3DL2, SH3BGRL3, EPCAM, CST7
## GZMM, CXCR3, ESYT2, MATK, CD52, MYO1E, XCL1, KLRC1, TRGV2, CD3G
## OCIAD2, KIR2DL4, PTPRC, KLRK1, LCK, SYNE2, IFITM1, RPS27, CD6, PTPN6
## Negative: NPM1, SEC11C, IL2RA, MTHFD2, HDGFL3, YBX3, VDAC1, MTDH, RBM17, C12orf75
## CCT8, KRT7, HINT2, MINDY3, CCND2, CTSH, RAD21, EGFL6, BATF3, RDH10
## SPATS2L, MIIP, PRELID1, CANX, HTATIP2, TNFRSF4, MIR155HG, SRM, SLC35F3, RAN
## PC_ 2
## Positive: NME2, RPL35, CHCHD2, RPL27A, RPS17, NME1, COX6A1, C1QBP, ATP5MC3, KIR3DL1
## DAD1, PFN1, HSPE1, CORO1B, ATP5MC1, SPINT2, ATP5F1B, SET, ENO1, HSP90AA1
## APRT, MATK, EPCAM, PPP1R14B, MRPL12, RPS18, RAB25, TRGV2, NDUFA4, GYPC
## Negative: B2M, RPL34, ANKRD12, CD2, MALAT1, SARAF, RPS4Y1, RPL39, LMNA, PNRC1
## PAGE5, MVP, TENM3, FYB1, PTPRC, ANXA1, ITGB1, RPL35A, BCL3, ERAP2
## RAP1A, PLD1, CLEC2D, DAZAP2, AHNAK, TSC22D3, CD74, GBP2, BTG1, IL7R
## PC_ 3
## Positive: TIGIT, TNFRSF4, HACD1, EGFL6, BACE2, RPL30, C12orf75, RPS27, PXYLP1, ARPC2
## ACTN1, ETS1, NET1, BTG1, RASGRP2, SYT4, C12orf57, GRIA4, SMIM3, LAPTM5
## CD3E, MAP1B, MCTP2, MT-CYB, UBE2D2, PHLDA2, FGGY, RPS4Y1, KRT7, DOCK8
## Negative: GAPDH, PAGE5, RPL22L1, NDUFV2, PSMB2, RPS15, STMN1, TUBB, RPL35A, CDKN2A
## RPS2, RPS23, CD74, RBPMS, EEF2, TYMS, PPBP, WDR34, GPX4, RPS14
## RPS3A, CENPX, RPLP0, TUBA1B, CCNA2, ACTG1, FTL, ACAT1, RPL19, NEURL1
## PC_ 4
## Positive: PPID, EIF5A, FCER2, HSPE1, PPBP, ODC1, DNAJC12, CHCHD10, RPS4Y1, FAM162A
## ATP5MC3, MTDH, MT-ND3, GSTP1, MT-ND1, YBX3, CYC1, HSP90B1, PRELID3B, TCF7
## FKBP11, COL19A1, GCSH, RPL34, CYCS, FKBP4, SLC7A11-AS1, HSPD1, PSMB6, MIR155HG
## Negative: RPS4X, S100A4, WFDC1, EGLN3, S100A11, IL32, GAS5, S100A6, KRT1, LINC02752
## TTC29, VIM, TBX4, RPLP1, PLCB1, TNS4, AC069410.1, SP5, GATA3, NKG7
## RPL13, IFNGR1, MYO1G, RCSD1, SEMA4A, FAM9C, PTGIS, LAT, IL4, RPS24
## PC_ 5
## Positive: PGAM1, MIIP, RDH10, PFKP, TP73, EEF1A2, HOXC9, GPAT2, GPAT3, TMEM163
## CEP135, FABP5, RIOX2, IL2RA, TPI1, TMSB10, PIM2, RBM38, LDHA, LIME1
## LGALS1, TAGLN2, DDIT4, PGK1, CA8, S100A11, RPL41, PXYLP1, IRX3, S100A10
## Negative: CCL17, MAP4K4, MYO1D, IGHE, MIR155HG, CFI, CA10, THY1, IL4I1, EZH2
## LRBA, CA2, BCAT1, RXFP1, HS3ST1, FRMD4A, AL590550.1, SETBP1, RANBP17, SYT4
## RYR2, MAGI1, RUNX1, ONECUT2, AC100801.1, LAYN, TNFRSF9, AKAP12, AL023574.1, MGST3## Warning: Number of dimensions changing from 50 to 90
7. Perform PCA TEST
library(ggplot2)
library(RColorBrewer)
# Assuming you have 10 different cell lines, generating a color palette with 10 colors
cell_line_colors <- brewer.pal(10, "Set3")
# Assuming All_samples_Merged$cell_line is a factor or character vector containing cell line names
data <- as.data.frame(table(All_samples_Merged$cell_line))
colnames(data) <- c("cell_line", "nUMI") # Change column name to nUMI
ncells <- ggplot(data, aes(x = cell_line, y = nUMI, fill = cell_line)) +
geom_col() +
theme_classic() +
geom_text(aes(label = nUMI),
position = position_dodge(width = 0.9),
vjust = -0.25) +
scale_fill_manual(values = cell_line_colors) +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(hjust = 0.5)) + # Adjust the title position
