BMI - Brain Cortex
sheng Qian
2021-2-6
Last updated: 2022-02-22
Checks: 6 1
Knit directory: cTWAS_analysis/
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|---|---|---|---|---|
| Rmd | 1058990 | sq-96 | 2022-02-22 | update |
Weight QC
#number of imputed weights
nrow(qclist_all)[1] 30990#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2930 2095 1825 1113 1310 1640 1721 1105 1264 1340 1822 1551 570 1023 1037 1575
17 18 19 20 21 22
2139 377 2185 993 407 968 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 28323#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.9139#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$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
0.0055304 0.0002828 #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
21.68 17.20 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 30990 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
0.01105 0.10902 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.7502 16.4671Genes with highest PIPs
genename region_tag susie_pip mu2 PVE
29806 SEMA3F 3_35 1.0000 72455.47 2.156e-01
30617 SLC38A2 12_29 1.0000 13778.71 4.100e-02
30928 ZNF559-ZNF177 19_9 1.0000 14911.89 4.437e-02
30135 DST 6_42 1.0000 15112.83 4.496e-02
30586 MRPL21 11_38 1.0000 24510.28 7.292e-02
30679 TMOD3 15_21 1.0000 32524.60 9.677e-02
30017 NSD1 5_106 0.9900 13186.57 3.884e-02
30188 PMS2P3 7_48 0.9753 97.68 2.834e-04
30762 TNFSF12-TNFSF13 17_7 0.9175 28.51 7.783e-05
30518 CD151 11_1 0.8857 35.56 9.371e-05
18878 SPG20 13_13 0.7925 12914.66 3.045e-02
16388 EFEMP2 11_36 0.7865 51.61 1.208e-04
17631 POU6F1 12_31 0.7830 30.57 7.122e-05
2226 EDEM3 1_92 0.7789 30.85 7.149e-05
1006 NASP 1_28 0.7676 29.44 6.724e-05
6904 ANAPC4 4_21 0.7611 108.50 2.457e-04
22411 COG4 16_37 0.7603 35.09 7.938e-05
5169 ANKRD28 3_11 0.7528 22.61 5.065e-05
3760 FAM178B 2_57 0.7452 2674.95 5.931e-03
12318 SLC25A37 8_24 0.7205 26.65 5.712e-05
intron_id z num_eqtl
29806 intron_3_50174350_50175096 -7.509 2
30617 intron_12_46367340_46370512 2.953 1
30928 intron_19_9364948_9371610 3.338 1
30135 intron_6_56468999_56469883 2.984 1
30586 intron_11_68898012_68900548 -4.372 1
30679 intron_15_51941598_51947292 4.606 1
30017 intron_5_177260168_177267562 2.934 1
30188 intron_7_75514779_75515718 10.412 2
30762 intron_17_7559702_7560049 -4.225 1
30518 intron_11_838367_838445 6.013 1
18878 intron_13_36312477_36313968 2.776 1
16388 intron_11_65872018_65872244 8.270 1
17631 intron_12_51206883_51217642 -4.747 1
2226 intron_1_184711877_184712433 -5.433 1
1006 intron_1_45600457_45602255 5.724 2
6904 intron_4_25407253_25409698 -10.773 2
22411 intron_16_70481144_70481359 -6.852 1
5169 intron_3_15677627_15678209 3.108 1
3760 intron_2_96902707_96921165 2.963 1
12318 intron_8_23529212_23566108 5.008 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE
