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] 25404#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2325 1736 1544 992 1008 1320 1439 902 1002 1176 1512 1297 482 858 850 1166
17 18 19 20 21 22
1693 349 1800 850 332 771 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 23231#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.9145#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$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
0.0076468 0.0002809 #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
23.24 16.96 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 25404 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
0.01343 0.10682 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.5317 15.9506Genes with highest PIPs
genename region_tag susie_pip mu2 PVE
24394 DST 6_42 1.0000 16171.48 4.811e-02
25367 ASCC2 22_10 0.9963 8472.85 2.512e-02
24792 TMOD3 15_21 0.9912 32984.25 9.727e-02
24171 MAST2 1_28 0.9826 25488.50 7.451e-02
25172 GIPR 19_32 0.9516 5431.93 1.538e-02
24981 TNFSF12-TNFSF13 17_8 0.9344 28.45 7.909e-05
24250 EIF2B5 3_113 0.8200 37.60 9.173e-05
5270 SERPINI1 3_103 0.7912 23.69 5.577e-05
12907 DPYSL4 10_83 0.7903 44.55 1.048e-04
6239 SCLT1 4_85 0.7880 6544.24 1.534e-02
6240 SCLT1 4_85 0.7880 6544.24 1.534e-02
2276 SH3YL1 2_1 0.7807 45.30 1.052e-04
10065 PTPRN2 7_98 0.7802 22.72 5.274e-05
20848 DOT1L 19_3 0.7756 22.73 5.245e-05
24622 NTM 11_82 0.7684 29.27 6.690e-05
20204 RPTOR 17_45 0.7645 47.83 1.088e-04
10323 SLC25A37 8_24 0.7641 27.22 6.187e-05
25136 ZNF559-ZNF177 19_9 0.7500 15834.62 3.533e-02
25135 ZNF559-ZNF177 19_9 0.7500 15834.62 3.533e-02
14863 CBX5 12_33 0.7448 25.94 5.748e-05
intron_id z num_eqtl
24394 intron_6_56468999_56469883 2.984 1
25367 intron_22_29822464_29825087 2.935 1
24792 intron_15_51934354_51946990 4.604 1
24171 intron_1_45997799_46000959 -4.325 1
25172 intron_19_45677779_45677906 10.801 1
24981 intron_17_7559702_7560049 -4.225 1
24250 intron_3_184278313_184283785 6.036 2
5270 intron_3_167735823_167789111 -4.409 2
12907 intron_10_132187102_132190747 -6.909 2
6239 intron_4_128959777_128965227 -2.051 1
6240 intron_4_128959777_128970378 2.051 1
2276 intron_2_253115_264782 5.990 1
10065 intron_7_157682937_157898673 -4.473 1
20848 intron_19_2199939_2202700 -4.106 1
24622 intron_11_131582378_131660929 -5.156 1
20204 intron_17_80730706_80754010 -6.093 1
10323 intron_8_23529212_23566108 5.008 1
25136 intron_19_9364948_9376316 -3.338 1
25135 intron_19_9364948_9371610 3.338 1
14863 intron_12_54257692_54280008 -4.732 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE
21319 MAST3 19_14 0.000e+00 49193 0.000e+00
21323 MAST3 19_14 0.000e+00 39707 0.000e+00
21324 MAST3 19_14 0.000e+00 39707 0.000e+00
24792 TMOD3 15_21 9.912e-01 32984 9.727e-02
