Closed huerqiang closed 10 months ago
（1）get_ont_info()的ontology == "MDO"数据中ancmap和termmap有错误，应该是从HDO获取，而不是从HPO获取。 （2）MPOMPMGI对象名误写成MPGMGIDO （3）get_dose_data（）中应该对EG2ALLTERM.df增加as.character()操作，将factor转为character，这样可以避免enricher()等函数报错。 例子：
get_ont_info()
MPOMPMGI
MPGMGIDO
enricher()
> library(DOSE) DOSE v3.29.1.991 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/ If you use DOSE in published research, please cite: Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609 > library(GOSemSim) GOSemSim v2.28.0 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/ If you use GOSemSim in published research, please cite: [36m-[39m Guangchuang Yu. Gene Ontology Semantic Similarity Analysis Using GOSemSim. In: Kidder B. (eds) Stem Cell Transcriptional Networks. Methods in Molecular Biology, 2020, 2117:207-215. Humana, New York, NY. doi:10.1007/978-1-0716-0301-7_11 [36m-[39m Guangchuang Yu, Fei Li, Yide Qin, Xiaochen Bo, Yibo Wu, Shengqi Wang. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products Bioinformatics 2010, 26(7):976-978. doi:10.1093/bioinformatics/btq064 载入程辑包：‘GOSemSim’ The following objects are masked from ‘package:DOSE’: clusterSim, geneSim, mclusterSim > library(MPO.db) MPO.db version 0.99.7 > library(HPO.db) HPO.db version 0.99.2 > genes <- keys(MPO.db, "mgi") > set.seed(123) > gene <- sample(genes, 100) > genelist <- runif(length(genes),-2,2) > names(genelist) <- genes > genelist <- sort(genelist, decreasing = TRUE) > edo = enrichDO(gene, pvalueCutoff = 1, qvalueCutoff = 1, + organism = "mmu", minGSSize = 1, maxGSSize = Inf) > head(edo) ID Description GeneRatio BgRatio pvalue p.adjust qvalue DOID:0060783 DOID:0060783 ectrodactyly, ectodermal dysplasia, and cleft lip-palate syndrome 3 16/82 1065/12553 0.001273699 0.7994451 0.7994451 DOID:0060347 DOID:0060347 acrorenal syndrome 12/82 715/12553 0.002236440 0.7994451 0.7994451 DOID:0110969 DOID:0110969 brachydactyly type B1 9/82 467/12553 0.003332270 0.7994451 0.7994451 DOID:0070061 DOID:0070061 autosomal dominant intellectual developmental disorder 31 2/82 15/12553 0.004188460 0.7994451 0.7994451 DOID:0111094 DOID:0111094 Fanconi anemia complementation group N 14/82 994/12553 0.004759278 0.7994451 0.7994451 DOID:0050833 DOID:0050833 orotic aciduria 6/82 244/12553 0.005187162 0.7994451 0.7994451 geneID Count DOID:0060783 110253/17390/13489/54635/69327/245884/64654/242022/414758/18992/387206/235504/67839/16206/23807/22287 16 DOID:0060347 17390/54635/69327/71846/245884/64654/414758/387206/67839/23807/72043/22287 12 DOID:0110969 208647/17390/19726/286940/69327/242022/19822/22627/16206 9 DOID:0070061 17390/18936 2 DOID:0111094 17390/19726/29859/13489/286940/69327/64654/18992/18049/21406/19822/235504/22627/16206 14 DOID:0050833 19726/69327/242022/19822/22627/22287 6 > gdo = gseDO(genelist, pvalueCutoff = 1, + organism = "mmu", minGSSize = 1, maxGSSize = Inf) preparing geneSet collections... GSEA analysis... leading edge analysis... done... > > head(gdo) ID Description setSize enrichmentScore NES pvalue p.adjust qvalue rank leading_edge core_enrichment DOID:643 DOID:643 progressive multifocal leukoencephalopathy 2 -0.9133641 -1.456511 0.03103367 0.9540816 0.9540816 295 tags=100%, list=2%, signal=98% 50701 DOID:0050890 DOID:0050890 synucleinopathy 5 -0.6306940 -1.394528 0.13133208 0.9540816 0.9540816 1609 tags=60%, list=11%, signal=53% 