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] 21015#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1988 1451 1226 814 817 1108 1205 768 844 935 1262 1061 401 722 697 946
17 18 19 20 21 22
1404 275 1490 695 258 648 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 19381#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.9222#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$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
0.0072760 0.0002872 #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
24.48 17.13 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 21015 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
0.01114 0.11031 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.4344 14.8260Genes with highest PIPs
genename region_tag susie_pip mu2 PVE
20492 NSD1 5_106 1.0000 13952.43 4.151e-02
20657 SLC38A2 12_29 1.0000 15720.07 4.677e-02
20521 DST 6_42 1.0000 15093.71 4.491e-02
20744 MOK 14_53 1.0000 9686.11 2.882e-02
20984 ASCC2 22_10 0.9911 8705.86 2.567e-02
20761 TNFSF12-TNFSF13 17_7 0.9426 29.32 8.224e-05
20638 GBF1 10_65 0.7791 291.14 6.749e-04
8294 FAM160B2 8_23 0.7784 26.68 6.180e-05
1804 SH3YL1 2_1 0.7781 45.46 1.052e-04
20279 MAST2 1_28 0.7748 26644.32 6.142e-02
17334 DOT1L 19_3 0.7711 22.94 5.262e-05
8140 PTPRN2 7_98 0.7659 22.89 5.217e-05
11308 EFEMP2 11_36 0.7597 50.86 1.150e-04
20873 ZNF559-ZNF177 19_9 0.7500 16816.72 3.753e-02
20874 ZNF559-ZNF177 19_9 0.7500 16816.72 3.753e-02
16827 RPTOR 17_45 0.7475 47.73 1.061e-04
10939 BDNF-AS 11_19 0.7427 120.71 2.667e-04
16218 MLX 17_25 0.7425 31.57 6.974e-05
7779 PTCD1 7_61 0.7360 55.22 1.209e-04
8343 SLC25A37 8_24 0.7311 26.64 5.795e-05
intron_id z num_eqtl
20492 intron_5_177235945_177239756 2.957 1
20657 intron_12_46367340_46370512 -2.953 1
20521 intron_6_56468999_56469883 2.984 1
20744 intron_14_102265912_102283478 2.804 1
20984 intron_22_29825257_29825622 -2.882 1
20761 intron_17_7559702_7560049 -4.226 2
20638 intron_10_102260116_102344051 -5.281 1
8294 intron_8_22094518_22096337 4.806 1
1804 intron_2_253115_263211 5.990 1
20279 intron_1_45997799_46000959 -4.325 1
17334 intron_19_2199939_2202700 -4.106 1
8140 intron_7_157682937_157690552 -4.473 1
11308 intron_11_65868629_65869360 -8.201 1
20873 intron_19_9364948_9371610 3.338 1
20874 intron_19_9364948_9376316 -3.338 1
16827 intron_17_80730706_80754010 -6.093 1
10939 intron_11_27658462_27659171 -13.138 1
16218 intron_17_42567655_42568837 5.692 1
7779 intron_7_99452440_99454041 7.169 1
8343 intron_8_23529212_23566108 5.008 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE intron_id
17747 MAST3 19_14 0.0000 54893 0.00000 intron_19_18107618_18121685
17748 MAST3 19_14 0.0000 46415 0.00000 intron_19_18110741_18121685
20281 MAST2 1_28 0.2252 26647 0.01786 intron_1_46019697_46021950
20279 MAST2 1_28 0.7748 26644 0.06142 intron_1_45997799_46000959
17751 MAST3 19_14 0.0000 26358 0.00000 intron_19_18147017_18147443
17752 MAST3 19_14 0.0000 24335 0.00000 intron_19_18147044_18147443
11418 IGHMBP2 11_38 0.0000 22271 0.00000 intron_11_68929357_68933299
20275 NASP 1_28 0.0000 22177 0.00000 intron_1_45616703_45618061
20274 NASP 1_28 0.0000 22176 0.00000 intron_1_45616393_45616626
20271 NASP 1_28 0.0000 21778 0.00000 intron_1_45591270_45600385
20272 NASP 1_28 0.0000 21778 0.00000 intron_1_45591270_45602255
14243 TMOD3 15_21 0.0000 18818 0.00000 intron_15_51938260_51947292
3684 HEMK1 3_35 0.0000 18621 0.00000 intron_3_50579740_50579844