29806 SEMA3F 3_35 1.000000 72455 0.2155726
29798 CTD-2330K9.3 3_35 0.000000 51230 0.0000000
13262 CCDC171 9_13 0.000000 44381 0.0000000
25966 MAST3 19_14 0.000000 40527 0.0000000
29804 RBM6 3_35 0.000000 38536 0.0000000
29800 RBM6 3_35 0.000000 38529 0.0000000
29801 RBM6 3_35 0.000000 38529 0.0000000
29726 NADK 1_1 0.000000 37896 0.0000000
25967 MAST3 19_14 0.000000 36621 0.0000000
13264 CCDC171 9_13 0.000000 35790 0.0000000
29727 NADK 1_1 0.000000 34985 0.0000000
29728 NADK 1_1 0.000000 34985 0.0000000
29736 CFAP74 1_1 0.000000 34426 0.0000000
30361 NT5C2 10_66 0.000000 33282 0.0000000
30358 NT5C2 10_66 0.000000 33282 0.0000000
30679 TMOD3 15_21 0.999991 32525 0.0967677
30676 TMOD3 15_21 0.003702 32490 0.0003578
30675 TMOD3 15_21 0.002310 32488 0.0002233
30677 TMOD3 15_21 0.000000 31963 0.0000000
30678 TMOD3 15_21 0.000000 30119 0.0000000
intron_id z num_eqtl
29806 intron_3_50174350_50175096 -7.509 2
29798 intron_3_49910884_49913220 -9.780 1
13262 intron_9_15888088_15920270 7.903 1
25966 intron_19_18147017_18147443 -6.726 1
29804 intron_3_50059746_50060956 12.537 1
29800 intron_3_49999513_50046716 12.539 1
29801 intron_3_49999513_50048245 -12.539 1
29726 intron_1_1753649_1754051 -5.047 1
25967 intron_19_18147044_18147443 5.863 2
13264 intron_9_15920422_15971609 -7.290 1
29727 intron_1_1753649_1754284 -4.754 1
29728 intron_1_1755476_1756258 -4.754 1
29736 intron_1_1944452_1946317 -5.444 1
30361 intron_10_103181328_103193236 5.042 1
30358 intron_10_103174982_103181185 5.037 1
30679 intron_15_51941598_51947292 4.606 1
30676 intron_15_51934354_51946990 4.604 1
30675 intron_15_51931133_51934250 4.580 1
30677 intron_15_51938260_51941501 4.653 1
30678 intron_15_51938260_51947292 -3.755 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE
29806 SEMA3F 3_35 1.00000 72455 0.215573
30679 TMOD3 15_21 0.99999 32525 0.096768
30586 MRPL21 11_38 1.00000 24510 0.072924
30135 DST 6_42 1.00000 15113 0.044964
30928 ZNF559-ZNF177 19_9 1.00000 14912 0.044366
30617 SLC38A2 12_29 1.00000 13779 0.040995
30017 NSD1 5_106 0.98999 13187 0.038840
18878 SPG20 13_13 0.79247 12915 0.030450
18877 SPG20 13_13 0.61162 12914 0.023501
30016 NSD1 5_106 0.51745 13176 0.020285
7641 SDHA 5_1 0.56230 10130 0.016947
7640 CCDC127 5_1 0.43693 10125 0.013162
29044 ASCC2 22_10 0.45428 8058 0.010892
7429 MFSD8 4_84 0.36378 6979 0.007554
7430 MFSD8 4_84 0.36378 6979 0.007554
29042 ASCC2 22_10 0.26808 8058 0.006427
3760 FAM178B 2_57 0.74524 2675 0.005931
7431 C4orf29 4_84 0.13189 6975 0.002737
7432 C4orf29 4_84 0.13189 6975 0.002737
18879 SPG20 13_13 0.05193 12890 0.001992
intron_id z num_eqtl
29806 intron_3_50174350_50175096 -7.509 2
30679 intron_15_51941598_51947292 4.606 1
30586 intron_11_68898012_68900548 -4.372 1
30135 intron_6_56468999_56469883 2.984 1
