5. LabelTransfer integrated

suppressPackageStartupMessages({
library(reticulate)
library(Seurat)
library(dplyr)
library(ggpubr)
library(sceasy)
library(future)
})

# utilities

o<- function(w,h) options(repr.plot.width=w, repr.plot.height=h)

manycolors=c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231',
             '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe',
             '#008080', '#e6beff', '#9a6324', '#fffac8', '#800000',
             '#aaffc3', '#808000', '#ffd8b1', '#000075', '#808080',
             '#4f34ff', '#f340F0')


covid.color.table= c(
    'B'="#FFF3B0", 'Plasma'='#E09F3E',
    'T'='#2F6D9D',
    'T CD4'='#68BDDF',
    'T CD8'='#FF046E',
    'CD8 cytotoxic T'='#9D2A2B',
    'T cycling'='#ADA7FF',
    'Treg'='#493A9D',
    'NKT'='#3300FF',
    'NK'='#D543FF',
    'gdT'='#6A24FF',
    'Neu'='#312697',
    'macrophage'='#429783',
    'Mast'='#FF6B3E',
    'cDC1'='#FF8F39',
    'cDC2'='#E7FF3F',
    'moDC'='#FFBCE1',
    'pDC'='#FF72DB',
    'DC'='#FF0022',
    'Mye'='#19C3BE'
)

heca.color.table=c(
    'B cell'='#FFF3B0',
    'CD8 T cell'='#FF046E',
    'Dendritic cell'='#FF0022',
    'Macrophage'='#429783',
    'Mast cell'='#FF6B3E',
    'Megakaryocyte'='gray',
    'Monocyte'='#429753',
    'Myeloid cell'='#19C3BE',
    'NK cell'='#D543FF',
    'Neutrophilic granunocyte'='#312697',
    'Plasma B cell'='#E09F3E',
    'T cell'='#2F6D9D'
)

load data

load hECA data

# convert h5ad to rds format so that R can read it
sceasy::convertFormat('./lungimm_heca.h5ad', from="anndata", to="seurat",
                       outFile='lungimm_heca.rds')

X -> counts

Warning message:
“Keys should be one or more alphanumeric characters followed by an underscore, setting key from DMAPharmony_bydonor_ to DMAPharmonybydonor_”
Warning message:
“All keys should be one or more alphanumeric characters followed by an underscore '_', setting key to DMAPharmonybydonor_”
Warning message:
“Keys should be one or more alphanumeric characters followed by an underscore, setting key from PCAharmony_bydonor_ to PCAharmonybydonor_”
Warning message:
“All keys should be one or more alphanumeric characters followed by an underscore '_', setting key to PCAharmonybydonor_”

An object of class Seurat
20492 features across 53299 samples within 1 assay
Active assay: RNA (20492 features, 0 variable features)
 6 dimensional reductions calculated: densmap, densmap_harmonyByDonor, dmap_harmony_bydonor, pca, pca_harmony_bydonor, umap

# read saved hECA data
heca = readRDS('./lungimm_heca.rds')

# create a clean seurat object
heca.full = CreateSeuratObject(heca@assays$RNA@data, meta.data = heca@meta.data)
heca.full@assays$RNA@data <- heca.full@assays$RNA@counts
heca.full[["umap"]] <- CreateDimReducObject(heca@reductions$umap@cell.embeddings, key = "umap_")

Warning message:
“No assay specified, setting assay as RNA by default.”

# show different batches
o(15,5)
DimPlot(heca.full, group.by='ann',cols = heca.color.table, split.by = 'study_id', pt.size=0.01, label=T, repel=T)

# print study_id
unique(heca.full@meta.data$study_id)
  1. 10.1038/s41591-019-0468-5
  2. 10.1186/s13059-019-1906-x
  3. 10.1038/s41586-020-2157-4
Levels:
  1. '10.1038/s41586-020-2157-4'
  2. '10.1038/s41591-019-0468-5'
  3. '10.1186/s13059-019-1906-x'
output_6_1.png 
#heca1 = subset(heca.full, study_id=='10.1038/s41586-020-2157-4')

