Last updated: 2022-02-27

Checks: 6 1

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.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

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 3dd5b4c. 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:    .ipynb_checkpoints/
    Ignored:    data/AF/

Untracked files:
    Untracked:  Rplot.png
    Untracked:  analysis/.ipynb_checkpoints/
    Untracked:  analysis/Autism_Brain_Amygdala.Rmd
    Untracked:  analysis/Autism_Brain_Anterior_cingulate_cortex_BA24.Rmd
    Untracked:  analysis/Autism_Brain_Caudate_basal_ganglia.Rmd
    Untracked:  analysis/Autism_Brain_Cerebellar_Hemisphere.Rmd
    Untracked:  analysis/Autism_Brain_Cerebellum.Rmd
    Untracked:  analysis/Autism_Brain_Cortex.Rmd
    Untracked:  analysis/Autism_Brain_Frontal_Cortex_BA9.Rmd
    Untracked:  analysis/Autism_Brain_Hippocampus.Rmd
    Untracked:  analysis/Autism_Brain_Hypothalamus.Rmd
    Untracked:  analysis/Autism_Brain_Nucleus_accumbens_basal_ganglia.Rmd
    Untracked:  analysis/Autism_Brain_Putamen_basal_ganglia.Rmd
    Untracked:  analysis/Autism_Brain_Spinal_cord_cervical_c-1.Rmd
    Untracked:  analysis/Autism_Brain_Substantia_nigra.Rmd
    Untracked:  analysis/Glucose_Adipose_Subcutaneous.Rmd
    Untracked:  analysis/Glucose_Adipose_Visceral_Omentum.Rmd
    Untracked:  analysis/Splicing_Test.Rmd
    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_out/
    Untracked:  code/T2D_out/
    Untracked:  code/ctwas_config.R
    Untracked:  code/mapping.R
    Untracked:  code/out/
    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_analysis.sbatch
    Untracked:  code/run_SCZ_analysis.sh
    Untracked:  code/run_SCZ_ctwas_rss_LDR.R
    Untracked:  code/run_T2D_analysis.sbatch
    Untracked:  code/run_T2D_analysis.sh
    Untracked:  code/run_T2D_ctwas_rss_LDR.R
    Untracked:  data/.ipynb_checkpoints/
    Untracked:  data/Autism/
    Untracked:  data/BMI/
    Untracked:  data/BMI_S/
    Untracked:  data/Glucose/
    Untracked:  data/LDL_S/
    Untracked:  data/SCZ/
    Untracked:  data/T2D/
    Untracked:  data/TEST/
    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/summary_known_genes_annotations.xlsx
    Untracked:  data/untitled.txt

Unstaged changes:
    Modified:   analysis/BMI_Brain_Amygdala_S.Rmd
    Modified:   analysis/BMI_Brain_Anterior_cingulate_cortex_BA24_S.Rmd
    Modified:   analysis/BMI_Brain_Caudate_basal_ganglia_S.Rmd
    Modified:   analysis/BMI_Brain_Cerebellar_Hemisphere_S.Rmd
    Modified:   analysis/BMI_Brain_Cerebellum_S.Rmd
    Modified:   analysis/BMI_Brain_Cortex.Rmd
    Modified:   analysis/BMI_Brain_Cortex_S.Rmd
    Modified:   analysis/BMI_Brain_Frontal_Cortex_BA9_S.Rmd
    Modified:   analysis/BMI_Brain_Hippocampus_S.Rmd
    Modified:   analysis/BMI_Brain_Hypothalamus_S.Rmd
    Modified:   analysis/BMI_Brain_Nucleus_accumbens_basal_ganglia_S.Rmd
    Modified:   analysis/BMI_Brain_Putamen_basal_ganglia_S.Rmd
    Modified:   analysis/BMI_Brain_Spinal_cord_cervical_c-1_S.Rmd
    Modified:   analysis/BMI_Brain_Substantia_nigra_S.Rmd
    Modified:   analysis/LDL_Liver_S.Rmd
    Modified:   analysis/index.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_Brain_Cortex.Rmd) and HTML (docs/SCZ_Brain_Cortex.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 3dd5b4c sq-96 2022-02-27 update

