BMI - Brain Cortex
sheng Qian
2021-2-6
Last updated: 2022-02-22
Checks: 6 1
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 18585#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1767 1246 1031 704 763 970 1076 667 724 846 1084 936 356 661 609 865
17 18 19 20 21 22
1252 230 1355 624 247 572 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 17196#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.9253#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$z <- z_gene[ctwas_gene_res$intron_id,]$z
z_snp <- z_snp[z_snp$id %in% ctwas_snp_res$id,]
ctwas_snp_res$z <- z_snp$z[match(ctwas_snp_res$id, z_snp$id)]
#merge gene and snp results with added information
ctwas_snp_res$genename=NA
ctwas_snp_res$gene_type=NA
ctwas_snp_res$intron_id=NA
ctwas_res <- rbind(ctwas_gene_res,
ctwas_snp_res[,colnames(ctwas_gene_res)])
#get number of eQTL for geens
num_eqtl <- c()
for (i in 1:22){
load(paste0(results_dir, "/", analysis_id, "_expr_chr", i, ".exprqc.Rd"))
num_eqtl <- c(num_eqtl, unlist(lapply(wgtlist, nrow)))
}
ctwas_gene_res$num_eqtl <- num_eqtl[ctwas_gene_res$intron_id]
#store columns to report
report_cols <- colnames(ctwas_gene_res)[!(colnames(ctwas_gene_res) %in% c("type", "region_tag1", "region_tag2", "cs_index", "gene_type", "z_flag", "id", "chrom", "pos"))]
first_cols <- c("genename", "region_tag")
report_cols <- c(first_cols, report_cols[!(report_cols %in% first_cols)])
report_cols_snps <- c("id", report_cols[-1])
report_cols_snps <- report_cols_snps[!(report_cols_snps %in% "num_eqtl")]
#get number of SNPs from s1 results; adjust for thin argument
ctwas_res_s1 <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas.s1.susieIrss.txt"))
n_snps <- sum(ctwas_res_s1$type=="SNP")/thin
rm(ctwas_res_s1)Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.005713 0.000292 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
26.34 17.29 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 18585 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.008322 0.113177 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.5046 15.5637Genes with highest PIPs
genename region_tag susie_pip mu2 PVE
18147 MRPL21 11_38 1.0000 28867.84 8.589e-02
18157 SLC38A2 12_29 1.0000 17149.91 5.103e-02
18424 ZNF559-ZNF177 19_9 1.0000 18202.44 5.416e-02
17838 MAST2 1_28 1.0000 28187.86 8.387e-02
17963 DST 6_42 1.0000 13123.33 3.905e-02
18256 MOK 14_53 1.0000 10211.28 3.038e-02
17928 SDHA 5_1 1.0000 12752.70 3.794e-02
17992 PMS2P3 7_48 0.9851 100.05 2.933e-04
18090 BRSK2 11_2 0.9201 32.26 8.831e-05
18327 TNFSF12-TNFSF13 17_7 0.9191 29.04 7.940e-05
17904 EIF2B5 3_113 0.8362 37.88 9.423e-05
304 MINOS1 1_13 0.7895 35.70 8.385e-05
7822 TBC1D31 8_80 0.7816 25.09 5.834e-05
17235 ADARB1 21_23 0.7693 47.27 1.082e-04
54 MIB2 1_1 0.7679 51.59 1.179e-04
7458 FAM160B2 8_23 0.7504 26.94 6.016e-05
10513 NTM 11_82 0.7324 29.63 6.456e-05
7979 SHARPIN 8_94 0.7104 25.71 5.435e-05
14859 RPTOR 17_45 0.7063 48.28 1.015e-04
15309 DOT1L 19_3 0.6993 25.09 5.221e-05
intron_id z num_eqtl
