SCZ 2018 - Brain_Substantia_nigra
sheng Qian
2021-2-6
Last updated: 2022-05-12
Checks: 5 2
Knit directory: cTWAS_analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20211220) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.
| absolute | relative |
|---|---|
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/ | data |
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config.R | code/ctwas_config.R |
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 011327d. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .ipynb_checkpoints/
Ignored: data/AF/
Untracked files:
Untracked: G_list.RData
Untracked: Rplot.png
Untracked: SCZ_annotation.xlsx
Untracked: analysis/.ipynb_checkpoints/
Untracked: code/.ipynb_checkpoints/
Untracked: code/AF_out/
Untracked: code/Autism_out/
Untracked: code/BMI_S_out/
Untracked: code/BMI_out/
Untracked: code/Glucose_out/
Untracked: code/LDL_S_out/
Untracked: code/SCZ_2014_EUR_out/
Untracked: code/SCZ_2018_S_out/
Untracked: code/SCZ_2018_out/
Untracked: code/SCZ_2020_Single_out/
Untracked: code/SCZ_2020_out/
Untracked: code/SCZ_S_out/
Untracked: code/SCZ_out/
Untracked: code/T2D_out/
Untracked: code/ctwas_config.R
Untracked: code/mapping.R
Untracked: code/out/
Untracked: code/process_scz_2018_snps.R
Untracked: code/run_AF_analysis.sbatch
Untracked: code/run_AF_analysis.sh
Untracked: code/run_AF_ctwas_rss_LDR.R
Untracked: code/run_Autism_analysis.sbatch
Untracked: code/run_Autism_analysis.sh
Untracked: code/run_Autism_ctwas_rss_LDR.R
Untracked: code/run_BMI_analysis.sbatch
Untracked: code/run_BMI_analysis.sh
Untracked: code/run_BMI_analysis_S.sbatch
Untracked: code/run_BMI_analysis_S.sh
Untracked: code/run_BMI_ctwas_rss_LDR.R
Untracked: code/run_BMI_ctwas_rss_LDR_S.R
Untracked: code/run_Glucose_analysis.sbatch
Untracked: code/run_Glucose_analysis.sh
Untracked: code/run_Glucose_ctwas_rss_LDR.R
Untracked: code/run_LDL_analysis_S.sbatch
Untracked: code/run_LDL_analysis_S.sh
Untracked: code/run_LDL_ctwas_rss_LDR_S.R
Untracked: code/run_SCZ_2014_EUR_analysis.sbatch
Untracked: code/run_SCZ_2014_EUR_analysis.sh
Untracked: code/run_SCZ_2014_EUR_ctwas_rss_LDR.R
Untracked: code/run_SCZ_2018_analysis.sbatch
Untracked: code/run_SCZ_2018_analysis.sh
Untracked: code/run_SCZ_2018_analysis_S.sbatch
Untracked: code/run_SCZ_2018_analysis_S.sh
Untracked: code/run_SCZ_2018_ctwas_rss_LDR.R
Untracked: code/run_SCZ_2018_ctwas_rss_LDR_S.R
Untracked: code/run_SCZ_2020_Single_analysis.sbatch
Untracked: code/run_SCZ_2020_Single_analysis.sh
Untracked: code/run_SCZ_2020_Single_ctwas_rss_LDR.R
Untracked: code/run_SCZ_2020_analysis.sbatch
Untracked: code/run_SCZ_2020_analysis.sh
Untracked: code/run_SCZ_2020_ctwas_rss_LDR.R
Untracked: code/run_SCZ_analysis.sbatch
Untracked: code/run_SCZ_analysis.sh
Untracked: code/run_SCZ_analysis_S.sbatch
Untracked: code/run_SCZ_analysis_S.sh
Untracked: code/run_SCZ_ctwas_rss_LDR.R
Untracked: code/run_SCZ_ctwas_rss_LDR_S.R