24789 TMOD3 15_21 9.186e-02 32978 9.013e-03
24790 TMOD3 15_21 9.186e-02 32978 9.013e-03
24791 TMOD3 15_21 0.000e+00 32449 0.000e+00
24793 LEO1 15_21 6.817e-14 32448 6.581e-15
24171 MAST2 1_28 9.826e-01 25489 7.451e-02
24170 MAST2 1_28 1.741e-02 25473 1.319e-03
24620 BDNF-AS 11_19 0.000e+00 25038 0.000e+00
24167 GPBP1L1 1_28 0.000e+00 21847 0.000e+00
17194 CATSPER2P1 15_16 0.000e+00 21782 0.000e+00
13863 IGHMBP2 11_38 0.000e+00 21753 0.000e+00
24166 NASP 1_28 0.000e+00 21214 0.000e+00
24162 NASP 1_28 0.000e+00 20834 0.000e+00
24163 NASP 1_28 0.000e+00 20834 0.000e+00
24617 BDNF-AS 11_19 0.000e+00 20652 0.000e+00
24204 R3HDM1 2_80 0.000e+00 18802 0.000e+00
24206 UBXN4 2_80 0.000e+00 18225 0.000e+00
intron_id z num_eqtl
21319 intron_19_18110741_18121685 -7.147 1
21323 intron_19_18147017_18147443 -6.726 1
21324 intron_19_18147044_18147443 6.726 1
24792 intron_15_51934354_51946990 4.604 1
24789 intron_15_51924578_51931031 -4.569 1
24790 intron_15_51924578_51934250 4.569 1
24791 intron_15_51931133_51934250 4.672 1
24793 intron_15_51958826_51959899 -4.665 1
24171 intron_1_45997799_46000959 -4.325 1
24170 intron_1_45882395_45959386 -4.311 1
24620 intron_11_27659228_27676982 -1.202 1
24167 intron_1_45642499_45654543 4.500 1
17194 intron_15_43744219_43744395 -4.508 1
13863 intron_11_68929357_68933299 4.493 1
24166 intron_1_45617591_45618061 4.297 1
24162 intron_1_45591270_45600385 3.951 1
24163 intron_1_45591270_45602255 -3.951 1
24617 intron_11_27640005_27658241 5.845 1
24204 intron_2_135680324_135709433 4.474 1
24206 intron_2_135754277_135755517 -3.871 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE
24792 TMOD3 15_21 0.99119 32984 0.097271
24171 MAST2 1_28 0.98257 25489 0.074513
24394 DST 6_42 1.00000 16171 0.048114
25136 ZNF559-ZNF177 19_9 0.75000 15835 0.035334
25135 ZNF559-ZNF177 19_9 0.75000 15835 0.035334
25367 ASCC2 22_10 0.99635 8473 0.025117
15729 SPG20 13_13 0.57737 11262 0.019346
15727 SPG20 13_13 0.57737 11262 0.019346
15726 SPG20 13_13 0.57737 11262 0.019346
15728 SPG20 13_13 0.57737 11262 0.019346
24279 CCDC127 5_1 0.47900 11416 0.016270
25172 GIPR 19_32 0.95160 5432 0.015379
6239 SCLT1 4_85 0.78796 6544 0.015342
6240 SCLT1 4_85 0.78796 6544 0.015342
6241 SCLT1 4_85 0.69420 6477 0.013378
24789 TMOD3 15_21 0.09186 32978 0.009013
24790 TMOD3 15_21 0.09186 32978 0.009013
24647 RP11-386G11.5 12_31 0.67889 1552 0.003135
1979 TATDN3 1_108 0.18847 3296 0.001848
1980 TATDN3 1_108 0.18498 3296 0.001814
intron_id z num_eqtl
24792 intron_15_51934354_51946990 4.604 1
24171 intron_1_45997799_46000959 -4.325 1
24394 intron_6_56468999_56469883 2.984 1
25136 intron_19_9364948_9376316 -3.338 1
25135 intron_19_9364948_9371610 3.338 1
25367 intron_22_29822464_29825087 2.935 1
15729 intron_13_36335832_36346582 2.819 1
