SCZ 2018 - Brain_Hippocampus

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-05-18

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.

---

R Markdown file: uncommitted changes

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.

Environment: empty

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.

Seed: set.seed(20211220)

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.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: absolute

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

Repository version: 2749be9

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 2749be9. 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/

Untracked files:
    Untracked:  G_list.RData
    Untracked:  Rplot.png
    Untracked:  SCZ_annotation.xlsx
    Untracked:  analysis/.ipynb_checkpoints/
    Untracked:  analysis/SCZ_E_S_Analysis.Rmd
    Untracked:  analysis/Untitled1.ipynb
    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/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_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
    Modified:   analysis/SCZ_Annotation_Analysis.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_Hippocampus_S.Rmd) and HTML (docs/SCZ_2018_Brain_Hippocampus_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
Rmd	2749be9	sq-96	2022-05-12	update
html	2749be9	sq-96	2022-05-12	update
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] 17848

#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 
1685 1258 1054  701  707  931 1057  622  737  814 1072  981  359  635  616  686 
  17   18   19   20   21   22 
1225  243 1275  611   30  549 

#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)

[1] 15888

#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)

[1] 0.8902

finish

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Check convergence of parameters

Past versions of unnamed-chunk-12-1.png

Version	Author	Date
2749be9	sq-96	2022-05-12

     gene       snp 
0.0079441 0.0003125 

 gene   snp 
13.20 10.19 

[1] 105318

[1]    7021 6309950

   gene     snp 
0.00699 0.19087 

[1] 0.01552 1.07050

Genes with highest PIPs

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
2749be9	sq-96	2022-05-12

      genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
3251      LRP8       1_33    1.1226 33.65 0.0003019  4.820          5        5
2405    GIGYF1       7_62    1.0221 34.53 0.0002916  5.266          3        3
2126  FAM177A1       14_9    1.0157 24.42 0.0002097 -4.872         11       13
3236    LPCAT4      15_10    0.9460 25.64 0.0002120  4.892          3        3
3155 LINC00320       21_6    0.9267 29.45 0.0002312  5.336          3        3
587     B3GAT1      11_84    0.8969 23.77 0.0001658 -4.448          8       12
4180      PAK6      15_14    0.8895 30.73 0.0002308  5.588          1        1
1450     CRTAP       3_24    0.8847 20.12 0.0001488  3.929          2        2
3327    MAD1L1        7_3    0.8812 69.62 0.0003687  8.182          6        7
6037     THAP8      19_25    0.8507 19.76 0.0001358 -3.847          2        2
4225     PCBP2      12_33    0.8426 26.30 0.0001773 -4.953          2        2
1660      DGKZ      11_28    0.8422 48.30 0.0003253  7.216          2        2
6256     TPGS2      18_20    0.8198 28.26 0.0001709 -4.088          4        4
1322      COA8      14_54    0.7858 46.02 0.0002582 -7.407          7       10
4042     NT5C2      10_66    0.7796 48.83 0.0002566 -8.541         11       13
112     ACTR1B       2_57    0.7780 20.17 0.0001136  3.978          5        5
298     ANAPC7      12_67    0.7579 38.23 0.0001921  6.385          4        4
5322     SF3B1      2_117    0.7509 46.51 0.0002443 -7.053          2        2
1799    DPYSL3       5_86    0.7392 22.59 0.0001172 -4.157          1        1
6768   ZDHHC20       13_2    0.7325 23.83 0.0001200 -4.615          2        2

Genes with highest PVE

      genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
3327    MAD1L1        7_3    0.8812 69.62 0.0003687  8.182          6        7
1660      DGKZ      11_28    0.8422 48.30 0.0003253  7.216          2        2
3251      LRP8       1_33    1.1226 33.65 0.0003019  4.820          5        5
2405    GIGYF1       7_62    1.0221 34.53 0.0002916  5.266          3        3
1322      COA8      14_54    0.7858 46.02 0.0002582 -7.407          7       10
4042     NT5C2      10_66    0.7796 48.83 0.0002566 -8.541         11       13
5322     SF3B1      2_117    0.7509 46.51 0.0002443 -7.053          2        2
3155 LINC00320       21_6    0.9267 29.45 0.0002312  5.336          3        3
4180      PAK6      15_14    0.8895 30.73 0.0002308  5.588          1        1
3236    LPCAT4      15_10    0.9460 25.64 0.0002120  4.892          3        3
2126  FAM177A1       14_9    1.0157 24.42 0.0002097 -4.872         11       13
298     ANAPC7      12_67    0.7579 38.23 0.0001921  6.385          4        4
4225     PCBP2      12_33    0.8426 26.30 0.0001773 -4.953          2        2
6256     TPGS2      18_20    0.8198 28.26 0.0001709 -4.088          4        4
587     B3GAT1      11_84    0.8969 23.77 0.0001658 -4.448          8       12
1450     CRTAP       3_24    0.8847 20.12 0.0001488  3.929          2        2
2318      FXR1      3_111    0.5794 44.40 0.0001380  6.837          4        4
6037     THAP8      19_25    0.8507 19.76 0.0001358 -3.847          2        2
6768   ZDHHC20       13_2    0.7325 23.83 0.0001200 -4.615          2        2
1667      DHPS      19_10    0.7106 24.82 0.0001190 -4.396          1        1

