An example script for this workflow can be finded with
system.file(package = 'easybio', 'example-single-cell.R') |> file.show()
head(pbmc.markers)
#> p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
#> RPS12 1.273332e-143 0.7387061 1.000 0.991 1.746248e-139 0 RPS12
#> RPS6 6.817653e-143 0.6934523 1.000 0.995 9.349729e-139 0 RPS6
#> RPS27 4.661810e-141 0.7372604 0.999 0.992 6.393206e-137 0 RPS27
#> RPL32 8.158412e-138 0.6266075 0.999 0.995 1.118845e-133 0 RPL32
#> RPS14 5.177478e-130 0.6336957 1.000 0.994 7.100394e-126 0 RPS14
#> RPS25 3.244898e-123 0.7689940 0.997 0.975 4.450053e-119 0 RPS25(marker <- matchCellMarker2(marker = pbmc.markers, n = 50, spc = 'Human')[, head(.SD, 2), by=cluster])
#> Key: <cluster>
#> cluster cell_name uniqueN N
#> <fctr> <char> <int> <int>
#> 1: 0 Naive CD8+ T cell 6 34
#> 2: 0 Naive T(Th0) cell 3 32
#> 3: 1 Monocyte 9 133
#> 4: 1 Macrophage 8 63
#> 5: 2 Regulatory T(Treg) cell 11 148
#> 6: 2 T cell 11 82
#> 7: 3 B cell 9 317
#> 8: 3 Naive B cell 6 33
#> 9: 4 T cell 15 104
#> 10: 4 Natural killer cell 17 99
#> 11: 5 Macrophage 4 34
#> 12: 5 Monocyte 3 10
#> 13: 6 Natural killer cell 14 196
#> 14: 6 Cytotoxic T cell 4 24
#> 15: 7 Plasmacytoid dendritic cell(pDC) 8 42
#> 16: 7 Dendritic cell 6 38
#> 17: 8 Megakaryocyte 9 52
#> 18: 8 Endothelial cell 6 41
#> ordered_symbol orderN
#> <list> <list>
#> 1: CCR7,LEF1,CD8B,MAL,NELL2,TSHZ2 14,12, 2, 2, 2, 2
#> 2: CCR7,LEF1,LRRN3 23, 8, 1
#> 3: CD14,S100A8,S100A9,S100A12,FCGR1A,MS4A6A,... 82,22,15, 5, 4, 2,...
#> 4: CD14,FCGR1A,CCL2,PLA2G7,RNASE1,S100A8,... 46, 6, 2, 2, 2, 2,...
#> 5: FOXP3,IL2RA,CTLA4,TNFRSF4,TNFRSF18,ICOS,... 55,45,22, 7, 6, 4,...
#> 6: CD2,CTLA4,FOXP3,IL2RA,CD40LG,CCR6,... 32,12, 8, 7, 6, 4,...
#> 7: CD79A,CD19,MS4A1,FCER2,TCL1A,IGLL5,... 102, 97, 97, 6, 5, 3,...
#> 8: TCL1A,MS4A1,CD19,FCER2,CD79A,PCDH9 13, 6, 5, 5, 3, 1
#> 9: CD8A,CD8B,GZMK,TIGIT,CCL5,GZMA,... 38,10, 7, 7, 6, 6,...
#> 10: NKG7,KLRB1,GZMA,CCL5,CD160,CD8A,... 50, 9, 6, 5, 5, 3,...
#> 11: C1QA,C1QB,MS4A7,MS4A4A 13,10, 7, 4
#> 12: MS4A7,C1QB,C1QA 7,2,1
#> 13: NCAM1,GNLY,KLRF1,GZMB,NCR1,XCL1,... 61,42,25,16,14,11,...
#> 14: PRF1,GZMB,GNLY,FGFBP2 9,8,6,1
#> 15: CLEC4C,LILRA4,SCT,LAMP5,LRRC26,SERPINF1,... 19,16, 2, 1, 1, 1,...
#> 16: FCER1A,CLEC10A,LILRA4,FLT3,CD1E,CLEC4C 16,11, 4, 3, 2, 2
#> 17: PPBP,PF4,ITGA2B,GP9,MYL9,TUBB1,... 15,12, 9, 4, 4, 3,...