78309/50873/56424 DOID:14330 DOID:14330 Parkinson's disease 5 -0.6306940 -1.394528 0.13133208 0.9540816 0.9540816 1609 tags=60%, list=11%, signal=53% 78309/50873/56424 DOID:0080001 DOID:0080001 bone disease 2 0.8383321 1.350160 0.10139165 0.9540816 0.9540816 1552 tags=50%, list=11%, signal=45% 80752 DOID:65 DOID:65 connective tissue disease 2 0.8383321 1.350160 0.10139165 0.9540816 0.9540816 1552 tags=50%, list=11%, signal=45% 80752 DOID:8283 DOID:8283 peritonitis 1 -0.9799548 -1.312227 0.05220884 0.9540816 0.9540816 295 tags=100%, list=2%, signal=98% > > # 小鼠表型富集分析 > empo = enrichMPO(gene, pvalueCutoff = 1, qvalueCutoff = 1, + minGSSize = 1, maxGSSize = Inf) > head(empo) ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count MP:0000166 MP:0000166 abnormal chondrocyte morphology 4/100 83/14617 0.002506417 0.5367257 0.5226013 208647/286940/64654/26562 4 MP:0006429 MP:0006429 abnormal hyaline cartilage morphology 3/100 46/14617 0.003810749 0.5367257 0.5226013 17390/286940/18936 3 MP:0000167 MP:0000167 decreased chondrocyte number 2/100 15/14617 0.004591140 0.5367257 0.5226013 208647/64654 2 MP:0008324 MP:0008324 abnormal melanotroph morphology 1/100 1/14617 0.006841349 0.5367257 0.5226013 13489 1 MP:0008331 MP:0008331 increased lactotroph cell number 1/100 1/14617 0.006841349 0.5367257 0.5226013 13489 1 MP:0008423 MP:0008423 decreased lactotroph cell size 1/100 1/14617 0.006841349 0.5367257 0.5226013 13489 1 > > gmpo = gseMPO(genelist, pvalueCutoff = 1, + minGSSize = 1, maxGSSize = Inf) preparing geneSet collections... GSEA analysis... leading edge analysis... done... > > head(gmpo) ID Description setSize enrichmentScore NES pvalue p.adjust qvalue rank leading_edge MP:0000405 MP:0000405 abnormal auchene hair morphology 14 0.6754086 2.151384 0.0002616932 0.9810542 0.9810542 2276 tags=57%, list=16%, signal=48% MP:0010762 MP:0010762 abnormal microglial cell activation 25 0.5616637 2.138701 0.0001743608 0.9810542 0.9810542 3303 tags=48%, list=23%, signal=37% MP:0000229 MP:0000229 abnormal megakaryocyte differentiation 22 -0.5989617 -2.132420 0.0002949974 0.9810542 0.9810542 2367 tags=50%, list=16%, signal=42% MP:0001224 MP:0001224 abnormal keratinocyte apoptosis 16 0.6354358 2.107630 0.0004873834 0.9810542 0.9810542 4544 tags=81%, list=31%, signal=56% MP:0001195 MP:0001195 flaky skin 22 0.5632225 2.052313 0.0016116417 0.9810542 0.9810542 1655 tags=41%, list=11%, signal=36% MP:0003846 MP:0003846 matted coat 8 0.7510336 1.988744 0.0021891237 0.9810542 0.9810542 1874 tags=50%, list=13%, signal=44% core_enrichment MP:0000405 23872/20674/20672/18426/14835/56460/18194/14176 MP:0010762 17184/100038570/19214/83433/216739/12774/237868/11516/18145/24088/245944/21929 MP:0000229 78933/69296/12394/14582/14460/64214/17886/16452/22145/22761/27260 MP:0001224 12608/21847/13983/17199/106025/56460/19015/22173/16664/16151/24102/12367/19698 MP:0001195 20249/18992/14246/16661/13122/16905/77055/170720/225049 MP:0003846 20249/68268/14034/56460 > > > # 人类表型富集分析 > data(geneList) > gene <- sample(names(geneList), 100) > gene [1] "80146" "55255" "9796" "9711" "9093" "10073" "9788" "2788" "55620" "6484" "8814" "10767" "374354" "9152" "23646" "27202" "55112" "2995" "6905" [20] "8848" "10328" "925" "1789" "23505" "10370" "10020" "54457" "10778" "29907" "813" "64600" "4594" "22858" "55113" "51192" "7867" "6898" "23111" [39] "55282" "3376" "51734" "28972" "5256" "22953" "29940" "287" "25852" "10439" "10268" "10733" "3565" "6617" "79018" "54718" "57226" "8639" "23011" [58] "51123" "84193" "23161" "1101" "57020" "1186" "79571" "10785" "4477" "267" "5027" "5462" "6386" "5721" "6233" "100293516" "81621" "6331" "23471" [77] "7015" "56311" "28983" "170680" "8793" "3300" "27236" "4701" "26586" "53826" "79166" "6536" "23186" "84975" "57830" "23526" "5311" "79888" "25909" [96] "4486" "79906" "2286" "794" "7247" > > ehpo = enrichHPO(gene, pvalueCutoff = 1, qvalueCutoff = 1, + minGSSize = 1, maxGSSize = Inf) > head(ehpo) ID Description GeneRatio BgRatio pvalue p.adjust qvalue geneID Count HP:0001279 HP:0001279 Syncope 5/31 111/4885 0.0005867311 0.4670664 0.4419055 9152/10370/287/6331/7015 5 HP:0001744 HP:0001744 Splenomegaly 8/31 340/4885 0.0009677496 0.4670664 0.4419055 374354/2995/10020/4594/3376/5256/1186/7015 8 HP:0004445 HP:0004445 Elliptocytosis 2/31 8/4885 0.0010657912 0.4670664 0.4419055 374354/2995 2 HP:0004312 HP:0004312 Abnormal reticulocyte morphology 4/31 82/4885 0.0016436366 0.4670664 0.4419055 374354/2995/1186/7015 4 HP:0001923 HP:0001923 Reticulocytosis 3/31 48/4885 0.0032998637 0.4670664 0.4419055 374354/2995/1186 3 HP:0002605 HP:0002605 Hepatic necrosis 2/31 14/4885 0.0033825831 0.4670664 0.4419055 374354/7015 2 > > ghpo = gseHPO(geneList, pvalueCutoff = 1, + minGSSize = 1, maxGSSize = Inf) preparing geneSet collections... GSEA analysis... leading edge analysis... done... Warning message: In fgseaMultilevel(pathways = pathways, stats = stats, minSize = minSize, : For some pathways, in reality P-values are less than 1e-10. You can set the `eps` argument to zero for better estimation. > > head(ghpo) ID Description setSize enrichmentScore NES pvalue p.adjust qvalue rank leading_edge HP:0011893 HP:0011893 Abnormal leukocyte count 380 0.4119412 1.982576 1.000000e-10 4.939000e-07 4.681053e-07 2481 tags=35%, list=20%, signal=29% HP:0032251 HP:0032251 Abnormal immune system morphology 550 0.3580838 1.783569 1.000000e-10 4.939000e-07 4.681053e-07 2487 tags=32%, list=20%, signal=27% HP:0001881 HP:0001881 Abnormal leukocyte morphology 540 0.3562669 1.768988 2.061199e-10 5.090130e-07 4.824290e-07 2487 tags=31%, list=20%, signal=26% HP:0010987 HP:0010987 Abnormal cellular immune system morphology 540 0.3562669 1.768988 2.061199e-10 5.090130e-07 4.824290e-07 2487 tags=31%, list=20%, signal=26% HP:0040088 HP:0040088 Abnormal lymphocyte count 187 0.4892985 2.152656 4.617016e-10 9.121377e-07 8.644998e-07 2508 tags=42%, list=20%, signal=34% HP:0032169 HP:0032169 Severe infection 59 0.6554646 2.401302 4.743272e-09 7.653309e-06 7.253602e-06 1358 tags=41%, list=11%, signal=36% core_enrichment HP:0011893 55388/9837/29851/55215/81570/1503/5888/1493/3070/7037/2175/3932/5551/3559/6772/51311/3507/5645/4609/3561/84823/917/9401/641/3654/5698/3574/54892/3575/919/81693/4860/915/22806/55159/2178/4938/3458/959/1789/5336/11151/3930/3702/925/79650/64135/5557/28755/974/2120/6897/6916/1991/867/11330/1794/3689/5788/916/4068/23250/83990/3937/30009/2539/3394/10525/100/2072/6696/5052/2189/5880/4522/7128/4683/81622/4210/6789/930/2542/26191/204/6850/9056/10095/6427/8882/56244/83737/7852/613/7454/5692/64170/833/843/2213/1053/10125/8456/8625/3071/672/2000/8676/4478/1080/356/1380/7319/958/4700/1773/675/1041/2215/5591/25839/54440/55636/4893/3570/3978/5371/8651/10625/7036/5889/64858/29927 