Comparing z scores and PIPs

Past versions of unnamed-chunk-16-1.png

Version	Author	Date
2749be9	sq-96	2022-05-12

Past versions of unnamed-chunk-16-2.png

Version	Author	Date
2749be9	sq-96	2022-05-12

[1] 0.01837

     genename region_tag susie_pip    mu2       PVE       z num_intron num_sqtl
3286     LSM2       6_26 9.745e-05 222.29 2.004e-11 -11.599          1        1
603      BAG6       6_26 1.162e-04 221.94 2.767e-11 -11.590          6        7
1604     DDR1       6_25 1.826e-01 105.59 3.276e-05  11.175          2        2
823  C6orf136       6_24 1.015e-01  82.59 8.072e-06 -11.031          2        2
2263    FLOT1       6_24 2.106e-01  81.23 3.399e-05 -10.981          6        6
724    BTN3A2       6_20 1.394e-01  92.71 8.680e-06 -10.665          5        5
4603     PPT2       6_26 3.691e-05 152.97 1.889e-12 -10.061          5        5
1892    EGFL8       6_26 2.777e-05 142.24 1.029e-12  -9.625          3        4
1014   CCHCR1       6_25 3.239e-02  68.64 3.490e-07  -9.508          6        9
2509    GPSM3       6_26 2.168e-06 124.08 5.539e-15   9.377          1        1
7016  ZSCAN31       6_22 1.725e-02  58.35 9.581e-08  -9.321          2        2
2674  HLA-DMA       6_27 1.319e-01  69.70 4.883e-06   8.727          6       10
4042    NT5C2      10_66 7.796e-01  48.83 2.566e-04  -8.541         11       13
3327   MAD1L1        7_3 8.812e-01  69.62 3.687e-04   8.182          6        7
6349  TSNARE1       8_93 3.299e-02  53.87 3.712e-07   7.961          4        6
4314    PGBD1       6_22 3.918e-02  40.35 3.080e-07  -7.746          2        2
722    BTN2A1       6_20 4.151e-02  51.43 3.563e-07  -7.727          3        3
7014  ZSCAN26       6_22 3.226e-02  38.78 2.883e-07   7.631          4        4
723    BTN3A1       6_20 5.359e-02  47.80 4.827e-07   7.490          4        4
700      BRD2       6_27 2.136e-01  46.77 1.498e-05   7.455          6        7

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)

[1] 50

Uploading 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"

Past versions of unnamed-chunk-19-1.png

Version	Author	Date
2749be9	sq-96	2022-05-12

[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"

Past versions of unnamed-chunk-19-2.png

Version	Author	Date
2749be9	sq-96	2022-05-12

                                              Term Overlap Adjusted.P.value
1 protein phosphatase type 2A complex (GO:0000159)    2/17          0.04714
2            microtubule cytoskeleton (GO:0015630)   5/331          0.04714
                                Genes
1                     PPP2R5B;PPP2R2A
2 DYNC1I2;ACTR1B;ANAPC7;KIF21B;MAD1L1
[1] "GO_Molecular_Function_2021"

Past versions of unnamed-chunk-19-3.png

Version	Author	Date
2749be9	sq-96	2022-05-12

[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
129                           Abnormality of head or neck 0.006894  2/22
7                              Adenocarcinoma of prostate 0.016828  2/22
29                                                Measles 0.016828  1/22
48                          Electroencephalogram abnormal 0.016828  1/22
97                              Sporadic Breast Carcinoma 0.016828  1/22
102                          Primary peritoneal carcinoma 0.016828  1/22
104                      Osteogenesis Imperfecta Type VII 0.016828  1/22
106 BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.016828  1/22
107         BREAST CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.016828  1/22
108        OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.016828  1/22
    BgRatio
129  5/9703
7   20/9703
29   1/9703
48   1/9703
97   1/9703
102  1/9703
104  1/9703
106  1/9703
107  1/9703
108  1/9703

WebGestalt 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!

NULL

PIP Manhattan Plot

Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Past versions of unnamed-chunk-22-1.png

Version	Author	Date
2749be9	sq-96	2022-05-12

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] 54

#significance threshold for TWAS
print(sig_thresh)

[1] 4.49

#number of ctwas genes
length(ctwas_genes)

[1] 13

#number of TWAS genes
length(twas_genes)

[1] 129

#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
587    B3GAT1      11_84    0.8969 23.77 0.0001658 -4.448          8       12
1450    CRTAP       3_24    0.8847 20.12 0.0001488  3.929          2        2
6037    THAP8      19_25    0.8507 19.76 0.0001358 -3.847          2        2
6256    TPGS2      18_20    0.8198 28.26 0.0001709 -4.088          4        4

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.03077 0.13846 

#specificity
print(specificity)

 ctwas   TWAS 
0.9987 0.9841 

#precision / PPV
print(precision)

 ctwas   TWAS 
0.3077 0.1395 

Session information

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