SCZ - Brain Substantia nigra
sheng Qian
2021-2-6
Last updated: 2022-03-03
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 10096#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
970 724 606 388 494 522 474 399 398 404 602 577 222 334 344 461 597 153 768 314
21 22
112 233 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8298#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8219Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0079186 0.0002633 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
11.493 8.624 #report sample size
print(sample_size)[1] 82315#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10096 7573890#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01116 0.20896 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04909 1.56331Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
4961 FURIN 15_42 0.9778 46.16 0.0005483 -7.000 1
10000 ZNF823 19_10 0.9709 29.75 0.0003509 5.455 1
11036 AC012074.2 2_15 0.8479 22.88 0.0002357 4.620 2
247 VSIG2 11_77 0.7885 26.23 0.0002512 -3.818 1
10995 HIST1H2BN 6_21 0.7676 94.50 0.0008812 10.773 1
8494 DIRAS1 19_3 0.7586 25.35 0.0002337 4.867 2
5687 ARFGAP2 11_29 0.6907 24.40 0.0002047 4.740 1
2668 PDCD10 3_103 0.5732 21.86 0.0001522 -4.033 2
2900 MAP7D1 1_22 0.5726 24.30 0.0001691 4.907 1
8526 LY6H 8_94 0.5552 21.43 0.0001446 4.042 2
12597 EBLN3P 9_28 0.5351 23.06 0.0001499 -4.450 1
10218 LIN28B-AS1 6_70 0.5284 23.56 0.0001512 -4.651 1
10923 LINC01305 2_105 0.5020 23.25 0.0001418 4.523 1
5807 PLBD2 12_68 0.4878 21.18 0.0001255 3.986 1
8611 ZNF354C 5_108 0.4600 24.48 0.0001368 -3.965 1
2371 MDK 11_28 0.4596 39.12 0.0002184 -6.357 1
5246 CEP170 1_128 0.4510 25.69 0.0001408 -4.678 1
98 ELAC2 17_11 0.4364 30.58 0.0001622 4.227 1
1618 PPP1R16B 20_23 0.4354 35.67 0.0001887 6.091 1
11529 RP11-65M17.3 11_66 0.4267 22.40 0.0001161 4.330 1Genes with largest effect sizes
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
6375 ARHGAP27 17_27 0.000e+00 2296.94 0.000e+00 -2.0935 1
62 KMT2E 7_65 1.106e-05 1474.23 1.981e-07 -5.7816 2
8739 HLA-DQB1 6_26 5.596e-14 848.22 5.766e-16 4.1487 1
10292 HSPA1A 6_26 3.034e-13 234.54 8.645e-16 7.1259 1
8149 DCAKD 17_27 0.000e+00 119.02 0.000e+00 -2.8009 2
4321 SRPK2 7_65 0.000e+00 99.65 0.000e+00 -1.1604 1
10995 HIST1H2BN 6_21 7.676e-01 94.50 8.812e-04 10.7729 1
8901 ACBD4 17_27 0.000e+00 91.02 0.000e+00 0.1106 2
10526 CLIC1 6_26 2.818e-13 85.62 2.931e-16 0.4634 1
9454 HEXIM1 17_27 0.000e+00 69.44 0.000e+00 -2.8451 1
9418 BTN3A2 6_20 1.425e-02 67.98 1.177e-05 9.0770 3
2212 GOSR2 17_27 0.000e+00 67.64 0.000e+00 -2.5096 1
10868 SAPCD1 6_26 3.779e-12 64.23 2.949e-15 2.7814 1
10298 MSH5 6_26 5.906e-14 57.52 4.127e-17 0.7907 2
1217 PUS7 7_65 0.000e+00 56.76 0.000e+00 -2.8339 2
8834 HIST1H2BC 6_20 1.538e-02 54.00 1.009e-05 -8.0277 1
9552 ZSCAN23 6_22 4.653e-02 52.42 2.963e-05 -7.5541 2
4600 PGBD1 6_22 8.004e-03 50.72 4.932e-06 -6.3599 2
11951 LINC01415 18_30 1.610e-01 49.98 9.776e-05 -5.3243 1
4961 FURIN 15_42 9.778e-01 46.16 5.483e-04 -7.0004 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
10995 HIST1H2BN 6_21 0.7676 94.50 0.0008812 10.773 1
4961 FURIN 15_42 0.9778 46.16 0.0005483 -7.000 1
10000 ZNF823 19_10 0.9709 29.75 0.0003509 5.455 1
247 VSIG2 11_77 0.7885 26.23 0.0002512 -3.818 1
11036 AC012074.2 2_15 0.8479 22.88 0.0002357 4.620 2
8494 DIRAS1 19_3 0.7586 25.35 0.0002337 4.867 2
2371 MDK 11_28 0.4596 39.12 0.0002184 -6.357 1
5687 ARFGAP2 11_29 0.6907 24.40 0.0002047 4.740 1
1618 PPP1R16B 20_23 0.4354 35.67 0.0001887 6.091 1
426 ARID1B 6_102 0.3795 37.06 0.0001709 -3.907 1