3683 HEMK1 3_35 0.0000 18620 0.00000 intron_3_50577573_50577826
3682 HEMK1 3_35 0.0000 18619 0.00000 intron_3_50571801_50577052
3681 C3orf18 3_35 0.0000 18615 0.00000 intron_3_50569574_50570931
2843 PLCL1 2_117 0.0000 18554 0.00000 intron_2_197805339_198001952
20467 FAM193A 4_3 0.0000 18231 0.00000 intron_4_2695129_2696363
4200 ARHGEF26 3_95 0.0000 17488 0.00000 intron_3_154152932_154187685
11416 MRPL21 11_38 0.0000 16886 0.00000 intron_11_68898012_68900548
z num_eqtl
17747 6.803 1
17748 -6.367 2
20281 -4.301 1
20279 -4.325 1
17751 -5.317 2
17752 4.715 3
11418 4.486 1
20275 4.287 1
20274 -4.289 1
20271 3.951 1
20272 -3.951 1
14243 -2.890 2
3684 4.698 1
3683 4.701 1
3682 4.704 1
3681 4.682 1
2843 -5.642 1
20467 5.435 1
4200 -4.115 1
11416 -3.950 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE
20279 MAST2 1_28 0.774763 26644.32 0.0614180
20657 SLC38A2 12_29 1.000000 15720.07 0.0467710
20521 DST 6_42 1.000000 15093.71 0.0449075
20492 NSD1 5_106 1.000000 13952.43 0.0415119
20873 ZNF559-ZNF177 19_9 0.750000 16816.72 0.0375254
20874 ZNF559-ZNF177 19_9 0.750000 16816.72 0.0375254
20744 MOK 14_53 0.999958 9686.11 0.0288173
20984 ASCC2 22_10 0.991073 8705.86 0.0256708
12984 SPG20 13_13 0.555556 13496.28 0.0223082
12982 SPG20 13_13 0.555556 13496.28 0.0223082
12983 SPG20 13_13 0.555556 13496.28 0.0223082
20281 MAST2 1_28 0.225237 26647.22 0.0178573
20638 GBF1 10_65 0.779128 291.14 0.0006749
10233 TNKS2 10_59 0.395483 401.72 0.0004727
3735 ITIH4 3_36 0.503272 179.62 0.0002690
10939 BDNF-AS 11_19 0.742708 120.71 0.0002667
20716 DUSP6 12_54 0.700419 104.11 0.0002170
18226 GIPR 19_32 0.550913 117.02 0.0001918
20520 DST 6_42 0.003621 15062.10 0.0001623
14369 MAP2K5 15_31 0.546286 93.09 0.0001513
intron_id z num_eqtl
20279 intron_1_45997799_46000959 -4.325 1
20657 intron_12_46367340_46370512 -2.953 1
20521 intron_6_56468999_56469883 2.984 1
20492 intron_5_177235945_177239756 2.957 1
20873 intron_19_9364948_9371610 3.338 1
20874 intron_19_9364948_9376316 -3.338 1
20744 intron_14_102265912_102283478 2.804 1
20984 intron_22_29825257_29825622 -2.882 1
12984 intron_13_36335832_36346582 2.819 1
12982 intron_13_36335832_36336287 2.819 1
12983 intron_13_36335832_36346225 -2.819 1
20281 intron_1_46019697_46021950 -4.301 1
20638 intron_10_102260116_102344051 -5.281 1
10233 intron_10_91817229_91819270 2.504 2
3735 intron_3_52818536_52819393 5.995 2
10939 intron_11_27658462_27659171 -13.138 1
20716 intron_12_89351025_89351640 6.438 1
18226 intron_19_45677779_45677906 10.801 1
20520 intron_6_56464756_56466078 2.892 1
14369 intron_15_67727945_67748231 -9.894 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE
10938 BDNF-AS 11_19 2.649e-01 119.76 9.439e-05
10939 BDNF-AS 11_19 7.427e-01 120.71 2.667e-04
15153 KCTD13 16_24 5.618e-02 107.69 1.800e-05
1922 DNAJC27-AS1 2_15 7.658e-05 191.49 4.363e-08
1924 DNAJC27-AS1 2_15 7.658e-05 191.49 4.363e-08
1923 DNAJC27-AS1 2_15 7.658e-05 191.49 4.363e-08
15165 DOC2A 16_24 3.116e-02 101.21 9.382e-06
18226 GIPR 19_32 5.509e-01 117.02 1.918e-04
18225 GIPR 19_32 3.890e-01 116.24 1.345e-04
15159 INO80E 16_24 2.218e-02 94.37 6.226e-06
3660 RBM6 3_35 8.382e-14 162.75 4.059e-17
15137 ATXN2L 16_23 2.003e-01 89.70 5.346e-05
15136 ATXN2L 16_23 1.989e-01 89.68 5.308e-05
15126 SULT1A2 16_23 1.625e-01 94.76 4.581e-05
15158 TAOK2 16_24 2.141e-02 93.56 5.958e-06
15134 ATXN2L 16_23 1.144e-01 88.01 2.994e-05
15135 ATXN2L 16_23 1.144e-01 88.01 2.994e-05