15727 intron_13_36335832_36346225 -2.819 1
15726 intron_13_36335832_36336287 2.819 1
15728 intron_13_36335832_36346240 -2.819 1
24279 intron_5_216859_218093 2.900 1
25172 intron_19_45677779_45677906 10.801 1
6239 intron_4_128959777_128965227 -2.051 1
6240 intron_4_128959777_128970378 2.051 1
6241 intron_4_129039096_129043395 2.284 1
24789 intron_15_51924578_51931031 -4.569 1
24790 intron_15_51924578_51934250 4.569 1
24647 intron_12_49005543_49005743 4.788 1
1979 intron_1_212804429_212804596 3.299 1
1980 intron_1_212804651_212807736 -3.301 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE
24619 BDNF-AS 11_19 0.000e+00 9402.82 0.000e+00
24618 BDNF-AS 11_19 0.000e+00 12261.81 0.000e+00
2436 DNAJC27-AS1 2_15 8.085e-05 190.45 4.581e-08
7880 C6orf106 6_28 4.500e-01 122.03 1.634e-04
18244 DOC2A 16_24 3.101e-02 100.85 9.303e-06
2434 DNAJC27-AS1 2_15 9.014e-05 183.83 4.930e-08
4514 RNF123 3_35 2.990e-11 793.04 7.055e-14
4516 RNF123 3_35 2.955e-11 792.32 6.966e-14
1838 LMOD1 1_102 3.299e-01 113.26 1.112e-04
25172 GIPR 19_32 9.516e-01 5431.93 1.538e-02
18210 SH2B1 16_23 9.945e-02 64.66 1.913e-05
18212 SH2B1 16_23 9.945e-02 64.66 1.913e-05
18211 SH2B1 16_23 9.994e-02 64.68 1.923e-05
18206 ATXN2L 16_23 9.083e-02 64.23 1.736e-05
18205 ATXN2L 16_23 8.963e-02 64.17 1.711e-05
18237 TAOK2 16_24 2.124e-02 93.45 5.906e-06
18238 TAOK2 16_24 2.124e-02 93.45 5.906e-06
18208 SH2B1 16_23 9.076e-02 64.30 1.736e-05
18207 SH2B1 16_23 7.320e-02 63.15 1.375e-05
18197 SULT1A2 16_23 2.247e-01 63.06 4.216e-05
intron_id z num_eqtl
24619 intron_11_27658462_27659171 -13.14 1
24618 intron_11_27640005_27659171 12.24 2
2436 intron_2_24972232_25001415 11.29 1
7880 intron_6_34654779_34696446 -11.24 1
18244 intron_16_30007090_30007173 -11.06 2
2434 intron_2_24972232_24972626 -11.02 2
4514 intron_3_49712656_49713513 10.96 1
4516 intron_3_49714174_49715575 -10.96 1
1838 intron_1_201900751_201946080 -10.89 1
25172 intron_19_45677779_45677906 10.80 1
18210 intron_16_28865624_28866012 -10.76 1
18212 intron_16_28869383_28871780 10.76 1
18211 intron_16_28867033_28867331 -10.76 1
18206 intron_16_28835399_28835549 -10.74 1
18205 intron_16_28835399_28835546 10.74 1
18237 intron_16_29989805_29990543 10.73 1
18238 intron_16_29989805_29990784 -10.73 1
18208 intron_16_28863754_28866012 -10.71 2
18207 intron_16_28846827_28863668 -10.69 1
18197 intron_16_28592481_28593252 -10.51 1Comparing z scores and PIPs
[1] 0.01819 genename region_tag susie_pip mu2 PVE
24619 BDNF-AS 11_19 0.000e+00 9402.82 0.000e+00
24618 BDNF-AS 11_19 0.000e+00 12261.81 0.000e+00
2436 DNAJC27-AS1 2_15 8.085e-05 190.45 4.581e-08
7880 C6orf106 6_28 4.500e-01 122.03 1.634e-04
18244 DOC2A 16_24 3.101e-02 100.85 9.303e-06
2434 DNAJC27-AS1 2_15 9.014e-05 183.83 4.930e-08
4514 RNF123 3_35 2.990e-11 793.04 7.055e-14
