Enrichment analysis(GSEA) 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"...
AI代码解释 CellStatPlot(srt=pancreas_sub,stat.by="CellQC",group.by="CellType",label=TRUE) 代码语言:javascript 代码运行次数:0 运行 AI代码解释 CellStatPlot(srt=pancreas_sub,stat.by=c("db_qc","outlier_qc","umi_qc","gene_qc","mito_qc","ribo_qc","ribo_mito_ratio_qc","species_qc"...
GSEAPlot( srt = pancreas_sub, group_by = "CellType", group_use = "Endocrine", plot_type = "bar", direction = "both", topTerm = 20 )GSEAPlot(srt = pancreas_sub, group_by = "CellType", plot_type = "comparison")Trajectory inferencepancreas_sub <- RunSlingshot(srt = pancreas_sub,...
GSEAPlot(srt = pancreas_sub, group_by = "CellType", group_use = "Endocrine", id_use = "GO:0007186") Dynamic features pancreas_sub<-RunDynamicFeatures(srt=pancreas_sub,lineages=c("Lineage1","Lineage2"),n_candidates=200)ht<-DynamicHeatmap(srt=pancreas_sub,lineages=c("Lineage1","Lineage...
GSEAPlot(srt = pancreas_sub, group_by ="CellType", plot_type ="comparison") ORA富集分析和GSEA富集分析的结果也均存于pancreas_sub@tools中 13、轨迹推断 SCP支持Slingshot, PAGA, scVelo, Palantir, Monocle2, Monocle3, WOT等轨迹推断方法,不过不论是什么方法都需要基于生物学知识库对算法进行合理的限制...
结果解释 - **gsea_result**对象包含了GSEA的结果,包括每个基因集的归一化富集得分(NES)、名义p值和错误发现率(FDR)等。 - 使用`summary()`函数可以快速查看结果的概览。 - `dotplot()`函数可以生成点图来可视化最显著的基因集。 ### 6. 结论 通过上述步骤,你可以在R中成功执行GSEA分析。根据分析结果,你...
GSEAPlot(srt = pancreas_sub, group_by ="CellType", plot_type ="comparison") ORA富集分析和GSEA富集分析的结果也均存于pancreas_sub@tools中 13、轨迹推断 SCP支持Slingshot, PAGA, scVelo, Palantir, Monocle2, Monocle3, WOT等轨迹推断方法,不过不论是什么方法都需要基于生物学知识库对算法进行合理的限制...