18147 intron_11_68898012_68900535 4.372 1
18157 intron_12_46366945_46367076 2.953 1
18424 intron_19_9364948_9376316 -3.338 1
17838 intron_1_46019697_46021950 -4.301 1
17963 intron_6_56464756_56466078 2.881 1
18256 intron_14_102265912_102283478 2.804 1
17928 intron_5_228333_230876 2.905 1
17992 intron_7_75514779_75515718 10.456 2
18090 intron_11_1449836_1450221 -5.470 1
18327 intron_17_7559702_7560049 -4.225 1
17904 intron_3_184278313_184283785 6.061 2
304 intron_1_19597109_19617258 -5.733 1
7822 intron_8_123130333_123140761 -4.335 1
17235 intron_21_45221014_45222018 6.895 1
54 intron_1_1628722_1629133 6.613 1
7458 intron_8_22094853_22096337 -4.806 1
10513 intron_11_131582378_131660929 -4.836 2
7979 intron_8_144103225_144104025 -4.431 1
14859 intron_17_80730706_80754010 -6.093 1
15309 intron_19_2199939_2202700 -4.106 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE
15686 MAST3 19_14 0.000000 41584 0.0000000
15684 MAST3 19_14 0.000000 29200 0.0000000
18147 MRPL21 11_38 1.000000 28868 0.0858888
17838 MAST2 1_28 1.000000 28188 0.0838657
18149 IGHMBP2 11_38 0.000000 27907 0.0000000
18267 TMOD3 15_21 0.001709 27558 0.0001402
15685 MAST3 19_14 0.000000 25134 0.0000000
17831 NASP 1_28 0.000000 23455 0.0000000
17828 NASP 1_28 0.000000 23034 0.0000000
17829 NASP 1_28 0.000000 23034 0.0000000
8060 CCDC171 9_13 0.000000 22540 0.0000000
18148 MRPL21 11_38 0.000000 21357 0.0000000
3326 HEMK1 3_35 0.000000 19065 0.0000000
18424 ZNF559-ZNF177 19_9 1.000000 18202 0.0541567
18427 ZNF266 19_9 0.000000 17634 0.0000000
18429 ZNF266 19_9 0.000000 17427 0.0000000
18157 SLC38A2 12_29 1.000000 17150 0.0510252
18425 ZNF266 19_9 0.000000 16967 0.0000000
18426 ZNF266 19_9 0.000000 16967 0.0000000
12549 ZSCAN29 15_16 0.000000 16904 0.0000000
intron_id z num_eqtl
15686 intron_19_18147044_18147443 6.726 1
15684 intron_19_18137361_18139015 5.986 1
18147 intron_11_68898012_68900535 4.372 1
17838 intron_1_46019697_46021950 -4.301 1
18149 intron_11_68930434_68933299 -4.493 1
18267 intron_15_51902036_51908776 -4.579 1
15685 intron_19_18147017_18147443 -4.750 3
17831 intron_1_45616393_45616626 -4.289 1
17828 intron_1_45591270_45600385 3.951 1
17829 intron_1_45591270_45602255 -3.951 1
8060 intron_9_15888088_15888983 -5.264 2
18148 intron_11_68898012_68900548 -3.964 2
3326 intron_3_50572208_50577052 -4.704 1
18424 intron_19_9364948_9376316 -3.338 1
18427 intron_19_9420218_9434798 3.116 1
18429 intron_19_9433786_9434075 -3.165 1
18157 intron_12_46366945_46367076 2.953 1
18425 intron_19_9414720_9415654 3.372 1
18426 intron_19_9415742_9417828 -3.401 1
12549 intron_15_43366808_43368923 -3.359 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE
18147 MRPL21 11_38 1.00000 28867.8 0.0858888
17838 MAST2 1_28 1.00000 28187.9 0.0838657
18424 ZNF559-ZNF177 19_9 1.00000 18202.4 0.0541567
18157 SLC38A2 12_29 1.00000 17149.9 0.0510252
17963 DST 6_42 1.00000 13123.3 0.0390451
17928 SDHA 5_1 0.99996 12752.7 0.0379408
18256 MOK 14_53 1.00000 10211.3 0.0303810
11486 SPG20 13_13 0.55556 14697.4 0.0242935