HP:0032251 55388/9837/3832/9319/29851/55215/701/81570/1503/5888/1493/3070/7037/2175/4173/1075/3932/3110/5551/3559/6772/51311/3507/7298/699/5645/4609/3561/84823/917/9401/641/10535/1029/3654/5698/3574/54892/3575/919/81693/4860/915/22806/55159/2178/4938/2821/1535/3458/959/1789/7112/5336/11151/3930/3702/925/4688/4436/79650/64135/5557/28755/974/8557/2120/6897/63976/6916/3514/1991/867/10507/25939/11330/1794/3689/5788/916/4068/23250/83990/3937/30009/2539/11200/3394/10525/100/2072/6696/940/26511/5052/2189/939/4689/5880/4522/7128/4683/81622/4210/6789/930/6573/4624/2542/1788/26191/204/6850/9056/10095/5604/6427/8882/56244/83737/7852/613/1410/7454/5692/348/64170/833/843/7273/2213/1053/1536/10125/8456/8625/3071/672/2000/8676/23092/4478/1080/356/1380/7319/7305/7133/958/4700/55505/1773/675/1041/2215/3587/5591/10430/88/5567/25839/54440/55636/4893/3570/3978/5371/8651/10625/7036/5889/64858/29927/5373 HP:0001881 55388/9837/3832/9319/29851/55215/701/81570/1503/5888/1493/3070/7037/2175/4173/3932/5551/3559/6772/51311/3507/7298/699/5645/4609/3561/84823/917/9401/641/10535/1029/3654/5698/3574/54892/3575/919/81693/4860/915/22806/55159/2178/4938/2821/1535/3458/959/1789/7112/5336/11151/3930/3702/925/4688/4436/79650/64135/5557/28755/974/8557/2120/6897/63976/6916/1991/867/10507/25939/11330/1794/3689/5788/916/4068/23250/83990/3937/30009/2539/11200/3394/10525/100/2072/6696/940/26511/5052/2189/939/4689/5880/4522/7128/4683/81622/4210/6789/930/6573/4624/2542/1788/26191/204/6850/9056/10095/5604/6427/8882/56244/83737/7852/613/1410/7454/5692/348/64170/833/843/7273/2213/1053/1536/10125/8456/8625/3071/672/2000/8676/23092/4478/1080/356/1380/7319/7305/7133/958/4700/55505/1773/675/1041/2215/5591/10430/88/5567/25839/54440/55636/4893/3570/3978/5371/8651/10625/7036/5889/64858/29927/5373 HP:0010987 55388/9837/3832/9319/29851/55215/701/81570/1503/5888/1493/3070/7037/2175/4173/3932/5551/3559/6772/51311/3507/7298/699/5645/4609/3561/84823/917/9401/641/10535/1029/3654/5698/3574/54892/3575/919/81693/4860/915/22806/55159/2178/4938/2821/1535/3458/959/1789/7112/5336/11151/3930/3702/925/4688/4436/79650/64135/5557/28755/974/8557/2120/6897/63976/6916/1991/867/10507/25939/11330/1794/3689/5788/916/4068/23250/83990/3937/30009/2539/11200/3394/10525/100/2072/6696/940/26511/5052/2189/939/4689/5880/4522/7128/4683/81622/4210/6789/930/6573/4624/2542/1788/26191/204/6850/9056/10095/5604/6427/8882/56244/83737/7852/613/1410/7454/5692/348/64170/833/843/7273/2213/1053/1536/10125/8456/8625/3071/672/2000/8676/23092/4478/1080/356/1380/7319/7305/7133/958/4700/55505/1773/675/1041/2215/5591/10430/88/5567/25839/54440/55636/4893/3570/3978/5371/8651/10625/7036/5889/64858/29927/5373 HP:0040088 55388/9837/29851/81570/1503/1493/3070/3932/3559/6772/51311/3507/4609/3561/84823/917/641/3654/5698/3574/54892/3575/919/4860/915/22806/1789/5336/11151/3930/3702/925/64135/5557/974/1991/1794/5788/916/4068/23250/3937/10525/100/6696/5880/4522/7128/4683/6789/930/204/6850/10095/56244/7852/7454/5692/64170/843/10125/8456/8625/3071/2000/4478/356/1380/5591/54440/55636/4893/3570/3978/8651/10625/7036/64858/51371 HP:0032169
（1）
get_ont_info()的ontology == "MDO"数据中ancmap和termmap有错误，应该是从HDO获取，而不是从HPO获取。 （2）MPOMPMGI对象名误写成MPGMGIDO（3）get_dose_data（）中应该对EG2ALLTERM.df增加as.character()操作，将factor转为character，这样可以避免enricher()等函数报错。 例子：