ggtitle("Filtered cells per sample") +
xlab("Cell lines") + # Adjust x-axis label
ylab("Frequency") # Adjust y-axis label
print(ncells)
# TEST-1
# given that the output of RunPCA is "pca"
# replace "so" by the name of your seurat object
pct <- All_samples_Merged[["pca"]]@stdev / sum(All_samples_Merged[["pca"]]@stdev) * 100
cumu <- cumsum(pct) # Calculate cumulative percents for each PC
# Determine the difference between variation of PC and subsequent PC
co2 <- sort(which((pct[-length(pct)] - pct[-1]) > 0.1), decreasing = T)[1] + 1
# last point where change of % of variation is more than 0.1%. -> co2
co2## [1] 12# TEST-2
# get significant PCs
stdv <- All_samples_Merged[["pca"]]@stdev
sum.stdv <- sum(All_samples_Merged[["pca"]]@stdev)
percent.stdv <- (stdv / sum.stdv) * 100
cumulative <- cumsum(percent.stdv)
co1 <- which(cumulative > 90 & percent.stdv < 5)[1]
co2 <- sort(which((percent.stdv[1:length(percent.stdv) - 1] -
percent.stdv[2:length(percent.stdv)]) > 0.1),
decreasing = T)[1] + 1
min.pc <- min(co1, co2)
min.pc## [1] 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()
8. Clustering
All_samples_Merged <- FindNeighbors(All_samples_Merged,
dims = 1:50,
verbose = FALSE)
# understanding resolution
All_samples_Merged <- FindClusters(All_samples_Merged,
resolution = c(0.4,0.5, 0.6, 0.7,0.8, 0.9, 1))## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 46976
## Number of edges: 1696701
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9565
## Number of communities: 16
## Elapsed time: 8 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 46976
## Number of edges: 1696701
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9489
## Number of communities: 18
## Elapsed time: 8 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 46976
## Number of edges: 1696701
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9414
## Number of communities: 22
## Elapsed time: 8 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 46976
## Number of edges: 1696701
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9345
## Number of communities: 25
## Elapsed time: 7 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 46976
## Number of edges: 1696701
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9275
## Number of communities: 25
## Elapsed time: 7 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 46976
## Number of edges: 1696701
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9209
## Number of communities: 28
## Elapsed time: 7 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 46976
## Number of edges: 1696701
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9153
## Number of communities: 32
## Elapsed time: 7 seconds# non-linear dimensionality reduction --------------
All_samples_Merged <- RunUMAP(All_samples_Merged,
dims = 1:50,
verbose = FALSE)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session# note that you can set `label = TRUE` or use the Label Clusters function to help label
