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] 31854#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2966 2106 1909 1122 1289 1627 1747 1117 1313 1326 1918 1617 589 1091 1116 1582
17 18 19 20 21 22
2254 398 2296 1033 414 1024 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 28977#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.9097#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$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
0.0072010 0.0002783 #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
18.26 17.28 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 31854 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
0.01246 0.10784 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.5037 16.7477Genes with highest PIPs
genename region_tag susie_pip mu2 PVE
30707 ANKRD28 3_11 1.0000 37497.55 1.116e-01
30814 DST 6_42 1.0000 12290.08 3.657e-02
31486 LEO1 15_21 0.9999 28041.27 8.342e-02
31385 SPG20 13_13 0.9841 10509.37 3.077e-02
30809 PHACTR1 6_11 0.9620 41.98 1.202e-04
31645 TNFSF12-TNFSF13 17_7 0.9349 28.18 7.839e-05
30840 SLC37A3 7_86 0.9055 24.04 6.476e-05
31207 EFEMP2 11_36 0.9007 52.47 1.406e-04
27934 PPP6R1 19_38 0.7975 23.40 5.551e-05
12641 FAM160B2 8_23 0.7838 28.91 6.743e-05
12715 SLC25A37 8_24 0.7813 25.55 5.938e-05
16323 TRIM66 11_7 0.7776 41.75 9.658e-05
30982 CTBP2 10_78 0.7490 31.62 7.046e-05
29527 BCL2L13 22_2 0.7464 28.04 6.228e-05
7162 ANAPC4 4_21 0.7441 106.81 2.365e-04
3169 BIRC6 2_20 0.7407 1159.91 2.556e-03
18013 SLC38A2 12_29 0.7321 11262.14 2.453e-02
8892 SFXN1 5_105 0.7321 645.50 1.406e-03
18125 CBX5 12_33 0.7316 25.44 5.537e-05
10951 ANKMY2 7_16 0.7292 23.06 5.003e-05
intron_id z num_eqtl
30707 intron_3_15713641_15714571 5.3854 1
30814 intron_6_56468999_56469883 2.9850 1
31486 intron_15_51954575_51958742 -4.6095 1
31385 intron_13_36312477_36313968 2.7757 1
30809 intron_6_13182686_13205815 -6.4898 2
31645 intron_17_7559702_7560049 -4.2247 1
30840 intron_7_140348767_140351273 -4.5689 1
31207 intron_11_65872018_65872244 8.2696 1
27934 intron_19_55247109_55258435 -4.1644 1
12641 intron_8_22098310_22098423 -5.0631 1
12715 intron_8_23529212_23566108 5.0076 1
16323 intron_11_8620572_8621032 6.7162 2
30982 intron_10_125039155_125160319 -5.4234 1
29527 intron_22_17655832_17683214 5.1962 2
7162 intron_4_25407253_25409698 -10.7729 2
3169 intron_2_32575366_32590806 -3.2457 1
18013 intron_12_46367340_46370512 2.9534 1
8892 intron_5_175512196_175513463 0.2299 1
18125 intron_12_54257692_54280008 -4.7317 1
10951 intron_7_16602509_16604721 3.8750 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE
13637 CCDC171 9_13 0.0000000 42556 0.000e+00
13639 CCDC171 9_13 0.0000000 40838 0.000e+00
13636 CCDC171 9_13 0.0000000 40403 0.000e+00
13638 CCDC171 9_13 0.0000000 38366 0.000e+00
30707 ANKRD28 3_11 0.9999999 37498 1.116e-01
30709 ANKRD28 3_11 0.0000000 36948 0.000e+00