Untracked: code/run_T2D_analysis.sbatch
Untracked: code/run_T2D_analysis.sh
Untracked: code/run_T2D_ctwas_rss_LDR.R
Untracked: code/wflow_build.R
Untracked: code/wflow_build.sbatch
Untracked: data/.ipynb_checkpoints/
Untracked: data/BMI/
Untracked: data/GO_Terms/
Untracked: data/PGC3_SCZ_wave3_public.v2.tsv
Untracked: data/SCZ/
Untracked: data/SCZ_2014_EUR/
Untracked: data/SCZ_2018/
Untracked: data/SCZ_2018_S/
Untracked: data/SCZ_2020/
Untracked: data/SCZ_2020_Single/
Untracked: data/SCZ_S/
Untracked: data/Supplementary Table 15 - MAGMA.xlsx
Untracked: data/Supplementary Table 20 - Prioritised Genes.xlsx
Untracked: data/T2D/
Untracked: data/UKBB/
Untracked: data/UKBB_SNPs_Info.text
Untracked: data/gene_OMIM.txt
Untracked: data/gene_pip_0.8.txt
Untracked: data/mashr_Heart_Atrial_Appendage.db
Untracked: data/mashr_sqtl/
Untracked: data/scz_2018.RDS
Untracked: data/summary_known_genes_annotations.xlsx
Untracked: data/untitled.txt
Untracked: top_genes_32.txt
Untracked: top_genes_37.txt
Untracked: top_genes_43.txt
Untracked: top_genes_81.txt
Untracked: z_snp_pos_SCZ.RData
Untracked: z_snp_pos_SCZ_2014_EUR.RData
Untracked: z_snp_pos_SCZ_2018.RData
Untracked: z_snp_pos_SCZ_2020.RData
Unstaged changes:
Deleted: analysis/BMI_S_results.Rmd
Modified: analysis/SCZ_2018_Brain_Amygdala_S.Rmd
Modified: analysis/SCZ_2018_Brain_Anterior_cingulate_cortex_BA24_S.Rmd
Modified: analysis/SCZ_2018_Brain_Caudate_basal_ganglia_S.Rmd
Modified: analysis/SCZ_2018_Brain_Cerebellar_Hemisphere_S.Rmd
Modified: analysis/SCZ_2018_Brain_Cerebellum_S.Rmd
Modified: analysis/SCZ_2018_Brain_Cortex_S.Rmd
Modified: analysis/SCZ_2018_Brain_Frontal_Cortex_BA9_S.Rmd
Modified: analysis/SCZ_2018_Brain_Hippocampus_S.Rmd
Modified: analysis/SCZ_2018_Brain_Hypothalamus_S.Rmd
Modified: analysis/SCZ_2018_Brain_Nucleus_accumbens_basal_ganglia_S.Rmd
Modified: analysis/SCZ_2018_Brain_Putamen_basal_ganglia_S.Rmd
Modified: analysis/SCZ_2018_Brain_Spinal_cord_cervical_c-1_S.Rmd
Modified: analysis/SCZ_2018_Brain_Substantia_nigra_S.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/SCZ_2018_Brain_Substantia_nigra_S.Rmd) and HTML (docs/SCZ_2018_Brain_Substantia_nigra_S.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 011327d | sq-96 | 2022-05-12 | update |
| Rmd | 6c6abbd | sq-96 | 2022-05-12 | update |
library(reticulate)
use_python("/scratch/midway2/shengqian/miniconda3/envs/PythonForR/bin/python",required=T)Weight QC
#number of imputed weights
nrow(qclist_all)[1] 15170#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1400 1082 880 623 588 770 877 489 649 714 913 791 301 549 531 626
17 18 19 20 21 22
1040 214 1116 542 34 441 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 13605#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8968finish
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionCheck convergence of parameters
gene snp
0.0065597 0.0003205 gene snp