2900 MAP7D1 1_22 0.5726 24.30 0.0001691 4.907 1
98 ELAC2 17_11 0.4364 30.58 0.0001622 4.227 1
2668 PDCD10 3_103 0.5732 21.86 0.0001522 -4.033 2
10218 LIN28B-AS1 6_70 0.5284 23.56 0.0001512 -4.651 1
12597 EBLN3P 9_28 0.5351 23.06 0.0001499 -4.450 1
8526 LY6H 8_94 0.5552 21.43 0.0001446 4.042 2
10923 LINC01305 2_105 0.5020 23.25 0.0001418 4.523 1
5246 CEP170 1_128 0.4510 25.69 0.0001408 -4.678 1
12007 RP11-247A12.7 9_66 0.2911 39.33 0.0001391 4.243 2
8611 ZNF354C 5_108 0.4600 24.48 0.0001368 -3.965 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
10995 HIST1H2BN 6_21 7.676e-01 94.50 8.812e-04 10.773 1
9418 BTN3A2 6_20 1.425e-02 67.98 1.177e-05 9.077 3
8834 HIST1H2BC 6_20 1.538e-02 54.00 1.009e-05 -8.028 1
9552 ZSCAN23 6_22 4.653e-02 52.42 2.963e-05 -7.554 2
2539 TRIM38 6_20 1.187e-02 45.99 6.630e-06 -7.478 2
6323 ZSCAN12 6_22 1.556e-02 39.73 7.511e-06 7.193 2
10292 HSPA1A 6_26 3.034e-13 234.54 8.645e-16 7.126 1
4961 FURIN 15_42 9.778e-01 46.16 5.483e-04 -7.000 1
5665 CYP17A1 10_66 4.682e-03 31.84 1.811e-06 -6.720 1
4600 PGBD1 6_22 8.004e-03 50.72 4.932e-06 -6.360 2
2371 MDK 11_28 4.596e-01 39.12 2.184e-04 -6.357 1
2778 KCNJ13 2_137 1.584e-01 35.34 6.800e-05 -6.333 1
1137 PPP1R13B 14_54 6.061e-02 44.26 3.259e-05 -6.297 1
8821 HARBI1 11_28 1.622e-01 36.44 7.181e-05 6.169 1
10439 DNAJC19 3_111 2.146e-01 37.89 9.879e-05 6.158 1
3921 C12orf65 12_75 3.691e-03 36.50 1.637e-06 -6.141 1
9960 NMB 15_39 1.706e-01 42.26 8.760e-05 6.132 1
9514 ZKSCAN4 6_22 1.111e-02 28.19 3.805e-06 -6.092 1
1618 PPP1R16B 20_23 4.354e-01 35.67 1.887e-04 6.091 1
10577 AS3MT 10_66 6.259e-03 31.85 2.422e-06 6.055 2Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] 0.006933GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 13Uploading 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"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
Term
1 positive regulation of MAP kinase activity (GO:0043406)
2 regulation of MAP kinase activity (GO:0043405)
3 positive regulation of protein serine/threonine kinase activity (GO:0071902)
4 negative regulation of transforming growth factor beta1 production (GO:0032911)
5 regulation of low-density lipoprotein particle receptor catabolic process (GO:0032803)
6 negative regulation of blood vessel endothelial cell proliferation involved in sprouting angiogenesis (GO:1903588)
7 Golgi transport vesicle coating (GO:0048200)
8 COPI coating of Golgi vesicle (GO:0048205)
9 COPI-coated vesicle budding (GO:0035964)
10 regulation of transforming growth factor beta1 production (GO:0032908)
11 establishment of Golgi localization (GO:0051683)
12 negative regulation of transforming growth factor beta production (GO:0071635)
13 negative regulation of cellular protein catabolic process (GO:1903363)
14 cellular response to acetylcholine (GO:1905145)
15 signal peptide processing (GO:0006465)
16 Golgi inheritance (GO:0048313)
17 intrinsic apoptotic signaling pathway in response to oxidative stress (GO:0008631)
18 Golgi localization (GO:0051645)
19 negative regulation of cell migration involved in sprouting angiogenesis (GO:0090051)
20 regulation of lipase activity (GO:0060191)
21 stress fiber assembly (GO:0043149)
22 positive regulation of membrane protein ectodomain proteolysis (GO:0051044)
23 contractile actin filament bundle assembly (GO:0030038)
24 epiboly involved in wound healing (GO:0090505)
25 regulation of blood vessel endothelial cell proliferation involved in sprouting angiogenesis (GO:1903587)
26 regulation of Golgi organization (GO:1903358)
27 regulation of catabolic process (GO:0009894)