30710 ANKRD28 3_11 0.0000000 36919 0.000e+00
26803 PIK3R2 19_14 0.0000000 35388 0.000e+00
30923 NT5C2 10_66 0.0000000 33060 0.000e+00
30920 NT5C2 10_66 0.0000000 33060 0.000e+00
30903 C10orf32-ASMT 10_66 0.0000000 28606 0.000e+00
30917 NT5C2 10_66 0.0000000 28568 0.000e+00
31486 LEO1 15_21 0.9999230 28041 8.342e-02
31484 TMOD3 15_21 0.0003981 27971 3.313e-05
30783 ANAPC10 4_94 0.0000000 27177 0.000e+00
30784 ANAPC10 4_94 0.0000000 27177 0.000e+00
30579 PIK3R3 1_29 0.7037875 26246 5.496e-02
30580 PIK3R3 1_29 0.7037875 26246 5.496e-02
30581 PIK3R3 1_29 0.1689523 26236 1.319e-02
30575 MAST2 1_29 0.0000000 25968 0.000e+00
intron_id z num_eqtl
13637 intron_9_15883191_15885514 8.979 1
13639 intron_9_15889101_15920270 -7.233 1
13636 intron_9_15874663_15920270 -8.163 1
13638 intron_9_15888088_15888983 -7.315 1
30707 intron_3_15713641_15714571 5.385 1
30709 intron_3_15714656_15720915 -5.253 1
30710 intron_3_15737233_15751750 5.255 1
26803 intron_19_18163388_18166160 -6.025 1
30923 intron_10_103181328_103193236 5.042 1
30920 intron_10_103174982_103181185 5.037 1
30903 intron_10_102870211_102872448 -4.742 1
30917 intron_10_103111805_103139406 -4.364 2
31486 intron_15_51954575_51958742 -4.610 1
31484 intron_15_51934354_51946990 4.604 1
30783 intron_4_144995603_145064572 4.514 1
30784 intron_4_144995603_145081660 -4.514 1
30579 intron_1_46046625_46055795 -4.435 1
30580 intron_1_46046625_46061929 -4.435 1
30581 intron_1_46055971_46061929 4.419 1
30575 intron_1_46010939_46021950 4.309 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE
30707 ANKRD28 3_11 1.0000 37497.6 0.111564
31486 LEO1 15_21 0.9999 28041.3 0.083423
30579 PIK3R3 1_29 0.7038 26245.8 0.054957
30580 PIK3R3 1_29 0.7038 26245.8 0.054957
30814 DST 6_42 1.0000 12290.1 0.036566
31385 SPG20 13_13 0.9841 10509.4 0.030771
18013 SLC38A2 12_29 0.7321 11262.1 0.024531
30581 PIK3R3 1_29 0.1690 26236.0 0.013188
26499 ZNF562 19_9 0.5344 6887.6 0.010950
7841 CCDC127 5_1 0.2443 9575.7 0.006961
7630 MFSD8 4_84 0.2688 6310.1 0.005047
7629 MFSD8 4_84 0.2688 6310.1 0.005047
31387 SPG20 13_13 0.1192 10490.6 0.003721
31386 SPG20 13_13 0.1192 10490.6 0.003721
26497 ZNF561-AS1 19_9 0.1757 6885.2 0.003599
26498 ZNF561-AS1 19_9 0.1757 6885.2 0.003599
3169 BIRC6 2_20 0.7407 1159.9 0.002556
7632 C4orf29 4_84 0.1291 6306.1 0.002422
7631 C4orf29 4_84 0.1203 6305.8 0.002257
8892 SFXN1 5_105 0.7321 645.5 0.001406
intron_id z num_eqtl
30707 intron_3_15713641_15714571 5.3854 1
31486 intron_15_51954575_51958742 -4.6095 1
30579 intron_1_46046625_46055795 -4.4353 1
30580 intron_1_46046625_46061929 -4.4353 1
30814 intron_6_56468999_56469883 2.9850 1
31385 intron_13_36312477_36313968 2.7757 1
18013 intron_12_46367340_46370512 2.9534 1
30581 intron_1_46055971_46061929 4.4189 1
26499 intron_19_9656653_9658009 -2.6445 1
7841 intron_5_216859_218093 2.9002 1
7630 intron_4_127957592_127965897 2.5077 1