4516 RNF123 3_35 2.955e-11 792.32 6.966e-14
1838 LMOD1 1_102 3.299e-01 113.26 1.112e-04
25172 GIPR 19_32 9.516e-01 5431.93 1.538e-02
18210 SH2B1 16_23 9.945e-02 64.66 1.913e-05
18212 SH2B1 16_23 9.945e-02 64.66 1.913e-05
18211 SH2B1 16_23 9.994e-02 64.68 1.923e-05
18206 ATXN2L 16_23 9.083e-02 64.23 1.736e-05
18205 ATXN2L 16_23 8.963e-02 64.17 1.711e-05
18237 TAOK2 16_24 2.124e-02 93.45 5.906e-06
18238 TAOK2 16_24 2.124e-02 93.45 5.906e-06
18208 SH2B1 16_23 9.076e-02 64.30 1.736e-05
18207 SH2B1 16_23 7.320e-02 63.15 1.375e-05
18197 SULT1A2 16_23 2.247e-01 63.06 4.216e-05
intron_id z num_eqtl
24619 intron_11_27658462_27659171 -13.14 1
24618 intron_11_27640005_27659171 12.24 2
2436 intron_2_24972232_25001415 11.29 1
7880 intron_6_34654779_34696446 -11.24 1
18244 intron_16_30007090_30007173 -11.06 2
2434 intron_2_24972232_24972626 -11.02 2
4514 intron_3_49712656_49713513 10.96 1
4516 intron_3_49714174_49715575 -10.96 1
1838 intron_1_201900751_201946080 -10.89 1
25172 intron_19_45677779_45677906 10.80 1
18210 intron_16_28865624_28866012 -10.76 1
18212 intron_16_28869383_28871780 10.76 1
18211 intron_16_28867033_28867331 -10.76 1
18206 intron_16_28835399_28835549 -10.74 1
18205 intron_16_28835399_28835546 10.74 1
18237 intron_16_29989805_29990543 10.73 1
18238 intron_16_29989805_29990784 -10.73 1
18208 intron_16_28863754_28866012 -10.71 2
18207 intron_16_28846827_28863668 -10.69 1
18197 intron_16_28592481_28593252 -10.51 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 67Uploading 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"
Term Overlap Adjusted.P.value
1 phosphatase binding (GO:0019902) 4/114 0.04466
Genes
1 SLC9A3R2;SH3YL1;DLG1;MAST2DisGeNET 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
9 Body Weight 0.05526 2/27
77 Progressive cerebellar ataxia 0.05526 1/27
109 Mucinous carcinoma of breast 0.05526 1/27
117 Familial encephalopathy with neuroserpin inclusion bodies 0.05526 1/27
130 NEUROPATHY, HEREDITARY SENSORY AND AUTONOMIC, TYPE VI 0.05526 1/27
132 EPIDERMOLYSIS BULLOSA SIMPLEX, AUTOSOMAL RECESSIVE 2 0.05526 1/27
133 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 28 0.05526 1/27
21 Diabetic Angiopathies 0.10910 1/27
22 Cardiac Arrest 0.10910 1/27
29 Long QT Syndrome 0.10910 1/27
BgRatio
9 15/9703
77 1/9703
109 1/9703
117 1/9703
130 1/9703
132 1/9703
133 1/9703
21 16/9703
22 5/9703
29 11/9703WebGestalt 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$genename))[1] 18#significance threshold for TWAS
print(sig_thresh)[1] 4.757#number of ctwas genes
length(ctwas_genes)[1] 7#number of TWAS genes
length(twas_genes)[1] 462#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE
24170 MAST2 1_28 0.017408 25472.560 1.319e-03
24171 MAST2 1_28 0.982568 25488.502 7.451e-02