30928 intron_19_9364948_9371610 3.338 1
30617 intron_12_46367340_46370512 2.953 1
30017 intron_5_177260168_177267562 2.934 1
18878 intron_13_36312477_36313968 2.776 1
18877 intron_13_36304632_36312145 2.769 1
30016 intron_5_177235945_177239756 -2.957 1
7641 intron_5_228333_233477 2.903 1
7640 intron_5_216859_218093 2.900 1
29044 intron_22_29825780_29832245 2.842 1
7429 intron_4_127957592_127965072 2.508 1
7430 intron_4_127957592_127965897 2.508 1
29042 intron_22_29825257_29825622 -2.882 1
3760 intron_2_96902707_96921165 2.963 1
7431 intron_4_127984423_127989721 2.510 1
7432 intron_4_127984423_128008920 2.510 1
18879 intron_13_36335832_36346225 -2.819 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE
30538 BDNF-AS 11_19 0.000e+00 9588.03 0.000e+00
29800 RBM6 3_35 0.000e+00 38528.75 0.000e+00
29801 RBM6 3_35 0.000e+00 38528.75 0.000e+00
29804 RBM6 3_35 0.000e+00 38536.48 0.000e+00
9380 C6orf106 6_28 4.624e-01 176.39 2.427e-04
22074 KCTD13 16_24 5.201e-02 107.43 1.662e-05
3014 DNAJC27-AS1 2_15 4.932e-05 189.67 2.783e-08
3015 DNAJC27-AS1 2_15 4.932e-05 189.67 2.783e-08
8011 POC5 5_44 6.145e-01 94.26 1.723e-04
3013 DNAJC27-AS1 2_15 5.333e-05 185.55 2.944e-08
9385 UHRF1BP1 6_28 3.834e-02 155.93 1.779e-05
9383 UHRF1BP1 6_28 3.213e-02 155.69 1.488e-05
29795 MST1R 3_35 0.000e+00 2132.87 0.000e+00
9384 UHRF1BP1 6_28 3.775e-02 156.13 1.753e-05
29786 RNF123 3_35 0.000e+00 21766.87 0.000e+00
29787 RNF123 3_35 0.000e+00 21766.87 0.000e+00
29784 RNF123 3_35 0.000e+00 21749.39 0.000e+00
29790 RNF123 3_35 0.000e+00 21764.16 0.000e+00
29789 RNF123 3_35 0.000e+00 21764.16 0.000e+00
9381 C6orf106 6_28 3.932e-03 142.15 1.663e-06
intron_id z num_eqtl
30538 intron_11_27658462_27659171 -13.14 1
29800 intron_3_49999513_50046716 12.54 1
29801 intron_3_49999513_50048245 -12.54 1
29804 intron_3_50059746_50060956 12.54 1
9380 intron_6_34654779_34696446 -11.54 2
22074 intron_16_29925581_29925790 11.38 1
3014 intron_2_24972232_24979060 11.29 1
3015 intron_2_24972232_25001415 11.29 1
8011 intron_5_75690562_75692396 11.29 1
3013 intron_2_24972232_24972626 -11.12 2
9385 intron_6_34859571_34864002 11.10 1
9383 intron_6_34835464_34836156 -11.08 1
29795 intron_3_49887562_49889924 11.08 2
9384 intron_6_34836348_34855617 11.06 1
29786 intron_3_49712656_49713513 10.96 1
29787 intron_3_49712892_49713513 -10.96 1
29784 intron_3_49698822_49698980 -10.96 1
29790 intron_3_49714174_49715575 -10.95 1
29789 intron_3_49714174_49715492 10.95 1
9381 intron_6_34688933_34696446 10.90 1Comparing z scores and PIPs
[1] 0.02114 genename region_tag susie_pip mu2 PVE
30538 BDNF-AS 11_19 0.000e+00 9588.03 0.000e+00
29800 RBM6 3_35 0.000e+00 38528.75 0.000e+00
29801 RBM6 3_35 0.000e+00 38528.75 0.000e+00
29804 RBM6 3_35 0.000e+00 38536.48 0.000e+00