7629 intron_4_127957592_127965072 -2.5077 1
31387 intron_13_36335832_36346582 2.8188 1
31386 intron_13_36335832_36346240 -2.8188 1
26497 intron_19_9632417_9633460 2.6214 1
26498 intron_19_9632417_9645074 -2.6214 1
3169 intron_2_32575366_32590806 -3.2457 1
7632 intron_4_127984423_128008920 2.5104 1
7631 intron_4_127984423_127989721 2.5099 1
8892 intron_5_175512196_175513463 0.2299 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE
5612 RBM6 3_35 1.240e-03 883.21 3.258e-06
5616 RBM6 3_35 1.201e-03 883.67 3.157e-06
5615 RBM6 3_35 1.201e-03 883.67 3.157e-06
31008 BDNF-AS 11_19 0.000e+00 10361.36 0.000e+00
9699 C6orf106 6_28 3.448e-01 174.17 1.787e-04
9700 C6orf106 6_28 1.741e-01 165.06 8.551e-05
5605 MST1R 3_35 1.761e-11 180.79 9.471e-15
3028 DNAJC27-AS1 2_15 4.842e-05 186.70 2.690e-08
8202 POC5 5_44 6.189e-01 92.55 1.704e-04
3026 DNAJC27-AS1 2_15 5.217e-05 182.88 2.839e-08
22656 KCTD13 16_24 1.666e-02 101.63 5.037e-06
9703 UHRF1BP1 6_28 3.333e-02 154.47 1.532e-05
5596 RNF123 3_35 1.462e-11 800.39 3.482e-14
5597 RNF123 3_35 1.462e-11 800.39 3.482e-14
5598 RNF123 3_35 1.214e-11 799.40 2.886e-14
5599 RNF123 3_35 1.214e-11 799.40 2.886e-14
9701 C6orf106 6_28 3.543e-03 139.75 1.473e-06
22631 SH2B1 16_23 1.279e-01 62.66 2.384e-05
22673 DOC2A 16_24 2.674e-02 92.65 7.370e-06
7162 ANAPC4 4_21 7.441e-01 106.81 2.365e-04
intron_id z num_eqtl
5612 intron_3_49999513_50048245 -12.54 1
5616 intron_3_50059746_50060956 12.54 1
5615 intron_3_50059746_50060801 -12.54 1
31008 intron_11_27640005_27659171 11.99 1
9699 intron_6_34654779_34688841 11.54 2
9700 intron_6_34654779_34696446 -11.49 2
5605 intron_3_49887562_49889924 11.31 2
3028 intron_2_24972232_25001415 11.29 1
8202 intron_5_75690562_75692396 11.29 1
3026 intron_2_24972232_24972626 -11.13 2
22656 intron_16_29923359_29925539 11.12 2
9703 intron_6_34836348_34855617 11.06 1
5596 intron_3_49712656_49713513 10.96 1
5597 intron_3_49712892_49713513 -10.96 1
5598 intron_3_49714174_49715492 10.95 1
5599 intron_3_49714174_49715575 -10.95 1
9701 intron_6_34688933_34696446 10.90 1
22631 intron_16_28869383_28870709 -10.81 2
22673 intron_16_30007090_30007173 -10.79 2
7162 intron_4_25407253_25409698 -10.77 2Comparing z scores and PIPs
[1] 0.02088 genename region_tag susie_pip mu2 PVE
5612 RBM6 3_35 1.240e-03 883.21 3.258e-06
5616 RBM6 3_35 1.201e-03 883.67 3.157e-06
5615 RBM6 3_35 1.201e-03 883.67 3.157e-06
31008 BDNF-AS 11_19 0.000e+00 10361.36 0.000e+00
9699 C6orf106 6_28 3.448e-01 174.17 1.787e-04
9700 C6orf106 6_28 1.741e-01 165.06 8.551e-05
5605 MST1R 3_35 1.761e-11 180.79 9.471e-15
3028 DNAJC27-AS1 2_15 4.842e-05 186.70 2.690e-08
8202 POC5 5_44 6.189e-01 92.55 1.704e-04
3026 DNAJC27-AS1 2_15 5.217e-05 182.88 2.839e-08
22656 KCTD13 16_24 1.666e-02 101.63 5.037e-06
9703 UHRF1BP1 6_28 3.333e-02 154.47 1.532e-05
5596 RNF123 3_35 1.462e-11 800.39 3.482e-14
5597 RNF123 3_35 1.462e-11 800.39 3.482e-14