11487 SPG20 13_13 0.55556 14697.4 0.0242935
11488 SPG20 13_13 0.55556 14697.4 0.0242935
5700 TAPBP 6_28 0.50000 6029.2 0.0089691
5701 TAPBP 6_28 0.50000 6029.2 0.0089691
1522 TATDN3 1_108 0.24419 3486.2 0.0025329
1517 TATDN3 1_108 0.16341 3484.2 0.0016939
1518 TATDN3 1_108 0.16083 3484.1 0.0016672
1520 TATDN3 1_108 0.13219 3485.2 0.0013707
1519 TATDN3 1_108 0.13219 3485.2 0.0013707
13343 TNRC6A 16_21 0.62408 486.2 0.0009028
1516 TATDN3 1_108 0.07623 3483.6 0.0007901
1513 NSL1 1_108 0.06936 3480.8 0.0007183
intron_id z num_eqtl
18147 intron_11_68898012_68900535 4.372 1
17838 intron_1_46019697_46021950 -4.301 1
18424 intron_19_9364948_9376316 -3.338 1
18157 intron_12_46366945_46367076 2.953 1
17963 intron_6_56464756_56466078 2.881 1
17928 intron_5_228333_230876 2.905 1
18256 intron_14_102265912_102283478 2.804 1
11486 intron_13_36335832_36336287 2.819 1
11487 intron_13_36335832_36346225 -2.819 1
11488 intron_13_36335832_36346582 2.819 1
5700 intron_6_33305387_33313217 -2.734 1
5701 intron_6_33305387_33313694 2.734 1
1522 intron_1_212812328_212815013 3.296 1
1517 intron_1_212804429_212804596 3.300 1
1518 intron_1_212804651_212807736 3.300 1
1520 intron_1_212807848_212812248 3.289 1
1519 intron_1_212807848_212812227 -3.289 1
13343 intron_16_24730300_24750726 5.887 1
1516 intron_1_212797196_212802701 3.275 1
1513 intron_1_212738686_212739534 -3.259 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE
18117 BDNF-AS 11_19 0.000e+00 9834.55 0.000e+00
3305 RBM6 3_35 2.867e-03 912.59 7.784e-06
1841 DNAJC27-AS1 2_15 6.169e-05 192.74 3.538e-08
1842 DNAJC27-AS1 2_15 6.169e-05 192.74 3.538e-08
1840 DNAJC27-AS1 2_15 6.169e-05 192.74 3.538e-08
5718 SNRPC 6_28 1.785e-01 121.46 6.450e-05
13397 DOC2A 16_24 2.075e-02 99.17 6.122e-06
3307 RBM6 3_35 9.492e-14 161.26 4.554e-17
13376 ATXN2L 16_23 2.444e-01 99.41 7.227e-05
13377 ATXN2L 16_23 2.444e-01 99.41 7.227e-05
13370 SULT1A2 16_23 1.759e-01 102.55 5.367e-05
1356 SEC16B 1_87 2.289e-02 95.88 6.529e-06
13371 SULT1A2 16_23 8.449e-02 103.05 2.591e-05
17992 PMS2P3 7_48 9.851e-01 100.05 2.933e-04
13372 SULT1A2 16_23 3.060e-02 96.20 8.759e-06
4840 POC5 5_44 9.577e-03 87.64 2.497e-06
13375 SULT1A1 16_23 1.706e-02 91.54 4.645e-06
17993 PMS2P3 7_48 1.530e-02 91.01 4.144e-06
13417 BCKDK 16_24 1.886e-02 76.35 4.284e-06
3304 CTD-2330K9.3 3_35 5.551e-16 1413.26 2.334e-18
intron_id z num_eqtl
18117 intron_11_27640005_27659171 13.138 1
3305 intron_3_49999513_50048245 -12.539 1
1841 intron_2_24972232_24979060 11.288 1
1842 intron_2_24972232_25001415 11.288 1
1840 intron_2_24972232_24972626 -11.288 1
5718 intron_6_34757954_34762595 -11.130 1
13397 intron_16_30007090_30007173 -10.811 2
3307 intron_3_50047326_50048245 10.783 2
13376 intron_16_28835399_28835546 10.745 1
13377 intron_16_28835399_28835549 -10.745 1
13370 intron_16_28592443_28593252 -10.684 2
1356 intron_1_177932569_177932698 10.585 1
13371 intron_16_28592481_28593252 -10.570 1
17992 intron_7_75514779_75515718 10.456 2
13372 intron_16_28593568_28595367 -10.400 1
4840 intron_5_75705787_75712854 -10.325 2