1402 SEC16B 1_87 2.992e-02 95.09 8.466e-06
15127 SULT1A2 16_23 7.048e-02 93.17 1.954e-05
15118 CLN3 16_23 4.389e-02 87.81 1.147e-05
intron_id z num_eqtl
10938 intron_11_27640005_27659171 13.36 2
10939 intron_11_27658462_27659171 -13.14 1
15153 intron_16_29923359_29925790 -11.30 2
1922 intron_2_24972232_24972626 -11.29 1
1924 intron_2_24972232_25001415 11.29 1
1923 intron_2_24972232_24979060 11.29 1
15165 intron_16_30007090_30007173 -11.08 2
18226 intron_19_45677779_45677906 10.80 1
18225 intron_19_45677108_45677710 10.77 1
15159 intron_16_30001040_30001212 10.77 1
3660 intron_3_50047326_50048245 10.75 2
15137 intron_16_28835399_28835549 -10.74 1
15136 intron_16_28835399_28835546 10.74 1
15126 intron_16_28592443_28593252 -10.74 2
15158 intron_16_29989805_29990784 -10.73 1
15134 intron_16_28826390_28826862 -10.68 1
15135 intron_16_28826986_28829401 -10.68 1
1402 intron_1_177932569_177932698 10.59 1
15127 intron_16_28592481_28593252 -10.51 1
15118 intron_16_28487541_28487662 -10.48 1Comparing z scores and PIPs
[1] 0.0188 genename region_tag susie_pip mu2 PVE
10938 BDNF-AS 11_19 2.649e-01 119.76 9.439e-05
10939 BDNF-AS 11_19 7.427e-01 120.71 2.667e-04
15153 KCTD13 16_24 5.618e-02 107.69 1.800e-05
1922 DNAJC27-AS1 2_15 7.658e-05 191.49 4.363e-08
1924 DNAJC27-AS1 2_15 7.658e-05 191.49 4.363e-08
1923 DNAJC27-AS1 2_15 7.658e-05 191.49 4.363e-08
15165 DOC2A 16_24 3.116e-02 101.21 9.382e-06
18226 GIPR 19_32 5.509e-01 117.02 1.918e-04
18225 GIPR 19_32 3.890e-01 116.24 1.345e-04
15159 INO80E 16_24 2.218e-02 94.37 6.226e-06
3660 RBM6 3_35 8.382e-14 162.75 4.059e-17
15137 ATXN2L 16_23 2.003e-01 89.70 5.346e-05
15136 ATXN2L 16_23 1.989e-01 89.68 5.308e-05
15126 SULT1A2 16_23 1.625e-01 94.76 4.581e-05
15158 TAOK2 16_24 2.141e-02 93.56 5.958e-06
15134 ATXN2L 16_23 1.144e-01 88.01 2.994e-05
15135 ATXN2L 16_23 1.144e-01 88.01 2.994e-05
1402 SEC16B 1_87 2.992e-02 95.09 8.466e-06
15127 SULT1A2 16_23 7.048e-02 93.17 1.954e-05
15118 CLN3 16_23 4.389e-02 87.81 1.147e-05
intron_id z num_eqtl
10938 intron_11_27640005_27659171 13.36 2
10939 intron_11_27658462_27659171 -13.14 1
15153 intron_16_29923359_29925790 -11.30 2
1922 intron_2_24972232_24972626 -11.29 1
1924 intron_2_24972232_25001415 11.29 1
1923 intron_2_24972232_24979060 11.29 1
15165 intron_16_30007090_30007173 -11.08 2
18226 intron_19_45677779_45677906 10.80 1
18225 intron_19_45677108_45677710 10.77 1
15159 intron_16_30001040_30001212 10.77 1
3660 intron_3_50047326_50048245 10.75 2
15137 intron_16_28835399_28835549 -10.74 1
15136 intron_16_28835399_28835546 10.74 1
15126 intron_16_28592443_28593252 -10.74 2
15158 intron_16_29989805_29990784 -10.73 1
15134 intron_16_28826390_28826862 -10.68 1
15135 intron_16_28826986_28829401 -10.68 1
1402 intron_1_177932569_177932698 10.59 1
15127 intron_16_28592481_28593252 -10.51 1
15118 intron_16_28487541_28487662 -10.48 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 62Uploading 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 0.04581
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
7 Body Weight 0.02296 2/27
75 Mucinous carcinoma of breast 0.02296 1/27
81 Familial encephalopathy with neuroserpin inclusion bodies 0.02296 1/27
87 FOCAL SEGMENTAL GLOMERULOSCLEROSIS 6 0.02296 1/27
88 CUTIS LAXA, AUTOSOMAL RECESSIVE, TYPE IB 0.02296 1/27
89 NEUROPATHY, HEREDITARY SENSORY AND AUTONOMIC, TYPE VI 0.02296 1/27
91 HYPOGONADOTROPIC HYPOGONADISM 19 WITH OR WITHOUT ANOSMIA 0.02296 1/27