5598 RNF123 3_35 1.214e-11 799.40 2.886e-14
5599 RNF123 3_35 1.214e-11 799.40 2.886e-14
9701 C6orf106 6_28 3.543e-03 139.75 1.473e-06
22631 SH2B1 16_23 1.279e-01 62.66 2.384e-05
22673 DOC2A 16_24 2.674e-02 92.65 7.370e-06
7162 ANAPC4 4_21 7.441e-01 106.81 2.365e-04
intron_id z num_eqtl
5612 intron_3_49999513_50048245 -12.54 1
5616 intron_3_50059746_50060956 12.54 1
5615 intron_3_50059746_50060801 -12.54 1
31008 intron_11_27640005_27659171 11.99 1
9699 intron_6_34654779_34688841 11.54 2
9700 intron_6_34654779_34696446 -11.49 2
5605 intron_3_49887562_49889924 11.31 2
3028 intron_2_24972232_25001415 11.29 1
8202 intron_5_75690562_75692396 11.29 1
3026 intron_2_24972232_24972626 -11.13 2
22656 intron_16_29923359_29925539 11.12 2
9703 intron_6_34836348_34855617 11.06 1
5596 intron_3_49712656_49713513 10.96 1
5597 intron_3_49712892_49713513 -10.96 1
5598 intron_3_49714174_49715492 10.95 1
5599 intron_3_49714174_49715575 -10.95 1
9701 intron_6_34688933_34696446 10.90 1
22631 intron_16_28869383_28870709 -10.81 2
22673 intron_16_30007090_30007173 -10.79 2
7162 intron_4_25407253_25409698 -10.77 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 71Uploading 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"
Term
1 protein import into mitochondrial matrix (GO:0030150)
2 protein transmembrane import into intracellular organelle (GO:0044743)
3 serine transport (GO:0032329)
Overlap Adjusted.P.value Genes
1 3/19 0.01920 TOMM40;TIMM17A;TIMM23
2 3/32 0.04321 TOMM40;TIMM17A;TIMM23
3 2/7 0.04321 SFXN1;SLC38A2
[1] "GO_Cellular_Component_2021"
Term
1 integral component of mitochondrial membrane (GO:0032592)
2 intrinsic component of mitochondrial inner membrane (GO:0031304)
3 integral component of mitochondrial inner membrane (GO:0031305)
4 TIM23 mitochondrial import inner membrane translocase complex (GO:0005744)
Overlap Adjusted.P.value Genes
1 4/66 0.006913 TOMM40;TIMM17A;TIMM23;SFXN1
2 3/39 0.014477 TIMM17A;TIMM23;SFXN1
3 3/51 0.021360 TIMM17A;TIMM23;SFXN1
4 2/14 0.023219 TIMM17A;TIMM23
[1] "GO_Molecular_Function_2021"
Term Overlap
1 L-serine transmembrane transporter activity (GO:0015194) 2/6
2 serine transmembrane transporter activity (GO:0022889) 2/8
Adjusted.P.value Genes
1 0.01967 SFXN1;SLC38A2
2 0.01967 SFXN1;SLC38A2DisGeNET 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
8 Anemia, Neonatal 0.04104 1/35
93 Sulfite oxidase deficiency 0.04104 1/35
129 Lewy Body Disease 0.04104 2/35
146 Familial encephalopathy with neuroserpin inclusion bodies 0.04104 1/35
148 ELLIPTOCYTOSIS 3 0.04104 1/35
156 SPHEROCYTOSIS, HEREDITARY, 2 0.04104 1/35
157 RETINITIS PIGMENTOSA 42 0.04104 1/35
162 Sulfocysteinuria 0.04104 1/35
166 CUTIS LAXA, AUTOSOMAL RECESSIVE, TYPE IB 0.04104 1/35
168 NEUROPATHY, HEREDITARY SENSORY AND AUTONOMIC, TYPE VI 0.04104 1/35
BgRatio
8 1/9703
93 1/9703
129 22/9703
146 1/9703
148 1/9703
156 1/9703
157 1/9703