#> 18: CLDN5,ESAM,GNG11,LCN2,SERPINE1,SPARC 36, 1, 1, 1, 1, 1
#> markerWith
#> <list>
#> 1: LEF1,CCR7,MAL,LEF1,TSHZ2,CCR7,...
#> 2: CCR7,CCR7,LRRN3,CCR7,CCR7,CCR7,...
#> 3: S100A9,S100A8,CD14,S100A8,S100A9,CD14,...
#> 4: CD14,CD14,CD14,CD14,CD14,CD14,...
#> 5: CTLA4,TNFRSF4,IL2RA,TNFRSF18,FOXP3,FOXP3,...
#> 6: IL2RA,IL2RA,CD40LG,CD40LG,CD2,CTLA4,...
#> 7: MS4A1,CD19,CD79A,CD79A,CD19,MS4A1,...
#> 8: MS4A1,TCL1A,PCDH9,CD79A,TCL1A,FCER2,...
#> 9: CD8A,CD8A,CD8A,GZMA,CD8B,CD8A,...
#> 10: NKG7,NKG7,KLRB1,CD8A,NKG7,NKG7,...
#> 11: MS4A7,C1QB,C1QA,MS4A7,C1QA,C1QB,...
#> 12: MS4A7,MS4A7,MS4A7,MS4A7,MS4A7,C1QB,...
#> 13: GNLY,NCAM1,NCAM1,KLRF1,GNLY,NCAM1,...
#> 14: PRF1,GZMB,FGFBP2,GNLY,GZMB,PRF1,...
#> 15: CLEC4C,LILRA4,SERPINF1,CLEC4C,LILRA4,LILRA4,...
#> 16: FCER1A,CLEC10A,FCER1A,LILRA4,CLEC10A,FCER1A,...
#> 17: PF4,PPBP,SPARC,PPBP,ITGA2B,TUBB1,...
#> 18: CLDN5,CLDN5,CLDN5,CLDN5,SPARC,CLDN5,...
Behind this function:
You can just use the top matched cell as the annotation:
cl2cell <- marker[, head(.SD, 1), by = .(cluster)]
cl2cell <- setNames(cl2cell[["cell_name"]], cl2cell[["cluster"]])
cl2cell
#> 0 1
#> "Naive CD8+ T cell" "Monocyte"
#> 2 3
#> "Regulatory T(Treg) cell" "B cell"
#> 4 5
#> "T cell" "Macrophage"
#> 6 7
#> "Natural killer cell" "Plasmacytoid dendritic cell(pDC)"
#> 8
#> "Megakaryocyte"Visualize marker dot plots for similar clusters:
cls <- list(
c(1, 5, 7),
c(8),
c(3),
c(0,2, 4, 6)
)
dotplotList <- plotSeuratDot(seuratObject, cls, marker = marker, n = 50, spc = 'Human', topcellN = 2)Behind this function:
Seurat::DotPlot to generate the
corresponding dot plot. The resulting plot will be saved in the
directory ‘tmp_check_marker’.Construct the named vector for your annotation:
cl2cell <- finsert(
expression(
c(1, 5) == "Monocyte",
c(7) == "DC",
c(8) == "megakaryocyte",
c(3) == "B.cell",
c(0, 2) == "Naive.CD8.T.cell",
c(4) == "Cytotoxic.T.Cell",
c(6) == "Natural.killer.cell",
), len = 9)
cl2cell
#> 0 1 2
#> "Naive.CD8.T.cell" "Monocyte" "Naive.CD8.T.cell"
#> 3 4 5
#> "B.cell" "Cytotoxic.T.Cell" "Monocyte"
#> 6 7 8
#> "Natural.killer.cell" "DC" "megakaryocyte"You can also directly obtain markers:
get_marker(spc = 'Human', cell = c('Monocyte', 'Neutrophil'), number = 5, min.count = 1)
#> $Monocyte
#> [1] "CD14" "FCGR3A" "LYZ" "S100A8" "FCN1"
#>
#> $Neutrophil
#> [1] "FCGR3B" "S100A9" "CSF3R" "S100A8" "FCGR3A"or Check the distribution of the marker directly.