#heca2 = subset(heca.full, study_id=='10.1186/s13059-019-1906-x')

created integrated hECA data

heca.list = SplitObject(heca.full, split.by = 'study_id')

heca.list

$`10.1038/s41591-019-0468-5`
An object of class Seurat
20492 features across 465 samples within 1 assay
Active assay: RNA (20492 features, 0 variable features)
 1 dimensional reduction calculated: umap

$`10.1186/s13059-019-1906-x`
An object of class Seurat
20492 features across 41940 samples within 1 assay
Active assay: RNA (20492 features, 0 variable features)
 1 dimensional reduction calculated: umap

$`10.1038/s41586-020-2157-4`
An object of class Seurat
20492 features across 10894 samples within 1 assay
Active assay: RNA (20492 features, 0 variable features)
 1 dimensional reduction calculated: umap

plan("sequential")
options(future.globals.maxSize = 1000 * 1024^2*4)
plan("multiprocess", workers = 14)
int.anchors <- FindIntegrationAnchors(object.list = heca.list, dims = 1:30)

plan("sequential")

Warning message:
“Strategy 'multiprocess' is deprecated in future (>= 1.20.0). Instead, explicitly specify either 'multisession' or 'multicore'. In the current R session, 'multiprocess' equals 'multicore'.”
Computing 2000 integration features

No variable features found for object1 in the object.list. Running FindVariableFeatures ...

No variable features found for object2 in the object.list. Running FindVariableFeatures ...

No variable features found for object3 in the object.list. Running FindVariableFeatures ...

Scaling features for provided objects

Finding all pairwise anchors

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 2280 anchors

Filtering anchors

    Retained 228 anchors

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 2238 anchors

Filtering anchors

    Retained 370 anchors

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 27018 anchors

Filtering anchors

    Retained 5907 anchors

heca.integrated <- IntegrateData(anchorset = int.anchors, dims = 1:30)

Merging dataset 1 into 3

Extracting anchors for merged samples

Finding integration vectors

Finding integration vector weights

Integrating data

Merging dataset 3 1 into 2

Extracting anchors for merged samples

Finding integration vectors

Finding integration vector weights

Integrating data

#mask horizontal genes
x=heca.integrated
masked.genes<-c(
    c(grep("^RPL", rownames(x), value = T),grep("^RPS", rownames(x), value = T)),

    grep("^MT-", rownames(x), value = T),

    c(grep("^IFI", rownames(x), value = T),grep("^ISG", rownames(x), value = T)),

    grep("^SMC[0-9]*", rownames(x), value = T),

    grep("^MCM[0-9]*", rownames(x), value = T),

    c(grep("^TUBA", rownames(x), value = T),
      grep("^TUBB", rownames(x), value = T),
      grep("^TUBD[0-9]", rownames(x), value = T),
      grep("^TUBE[0-9]", rownames(x), value = T),
      grep("^TUBG[0-9]", rownames(x), value = T)
    ),

    c(Seurat::cc.genes.updated.2019$s.genes,
      Seurat::cc.genes.updated.2019$g2m.genes
    ),

    c("H1-0","H1-1","H1-10","H1-12P","H1-2","H1-3","H1-4","H1-5","H1-6","H1-7","H1-8",
    "H1-9P","H2AB1","H2AB2","H2AB3","H2AC1","H2AC10P","H2AC11","H2AC12","H2AC13","H2AC14",
    "H2AC15","H2AC16","H2AC17","H2AC18","H2AC19","H2AC20","H2AC21","H2AC2P","H2AC3P","H2AC4",
    "H2AC5P","H2AC6","H2AC7","H2AC8","H2AC9P","H2AJ","H2AL1MP","H2AL1Q","H2AL3",
    "H2AP","H2AQ1P","H2AW","H2AX","H2AZ1","H2AZ2","MACROH2A1","MACROH2A2","H2BC1","H2BC10",
    "H2BC11","H2BC12","H2BC13","H2BC14","H2BC15","H2BC16P","H2BC17","H2BC18",
    "H2BC19P","H2BC20P","H2BC21","H2BC2P","H2BC3","H2BC4","H2BC5","H2BC6","H2BC7","H2BC8",
    "H2BC9","H2BK1","H2BL1P","H2BN1","H2BC12L","H2BU1","H2BU2P","H2BW1","H2BW2",
    "H2BW3P","H2BW4P","H3-7","H3-3A","H3-3B","H3-4","H3-5","H3C1","H3C10","H3C11","H3C12",
    "H3C13","H3C14","H3C15","H3C2","H3C3","H3C4","H3C5P","H3C6","H3C7","H3C8",
    "H3C9P","H3Y1","H3Y2","CENPA","H4-16","H4C1","H4C10P","H4C11","H4C12","H4C13","H4C14",
    "H4C15","H4C2","H4C3","H4C4","H4C5","H4C6","H4C7","H4C8","H4C9"),
    grep("^HIST", rownames(x), value=T)
)