9.522 10.339 [1] 105318[1] 6393 6309950 gene snp
0.003791 0.198505 [1] 0.009054 1.088938Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3010 LRP8 1_33 1.0645 32.61 2.549e-04 -4.624 6 6
1337 CRTAP 3_24 0.8430 20.01 1.343e-04 3.929 2 2
5499 THAP8 19_25 0.8114 20.21 1.261e-04 3.847 2 2
1220 COA8 14_54 0.8024 44.24 2.642e-04 7.429 6 9
1639 DPYSL3 5_86 0.7628 23.36 1.291e-04 4.157 1 1
596 BDNF 11_19 0.7481 22.63 1.203e-04 4.348 1 1
540 B3GAT1 11_84 0.6722 31.39 1.272e-04 -4.516 6 10
214 AKT3 1_128 0.6638 34.01 1.337e-04 -6.291 5 5
4063 PLCB2 15_14 0.6378 24.42 8.300e-05 4.470 3 4
3677 NPIPB14P 16_37 0.6317 17.28 6.162e-05 3.742 10 11
6007 VPS41 7_28 0.6151 25.12 8.874e-05 -4.509 2 2
99 ACTR1B 2_57 0.5934 22.34 7.367e-05 3.978 3 3
2360 GUSBP11 22_6 0.5870 19.36 4.733e-05 2.862 16 20
2270 GON4L 1_76 0.5783 27.63 8.773e-05 4.084 1 1
3185 MDK 11_28 0.5743 45.88 1.437e-04 7.159 1 1
2847 LAMA5 20_36 0.5692 32.47 8.011e-05 3.967 10 15
4383 PYROXD2 10_62 0.5608 33.32 7.427e-05 -3.718 10 11
2122 FXR1 3_111 0.5532 42.91 1.221e-04 -6.873 4 4
972 CD46 1_105 0.5515 18.45 5.268e-05 -3.654 6 6
4336 PTK2B 8_27 0.5318 26.09 6.953e-05 4.730 2 3Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
1220 COA8 14_54 0.8024 44.24 2.642e-04 7.429 6 9
3010 LRP8 1_33 1.0645 32.61 2.549e-04 -4.624 6 6
3185 MDK 11_28 0.5743 45.88 1.437e-04 7.159 1 1
1337 CRTAP 3_24 0.8430 20.01 1.343e-04 3.929 2 2
214 AKT3 1_128 0.6638 34.01 1.337e-04 -6.291 5 5
1639 DPYSL3 5_86 0.7628 23.36 1.291e-04 4.157 1 1
540 B3GAT1 11_84 0.6722 31.39 1.272e-04 -4.516 6 10
5499 THAP8 19_25 0.8114 20.21 1.261e-04 3.847 2 2
2122 FXR1 3_111 0.5532 42.91 1.221e-04 -6.873 4 4
596 BDNF 11_19 0.7481 22.63 1.203e-04 4.348 1 1
2163 GATAD2A 19_15 0.4632 45.09 9.073e-05 -6.640 4 4
6007 VPS41 7_28 0.6151 25.12 8.874e-05 -4.509 2 2
2270 GON4L 1_76 0.5783 27.63 8.773e-05 4.084 1 1
4063 PLCB2 15_14 0.6378 24.42 8.300e-05 4.470 3 4
2847 LAMA5 20_36 0.5692 32.47 8.011e-05 3.967 10 15
4383 PYROXD2 10_62 0.5608 33.32 7.427e-05 -3.718 10 11
99 ACTR1B 2_57 0.5934 22.34 7.367e-05 3.978 3 3
4336 PTK2B 8_27 0.5318 26.09 6.953e-05 4.730 2 3
5440 TCAIM 3_31 0.4480 35.10 6.170e-05 4.053 5 5
3677 NPIPB14P 16_37 0.6317 17.28 6.162e-05 3.742 10 11Comparing z scores and PIPs
[1] 0.01502 genename region_tag susie_pip mu2 PVE z num_intron
3981 PGBD1 6_22 4.087e-02 155.68 9.882e-07 -13.087 2
1471 DDR1 6_25 1.261e-01 100.79 1.477e-05 11.175 3
2072 FLOT1 6_24 1.104e-01 77.49 8.920e-06 -10.944 5
672 BTN3A2 6_20 1.060e-01 88.48 4.796e-06 -10.665 4
551 BAG6 6_26 3.830e-05 160.21 1.660e-12 -10.247 6
932 CCHCR1 6_25 2.958e-02 62.23 3.774e-07 -9.378 5
2303 GPSM3 6_26 1.690e-06 118.03 3.202e-15 -9.377 1
6388 ZSCAN31 6_22 1.677e-02 55.20 8.559e-08 -9.321 2
3726 NT5C2 10_66 3.297e-01 46.96 4.551e-05 -8.541 8
6386 ZSCAN26 6_22 3.391e-02 45.29 3.198e-07 -8.313 4
3082 MAD1L1 7_3 3.735e-01 62.14 5.662e-05 8.215 4
434 AS3MT 10_66 2.402e-01 44.47 2.402e-05 8.051 3
