SingleR annotations
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4 months ago

I have been working on a 5 umaps which takes annotations from the 5 databases and annotate single cells. My aim is to annotate a cell type with same color across five databases in 5 umaps. I wrote this code -

setwd("F:\NEW\Projects")
pacman::p_load(dplyr, Seurat, patchwork, scSorter, SingleR, celldex,,writexl, ggplot2, stringr, tidyverse)

pbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/")

pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc", min.cells = 3, min.features = 200)
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)

pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 0.5)

Creating different objects for 5 databases

hpca.data <- pbmc
bped.data <- pbmc
dice.data <- pbmc
hd.data <- pbmc
mid.data <- pbmc

Loading data

hpca.se <- HumanPrimaryCellAtlasData()
bped.se <- BlueprintEncodeData()
dice.se <- DatabaseImmuneCellExpressionData()
hd.se <- NovershternHematopoieticData()
mid.se <- MonacoImmuneData()

Setting labels

hpca.se$label.main <- str_replace_all(hpca.se$label.main, c("NK_cell" = "NK cells", "B_cell" = "B cells", "DC" = "Dendritic cells","HSC_-G-CSF" = "HSC G-CSF", "Monocyte" = "Monocytes", "Astrocyte" = "Astrocytes", "Erythroblast" = "Erythroblasts", "Macrophage" = "Macrophages","BM" = "BMs", "MSC"="MSCs","CMP" = "CMPs","GMP" = "GMPs", "MEP"="MEPs", "Myelocyte"="Myelocytes","Neuroepithelial_cell"="Neuroepithelial cells", "T_cells" = "T cells", "iPS_cells" = "iPS cells","Endothelial_cells" = "Endothelial cells", "Tissue_stem_cells" = "Tissue stem cells","Embryonic_stem_cells" = "Embryonic stem cells","Smooth_muscle_cells" = "Smooth muscle cells","Epithelial_cells" = "Epithelial cells"))
hpca.se$label.main[hpca.se$label.main == "HSC_CD34+"] <-"HSC CD34"
hpca.se$label.main[hpca.se$label.main == "Pro-B cells_CD34+"] <-"Pro-B cells CD34 Pos"
hpca.se$label.main[hpca.se$label.main == "Pre-B cells_CD34-"] <-"Pre-B cells CD34 Neg"

bped.se$label.main <- str_replace_all(bped.se$label.main, c("B-cells" = "B cells", "HSC" = "HSCs","DC" = "Dendritic cells"))
bped.se$label.main[bped.se$label.main == "CD4+ T-cells"] <- "CD4 T cells"
bped.se$label.main[bped.se$label.main == "CD8+ T-cells"] <- "CD8 T cells"

dice.se$label.main[dice.se$label.main == "T cells, CD4+"] <- "CD4 T cells"
dice.se$label.main[dice.se$label.main == "T cells, CD8+"] <- "CD8 T cells"

hd.se$label.main[hd.se$label.main == "CD4+ T cells"] <- "CD4 T cells"
hd.se$label.main[hd.se$label.main == "CD8+ T cells"] <- "CD8 T cells"

mid.se$label.main[mid.se$label.main == "CD4+ T cells"] <- "CD4 T cells"
mid.se$label.main[mid.se$label.main == "CD8+ T cells"] <- "CD8 T cells"

#------Humanprimarycellatlas
pred.hesc <- SingleR(test = counts, ref = hpca.se, assay.type.test=1,
labels = hpca.se$label.main)
pred.hesc.transform <- t(pred.hesc)
hesc_cells_names <- pred.hesc.transform$labels
hpca.data <- AddMetaData(object = hpca.data,metadata = hesc_cells_names,col.name = 'type')
Idents(hpca.data) <- hpca.data@meta.data$type

##---- BlueprintEncodeData()

pred.bped <- SingleR(test = counts, ref = bped.se, assay.type.test=1,
labels = bped.se$label.main)
pred.bped.transform <- t(pred.bped)
bped_cells_names <- pred.bped.transform$labels
bped.data <- AddMetaData(object = bped.data,metadata = bped_cells_names,col.name = 'type')
Idents(bped.data) <- bped.data@meta.data$type

DatabaseImmuneCellExpressionData()
pred.dice <- SingleR(test = counts, ref = dice.se, assay.type.test=1,
labels = dice.se$label.main)
pred.dice.transform <- t(pred.dice)
dice_cells_names <- pred.dice.transform$labels
dice.data <- AddMetaData(object = dice.data,metadata = dice_cells_names,col.name = 'type')
Idents(dice.data) <- dice.data@meta.data$type

NovershternHematopoieticData()
pred.hd <- SingleR(test = counts, ref = hd.se, assay.type.test=1,
labels = hd.se$label.main)
pred.hd.transform <- t(pred.hd)
hd_cells_names <- pred.hd.transform$labels
hd.data <- AddMetaData(object = hd.data,metadata = hd_cells_names,col.name = 'type')
Idents(hd.data) <- hd.data@meta.data$type

MonacoImmuneData()
pred.mid <- SingleR(test = counts, ref = mid.se, assay.type.test=1,
labels = mid.se$label.main)
pred.mid.transform <- t(pred.mid)
mid_cells_names <- pred.mid.transform$labels
mid.data <- AddMetaData(object = mid.data,metadata = mid_cells_names,col.name = 'type')
Idents(mid.data) <- mid.data@meta.data$type

colors50<- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c","#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928","#01665e","#c51b7d","#67001f","#53061","#4d4d4d","#f768a1","#276419","#8e0152","#7a0177","#80cdc1","#f1b6da","#e0e0e0","#f46d43","#878787","#045a8d","#fcc5c0","#1a1a1a","#4d004b","#4d4d4d","#f768a1","#276419","#8e0152","#7a0177","#80cdc1","#f1b6da","#e0e0e0","#f46d43","#878787","#045a8d","#fcc5c0","#1a1a1a","#4d004b","#4d4d4d","#f768a1","#276419","#8e0152","#7a0177","#80cdc1","#f1b6da","#e0e0e0","#f46d43","#878787","#045a8d","#fcc5c0","#1a1a1a","#4d004b")

hpca.data <- RunUMAP(hpca.data, dims = 1:10)
bped.data <- RunUMAP(bped.data, dims = 1:10)
dice.data <- RunUMAP(dice.data, dims = 1:10)
hd.data <- RunUMAP(hd.data, dims = 1:10)
mid.data <- RunUMAP(mid.data, dims = 1:10)

hpca.clusters <- Idents(hpca.data)
bped.clusters <- Idents(bped.data)
dice.clusters <- Idents(dice.data)
hd.clusters <- Idents(hd.data)
mid.clusters <- Idents(mid.data)

Assigning same colors to the same cell annotated across five databases
clusters <- c(hpca.clusters,bped.clusters,dice.clusters,hd.clusters,mid.clusters)
unique.clusters <- unique(clusters)
cluster.colors <- colors50[1:length(unique.clusters)]
names(cluster.colors) <- unique.clusters
cols <- cluster.colors[order(as.integer(names(cluster.colors)))]

DimPlot(object = hpca.data, reduction = "umap", cols = cluster.colors)
DimPlot(object = hpca.data, reduction = "umap", cols = cols)

These both gave me grey umap in my rmd on the server but its showing me correct colours with the rstudio. Is the code correct and well logically made?

cell seurat singler single 10x • 301 views
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