goi.int = setdiff( VariableFeatures(heca.integrated), masked.genes)

length(goi.int)
1919
DefaultAssay(heca.integrated) <- "integrated"
heca.integrated <- ScaleData(heca.integrated, verbose = FALSE, features = goi.int)
heca.integrated <- RunPCA(heca.integrated, npcs = 30, verbose = FALSE, features = goi.int)
heca.integrated <- RunUMAP(heca.integrated, reduction = "pca", dims = 1:30, verbose = FALSE)

Warning message:
“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”

# show different batches
o(15,5)
DimPlot(heca.integrated, group.by='ann',cols = heca.color.table, split.by = 'study_id', pt.size=0.01, label=T, repel=T)

# print study_id
unique(heca.integrated@meta.data$study_id)
  1. '10.1038/s41591-019-0468-5'
  2. '10.1186/s13059-019-1906-x'
  3. '10.1038/s41586-020-2157-4'
output_16_1.png

load COVID data

load("./nCoV.integrated.annotated.rda")

o(9,8)
DimPlot(immune.combined, group.by='persample_ann', label=T, repel=T, cols=covid.color.table) + ggtitle(
    "COVID19 lung immune cells")&
theme(axis.title = element_blank(),
      axis.text = element_blank(),
      axis.ticks=element_blank())

cairo_pdf(filename = 'covid19.umap.pdf', width = 9,height = 8)
DimPlot(immune.combined, group.by='persample_ann', label=T, repel=T, cols=covid.color.table) + ggtitle(
    "COVID19 lung immune cells")&
theme(axis.title = element_blank(),
      axis.text = element_blank(),
      axis.ticks=element_blank())
dev.off()
png: 2output_19_1.png

full batches - integrated data transfer

plan("sequential")
options(future.globals.maxSize = 1000 * 1024^2*4)
plan("multiprocess", workers = 14)

int.anchors <- FindTransferAnchors(reference = heca.integrated, query = immune.combined,
                                   dims = 1:30, reference.reduction = "pca")
predictions <- TransferData(anchorset = int.anchors, refdata = heca.integrated$cell_type,
                            dims = 1:30)
immune.combined <- AddMetaData(immune.combined, metadata = predictions)

plan("sequential")