13375 intron_16_28620133_28623131 10.125 1
17993 intron_7_75514784_75515718 -9.959 1
13417 intron_16_31110499_31110688 -9.866 1
3304 intron_3_49910884_49913220 -9.780 1Comparing z scores and PIPs
[1] 0.01851 genename region_tag susie_pip mu2 PVE
18117 BDNF-AS 11_19 0.000e+00 9834.55 0.000e+00
3305 RBM6 3_35 2.867e-03 912.59 7.784e-06
1841 DNAJC27-AS1 2_15 6.169e-05 192.74 3.538e-08
1842 DNAJC27-AS1 2_15 6.169e-05 192.74 3.538e-08
1840 DNAJC27-AS1 2_15 6.169e-05 192.74 3.538e-08
5718 SNRPC 6_28 1.785e-01 121.46 6.450e-05
13397 DOC2A 16_24 2.075e-02 99.17 6.122e-06
3307 RBM6 3_35 9.492e-14 161.26 4.554e-17
13376 ATXN2L 16_23 2.444e-01 99.41 7.227e-05
13377 ATXN2L 16_23 2.444e-01 99.41 7.227e-05
13370 SULT1A2 16_23 1.759e-01 102.55 5.367e-05
1356 SEC16B 1_87 2.289e-02 95.88 6.529e-06
13371 SULT1A2 16_23 8.449e-02 103.05 2.591e-05
17992 PMS2P3 7_48 9.851e-01 100.05 2.933e-04
13372 SULT1A2 16_23 3.060e-02 96.20 8.759e-06
4840 POC5 5_44 9.577e-03 87.64 2.497e-06
13375 SULT1A1 16_23 1.706e-02 91.54 4.645e-06
17993 PMS2P3 7_48 1.530e-02 91.01 4.144e-06
13417 BCKDK 16_24 1.886e-02 76.35 4.284e-06
3304 CTD-2330K9.3 3_35 5.551e-16 1413.26 2.334e-18
intron_id z num_eqtl
18117 intron_11_27640005_27659171 13.138 1
3305 intron_3_49999513_50048245 -12.539 1
1841 intron_2_24972232_24979060 11.288 1
1842 intron_2_24972232_25001415 11.288 1
1840 intron_2_24972232_24972626 -11.288 1
5718 intron_6_34757954_34762595 -11.130 1
13397 intron_16_30007090_30007173 -10.811 2
3307 intron_3_50047326_50048245 10.783 2
13376 intron_16_28835399_28835546 10.745 1
13377 intron_16_28835399_28835549 -10.745 1
13370 intron_16_28592443_28593252 -10.684 2
1356 intron_1_177932569_177932698 10.585 1
13371 intron_16_28592481_28593252 -10.570 1
17992 intron_7_75514779_75515718 10.456 2
13372 intron_16_28593568_28595367 -10.400 1
4840 intron_5_75705787_75712854 -10.325 2
13375 intron_16_28620133_28623131 10.125 1
17993 intron_7_75514784_75515718 -9.959 1
13417 intron_16_31110499_31110688 -9.866 1
3304 intron_3_49910884_49913220 -9.780 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 49Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)DisGeNET enrichment analysis for genes with PIP>0.5
Warning in disease_enrichment(entities = genes, vocabulary = "HGNC", database =
"CURATED"): Removing duplicates from input list. Description FDR Ratio
42 Progressive cerebellar ataxia 0.02466 1/22
59 Mucinous carcinoma of breast 0.02466 1/22
78 CARDIOMYOPATHY, DILATED, 1GG 0.02466 1/22
80 PARAGANGLIOMAS 5 0.02466 1/22
81 NEUROPATHY, HEREDITARY SENSORY AND AUTONOMIC, TYPE VI 0.02466 1/22
83 EPIDERMOLYSIS BULLOSA SIMPLEX, AUTOSOMAL RECESSIVE 2 0.02466 1/22
84 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 28 0.02466 1/22
87 RETINITIS PIGMENTOSA 84 0.02466 1/22
40 Familial dilated cardiomyopathy 0.04238 2/22
66 EPIDERMOLYSIS BULLOSA SIMPLEX, AUTOSOMAL RECESSIVE (disorder) 0.05906 1/22
BgRatio
42 1/9703
59 1/9703
78 1/9703
80 1/9703
81 1/9703
83 1/9703
84 1/9703
87 1/9703
40 44/9703