162 1/9703
166 1/9703
168 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: 33 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$genename))[1] 20#significance threshold for TWAS
print(sig_thresh)[1] 4.802#number of ctwas genes
length(ctwas_genes)[1] 8#number of TWAS genes
length(twas_genes)[1] 665#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE
30810 DST 6_42 1.725e-04 12264.281 6.296e-06
30811 DST 6_42 0.000e+00 6635.362 0.000e+00
30812 DST 6_42 0.000e+00 6726.554 0.000e+00
30813 DST 6_42 7.097e-05 12263.018 2.589e-06
30814 DST 6_42 1.000e+00 12290.078 3.657e-02
30815 DST 6_42 0.000e+00 393.267 0.000e+00
30816 DST 6_42 0.000e+00 133.519 0.000e+00
30838 SLC37A3 7_86 2.413e-02 14.326 1.029e-06
30839 SLC37A3 7_86 8.301e-03 6.512 1.608e-07
30840 SLC37A3 7_86 9.055e-01 24.039 6.476e-05
30841 SLC37A3 7_86 8.734e-03 7.509 1.951e-07
30842 SLC37A3 7_86 9.838e-03 8.912 2.609e-07
30843 SLC37A3 7_86 8.244e-03 6.456 1.583e-07
30844 SLC37A3 7_86 1.153e-02 9.935 3.408e-07
30845 SLC37A3 7_86 1.029e-02 9.892 3.029e-07
30846 SLC37A3 7_86 1.029e-02 9.892 3.029e-07
30847 SLC37A3 7_86 1.211e-02 9.931 3.579e-07
31385 SPG20 13_13 9.841e-01 10509.371 3.077e-02
31386 SPG20 13_13 1.192e-01 10490.574 3.721e-03
31387 SPG20 13_13 1.192e-01 10490.574 3.721e-03
31486 LEO1 15_21 9.999e-01 28041.266 8.342e-02
31642 TNFSF12-TNFSF13 17_7 6.791e-03 9.367 1.893e-07
31643 TNFSF12-TNFSF13 17_7 6.791e-03 9.367 1.893e-07
31644 TNFSF12-TNFSF13 17_7 6.791e-03 9.367 1.893e-07
31645 TNFSF12-TNFSF13 17_7 9.349e-01 28.181 7.839e-05
intron_id z num_eqtl
30810 intron_6_56463156_56463565 2.9051 1
30811 intron_6_56463764_56464685 2.1176 2
30812 intron_6_56463764_56466078 -2.1324 2
30813 intron_6_56464756_56466078 2.8924 1
30814 intron_6_56468999_56469883 2.9850 1
30815 intron_6_56598006_56598476 3.7368 1
30816 intron_6_56851604_56900421 1.5511 1
30838 intron_7_140337349_140348626 0.9305 1
30839 intron_7_140345263_140345869 -1.0554 1
30840 intron_7_140348767_140351273 -4.5689 1
30841 intron_7_140351451_140352062 -1.4265 3
30842 intron_7_140351969_140352062 1.6409 3
30843 intron_7_140352181_140355668 1.0749 2
30844 intron_7_140355764_140364408 1.4247 1
30845 intron_7_140369682_140379214 1.7377 1
30846 intron_7_140369682_140380282 1.7377 1
30847 intron_7_140380390_140382438 -1.2641 1
31385 intron_13_36312477_36313968 2.7757 1
31386 intron_13_36335832_36346240 -2.8188 1
31387 intron_13_36335832_36346582 2.8188 1
31486 intron_15_51954575_51958742 -4.6095 1
31642 intron_17_7559297_7559846 1.3318 1
31643 intron_17_7559297_7559851 1.3318 1
31644 intron_17_7559702_7559846 -1.3318 1
31645 intron_17_7559702_7560049 -4.2247 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.0000 0.2683 #specificity
print(specificity) ctwas TWAS
0.9988 0.9532 #precision / PPV
print(precision) ctwas TWAS
0.00000 0.01654 
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