6381 ZSCAN16 6_22 2.003e-02 52.38 1.007e-07 7.468 2
1220 COA8 14_54 8.024e-01 44.24 2.642e-04 7.429 6
6382 ZSCAN16-AS1 6_22 7.565e-03 51.85 2.817e-08 -7.421 1
6220 ZNF192P1 6_22 1.526e-02 51.40 1.136e-07 7.378 2
3185 MDK 11_28 5.743e-01 45.88 1.437e-04 7.159 1
181 AIF1 6_26 1.311e-02 59.25 9.670e-08 -7.131 5
5600 TMEM219 16_24 3.359e-01 45.65 4.891e-05 -7.020 1
1524 DGKZ 11_28 1.388e-01 43.55 7.970e-06 -6.964 1
num_sqtl
3981 2
1471 3
2072 5
672 4
551 8
932 8
2303 1
6388 2
3726 11
6386 5
3082 6
434 3
6381 2
1220 9
6382 1
6220 2
3185 1
181 5
5600 1
1524 2GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 22Uploading 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 positive regulation of neuron projection development (GO:0010976)
2 positive regulation of cell projection organization (GO:0031346)
3 regulation of neuron projection development (GO:0010975)
4 negative regulation of neuron apoptotic process (GO:0043524)
5 positive regulation of vascular endothelial cell proliferation (GO:1905564)
6 negative regulation of neuron death (GO:1901215)
7 regulation of neuron apoptotic process (GO:0043523)
8 regulation of vascular endothelial cell proliferation (GO:1905562)
9 regulation of leukocyte chemotaxis (GO:0002688)
10 regulation of macrophage chemotaxis (GO:0010758)
11 negative regulation of ossification (GO:0030279)
12 regulation of regulatory T cell differentiation (GO:0045589)
13 activation of phospholipase C activity (GO:0007202)
14 positive regulation of protein phosphorylation (GO:0001934)
15 regulation of trans-synaptic signaling (GO:0099177)
16 regulation of actin cytoskeleton reorganization (GO:2000249)
17 regulation of filopodium assembly (GO:0051489)
18 positive regulation of protein tyrosine kinase activity (GO:0061098)
19 positive regulation of phospholipase C activity (GO:0010863)
20 positive regulation of T cell differentiation (GO:0045582)
21 negative regulation of apoptotic process (GO:0043066)
22 apoptotic process (GO:0006915)
23 positive regulation of cell-substrate adhesion (GO:0010811)
Overlap Adjusted.P.value Genes
1 5/88 1.565e-05 BDNF;MDK;DPYSL3;PTK2B;LRP8
2 4/117 1.609e-03 BDNF;MDK;DPYSL3;PTK2B
3 4/165 4.161e-03 BDNF;MDK;DPYSL3;PTK2B
4 3/71 6.746e-03 BDNF;MDK;PTK2B
5 2/13 7.673e-03 MDK;AKT3
6 3/98 9.308e-03 BDNF;MDK;PTK2B
7 3/98 9.308e-03 BDNF;MDK;PTK2B
8 2/18 9.308e-03 MDK;AKT3
9 2/19 9.308e-03 MDK;PTK2B
10 2/22 1.129e-02 MDK;PTK2B
11 2/24 1.225e-02 MDK;PTK2B
12 2/26 1.321e-02 MDK;CD46
13 2/32 1.853e-02 BDNF;PLCB2
14 4/371 1.923e-02 FXR1;BDNF;PTK2B;LRP8
15 2/35 1.923e-02 BDNF;LRP8
16 2/37 2.015e-02 MDK;PTK2B
17 2/41 2.177e-02 FXR1;DPYSL3
18 2/42 2.177e-02 BDNF;LRP8
19 2/43 2.177e-02 BDNF;PLCB2
20 2/43 2.177e-02 MDK;CD46
21 4/485 3.607e-02 BDNF;MDK;CASP2;PTK2B
22 3/231 3.883e-02 FXR1;CASP2;PTK2B
23 2/70 4.973e-02 MDK;PTK2B
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[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