Projecting cell embeddings

Finding neighborhoods

Finding anchors

    Found 7380 anchors

Filtering anchors

    Retained 2966 anchors

Finding integration vectors

Finding integration vector weights

Predicting cell labels

o(10,8)
DimPlot(immune.combined, group.by='predicted.id', label=T, repel=T, cols=heca.color.table, order='Neutrophilic granunocyte')
DimPlot(immune.combined, group.by='persample_ann', label=T, repel=T, cols=covid.color.table)
output_22_0.png output_22_1.png 
matched <- c(
    paste('B' , 'B cell', sep="_"),
    paste('Plasma' , 'Plasma B cell', sep="_"),
    paste('Plasma' , 'B cell', sep="_"),
    paste('T' , 'CD8 T cell', sep="_"),
    paste('T' , 'T cell', sep="_"),
    paste('T CD4' , 'T cell', sep="_"),
    paste('T CD8' , 'T cell', sep="_"),
    paste('T CD8' , 'CD8 T cell', sep="_"),
    paste('CD8 cytotoxic T' , 'T cell', sep="_"),
    paste('CD8 cytotoxic T' , 'CD8 T cell', sep="_"),
    paste('T cycling' , 'T cell', sep="_"),
    paste('T cycling' , 'CD8 T cell', sep="_"),
    paste('Treg' , 'T cell', sep="_"),
    paste('gdT' , 'T cell', sep="_"),
    paste('NKT' , 'T cell', sep="_"),
    paste('NK' , 'NK cell', sep="_"),
    paste('Neu' , 'Neutrophilic granulocyte', sep="_"),
    paste('macrophage' , 'Macrophage', sep="_"),
    paste('macrophage' , 'Monocyte', sep="_"),
    paste('macrophage' , 'Myeloid cell', sep="_"),

    paste('Mast' , 'Mast cell', sep="_"),
    paste('cDC1' , 'Dendritic cell', sep="_"),
    paste('cDC1' , 'Myeloid cell', sep="_"),
    paste('cDC2' , 'Dendritic cell', sep="_"),
    paste('cDC2' , 'Myeloid cell', sep="_"),
    paste('moDC' , 'Dendritic cell', sep="_"),
    paste('pDC' , 'Dendritic cell', sep="_"),
    paste('DC'  , 'Dendritic cell', sep="_"),
    paste('Mye' , 'Macrophage', sep="_"),
    paste('Mye' , 'Mast cell', sep="_"),
    paste('Mye' , 'Megakaryocyte', sep="_"),
    paste('Mye' , 'Dendritic cell', sep="_"),
    paste('Mye' , 'Monocyte', sep="_"),
    paste('Mye' , 'Neutrophilic granulocyte', sep="_"),
    paste('Mye' , 'Myeloid cell', sep="_")
)

# calculate accuracy
meta=immune.combined@meta.data
meta = meta[, c('persample_ann','predicted.id')]
meta$joint <- paste(meta$persample_ann, meta$predicted.id, sep='_')
meta$isMatched <- (meta$joint %in% matched) | (meta$persample_ann == meta$predicted.id)
acc = (meta %>% filter(isMatched==T) %>% nrow) / (meta %>% nrow)
print( acc )

[1] 0.9250865

# draw matrix

## create an empty adj matrix
rows = as.character( unique(immune.combined@meta.data$persample_ann) ) # from:covid ground-truth
cols = as.character( unique(heca.full@meta.data$cell_type) ) # to:predicted to
mat <- matrix(0, length(rows), length(cols))
rownames(mat) <- rows
colnames(mat) <- cols

# get an edge list
edge.list = meta %>% count(persample_ann, predicted.id, sort = TRUE)

# fill in the edge list
for(i in 1:NROW(edge.list)) mat[ edge.list[i,1], edge.list[i,2] ] <- edge.list[i,3]  # SEE UPDATE

row.order= c('Plasma','B',
'Treg','T cycling','T CD4','T','NKT','gdT','CD8 cytotoxic T',
'T CD8','NK',
'Neu','DC','cDC2','cDC1','macrophage','moDC','pDC','Mast'
)
col.order=c('Plasma B cell','B cell',
'T cell','CD8 T cell','NK cell',
'Neutrophilic granulocyte',
'Macrophage','Dendritic cell','Myeloid cell','Mast cell','Monocyte','Megakaryocyte')


# row-sum scaling
mat = mat/rowSums(mat)
mat = mat[row.order,col.order]
library(pheatmap)
o(5,5)
pheatmap(mat, cluster_rows = F, cluster_cols = F, main='hECA integrated reference')
output_25_0.png 
library('mclust')
adjustedRandIndex(meta$persample_ann, meta$predicted.id)

Package 'mclust' version 5.4.9
Type 'citation("mclust")' for citing this R package in publications.
0.379420559941594