28 positive regulation of stress-activated protein kinase signaling cascade (GO:0070304)
29 positive regulation of MAPK cascade (GO:0043410)
30 acetylcholine receptor signaling pathway (GO:0095500)
31 regulation of lipoprotein lipase activity (GO:0051004)
32 regulation of membrane protein ectodomain proteolysis (GO:0051043)
33 regulation of peptidase activity (GO:0052547)
34 wound healing, spreading of cells (GO:0044319)
Overlap Adjusted.P.value Genes
1 2/69 0.04473 PDCD10;DIRAS1
2 2/97 0.04473 PDCD10;DIRAS1
3 2/106 0.04473 PDCD10;DIRAS1
4 1/5 0.04473 FURIN
5 1/5 0.04473 FURIN
6 1/6 0.04473 PDCD10
7 1/6 0.04473 ARFGAP2
8 1/6 0.04473 ARFGAP2
9 1/6 0.04473 ARFGAP2
10 1/7 0.04473 FURIN
11 1/8 0.04473 PDCD10
12 1/10 0.04473 FURIN
13 1/10 0.04473 FURIN
14 1/10 0.04473 LY6H
15 1/11 0.04473 FURIN
16 1/11 0.04473 PDCD10
17 1/12 0.04473 PDCD10
18 1/12 0.04473 PDCD10
19 1/14 0.04473 PDCD10
20 1/14 0.04473 FURIN
21 1/15 0.04473 PDCD10
22 1/15 0.04473 FURIN
23 1/15 0.04473 PDCD10
24 1/16 0.04473 PDCD10
25 1/16 0.04473 PDCD10
26 1/17 0.04490 PDCD10
27 1/18 0.04490 FURIN
28 1/18 0.04490 PDCD10
29 2/274 0.04727 PDCD10;DIRAS1
30 1/21 0.04727 LY6H
31 1/21 0.04727 FURIN
32 1/22 0.04796 FURIN
33 1/23 0.04861 FURIN
34 1/24 0.04921 PDCD10
[1] "GO_Cellular_Component_2021"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
Term Overlap Adjusted.P.value Genes
1 nerve growth factor binding (GO:0048406) 1/5 0.03978 FURIN
2 acetylcholine receptor binding (GO:0033130) 1/8 0.03978 LY6H
3 neurotrophin binding (GO:0043121) 1/8 0.03978 FURINDisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio BgRatio
13 Cerebral Cavernous Malformations 3 0.002319 1/3 1/9703
15 Familial cerebral cavernous malformation 0.002319 1/3 1/9703
14 Cavernous Hemangioma of Brain 0.004637 1/3 3/9703
1 Carcinoma 0.062703 1/3 164/9703
3 Animal Mammary Neoplasms 0.062703 1/3 142/9703
4 Mammary Neoplasms, Experimental 0.062703 1/3 155/9703
6 Anaplastic carcinoma 0.062703 1/3 163/9703
7 Carcinoma, Spindle-Cell 0.062703 1/3 163/9703
8 Undifferentiated carcinoma 0.062703 1/3 163/9703
9 Carcinomatosis 0.062703 1/3 163/9703WebGestalt 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!NULL
Sensitivity, specificity and precision for silver standard genes
#number of genes in known annotations
print(length(known_annotations))[1] 130#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 58#significance threshold for TWAS
print(sig_thresh)[1] 4.567#number of ctwas genes
length(ctwas_genes)[1] 3#number of TWAS genes
length(twas_genes)[1] 70#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols][1] genename region_tag susie_pip mu2 PVE z num_eqtl
<0 rows> (or 0-length row.names)#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.04615 #specificity
print(specificity) ctwas TWAS
0.9999 0.9936 #precision / PPV
print(precision) ctwas TWAS
0.66667 0.08571 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 58#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 619#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$genename %in% c(known_annotations, unrelated_genes),]
#assign ctwas and TWAS genes
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
#significance threshold for TWAS
print(sig_thresh)[1] 4.567#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 2#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 22#sensitivity / recall
sensitivity ctwas TWAS
0.03448 0.10345 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9742 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.2727 