Description FDR Ratio BgRatio
3 Alcoholic Intoxication, Chronic 0.02335 4/14 268/9703
34 Profound Mental Retardation 0.02335 3/14 139/9703
41 Measles 0.02335 1/14 1/9703
44 Memory Disorders 0.02335 2/14 43/9703
45 Mental Retardation, Psychosocial 0.02335 3/14 139/9703
77 Memory impairment 0.02335 2/14 44/9703
135 Age-Related Memory Disorders 0.02335 2/14 43/9703
136 Memory Disorder, Semantic 0.02335 2/14 43/9703
137 Memory Disorder, Spatial 0.02335 2/14 43/9703
138 Memory Loss 0.02335 2/14 43/9703WebGestalt enrichment analysis for genes with PIP>0.5
Warning: replacing previous import 'lifecycle::last_warnings' by
'rlang::last_warnings' when loading 'hms'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
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] 44#significance threshold for TWAS
print(sig_thresh)[1] 4.47#number of ctwas genes
length(ctwas_genes)[1] 4#number of TWAS genes
length(twas_genes)[1] 96#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 z num_intron num_sqtl
1337 CRTAP 3_24 0.8430 20.01 0.0001343 3.929 2 2
5499 THAP8 19_25 0.8114 20.21 0.0001261 3.847 2 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.007692 0.076923 #specificity
print(specificity) ctwas TWAS
0.9995 0.9865 #precision / PPV
print(precision) ctwas TWAS
0.2500 0.1042
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readxl_1.4.0 forcats_0.5.1 stringr_1.4.0 purrr_0.3.4
[5] readr_1.4.0 tidyr_1.1.3 tidyverse_1.3.1 tibble_3.1.7
[9] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0 cowplot_1.1.1
[13] ggplot2_3.3.5 dplyr_1.0.7 reticulate_1.20 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.10 doParallel_1.0.16 httr_1.4.2
[5] rprojroot_2.0.2 tools_4.1.0 backports_1.2.1 doRNG_1.8.2
[9] bslib_0.2.5.1 utf8_1.2.1 R6_2.5.0 vipor_0.4.5
[13] DBI_1.1.1 colorspace_2.0-2 withr_2.4.2 ggrastr_1.0.1
[17] tidyselect_1.1.1 curl_4.3.2 compiler_4.1.0 git2r_0.28.0
[21] rvest_1.0.0 cli_3.0.0 Cairo_1.5-15 xml2_1.3.2
[25] labeling_0.4.2 sass_0.4.0 scales_1.1.1 systemfonts_1.0.4
[29] apcluster_1.4.9 digest_0.6.27 rmarkdown_2.9 svglite_2.0.0
[33] pkgconfig_2.0.3 htmltools_0.5.1.1 dbplyr_2.1.1 highr_0.9
[37] rlang_1.0.2 rstudioapi_0.13 jquerylib_0.1.4 farver_2.1.0
[41] generics_0.1.0 jsonlite_1.7.2 magrittr_2.0.1 Matrix_1.3-3
[45] ggbeeswarm_0.6.0 Rcpp_1.0.7 munsell_0.5.0 fansi_0.5.0
[49] lifecycle_1.0.0 stringi_1.6.2 whisker_0.4 yaml_2.2.1
[53] plyr_1.8.6 grid_4.1.0 ggrepel_0.9.1 parallel_4.1.0
[57] promises_1.2.0.1 crayon_1.4.1 lattice_0.20-44 haven_2.4.1
[61] hms_1.1.0 knitr_1.33 pillar_1.7.0 igraph_1.2.6
[65] rjson_0.2.20 rngtools_1.5 reshape2_1.4.4 codetools_0.2-18
[69] reprex_2.0.0 glue_1.4.2 evaluate_0.14 data.table_1.14.0
[73] modelr_0.1.8 png_0.1-7 vctrs_0.3.8 httpuv_1.6.1
[77] foreach_1.5.1 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[81] xfun_0.24 broom_0.7.8 later_1.2.0 iterators_1.0.13
[85] beeswarm_0.4.0 ellipsis_0.3.2