SingleR annotation
library(SingleR)
## Loading required package: SummarizedExperiment
## Loading required package: MatrixGenerics
## Loading required package: matrixStats
##
## 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
## Loading required package: parallel
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## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:parallel':
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## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB
## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
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## anyDuplicated, 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
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## Attaching package: 'S4Vectors'
## The following object is masked from 'package:base':
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## expand.grid
## Loading required package: IRanges
## Loading required package: GenomeInfoDb
## 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':
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## rowMedians
## The following objects are masked from 'package:matrixStats':
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## anyMissing, rowMedians
library(Seurat)
## Attaching SeuratObject
##
## Attaching package: 'Seurat'
## The following object is masked from 'package:SummarizedExperiment':
##
## Assays
library(celldex)
##
## Attaching package: 'celldex'
## The following objects are masked from 'package:SingleR':
##
## BlueprintEncodeData, DatabaseImmuneCellExpressionData,
## HumanPrimaryCellAtlasData, ImmGenData, MonacoImmuneData,
## MouseRNAseqData, NovershternHematopoieticData
library(SingleCellExperiment)
library(devtools)
## Loading required package: usethis
pbmc <- readRDS("/mnt/nectar_volume/home/eraz0001/KELLY 2020/E11.5/Final_15_clusters.RDS")
mimd.sc <- SingleR::MouseRNAseqData()
## Warning: 'SingleR::MouseRNAseqData' is deprecated.
## Use 'celldex::MouseRNAseqData' instead.
## See help("Deprecated")
## snapshotDate(): 2020-10-27
## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache
## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache
mimd.sc
## class: SummarizedExperiment
## dim: 21214 358
## metadata(0):
## assays(1): logcounts
## rownames(21214): Xkr4 Rp1 ... LOC100039574 LOC100039753
## rowData names(0):
## colnames(358): ERR525589Aligned ERR525592Aligned ... SRR1044043Aligned
## SRR1044044Aligned
## colData names(3): label.main label.fine label.ont
sce <- as.SingleCellExperiment(pbmc)
sce
## class: SingleCellExperiment
## dim: 16968 2333
## metadata(0):
## assays(3): counts logcounts scaledata
## rownames(16968): Rp1 Sox17 ... mt-Cytb Cd99
## rowData names(0):
## colnames(2333): AAACCTGAGACGCACA-1 AAACCTGCACAGACAG-1 ...
## TTTGTCATCTAGAGTC-1 TTTGTCATCTTTAGGG-1
## colData names(19): orig.ident nCount_RNA ... RNA_snn_res.2 ident
## reducedDimNames(2): PCA UMAP
## altExpNames(0):
main.group <- SingleR(test = sce,ref = mimd.sc, labels = mimd.sc$label.main)
fine.group <- SingleR(test = sce, ref = mimd.sc,labels = mimd.sc$label.fine)
table(main.group$pruned.labels)
##
## B cells Cardiomyocytes Endothelial cells Epithelial cells
## 2 1 318 1
## Erythrocytes Fibroblasts Hepatocytes Macrophages
## 8 1930 27 3
## Monocytes Neurons NK cells T cells
## 18 3 1 7
table(fine.group$pruned.labels)
##
## aNSCs B cells Cardiomyocytes
## 11 3 1
## Endothelial cells Erythrocytes Fibroblasts
## 319 3 23
## Fibroblasts senescent Granulocytes Hepatocytes
## 594 1 2
## Macrophages Monocytes NK cells
## 2 19 2
## NPCs OPCs T cells
## 1332 8 5
plotScoreHeatmap(main.group, max.labels = 8, clusters = pbmc$seurat_clusters, order.by = "clusters", show_colnames = F)
plotScoreHeatmap(fine.group, max.labels = 8, clusters = pbmc$seurat_clusters, order.by = "clusters", show_colnames = F)
mimd.sc@assays@data@listData[["logcounts"]][1:30]
## [1] 4.377727 3.336021 3.940646 9.042406 8.661706 8.974992 4.189599
## [8] 7.768620 3.336021 3.336021 9.225259 3.336021 3.336021 10.498472
## [15] 3.336021 5.291547 9.940517 8.128016 3.763161 3.940646 8.451546
## [22] 4.668700 5.499514 4.668700 5.043116 3.336021 3.336021 7.837134
## [29] 6.603874 8.665132
pred.comb <- combineCommonResults(list("broad" = main.group, "Fine" = fine.group))
## Warning in combineCommonResults(list(broad = main.group, Fine = fine.group)):
## common genes are not identical
plotScoreHeatmap(pred.comb, max.labels = 8, clusters = pbmc$seurat_clusters, order.by = "clusters", show_colnames = F)
pbmc$predicted_ID <- pred.comb$pruned.labels
table(pbmc$predicted_ID, pbmc$seurat_clusters)
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13
## aNSCs 1 3 0 3 0 0 0 0 0 1 1 0 0 1
## B cells 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Cardiomyocytes 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## Endothelial cells 0 1 0 0 0 0 172 0 145 0 0 0 0 1
## Erythrocytes 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## Fibroblasts 0 3 1 0 6 7 0 0 0 2 14 5 0 1
## Fibroblasts senescent 19 44 40 20 121 75 0 15 0 30 59 54 67 29
## Granulocytes 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Hepatocytes 0 0 0 0 0 0 0 0 0 0 0 0 0 2
## Macrophages 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Monocytes 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## NK cells 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## NPCs 222 146 152 166 57 92 1 133 0 100 59 59 47 64
## OPCs 1 2 0 1 0 3 0 0 0 0 0 0 0 1
## T cells 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## 14 15
## aNSCs 1 0
## B cells 0 3
## Cardiomyocytes 0 0
## Endothelial cells 0 0
## Erythrocytes 0 2
## Fibroblasts 0 0
## Fibroblasts senescent 10 0
## Granulocytes 0 1
## Hepatocytes 0 0
## Macrophages 0 3
## Monocytes 0 18
## NK cells 0 2
## NPCs 29 0
## OPCs 0 0
## T cells 0 5
pbmc@meta.data$main.group <- main.group$pruned.labels
pbmc@meta.data$fine.group <- fine.group$pruned.labels
pbmc <- SetIdent(pbmc, value = "fine.group")
a <- DimPlot(pbmc, label = T , repel = T,pt.size = 2, label.size = 4)
a
## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.2 (2020-06-22)
## os Ubuntu 20.04.4 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_AU.UTF-8
## ctype en_AU.UTF-8
## tz Australia/Melbourne
## date 2022-06-20
## pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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## BiocSingular 1.6.0 2020-10-27 [1] Bioconductor
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##
## [1] /mnt/nectar_volume/home/eraz0001/R/x86_64-pc-linux-gnu-library/4.0
## [2] /mnt/nectar_volume/software/apps/R/4.0.2/lib/R/library
##
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