66 3/9703WebGestalt enrichment analysis for genes with PIP>0.5
Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!NULLPIP Manhattan Plot
Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
Sensitivity, specificity and precision for silver standard genes
#number of genes in known annotations
print(length(known_annotations))[1] 41#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 17#significance threshold for TWAS
print(sig_thresh)[1] 4.693#number of ctwas genes
length(ctwas_genes)[1] 11#number of TWAS genes
length(twas_genes)[1] 344#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE
17838 MAST2 1_28 1.000e+00 28187.856 8.387e-02
17839 MAST2 1_28 0.000e+00 3743.189 0.000e+00
17840 MAST2 1_28 0.000e+00 972.435 0.000e+00
17841 MAST2 1_28 0.000e+00 987.956 0.000e+00
17928 SDHA 5_1 1.000e+00 12752.696 3.794e-02
17929 SDHA 5_1 0.000e+00 12151.563 0.000e+00
17960 DST 6_42 0.000e+00 3180.861 0.000e+00
17961 DST 6_42 0.000e+00 1058.014 0.000e+00
17962 DST 6_42 0.000e+00 1081.305 0.000e+00
17963 DST 6_42 1.000e+00 13123.329 3.905e-02
17964 DST 6_42 0.000e+00 223.862 0.000e+00
17965 DST 6_42 0.000e+00 419.570 0.000e+00
18147 MRPL21 11_38 1.000e+00 28867.838 8.589e-02
18148 MRPL21 11_38 0.000e+00 21357.221 0.000e+00
18157 SLC38A2 12_29 1.000e+00 17149.914 5.103e-02
18244 MOK 14_53 5.064e-05 10000.516 1.507e-06
18245 MOK 14_53 7.391e-06 74.914 1.647e-09
18246 MOK 14_53 7.391e-06 74.914 1.647e-09
18247 MOK 14_53 4.217e-06 1032.993 1.296e-08
18248 MOK 14_53 3.976e-06 3226.642 3.817e-08
18249 MOK 14_53 3.972e-06 3208.830 3.792e-08
18250 MOK 14_53 3.978e-06 3234.584 3.828e-08
18251 MOK 14_53 3.788e-06 2207.862 2.488e-08
18252 MOK 14_53 3.668e-06 6820.017 7.442e-08
18253 MOK 14_53 3.673e-06 6699.666 7.321e-08
18254 MOK 14_53 4.288e-06 4297.020 5.482e-08
18255 MOK 14_53 4.298e-06 4294.182 5.492e-08
18256 MOK 14_53 1.000e+00 10211.281 3.038e-02
18326 TNFSF12-TNFSF13 17_7 5.887e-03 9.464 1.658e-07
18327 TNFSF12-TNFSF13 17_7 9.191e-01 29.037 7.940e-05
18424 ZNF559-ZNF177 19_9 1.000e+00 18202.443 5.416e-02
intron_id z num_eqtl
17838 intron_1_46019697_46021950 -4.30144 1
17839 intron_1_46028933_46029466 -0.21936 1
17840 intron_1_46034266_46034538 0.79737 1
17841 intron_1_46034266_46034827 -1.03108 1
17928 intron_5_228333_230876 2.90472 1
17929 intron_5_240476_250992 -2.69645 2
17960 intron_6_56463156_56463565 1.73888 2
17961 intron_6_56463764_56464685 -0.85070 2
17962 intron_6_56463764_56466078 0.85970 2
17963 intron_6_56464756_56466078 2.88062 1
17964 intron_6_56529774_56529974 0.16085 1
17965 intron_6_56598006_56598476 3.73675 1
18147 intron_11_68898012_68900535 4.37175 1
18148 intron_11_68898012_68900548 -3.96402 2
18157 intron_12_46366945_46367076 2.95342 1
18244 intron_14_102226408_102232535 2.93264 1
18245 intron_14_102229657_102231707 -1.27036 1
18246 intron_14_102229657_102232535 1.27036 1
18247 intron_14_102232708_102233688 0.73816 1
18248 intron_14_102250987_102251444 -0.07538 2
18249 intron_14_102250987_102251756 0.07772 2
18250 intron_14_102250990_102251756 0.07434 2