9380 C6orf106 6_28 4.624e-01 176.39 2.427e-04
22074 KCTD13 16_24 5.201e-02 107.43 1.662e-05
3014 DNAJC27-AS1 2_15 4.932e-05 189.67 2.783e-08
3015 DNAJC27-AS1 2_15 4.932e-05 189.67 2.783e-08
8011 POC5 5_44 6.145e-01 94.26 1.723e-04
3013 DNAJC27-AS1 2_15 5.333e-05 185.55 2.944e-08
9385 UHRF1BP1 6_28 3.834e-02 155.93 1.779e-05
9383 UHRF1BP1 6_28 3.213e-02 155.69 1.488e-05
29795 MST1R 3_35 0.000e+00 2132.87 0.000e+00
9384 UHRF1BP1 6_28 3.775e-02 156.13 1.753e-05
29786 RNF123 3_35 0.000e+00 21766.87 0.000e+00
29787 RNF123 3_35 0.000e+00 21766.87 0.000e+00
29784 RNF123 3_35 0.000e+00 21749.39 0.000e+00
29790 RNF123 3_35 0.000e+00 21764.16 0.000e+00
29789 RNF123 3_35 0.000e+00 21764.16 0.000e+00
9381 C6orf106 6_28 3.932e-03 142.15 1.663e-06
intron_id z num_eqtl
30538 intron_11_27658462_27659171 -13.14 1
29800 intron_3_49999513_50046716 12.54 1
29801 intron_3_49999513_50048245 -12.54 1
29804 intron_3_50059746_50060956 12.54 1
9380 intron_6_34654779_34696446 -11.54 2
22074 intron_16_29925581_29925790 11.38 1
3014 intron_2_24972232_24979060 11.29 1
3015 intron_2_24972232_25001415 11.29 1
8011 intron_5_75690562_75692396 11.29 1
3013 intron_2_24972232_24972626 -11.12 2
9385 intron_6_34859571_34864002 11.10 1
9383 intron_6_34835464_34836156 -11.08 1
29795 intron_3_49887562_49889924 11.08 2
9384 intron_6_34836348_34855617 11.06 1
29786 intron_3_49712656_49713513 10.96 1
29787 intron_3_49712892_49713513 -10.96 1
29784 intron_3_49698822_49698980 -10.96 1
29790 intron_3_49714174_49715575 -10.95 1
29789 intron_3_49714174_49715492 10.95 1
9381 intron_6_34688933_34696446 10.90 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 55Uploading 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"
Term Overlap Adjusted.P.value
1 clathrin-coated vesicle membrane (GO:0030665) 4/90 0.007392
2 basement membrane (GO:0005604) 3/52 0.014063
Genes
1 NECAP2;AP1G1;DENND1A;SYT9
2 EFEMP2;CD151;DST
[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
88 SAUL-WILSON SYNDROME 0.0238 1/20
94 Nephropathy with Pretibial Epidermolysis Bullosa and Deafness 0.0238 1/20
111 Bardet-Biedl syndrome 4 (disorder) 0.0238 1/20
112 CONGENITAL DISORDER OF GLYCOSYLATION, TYPE IIj 0.0238 1/20
113 CARDIOMYOPATHY, DILATED, 1GG 0.0238 1/20
115 PARAGANGLIOMAS 5 0.0238 1/20
116 CUTIS LAXA, AUTOSOMAL RECESSIVE, TYPE IB 0.0238 1/20
117 NEUROPATHY, HEREDITARY SENSORY AND AUTONOMIC, TYPE VI 0.0238 1/20
120 EPIDERMOLYSIS BULLOSA SIMPLEX, AUTOSOMAL RECESSIVE 2 0.0238 1/20
124 5q35 microduplication syndrome 0.0238 1/20
BgRatio
88 1/9703
94 1/9703
111 1/9703
112 1/9703
113 1/9703
115 1/9703
116 1/9703
117 1/9703
120 1/9703
124 1/9703WebGestalt 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: 13 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$genename))[1] 19#significance threshold for TWAS
