pancreas_sub <- RunGSEA( srt = pancreas_sub, group_by = "CellType", db = "GO_BP", species = "Mus_musculus", DE_threshold = "p_val_adj < 0.05" ) GSEAPlot(srt = pancreas_sub, group_by = "CellType", group_use = "Endocrine", id_use = "GO:0007186") Dynamic features pancreas...
4种差异基因鉴定流程(All, Paired, Conserved, Distrubed) ORA和GSEA两种富集分析方法 3种特征打分方法(Seurat, Ucell, AUcell) 5种细胞映射方法(KNNMap, PCAMap, SeuratMap, CSSMap, SymphonyMap)和3种细胞自动注释方法(KNNpredict, scmap, SingleR) 7种细胞轨迹推断方法(Slingshot, PAGA, scVelo, Palantir, Mo...
4种差异基因鉴定流程(All, Paired, Conserved, Distrubed) ORA和GSEA两种富集分析方法 3种特征打分方法(Seurat, Ucell, AUcell) 5种细胞映射方法(KNNMap, PCAMap, SeuratMap, CSSMap, SymphonyMap)和3种细胞自动注释方法(KNNpredict, scmap, SingleR) 7种细胞轨迹推断方法(Slingshot, PAGA, scVelo, Palantir, Mo...
RunEnrichment RunGSEA RunDynamicFeatures RunDynamicEnrichment CellScoring panel_fix 然后可以使用一些函数对数据进行探索性的可视化: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 CellDimPlot(srt=pancreas_sub,group.by=c("CellType","SubCellType"),reduction="UMAP",theme_use="theme_blank") 代码语言...
ORA和GSEA两种富集分析方法 3种特征打分方法(Seurat, Ucell, AUcell) 5种细胞映射方法(KNNMap, PCAMap, SeuratMap, CSSMap,SymphonyMap)和3种细胞自动注释方法(KNNpredict, scmap, SingleR) 7种细胞轨迹推断方法(Slingshot, PAGA, scVelo, Palantir, Monocle2,Monocle3, WOT)和基于pseudotime的动态特征鉴定方法 可...
5A). Additionally, GSEA analysis confirmed a positive correlation between circSCP2 expression and EMT (Fig. 5B). To identify downstream targets through which circSCP2 functions as a miRNA sponge for miR-92a-1-5, we used TargetScan, miRNA-walk, and miRDB with our RNA-seq results, and IGF2...
Multiple single-cell downstream analyses such as identification of differential features, enrichment analysis, GSEA analysis, identification of dynamic features, PAGA, RNA velocity, Palantir, Monocle2, Monocle3, etc. Multiple methods for automatic annotation of single-cell data and methods for projection...
5A). Additionally, GSEA analysis confirmed a positive correlation between circSCP2 expression and EMT (Fig. 5B). To identify downstream targets through which circSCP2 functions as a miRNA sponge for miR-92a-1-5, we used TargetScan, miRNA-walk, and miRDB with our RNA-seq results, and IGF2...
pancreas_sub <- RunGSEA( srt = pancreas_sub, group_by = "CellType", db = "GO_BP", species = "Mus_musculus", DE_threshold = "p_val_adj < 0.05" ) GSEAPlot(srt = pancreas_sub, group_by = "CellType", group_use = "Endocrine", id_use = "GO:0007186") ...
RunGSEA RunDynamicFeatures RunDynamicEnrichment CellScoring panel_fix 然后可以使用一些函数对数据进行探索性的可视化: CellDimPlot( srt = pancreas_sub, group.by = c("CellType","SubCellType"), reduction ="UMAP", theme_use ="theme_blank"