18251 intron_14_102251804_102251917 0.19341 1
18252 intron_14_102251995_102263546 0.72392 2
18253 intron_14_102251995_102283478 -0.71673 2
18254 intron_14_102263616_102265823 -0.15763 1
18255 intron_14_102263616_102283478 -0.16195 2
18256 intron_14_102265912_102283478 2.80412 1
18326 intron_17_7559297_7559846 1.33178 1
18327 intron_17_7559702_7560049 -4.22468 1
18424 intron_19_9364948_9376316 -3.33796 1#sensitivity / recall
print(sensitivity)ctwas TWAS
0.000 0.122 #specificity
print(specificity) ctwas TWAS
0.9968 0.9626 #precision / PPV
print(precision) ctwas TWAS
0.00000 0.01453 
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] readxl_1.3.1 forcats_0.5.1
[3] stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_2.1.1
[7] tidyr_1.1.4 tidyverse_1.3.1
[9] tibble_3.1.6 WebGestaltR_0.4.4
[11] disgenet2r_0.99.2 enrichR_3.0
[13] cowplot_1.0.0 ggplot2_3.3.5
[15] EnsDb.Hsapiens.v79_2.99.0 ensembldb_2.8.0
[17] AnnotationFilter_1.8.0 GenomicFeatures_1.36.3
[19] AnnotationDbi_1.46.0 Biobase_2.44.0
[21] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0
[23] IRanges_2.18.1 S4Vectors_0.22.1
[25] BiocGenerics_0.30.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] ggbeeswarm_0.6.0 colorspace_2.0-2
[3] rjson_0.2.20 ellipsis_0.3.2
[5] rprojroot_2.0.2 XVector_0.24.0
[7] fs_1.5.2 rstudioapi_0.13
[9] farver_2.1.0 ggrepel_0.9.1
[11] bit64_4.0.5 lubridate_1.8.0
[13] fansi_0.5.0 xml2_1.3.3
[15] codetools_0.2-16 doParallel_1.0.16
[17] cachem_1.0.6 knitr_1.36
[19] jsonlite_1.7.2 apcluster_1.4.8
[21] Cairo_1.5-12.2 Rsamtools_2.0.0
[23] broom_0.7.10 dbplyr_2.1.1
[25] compiler_3.6.1 httr_1.4.2
[27] backports_1.4.1 assertthat_0.2.1
[29] Matrix_1.2-18 fastmap_1.1.0
[31] lazyeval_0.2.2 cli_3.1.0
[33] later_0.8.0 htmltools_0.5.2
[35] prettyunits_1.1.1 tools_3.6.1
[37] igraph_1.2.10 gtable_0.3.0
[39] glue_1.5.1 GenomeInfoDbData_1.2.1
[41] reshape2_1.4.4 doRNG_1.8.2
[43] Rcpp_1.0.7 cellranger_1.1.0
[45] jquerylib_0.1.4 vctrs_0.3.8
[47] Biostrings_2.52.0 svglite_1.2.2
[49] rtracklayer_1.44.4 iterators_1.0.13
[51] xfun_0.29 rvest_1.0.2
[53] lifecycle_1.0.1 rngtools_1.5.2
[55] XML_3.99-0.3 zlibbioc_1.30.0
[57] scales_1.1.1 vroom_1.5.7
[59] hms_1.1.1 promises_1.0.1
[61] ProtGenerics_1.16.0 SummarizedExperiment_1.14.1
[63] yaml_2.2.1 curl_4.3.2
[65] memoise_2.0.1 ggrastr_1.0.1
[67] gdtools_0.1.9 biomaRt_2.40.1
[69] stringi_1.7.6 RSQLite_2.2.8
[71] highr_0.9 foreach_1.5.1
[73] BiocParallel_1.18.0 rlang_0.4.12
[75] pkgconfig_2.0.3 matrixStats_0.57.0
[77] bitops_1.0-7 evaluate_0.14
[79] lattice_0.20-38 GenomicAlignments_1.20.1
[81] labeling_0.4.2 bit_4.0.4
[83] tidyselect_1.1.1 plyr_1.8.6
[85] magrittr_2.0.1 R6_2.5.1
[87] generics_0.1.1 DelayedArray_0.10.0
[89] DBI_1.1.1 haven_2.4.3
[91] pillar_1.6.4 whisker_0.3-2
[93] withr_2.4.3 RCurl_1.98-1.5
[95] modelr_0.1.8 crayon_1.4.2
[97] utf8_1.2.2 tzdb_0.2.0
[99] rmarkdown_2.11 progress_1.2.2
[101] grid_3.6.1 data.table_1.14.2
[103] blob_1.2.2 git2r_0.26.1
[105] reprex_2.0.1 digest_0.6.29
[107] httpuv_1.5.1 munsell_0.5.0
[109] beeswarm_0.2.3 vipor_0.4.5