Annotate data for DEG

o(8,7)
DimPlot(heca.integrated, group.by='ann',cols = heca.color.table, pt.size=0.01, label=T, repel=T)
output_28_0.png 
o(9,8)
DimPlot(heca.integrated, group.by='ann',cols = heca.color.table, pt.size=0.01, label=T, repel=T)&
theme(axis.title = element_blank(),
      axis.text = element_blank(),
      axis.ticks=element_blank())

cairo_pdf(filename = 'heca.umap.predicted.pdf', width = 9,height = 8)
DimPlot(heca.integrated, group.by='ann',cols = heca.color.table, pt.size=0.01, label=T, repel=T)&
theme(axis.title = element_blank(),
      axis.text = element_blank(),
      axis.ticks=element_blank())

dev.off()
png: 2output_29_1.png 
DefaultAssay(heca.integrated)<-'integrated'
heca.integrated <- FindNeighbors(heca.integrated, reduction = "pca", dims = 1:30)
heca.integrated <- FindClusters( heca.integrated, resolution = c(0.5,0.6,0.7,0.8,0.9))

Computing nearest neighbor graph

Computing SNN

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

Number of nodes: 53299
Number of edges: 1935710

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9299
Number of communities: 22
Elapsed time: 11 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 53299
Number of edges: 1935710

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9233
Number of communities: 26
Elapsed time: 10 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 53299
Number of edges: 1935710

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9177
Number of communities: 27
Elapsed time: 11 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 53299
Number of edges: 1935710

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9121
Number of communities: 33
Elapsed time: 11 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 53299
Number of edges: 1935710

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9072
Number of communities: 34
Elapsed time: 11 seconds

DimPlot(heca.integrated, group.by='integrated_snn_res.0.5',label=T)
output_31_0.png 
Idents(heca.integrated)<-'integrated_snn_res.0.5'
o(15,13)
VlnPlot(heca.integrated, features =c( 'CD8A','CD3E','CD69','GZMK','GZMB','TRDC'), pt.size=0.0)
output_32_0.png 
o(6*4, 6*2)
FeaturePlot(heca.integrated, pt.size=0.01,ncol=4, features = c('CD3E','NKG7','CD4','CD8A',
                                                               'CD8B','TRDC'))

Warning message:
“Could not find CD4 in the default search locations, found in RNA assay instead”
output_33_1.png 
Idents(heca.integrated)<-'integrated_snn_res.0.5'
deg12 = FindMarkers(heca.integrated, only.pos = T, ident.1=12)

