Guided Clustering Tutorial, Single cell data analysis by R studio 2편
[Seurat] Guided Clustering Tutorial, Single cell data analysis by R studio 2편
Guided Clustering Tutorial, Single cell data analysis by R studio 1편 [Seurat] Guided Clustering Tutorial, Single cell data analysis by R studio 안녕하세요, 꿈꾸는 약사입니다. 이번에는 R을 이용한 Si..
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안녕하세요, 꿈꾸는 약사입니다. Seurat tutorial 2편에 이어서 3편 진행하도록 하겠습니다.
목차는 오른쪽에 있습니다.
Finding differentially expressed features (cluster biomarkers)
# Differential expression을 통해 cluster를 정의하는 marker들을 찾는 방법
# min.pict는 두 개의 group간 최소 percentage의 공통 특성을 갖는 gene들에 대해 test하게 함. Default=0.1
cluster2.markers <- FindMarkers(pbmc, ident.1 = 2, min.pct = 0.25)
head(cluster2.markers, n = 5)# 실행 결과
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## IL32 2.892340e-90 1.2013522 0.947 0.465 3.966555e-86
## LTB 1.060121e-86 1.2695776 0.981 0.643 1.453850e-82
## CD3D 8.794641e-71 0.9389621 0.922 0.432 1.206097e-66
## IL7R 3.516098e-68 1.1873213 0.750 0.326 4.821977e-64
## LDHB 1.642480e-67 0.8969774 0.954 0.614 2.252497e-63
# Cluster 0과 3으로부터 Cluster 5를 distingushing하는 모든 marker들을 finding
cluster5.markers <- FindMarkers(pbmc, ident.1 = 5, ident.2 = c(0, 3), min.pct = 0.25)
head(cluster5.markers, n = 5)# 실행 결과
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## FCGR3A 8.246578e-205 4.261495 0.975 0.040 1.130936e-200
## IFITM3 1.677613e-195 3.879339 0.975 0.049 2.300678e-191
## CFD 2.401156e-193 3.405492 0.938 0.038 3.292945e-189
## CD68 2.900384e-191 3.020484 0.926 0.035 3.977587e-187
## RP11-290F20.3 2.513244e-186 2.720057 0.840 0.017 3.446663e-182# 각각의 cluster에서 나머지 모든 cluster의 cell과 비교하여 positively expressing되는 것들만 identification
pbmc.markers <- FindAllMarkers(pbmc, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
pbmc.markers %>%
group_by(cluster) %>%
slice_max(n = 2, order_by = avg_log2FC)# 실행 결과
## # A tibble: 18 × 7
## # Groups: cluster [9]
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 9.57e- 88 1.36 0.447 0.108 1.31e- 83 0 CCR7
## 2 3.75e-112 1.09 0.912 0.592 5.14e-108 0 LDHB
## 3 0 5.57 0.996 0.215 0 1 S100A9
## 4 0 5.48 0.975 0.121 0 1 S100A8
## 5 1.06e- 86 1.27 0.981 0.643 1.45e- 82 2 LTB
## 6 2.97e- 58 1.23 0.42 0.111 4.07e- 54 2 AQP3
## 7 0 4.31 0.936 0.041 0 3 CD79A
## 8 9.48e-271 3.59 0.622 0.022 1.30e-266 3 TCL1A
## 9 5.61e-202 3.10 0.983 0.234 7.70e-198 4 CCL5
## 10 7.25e-165 3.00 0.577 0.055 9.95e-161 4 GZMK
## 11 3.51e-184 3.31 0.975 0.134 4.82e-180 5 FCGR3A
## 12 2.03e-125 3.09 1 0.315 2.78e-121 5 LST1
## 13 3.13e-191 5.32 0.961 0.131 4.30e-187 6 GNLY
## 14 7.95e-269 4.83 0.961 0.068 1.09e-264 6 GZMB
## 15 1.48e-220 3.87 0.812 0.011 2.03e-216 7 FCER1A
## 16 1.67e- 21 2.87 1 0.513 2.28e- 17 7 HLA-DPB1
## 17 1.92e-102 8.59 1 0.024 2.63e- 98 8 PPBP
## 18 9.25e-186 7.29 1 0.011 1.27e-181 8 PF4# Roc analysis 기반하여 expressing 되는 gene 중에 marker를 identification
cluster0.markers <- FindMarkers(pbmc, ident.1 = 0, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)Visualizing marker expression - VlnPlot()
VlnPlot(pbmc, features = c("NKG7", "PF4"), slot = "counts", log = TRUE)# 실행 결과
Visualizing marker expression - FeaturePlot()
FeaturePlot(pbmc, features = c("MS4A1", "GNLY", "CD3E", "CD14", "FCER1A", "FCGR3A", "LYZ", "PPBP",
"CD8A"))# 실행 결과
Visualizing marker expression - DoHeatmap()
pbmc.markers %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10
DoHeatmap(pbmc, features = top10$gene) + NoLegend()# 실행 결과
Assigning cell type identity to clusters
# 기존에 알려진 canonical markers을 이용하여 cell type과 cluster를 매치
new.cluster.ids <- c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T", "B", "CD8 T", "FCGR3A+ Mono",
"NK", "DC", "Platelet")
names(new.cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, new.cluster.ids)
DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()# 실행 결과
# rds format으로 저장
saveRDS(pbmc, file = "C:/Rstuido/Rdata/pbmc3k_final.rds")