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip]
sensitivity[index] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
}
plot(1-specificity, sensitivity, type="l", xlim=c(0,1), ylim=c(0,1), main="", xlab="1 - Specificity", ylab="Sensitivity")
title(expression("ROC Curve for cTWAS (black) and TWAS (" * phantom("red") * ")"))
title(expression(phantom("ROC Curve for cTWAS (black) and TWAS (") * "red" * phantom(")")), col.main="red")
sig_thresh_range <- seq(from=0, to=max(abs(ctwas_gene_res_subset$z)), length.out=length(pip_range))
for (index in 1:length(sig_thresh_range)){
sig_thresh_plot <- sig_thresh_range[index]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>=sig_thresh_plot]
sensitivity[index] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
}
lines(1-specificity, sensitivity, xlim=c(0,1), ylim=c(0,1), col="red", lty=1)
abline(a=0,b=1,lty=3)
#add previously computed points from the analysis
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
points(1-specificity_plot["ctwas"], sensitivity_plot["ctwas"], pch=21, bg="black")
points(1-specificity_plot["TWAS"], sensitivity_plot["TWAS"], pch=21, bg="red")
Undetected silver standard genes have low TWAS z-scores or stronger signal from nearby variants
#table of outcomes for silver standard genes
-sort(-table(silver_standard_case))silver_standard_case
Not Imputed Insignificant z-score Nearby SNP(s)
72 52 4
Detected (PIP > 0.8)
2 #show inconclusive genes
silver_standard_case[silver_standard_case=="Inconclusive"]named character(0)
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0 IRanges_2.18.1
[4] S4Vectors_0.22.1 BiocGenerics_0.30.0 biomaRt_2.40.1
[7] readxl_1.3.1 forcats_0.5.1 stringr_1.4.0
[10] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[13] tidyr_1.1.4 tidyverse_1.3.1 tibble_3.1.6
[16] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0
[19] cowplot_1.0.0 ggplot2_3.3.5 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] ggbeeswarm_0.6.0 colorspace_2.0-2 rjson_0.2.20
[4] ellipsis_0.3.2 rprojroot_2.0.2 XVector_0.24.0
[7] fs_1.5.2 rstudioapi_0.13 farver_2.1.0
[10] ggrepel_0.9.1 bit64_4.0.5 AnnotationDbi_1.46.0
[13] fansi_1.0.2 lubridate_1.8.0 xml2_1.3.3
[16] codetools_0.2-16 doParallel_1.0.17 cachem_1.0.6
[19] knitr_1.36 jsonlite_1.7.2 apcluster_1.4.8
[22] Cairo_1.5-12.2 broom_0.7.10 dbplyr_2.1.1
[25] compiler_3.6.1 httr_1.4.2 backports_1.4.1
[28] assertthat_0.2.1 Matrix_1.2-18 fastmap_1.1.0
[31] cli_3.1.0 later_0.8.0 prettyunits_1.1.1
[34] htmltools_0.5.2 tools_3.6.1 igraph_1.2.10
[37] GenomeInfoDbData_1.2.1 gtable_0.3.0 glue_1.6.2
[40] reshape2_1.4.4 doRNG_1.8.2 Rcpp_1.0.8
[43] Biobase_2.44.0 cellranger_1.1.0 jquerylib_0.1.4
[46] vctrs_0.3.8 svglite_1.2.2 iterators_1.0.14
[49] xfun_0.29 ps_1.6.0 rvest_1.0.2
[52] lifecycle_1.0.1 rngtools_1.5.2 XML_3.99-0.3
[55] zlibbioc_1.30.0 getPass_0.2-2 scales_1.1.1
[58] vroom_1.5.7 hms_1.1.1 promises_1.0.1
[61] yaml_2.2.1 curl_4.3.2 memoise_2.0.1
[64] ggrastr_1.0.1 gdtools_0.1.9 stringi_1.7.6
[67] RSQLite_2.2.8 highr_0.9 foreach_1.5.2
[70] rlang_1.0.1 pkgconfig_2.0.3 bitops_1.0-7
[73] evaluate_0.14 lattice_0.20-38 labeling_0.4.2
[76] bit_4.0.4 processx_3.5.2 tidyselect_1.1.1
[79] plyr_1.8.6 magrittr_2.0.2 R6_2.5.1
[82] generics_0.1.1 DBI_1.1.2 pillar_1.6.4
[85] haven_2.4.3 whisker_0.3-2 withr_2.4.3
[88] RCurl_1.98-1.5 modelr_0.1.8 crayon_1.5.0
[91] utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11
[94] progress_1.2.2 grid_3.6.1 data.table_1.14.2
[97] blob_1.2.2 callr_3.7.0 git2r_0.26.1
[100] reprex_2.0.1 digest_0.6.29 httpuv_1.5.1
[103] munsell_0.5.0 beeswarm_0.2.3 vipor_0.4.5