deg12%>% arrange(desc(avg_log2FC))
A data.frame: 115 × 5
p_valavg_log2FCpct.1pct.2p_val_adj
<dbl><dbl><dbl><dbl><dbl>
GZMH 0.000000e+002.33176120.8920.368 0.000000e+00
CD3D 0.000000e+001.98725580.9640.407 0.000000e+00
SCGB3A1 0.000000e+001.70287280.8280.307 0.000000e+00
TRGC25.580196e-2661.62345420.7000.3041.116039e-262
IFITM1 0.000000e+001.62089030.8430.318 0.000000e+00
TRAC 0.000000e+001.57333750.9430.461 0.000000e+00
TNF1.575880e-1581.55377260.5370.2243.151760e-155
CD3G2.002996e-3011.46699200.7140.2544.005991e-298
NKG77.615188e-3051.45838270.9900.5681.523038e-301
CCL53.413545e-2971.41281910.9720.4936.827090e-294
IL32 0.000000e+001.36565490.9680.544 0.000000e+00
SCGB1A1 0.000000e+001.34413760.8120.335 0.000000e+00
CD3E3.127342e-2761.25443170.8490.3996.254685e-273
GIMAP73.283782e-2531.25184230.8350.4346.567565e-250
GZMA4.304799e-2861.24140300.9460.5118.609598e-283
FGFBP21.511492e-1471.17898370.6480.3383.022983e-144
TRBC18.220764e-1851.13837990.8010.4291.644153e-181
GZMK 1.388654e-411.04807180.3730.242 2.777308e-38
CD8A 1.875675e-831.00576760.4640.258 3.751349e-80
CD21.581810e-2180.99743080.8210.3823.163619e-215
GZMM8.816690e-1620.98807190.7230.3971.763338e-158
CORO1A1.566353e-2290.97669450.9680.7353.132705e-226
MGP2.381943e-1530.95310540.6650.3854.763887e-150
ACKR1 2.414332e-780.94860060.3570.185 4.828664e-75
TRBC22.861630e-1130.94427780.7170.4365.723259e-110
CCL21 1.658734e-540.91739240.3580.222 3.317469e-51
PRF13.529078e-2270.89866980.8880.4657.058156e-224
C12orf752.574815e-1180.88996270.6280.3575.149629e-115
FCRL68.443549e-1300.87870690.4500.1771.688710e-126
S1PR57.000118e-1040.87086950.4910.2421.400024e-100
⋮⋮⋮⋮⋮⋮
GZMB3.990067e-1550.38383380.8490.4687.980133e-152
NCR3 1.448325e-160.37654780.3710.290 2.896650e-13
RAB37 1.611220e-080.37534580.1940.178 3.222440e-05
CXCR3 3.049344e-140.37500410.2090.179 6.098688e-11
SYNE1 1.760211e-080.37300220.2150.210 3.520422e-05
RIPOR2 2.703798e-140.36610110.3520.304 5.407596e-11
TUBA4A 1.442729e-270.35856170.5080.407 2.885458e-24
ZNF683 1.843016e-010.35540560.1500.157 1.000000e+00
PTPN7 8.481600e-180.34794540.4500.383 1.696320e-14
C1orf21 2.250568e-110.34679130.2360.216 4.501136e-08
CCN1 2.585146e-100.33618220.1790.170 5.170293e-07
F2R 3.401221e-080.33335280.1980.160 6.802441e-05
B2M 8.769744e-880.32396781.0000.992 1.753949e-84
PPP2R2B 4.637321e-020.31843410.1560.152 1.000000e+00
PYHIN1 8.634472e-160.30388160.3810.302 1.726894e-12
GNG2 7.410162e-320.30182190.5140.386 1.482032e-28
DCN 6.905787e-090.28761150.2750.260 1.381157e-05
ZNF600 6.203896e-040.28512270.1730.195 1.000000e+00
SFTPB 3.032331e-240.28443820.4340.345 6.064663e-21
IFITM3 5.037570e-670.28280940.6920.422 1.007514e-63
PTPRC 7.126091e-540.28262170.8140.623 1.425218e-50
CD300A 4.182796e-060.27943540.3570.356 8.365592e-03
LPCAT1 8.864149e-210.27903470.3000.242 1.772830e-17
ADIRF 2.973978e-130.27558990.1850.164 5.947956e-10
HBB 4.200590e-720.27356060.4420.275 8.401180e-69
ID1 2.701749e-090.27239890.2090.201 5.403498e-06
GBP5 2.446865e-120.25858510.2150.197 4.893729e-09
MLLT6 1.151223e-010.25726200.1850.229 1.000000e+00
H2BC8 5.723035e-100.25676490.1040.218 1.144607e-06
TM4SF1 4.001418e-170.25189250.1550.133 8.002836e-14
 
Idents(heca.integrated)<-'integrated_snn_res.0.5'
deg15 = FindMarkers(heca.integrated, only.pos = T, ident.1=15)