92 EPIDERMOLYSIS BULLOSA SIMPLEX, AUTOSOMAL RECESSIVE 2 0.02296 1/27
94 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 28 0.02296 1/27
95 5q35 microduplication syndrome 0.02296 1/27
BgRatio
7 15/9703
75 1/9703
81 1/9703
87 1/9703
88 1/9703
89 1/9703
91 1/9703
92 1/9703
94 1/9703
95 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: 26 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$genename))[1] 20#significance threshold for TWAS
print(sig_thresh)[1] 4.718#number of ctwas genes
length(ctwas_genes)[1] 6#number of TWAS genes
length(twas_genes)[1] 395#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE
20490 NSD1 5_106 0.000e+00 6811.096 0.000e+00
20491 NSD1 5_106 0.000e+00 5205.305 0.000e+00
20492 NSD1 5_106 1.000e+00 13952.430 4.151e-02
20517 DST 6_42 0.000e+00 2414.058 0.000e+00
20518 DST 6_42 0.000e+00 621.083 0.000e+00
20519 DST 6_42 0.000e+00 453.810 0.000e+00
20520 DST 6_42 3.621e-03 15062.096 1.623e-04
20521 DST 6_42 1.000e+00 15093.715 4.491e-02
20522 DST 6_42 0.000e+00 165.192 0.000e+00
20657 SLC38A2 12_29 1.000e+00 15720.069 4.677e-02
20734 MOK 14_53 2.093e-05 7518.338 4.683e-07
20735 MOK 14_53 1.003e-05 71.089 2.121e-09
20736 MOK 14_53 5.982e-06 195.337 3.477e-09
20737 MOK 14_53 9.597e-06 68.300 1.950e-09
20738 MOK 14_53 5.256e-06 2869.037 4.486e-08
20739 MOK 14_53 5.292e-06 3017.702 4.752e-08
20740 MOK 14_53 5.096e-06 2093.290 3.174e-08
20741 MOK 14_53 1.914e-05 7538.181 4.293e-07
20742 MOK 14_53 5.147e-06 6423.996 9.838e-08
20743 MOK 14_53 5.623e-06 4083.485 6.831e-08
20744 MOK 14_53 1.000e+00 9686.112 2.882e-02
20745 MOK 14_53 7.981e-06 3034.162 7.205e-08
20759 TNFSF12-TNFSF13 17_7 6.706e-03 9.243 1.844e-07
20760 TNFSF12-TNFSF13 17_7 6.706e-03 9.243 1.844e-07
20761 TNFSF12-TNFSF13 17_7 9.426e-01 29.325 8.224e-05
20983 ASCC2 22_10 0.000e+00 35.450 0.000e+00
20984 ASCC2 22_10 9.911e-01 8705.864 2.567e-02
20985 ASCC2 22_10 0.000e+00 53.437 0.000e+00
intron_id z num_eqtl
20490 intron_5_177136030_177136873 0.31367 1
20491 intron_5_177136030_177191884 0.10561 2
20492 intron_5_177235945_177239756 2.95697 1
20517 intron_6_56463156_56463565 1.49128 2
20518 intron_6_56463764_56464685 -0.61897 2
20519 intron_6_56463764_56466078 0.53305 2
20520 intron_6_56464756_56466078 2.89236 1
20521 intron_6_56468999_56469883 2.98420 1
20522 intron_6_56861915_56863071 -1.55200 1
20657 intron_12_46367340_46370512 -2.95342 1
20734 intron_14_102226408_102232535 2.16037 1
20735 intron_14_102229657_102231707 -1.25920 1
20736 intron_14_102229657_102232535 0.57789 2
20737 intron_14_102231821_102232535 -1.22029 2
20738 intron_14_102250987_102251756 0.10090 2
20739 intron_14_102250990_102251756 0.08111 2
20740 intron_14_102251804_102251917 0.19341 1
20741 intron_14_102251995_102263546 2.09729 1
20742 intron_14_102251995_102283478 -0.74917 2
20743 intron_14_102263616_102283478 -0.14125 2
20744 intron_14_102265912_102283478 2.80412 1
20745 intron_14_102283592_102304962 1.04323 1
20759 intron_17_7559297_7559846 1.33178 1
20760 intron_17_7559702_7559846 -1.33178 1
20761 intron_17_7559702_7560049 -4.22617 2
20983 intron_22_29802208_29804638 0.08732 1
20984 intron_22_29825257_29825622 -2.88165 1
20985 intron_22_29825257_29832245 -0.60398 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.00000 0.09756 #specificity
print(specificity) ctwas TWAS
0.9987 0.9598 #precision / PPV
print(precision) ctwas TWAS
0.00000 0.01013 
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