effect size

  • add a new annotation mixture of spidex_low3% and merged spliceai, without PTVs, and remove the original ones.
  • the difference of effect size between fix pi and estimate pi is small.
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readRDS("/storage11_7T/fuy/TADA-A/cell_WES/DNM/2021-05-21_selected14_fix_pi_estim_pi_joint_sep_rr.rds")
A data.frame: 14 × 6
fix_pi0.05_joint_logRRestim_pi_joint_logRR, pi = 0.12annotaseparate_logRRupper_boundlower_bound
<dbl><dbl><chr><dbl><dbl><dbl>
11.11009170.7600744hnRNPL binding regions 1.5994883.0837530.1152234
20.35103680.2743483coding constraint >= 90 1.8974442.1033771.6915119
30.89900060.5676109mixture of spidex_low3% and merged spliceai, without PTVs 1.3507662.0511450.6503864
41.02554590.8607562dbSNP.RBP-Var: likely to affcted RNA-bind, RNA 2nd structure 1.8442032.2785401.4098657
50.84140960.6799831RNA modifications, including m6a, m1A, m5C, and etc.(filter_low_RMVar_hb) 1.3120181.6459880.9780486
60.55130900.5873327deepsea: mix ago_adult_brain.BA4 & ago_adult_brain.Cingulate.gyrus 2.4839182.9038372.0639991
70.10646220.5020994deepsea: mix elavl_Adult_brain.all_human_samples & elavl_Adult_brain.BA9_Alzheimer & elavl_Adult_brain.BA92.5928613.0063682.1793531
80.27036710.24922571<= mpc score < 2 1.6216011.8358551.4073458
90.79943360.7232636mpc score >= 2 2.3214052.5479512.0948598
100.97201780.7773866interaction-disrupting: mutations annotated as interface residues and probably damaging by PolyPhen-2 2.6004932.8280702.3729150
111.00883340.7638980pathogenic missense: primateAI_MVP_mix 1.7571851.8924051.6219658
121.94446201.3522520ptv in [0,0.5) 2.0815982.5579301.6052671
131.43602161.1693845ptv in [0.5,0.995) 2.2097102.9645291.4548916
143.15582382.7545241ptv in [0.995,1) 3.4937913.7594863.2280951

genes identified

  • the number of risk genes identified via estimate pi is more than that identified via fix pi
  • risk genes identified via estimate pi have higher enrichment in ASD-associated genesets.
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data.frame(pi=c("fixed","estimated") ,num_gene_identified = c(66,81))
mg
A data.frame: 2 × 2
pinum_gene_identified
<fct><dbl>
fixed 66
estimated81
A data.frame: 13 × 9
Genesetfixed:enrichfixed:pValfixed: gene numfixed:in genesetestim:enrichestim:pValestim:gene numestim:in geneset
<chr><dbl><dbl><int><int><dbl><dbl><int><int>
1haploinsufficiency_including_all_without_ncscore 4.20950.0000006622 4.52140.0000008129
2brainspan/top5%_brainspan_exp.gene.lst 1.56330.21518766 5 2.29280.01628581 9
3RVIS/Petrovski_plosgen_RVIS_score_top5_pct 6.55490.0000006620 6.94330.0000008126
4160210_GO_brain_genes.txt 2.84260.0000006631 2.76450.0000008137
5AutismKB 29.42420.0000006614 29.11290.0000008117
6Olfactory 0.00001.00000066 0 0.00001.00000081 0
7sfrai_genes_high_confidence_170717 180.55790.0000006615176.54550.0000008118
8sfrai/sfrai_curated_ASD_associated 13.52260.0000006636 12.54880.0000008141
9Darnell_Cell_2011_FMRP_targets 9.13170.0000006626 10.30240.0000008136
10DAWN_new_q0.05 14.38810.0000006637 13.94160.0000008144
11Kenny_MP_2014_brain_functional 11.74070.00000066 9 12.75530.0000008112
12Irimia_cell_2014_neuron_specific_alternative_splicing 1.76310.0543226610 2.01120.0087078114
13samocha_NG_2014_top5_percent 8.11850.0000006621 9.13510.0000008129
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# fix = readRDS(paste0("/storage11_7T/fuy/TADA-A/cell_WES/DNM/",Sys.Date(),"_selected14_fix_pi_risk_genes.rds"))
# estim = readRDS(paste0("/storage11_7T/fuy/TADA-A/cell_WES/DNM/",Sys.Date(),"_selected14_estim_pi_risk_genes.rds"))
# mg = cbind(df.fix[,1:5],df.estim[,2:5])[1:13,]
# colnames(mg) = c("Geneset","fixed:enrich","fixed:pVal","fixed: gene num","fixed:in geneset",
# "estim:enrich","estim:pVal","estim:gene num","estim:in geneset")
# mg$Geneset = as.character(mg$Geneset)
# mg$Geneset[1]="haploinsufficiency_including_all_without_ncscore_genelist.txt"
# mg$Geneset = gsub("_genelist.txt", "", mg$Geneset)
# mg$Geneset[7] = "sfrai_genes_high_confidence_170717"
# saveRDS(mg,"/storage11_7T/fuy/TADA-A/cell_WES/DNM/2021-05-21_selected14_fix_vs_estim_pi_EA.rds")