SCZ - Brain Cortex

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_Cortex_S.Rmd) and HTML (docs/SCZ_2018_Brain_Cortex_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] 23372

#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 
2106 1670 1401  900  973 1205 1349  834  978 1043 1384 1297  477  810  795  919 
  17   18   19   20   21   22 
1718  307 1661  776   45  724 

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

[1] 20390

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

[1] 0.8724

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 
0.0082338 0.0003052 

 gene   snp 
10.48 10.44 

[1] 105318

[1]    7742 6309950

   gene     snp 
0.00634 0.19088 

[1] 0.02217 1.09222

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
3623       LRP8       1_33    1.2198 32.31 0.0003555 -5.003         10       10
6190     SLC8B1      12_68    1.2142 22.26 0.0002788 -4.047         10       10
3510  LINC00320       21_6    1.0957 28.55 0.0003103  5.336          5        5
5276     R3HDM2      12_36    1.0921 42.83 0.0004461 -6.634          7        9
2356   FAM177A1       14_9    1.0212 23.31 0.0001933  4.849         13       16
3436      LAMA5      20_36    0.9539 28.66 0.0002078 -4.269         16       21
838  BUB1B-PAK6      15_14    0.9292 29.77 0.0002353  5.588          4        4
749        BDNF      11_19    0.9142 23.00 0.0001788  4.348          3        4
1042     CAMKK2      12_74    0.9028 35.27 0.0002014  4.060          8       10
5228      PTPRF       1_27    0.8882 35.71 0.0002541  6.680          6        6
2682     GIGYF1       7_62    0.8820 26.71 0.0001958 -5.266          3        3
1613      CRTAP       3_24    0.8650 19.74 0.0001395  3.929          2        2
6274     SNRPA1      15_50    0.8489 20.88 0.0001372 -4.098          4        5
623      ATP2B2        3_8    0.8284 25.09 0.0001526  4.229          5        7
5264    PYROXD2      10_62    0.8166 23.48 0.0001259  3.755         11       15
1215       CD46      1_105    0.8156 19.39 0.0001137 -3.933         11       14
4482      NTRK3      15_41    0.7867 23.89 0.0001304  4.457          3        3
352      ANAPC7      12_67    0.7853 39.18 0.0002002  6.385          6        6
154      ACTR1B       2_57    0.7830 20.03 0.0001145  3.978          5        5
674      B3GAT1      11_84    0.7565 23.78 0.0001131  4.248          8       13

Genes with highest PVE

       genename region_tag susie_pip    mu2       PVE      z num_intron
5276     R3HDM2      12_36    1.0921  42.83 0.0004461 -6.634          7
3623       LRP8       1_33    1.2198  32.31 0.0003555 -5.003         10
3510  LINC00320       21_6    1.0957  28.55 0.0003103  5.336          5
6190     SLC8B1      12_68    1.2142  22.26 0.0002788 -4.047         10
693        BAG6       6_26    0.2068 637.42 0.0002588 11.590          9
455        APOM       6_26    0.2068 637.42 0.0002588 11.590          2
5228      PTPRF       1_27    0.8882  35.71 0.0002541  6.680          6
838  BUB1B-PAK6      15_14    0.9292  29.77 0.0002353  5.588          4
456      APOPT1      14_54    0.7266  43.77 0.0002146  7.429          4
5969      SF3B1      2_117    0.7200  45.12 0.0002121  7.053          3
3436      LAMA5      20_36    0.9539  28.66 0.0002078 -4.269         16
1042     CAMKK2      12_74    0.9028  35.27 0.0002014  4.060          8
352      ANAPC7      12_67    0.7853  39.18 0.0002002  6.385          6
2682     GIGYF1       7_62    0.8820  26.71 0.0001958 -5.266          3
2356   FAM177A1       14_9    1.0212  23.31 0.0001933  4.849         13
3193       IRF3      19_34    0.7132  39.57 0.0001907 -6.461          2
1871       DGKZ      11_28    0.6445  46.77 0.0001845  7.216          2
749        BDNF      11_19    0.9142  23.00 0.0001788  4.348          3
292        AKT3      1_128    0.7447  34.40 0.0001716 -6.291          5
2636    GATAD2A      19_15    0.6131  46.80 0.0001634 -6.668          5
     num_sqtl
5276        9
3623       10
3510        5
6190       10
693         9
455         2
5228        6
838         4
456         7
5969        3
3436       21
1042       10
352         6
2682        3
2356       16
3193        2
1871        2
749         4
292         5
2636        5