print(sig_thresh)[1] 4.797#number of ctwas genes
length(ctwas_genes)[1] 10#number of TWAS genes
length(twas_genes)[1] 655#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE
30015 NSD1 5_106 0.000e+00 6431.23 0.000e+00
30016 NSD1 5_106 5.174e-01 13175.97 2.028e-02
30017 NSD1 5_106 9.900e-01 13186.57 3.884e-02
30018 NSD1 5_106 0.000e+00 3148.36 0.000e+00
30131 DST 6_42 4.267e-05 15082.08 1.915e-06
30132 DST 6_42 0.000e+00 4270.66 0.000e+00
30133 DST 6_42 0.000e+00 6249.56 0.000e+00
30134 DST 6_42 0.000e+00 8630.70 0.000e+00
30135 DST 6_42 1.000e+00 15112.83 4.496e-02
30136 DST 6_42 0.000e+00 468.01 0.000e+00
30137 DST 6_42 0.000e+00 161.41 0.000e+00
30585 MRPL21 11_38 0.000e+00 23808.74 0.000e+00
30586 MRPL21 11_38 1.000e+00 24510.28 7.292e-02
30617 SLC38A2 12_29 1.000e+00 13778.71 4.100e-02
30675 TMOD3 15_21 2.310e-03 32487.98 2.233e-04
30676 TMOD3 15_21 3.702e-03 32490.24 3.578e-04
30677 TMOD3 15_21 0.000e+00 31963.21 0.000e+00
30678 TMOD3 15_21 0.000e+00 30118.90 0.000e+00
30679 TMOD3 15_21 1.000e+00 32524.60 9.677e-02
30761 TNFSF12-TNFSF13 17_7 4.529e-03 6.93 9.338e-08
30762 TNFSF12-TNFSF13 17_7 9.175e-01 28.51 7.783e-05
30926 ZNF559-ZNF177 19_9 0.000e+00 14360.50 0.000e+00
30927 ZNF559-ZNF177 19_9 0.000e+00 14360.50 0.000e+00
30928 ZNF559-ZNF177 19_9 1.000e+00 14911.89 4.437e-02
30929 ZNF559-ZNF177 19_9 0.000e+00 13792.34 0.000e+00
30930 ZNF559-ZNF177 19_9 0.000e+00 14306.95 0.000e+00
intron_id z num_eqtl
30015 intron_5_177136030_177191884 -0.3137 1
30016 intron_5_177235945_177239756 -2.9570 1
30017 intron_5_177260168_177267562 2.9345 1
30018 intron_5_177273784_177280565 -2.2269 1
30131 intron_6_56463156_56463565 2.9051 1
30132 intron_6_56463764_56464685 1.5193 2
30133 intron_6_56463764_56466078 -1.8465 2
30134 intron_6_56464756_56466078 2.4674 2
30135 intron_6_56468999_56469883 2.9842 1
30136 intron_6_56598006_56598476 3.7368 1
30137 intron_6_56851604_56900421 1.5511 1
30585 intron_11_68898012_68900535 4.2403 2
30586 intron_11_68898012_68900548 -4.3717 1
30617 intron_12_46367340_46370512 2.9534 1
30675 intron_15_51931133_51934250 4.5797 1
30676 intron_15_51934354_51946990 4.6040 1
30677 intron_15_51938260_51941501 4.6530 1
30678 intron_15_51938260_51947292 -3.7554 2
30679 intron_15_51941598_51947292 4.6062 1
30761 intron_17_7559297_7559846 1.6079 2
30762 intron_17_7559702_7560049 -4.2247 1
30926 intron_19_9352004_9364862 3.5900 1
30927 intron_19_9355721_9357828 -3.5900 1
30928 intron_19_9364948_9371610 3.3380 1
30929 intron_19_9364948_9376316 -2.8723 2
30930 intron_19_9371752_9376316 3.4833 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.0000 0.2439 #specificity
print(specificity) ctwas TWAS
0.9987 0.9519 #precision / PPV
print(precision) ctwas TWAS
0.00000 0.01527 
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