deg15
A data.frame: 152 × 5
p_valavg_log2FCpct.1pct.2p_val_adj
<dbl><dbl><dbl><dbl><dbl>
GNLY 0.000000e+002.06967620.9950.524 0.000000e+00
PRF1 0.000000e+002.24716470.9860.466 0.000000e+00
CD247 0.000000e+001.77674130.9310.399 0.000000e+00
IFITM1 0.000000e+002.06924610.8740.320 0.000000e+00
NKG71.469025e-3021.86971030.9970.5702.938049e-299
KLRD11.069487e-2991.63948700.9670.4382.138975e-296
CTSW1.332831e-2991.72165660.9690.4922.665663e-296
SCGB3A11.197749e-2981.69345470.8160.3102.395498e-295
KLRF12.534747e-2941.56272280.8290.2945.069494e-291
GZMB1.259607e-2871.69065660.9760.4682.519214e-284
GZMA1.498221e-2821.70293050.9720.5132.996441e-279
SPON21.877297e-2811.79171680.8740.3863.754595e-278
HOPX1.411750e-2721.64247760.9320.4542.823501e-269
MYOM21.742244e-2412.02887100.6760.2413.484489e-238
SCGB1A19.854206e-2381.44114650.7830.3381.970841e-234
FCGR3A1.444223e-2311.50080750.9210.4442.888446e-228
KLRB13.819865e-2261.28268100.9150.4317.639731e-223
FGFBP21.663208e-2241.62260700.7950.3373.326416e-221
TRDC1.901180e-2061.47418510.7370.2933.802360e-203
CD72.533214e-2051.29911350.9050.4885.066429e-202
IGHG32.823892e-1990.38448240.5960.2005.647783e-196
CST71.957965e-1931.17773710.9360.5163.915930e-190
NCR31.599390e-1841.39578470.6820.2863.198781e-181
GZMM4.910456e-1761.31474460.7820.3989.820912e-173
CCL51.652247e-1701.24589280.9340.4963.304493e-167
CCL34.604424e-1691.16877780.9580.5789.208848e-166
LGALS132.207733e-1641.12944790.8580.4924.415465e-161
IFITM21.189061e-1621.00951390.9630.7122.378122e-159
S1PR51.006570e-1601.34398290.6020.2422.013141e-157
CORO1A3.843575e-1570.99570980.9540.7367.687149e-154
⋮⋮⋮⋮⋮⋮
H4C32.676584e-050.27565150.3830.3790.05353168
DCN6.476969e-050.30022190.2590.2600.12953938
RAB379.406866e-050.37845170.1800.1780.18813732
FBXO441.205780e-040.33849890.1480.1660.24115597
MLLT63.747354e-040.32819900.2180.2290.74947080
S100B4.016167e-040.78412120.2130.2120.80323349
FCRL36.314218e-040.29500550.1080.0841.00000000
RIPOR21.591798e-030.36402700.2930.3061.00000000
PLA2G2A3.044196e-030.61364640.2450.2521.00000000
PIF13.263715e-030.28866110.0640.1281.00000000
FAM43A4.691287e-030.32050020.1460.2301.00000000
NNMT4.722701e-030.47594930.2070.2181.00000000
ADAMTS15.144521e-030.40077610.1220.1411.00000000
MCTP21.010765e-020.31662270.1600.1581.00000000
CXCR11.058021e-020.32133120.1020.0841.00000000
RNF1251.140217e-020.26679880.2250.2481.00000000
KIR3DL21.431669e-020.31418920.1250.1111.00000000
CRTAM1.467915e-020.52944610.1420.2331.00000000
CEP782.026967e-020.39320910.1950.2171.00000000
TPM23.734433e-020.26358150.1300.1751.00000000
PTGDS4.187003e-020.69702240.1900.2221.00000000
SH2D2A6.854395e-020.36481990.2550.2691.00000000
TNFSF141.114615e-010.38690450.1950.2301.00000000
SLFN12L1.769912e-010.33254730.1300.1811.00000000
NCALD2.030238e-010.28201670.1270.1381.00000000
ARRDC32.532969e-010.25200300.2760.3211.00000000
NEIL12.697969e-010.44164210.1560.1861.00000000
DTHD13.087544e-010.40261170.1710.1821.00000000
TNFRSF94.500554e-010.34008360.1140.1511.00000000
PPP2R2B8.506222e-010.30383830.1370.1521.00000000
 
coi = Cells(subset(heca.integrated, integrated_snn_res.0.5=='4'))
heca.integrated@meta.data[coi, 'ann']<-'CD8 T cell'

coi = Cells(subset(heca.integrated, integrated_snn_res.0.5=='12'))
heca.integrated@meta.data[coi, 'ann']<-'CD8 T cell'

o(10,10)
DimPlot(heca.integrated, group.by='ann',label=T,cols = heca.color.table)
output_39_0.png 
save(heca.integrated, file = 'heca.integrated.annotated.rda', compress = T, compression_level = 9)