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] 0.01899

     genename region_tag susie_pip    mu2       PVE       z num_intron num_sqtl
4777    PGBD1       6_22 6.697e-02 158.26 2.166e-06 -13.087          3        5
455      APOM       6_26 2.068e-01 637.42 2.588e-04  11.590          2        2
693      BAG6       6_26 2.068e-01 637.42 2.588e-04  11.590          9        9
7287     VARS       6_26 1.246e-01 638.44 9.411e-05 -11.548          1        1
1821     DDR1       6_25 1.135e-01 101.67 1.195e-05 -11.175          2        2
966  C6orf136       6_24 8.036e-02  79.81 4.893e-06  11.031          2        2
2518    FLOT1       6_24 1.604e-01  78.48 1.914e-05  10.981          5        6
834    BTN3A2       6_20 8.836e-02  91.92 4.950e-06 -10.759          5        5
831    BTN2A1       6_20 1.160e-01  83.45 4.270e-06  10.185          6        7
2975    HLA-B       6_25 8.535e-02  77.13 1.824e-06  10.155         12       31
5078     PPT2       6_26 9.858e-12 474.58 4.379e-25 -10.061          5        5
2105    EGFL8       6_26 4.045e-12 473.96 7.332e-26  10.036          7        8
5142    PRRT1       6_26 3.440e-12 472.86 5.315e-26 -10.018          1        1
5558     RNF5       6_26 7.171e-13 467.32 2.282e-27  -9.714          1        1
2806    GPSM3       6_26 2.278e-13 424.06 2.090e-28   9.377          2        2
1176   CCHCR1       6_25 9.482e-02  62.94 2.689e-06  -9.376         11       15
7545  ZKSCAN3       6_22 2.297e-02  55.91 1.900e-07   9.321          2        3
2977  HLA-DMB       6_27 4.018e-02  68.96 8.983e-07   8.860          2        2
7734  ZSCAN23       6_22 1.033e-02  45.88 4.652e-08  -8.541          1        1
4470    NT5C2      10_66 5.993e-01  47.55 1.518e-04  -8.511         12       16

GO enrichment analysis for genes with PIP>0.5

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

[1] 76

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

                                                               Term Overlap
1 positive regulation of neuron projection development (GO:0010976)    5/88
  Adjusted.P.value                           Genes
1          0.01519 BDNF;NTRK3;DPYSL3;SERPINI1;LRP8
[1] "GO_Cellular_Component_2021"

Past versions of unnamed-chunk-19-2.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_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
11                                                Body Weight 0.0486  2/41
60                                                    Measles 0.0486  1/41
88                                              Schizophrenia 0.0486 11/41
116                                  Congenital absent nipple 0.0486  1/41
181           Congenital absence of breast with absent nipple 0.0486  1/41
244                                 Sporadic Breast Carcinoma 0.0486  1/41
249                              Primary peritoneal carcinoma 0.0486  1/41
257                          Osteogenesis Imperfecta Type VII 0.0486  1/41
258 Familial encephalopathy with neuroserpin inclusion bodies 0.0486  1/41
264                                   Retinitis Pigmentosa 46 0.0486  1/41
     BgRatio
11   15/9703
60    1/9703
88  883/9703
116   1/9703
181   1/9703
244   1/9703
249   1/9703
257   1/9703
258   1/9703
264   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: 37 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] 53

#significance threshold for TWAS
print(sig_thresh)

[1] 4.511

#number of ctwas genes
length(ctwas_genes)

[1] 16

#number of TWAS genes
length(twas_genes)

[1] 147

#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
623    ATP2B2        3_8    0.8284 25.09 0.0001526  4.229          5        7
749      BDNF      11_19    0.9142 23.00 0.0001788  4.348          3        4
1042   CAMKK2      12_74    0.9028 35.27 0.0002014  4.060          8       10
1215     CD46      1_105    0.8156 19.39 0.0001137 -3.933         11       14
1613    CRTAP       3_24    0.8650 19.74 0.0001395  3.929          2        2
3436    LAMA5      20_36    0.9539 28.66 0.0002078 -4.269         16       21
5264  PYROXD2      10_62    0.8166 23.48 0.0001259  3.755         11       15
6190   SLC8B1      12_68    1.2142 22.26 0.0002788 -4.047         10       10
6274   SNRPA1      15_50    0.8489 20.88 0.0001372 -4.098          4        5

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.01538 0.10769 

#specificity
print(specificity)

 ctwas   TWAS 
0.9982 0.9827 

#precision / PPV
print(precision)

  ctwas    TWAS 
0.12500 0.09524 

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