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博客园 - Life·Intelligence

SSHFS + VS Code 挂载集群代码目录(macOS)| 集群vibe coding OpenClaw 多 Channel 实战总结(Windows 环境) OpenClaw Windows 安装与 Debug 最终版教程(适用于 MiniPC i3-N305 / 无 GPU) miniconda转miniforge | conda | license LightDock | 蛋白质-多肽对接 | peptide-protein docking 蛋白结构预测 | alphafold | colabfold | docking 全栈生信 | PyMol使用教程 细胞通讯推断 | CCI | CellChat | CellphoneDB | iTALK | NicheNet ChromHMM教程 极简 | GRN | SCENIC | pySCENIC | 安装使用最新版scenicplus Linux下载zenodo数据 共定位 | colocalization 分析 | 表观因子 根据基因名批量查询下载PDB蛋白结构数据库 R小技巧汇总 Signac处理bulk ATAC-seq数据 Differential motif enrichment | CentriMo | meme 亚马逊云 | AWS S3 | 基本操作 ATAC-seq | TOBIAS | footprint分析 TCGA+GTEx基因表达数据合并 | 多癌种表达分析 药物筛选 | drug screen
findOverlappingPeaks | peak取交集操作
Life·Intelligence · 2024-12-17 · via 博客园 - Life·Intelligence

参考:

  • http://localhost:17449/lab/tree/projects/BAF_SOX9/diffbind/6.DMSO_only.ipynb
  • http://localhost:17449/lab/tree/projects/BAF_SOX9/diffbind/2.2-Diffbind-Raghwan.ipynb#findOverlapsOfPeaks

venn图

options(repr.plot.width=5.5, repr.plot.height=5.5)
p <- ChIPpeakAnno::makeVennDiagram(list(#GRanges(subset(selected.norm.count, DMSO_SOX9_2_Paul>5)[,1:3]),
                                   GRanges(subset(selected.norm.count, dT29_SC1_DM1_Rag_b4>5)[,1:3]),
                                   GRanges(subset(selected.norm.count, dT29_ARD1_DM2_Rag_b4>5)[,1:3]),
                                   GRanges(subset(selected.norm.count, dT29_BRD9_DM1_Rag_b4>5)[,1:3]),
                                   GRanges(subset(selected.norm.count, dT29_PBR1_DM1_Rag_b4>5)[,1:3])
                                  ), 
                              NameOfPeaks=c("SMARCC1","ARID1","BRD9","PBR1"), minoverlap = 1, 
                scaled=FALSE, euler.d=FALSE, # totalTest=100, 
                fill=tmp.colors[2:5], # circle fill color
                col=tmp.colors[2:5] #circle border color
                # cat.col=c("#D55E00", "#0072B2")
                             )
p

UpSetR图

require(UpSetR)
p1 <- upset(tmp.df, text.scale = 2, keep.order = T, intersections = tmp.intersections)

findOverlapsOfPeaks取交集

t1 <- ChIPpeakAnno::findOverlapsOfPeaks(PBR1.peak.list$`4074 Loss`,
                                   PBR1.peak.list$`5758 Gain`,
                                   BRD9.peak.list$`4665 Loss`,
                                   BRD9.peak.list$`5755 Gain`)

findOverlappingPeaks取交集【这个函数对两个set很友好,可以直接得到peak name】

peak.no.comp <- ChIPpeakAnno::findOverlappingPeaks(GRanges(norm.count[,1:3]), peak.list$`4074 Loss`)
peak.comp <- ChIPpeakAnno::findOverlappingPeaks(GRanges(norm.count[,1:3]), peak.list$`5758 Gain`)
peak.no.comp2 <- as.data.frame(peak.no.comp$Peaks1withOverlaps)
peak.comp2 <- as.data.frame(peak.comp$Peaks1withOverlaps)