24172 MAST2 1_28 0.000000 8460.213 0.000e+00
24173 MAST2 1_28 0.000000 879.923 0.000e+00
24174 MAST2 1_28 0.000000 879.923 0.000e+00
24390 DST 6_42 0.000000 1986.237 0.000e+00
24391 DST 6_42 0.000000 1138.577 0.000e+00
24392 DST 6_42 0.000000 501.263 0.000e+00
24393 DST 6_42 0.000000 2800.564 0.000e+00
24394 DST 6_42 1.000000 16171.480 4.811e-02
24395 DST 6_42 0.000000 171.908 0.000e+00
24789 TMOD3 15_21 0.091864 32977.971 9.013e-03
24790 TMOD3 15_21 0.091864 32977.971 9.013e-03
24791 TMOD3 15_21 0.000000 32449.235 0.000e+00
24792 TMOD3 15_21 0.991186 32984.248 9.727e-02
24977 TNFSF12-TNFSF13 17_8 0.001220 9.718 3.527e-08
24978 TNFSF12-TNFSF13 17_8 0.001220 9.718 3.527e-08
24979 TNFSF12-TNFSF13 17_8 0.001220 9.718 3.527e-08
24980 TNFSF12-TNFSF13 17_8 0.001220 9.718 3.527e-08
24981 TNFSF12-TNFSF13 17_8 0.934350 28.451 7.909e-05
24982 TNFSF12-TNFSF13 17_8 0.001220 9.718 3.527e-08
24983 TNFSF12-TNFSF13 17_8 0.001886 10.016 5.620e-08
24984 TNFSF12-TNFSF13 17_8 0.001886 10.016 5.620e-08
24985 TNFSF12-TNFSF13 17_8 0.001886 10.016 5.620e-08
25366 ASCC2 22_10 0.000000 527.895 0.000e+00
25367 ASCC2 22_10 0.996349 8472.849 2.512e-02
25368 ASCC2 22_10 0.000000 7460.871 0.000e+00
25369 ASCC2 22_10 0.000000 52.548 0.000e+00
25370 ASCC2 22_10 0.000000 52.974 0.000e+00
intron_id z num_eqtl
24170 intron_1_45882395_45959386 -4.3109 1
24171 intron_1_45997799_46000959 -4.3247 1
24172 intron_1_46000979_46002805 3.8652 1
24173 intron_1_46034266_46034538 0.7974 1
24174 intron_1_46034266_46034827 -0.7974 1
24390 intron_6_56460254_56463046 1.0487 2
24391 intron_6_56463764_56464685 -0.8008 2
24392 intron_6_56463764_56466078 0.5407 2
24393 intron_6_56464756_56466078 1.5954 2
24394 intron_6_56468999_56469883 2.9842 1
24395 intron_6_56851604_56900421 1.5511 1
24789 intron_15_51924578_51931031 -4.5690 1
24790 intron_15_51924578_51934250 4.5690 1
24791 intron_15_51931133_51934250 4.6716 1
24792 intron_15_51934354_51946990 4.6040 1
24977 intron_17_7559297_7559624 -1.3318 1
24978 intron_17_7559297_7559846 1.3318 1
24979 intron_17_7559297_7559851 1.3318 1
24980 intron_17_7559702_7559846 -1.3318 1
24981 intron_17_7559702_7560049 -4.2247 1
24982 intron_17_7559893_7560049 1.3318 1
24983 intron_17_7560488_7560724 1.2342 1
24984 intron_17_7560817_7560999 -1.2342 1
24985 intron_17_7560872_7560999 -1.2342 1
25366 intron_22_29806904_29808111 -0.4356 1
25367 intron_22_29822464_29825087 2.9349 1
25368 intron_22_29825257_29825622 -2.4259 2
25369 intron_22_29825257_29832245 -0.6040 1
25370 intron_22_29832342_29834489 -0.6278 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02439 0.26829 #specificity
print(specificity) ctwas TWAS
0.9979 0.9615 #precision / PPV
print(precision) ctwas TWAS
0.14286 0.02381 
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