# individual clusters
DimPlot(All_samples_Merged,group.by = "cell_line",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.4",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.5",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.6",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.7",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.8",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.0.9",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.1.1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged,
group.by = "SCT_snn_res.1.2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
# Set identity classes to an existing column in meta data
Idents(object = All_samples_Merged) <- "SCT_snn_res.0.4"
cluster_table <- table(Idents(All_samples_Merged))
barplot(cluster_table, main = "Number of Cells in Each Cluster",
xlab = "Cluster",
ylab = "Number of Cells",
col = rainbow(length(cluster_table)))
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 6428 5934 4888 4500 4042 3329 2819 2787 2704 2494 1966 1906 1436 868 530 345##
## 0 1 2 3 4 5 6 7 8
## CD4 CTL 0 6 0 0 0 0 0 0 0
## CD4 Naive 0 705 0 0 1 0 0 0 0
## CD4 Proliferating 5203 2 5347 3010 2425 3955 4011 3127 1299
## CD4 TCM 1007 4359 571 287 3237 615 543 136 33
## CD4 TEM 0 46 0 0 23 0 0 0 0
## CD8 Naive 6 380 2 0 0 19 2 3 1
## CD8 TCM 0 255 0 10 148 0 0 0 0
## CD8 TEM 0 209 0 8 0 0 0 0 0
## cDC2 11 1 0 0 0 72 179 65 8
## dnT 1 50 0 1 5 0 4 0 0
## gdT 0 13 0 0 0 0 0 0 0
## HSPC 173 2 9 1 13 666 304 789 400
## ILC 0 1 0 0 0 0 0 0 0
## MAIT 0 56 0 0 0 0 0 0 0
## NK 0 92 0 0 0 0 0 0 0
## NK Proliferating 7 0 11 2615 20 14 193 4 0
## Treg 11 170 0 0 8 0 15 1 0
10. Azimuth Visualization
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l1",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l1",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = F)
DimPlot(All_samples_Merged, group.by = "predicted.celltype.l2",
reduction = "umap",
label.size = 3,
repel = T,
label = T, label.box = T)## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
##
## 0 1 2 3 4 5 6 7 8 9 10 11
## CD4 CTL 0 0 0 0 0 0 6 0 0 0 0 0
## CD4 Naive 0 0 0 0 0 1 4 0 661 0 0 0
## CD4 Proliferating 5208 3011 3806 3936 3056 1073 0 2181 0 1702 922 1431
## CD4 TCM 1010 287 477 553 140 2130 2039 19 1651 605 979 45
## CD4 TEM 0 0 0 0 0 22 44 0 1 0 0 0
## CD8 Naive 6 0 2 2 3 0 13 0 331 19 0 1
## CD8 TCM 0 10 0 0 0 92 230 0 19 0 53 0
## CD8 TEM 0 8 0 0 0 0 209 0 0 0 0 0
## cDC2 11 0 148 0 67 0 0 3 0 73 0 11
## dnT 1 2 1 0 0 1 11 0 4 0 2 0
## gdT 0 0 0 0 0 0 13 0 0 0 0 0
## HSPC 173 1 288 1 771 0 0 580 1 86 2 418
## ILC 0 0 0 0 0 0 1 0 0 0 0 0
## MAIT 0 0 0 0 0 0 55 0 0 0 0 0
## NK 0 0 0 0 0 0 91 0 0 0 0 0
## NK Proliferating 8 2615 164 8 4 10 0 4 0 9 8 0
## Treg 11 0 2 0 1 0 103 0 36 0 0 0
##
## 12 13 14 15
## CD4 CTL 0 0 0 0
## CD4 Naive 0 40 0 0
## CD4 Proliferating 1408 5 424 216
## CD4 TCM 18 693 100 42
## CD4 TEM 0 1 1 0
## CD8 Naive 0 36 0 0
## CD8 TCM 0 6 3 0
## CD8 TEM 0 0 0 0
## cDC2 0 0 0 23
## dnT 0 36 0 3
## gdT 0 0 0 0
## HSPC 8 10 0 18
## ILC 0 0 0 0
## MAIT 0 1 0 0
## NK 0 1 0 0
## NK Proliferating 2 0 2 30
## Treg 0 39 0 13