Weight QC

#number of imputed weights
nrow(qclist_all)
[1] 11805
#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 
1182  833  689  456  564  597  567  438  449  491  708  673  233  392  390  558 
  17   18   19   20   21   22 
 697  184  896  372  130  306 
#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)
[1] 9268
#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)
[1] 0.7851

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 
0.0117819 0.0002498 
#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 
10.115  8.745 
#report sample size
print(sample_size)
[1] 82315
#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)
[1]   11805 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 
0.01709 0.20104 
#compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))
[1] 0.07764 1.58587

Genes with highest PIPs

          genename region_tag susie_pip   mu2       PVE      z num_eqtl
11314       ZNF823      19_10    0.9894 30.41 0.0003655  5.576        2
13679 RP11-230C9.4      6_102    0.9655 24.38 0.0002859 -4.864        2
3165         SF3B1      2_117    0.8406 43.74 0.0004467  6.725        1
3085         SPCS1       3_36    0.8248 35.15 0.0003522 -6.504        1
11176        PCBP2      12_33    0.7873 20.60 0.0001970  4.202        1
5055        RCBTB1      13_21    0.7808 21.04 0.0001996 -4.143        2
421          TRIT1       1_25    0.7795 21.04 0.0001992 -4.073        3
11969        AS3MT      10_66    0.7577 38.26 0.0003522  6.688        3
6435       ARFGAP2      11_29    0.7533 24.88 0.0002277  4.740        1
6035      METTL21A      2_122    0.7032 22.51 0.0001923 -4.406        1
13958        CWC25      17_23    0.6607 22.97 0.0001843 -3.926        2
376           CUL3      2_132    0.6584 29.41 0.0002352 -5.491        1
3183        CNPPD1      2_129    0.6333 23.74 0.0001827 -4.678        2
4002         ARMC7      17_42    0.5918 23.73 0.0001706  4.133        2
9752       ZNF354C      5_108    0.5861 21.75 0.0001549 -3.965        1
12379    LINC01305      2_105    0.5824 22.86 0.0001617  4.523        1
4909       CCDC146       7_49    0.5702 20.79 0.0001441  3.799        3
5958        CEP170      1_128    0.5671 24.30 0.0001674  4.678        1
752        PPP2R5B      11_36    0.5634 24.18 0.0001655 -4.577        1
10297        PCBP3      21_23    0.5553 21.27 0.0001435  4.308        1

Genes with largest effect sizes

      genename region_tag susie_pip     mu2       PVE      z num_eqtl
7218  ARHGAP27      17_27   0.00000 2129.27 0.000e+00 -1.847        2
3591     CRHR1      17_27   0.00000 2068.72 0.000e+00 -3.270        1
11645   LY6G6C       6_26   0.00000  960.79 0.000e+00  8.872        1
11907    CLIC1       6_26   0.00000  949.93 0.000e+00  9.312        2
10504   SPPL2C      17_27   0.00000  752.27 0.000e+00 -1.978        1
10996 HLA-DRB1       6_26   0.00000  525.39 0.000e+00  4.535        1
11640   HSPA1L       6_26   0.00000  401.95 0.000e+00 -7.126        1
11118 HLA-DQA1       6_26   0.00000  166.39 0.000e+00  1.889        1
4915     SRPK2       7_65   0.00000  128.12 0.000e+00 -1.338        1
10699   HEXIM1      17_27   0.00000  122.54 0.000e+00 -3.372        1
10447    FMNL1      17_27   0.00000  109.85 0.000e+00  1.802        2
12300   SAPCD1       6_26   0.00000  107.97 0.000e+00 -2.781        1
9224     DCAKD      17_27   0.00000   91.74 0.000e+00 -2.216        3
12783      C4A       6_26   0.00000   77.97 0.000e+00  3.137        2
9902  HLA-DQB1       6_26   0.00000   76.18 0.000e+00  1.677        2
10083    ACBD4      17_27   0.00000   68.16 0.000e+00  1.719        2
2927    PRSS16       6_21   0.11206   62.33 8.485e-05 -8.564        2
2503     GOSR2      17_27   0.00000   57.01 0.000e+00 -3.444        2
958      NT5C2      10_66   0.50836   51.68 3.192e-04 -8.066        1
6404       INA      10_66   0.02367   48.25 1.387e-05 -7.264        1

Genes with highest PVE

          genename region_tag susie_pip   mu2       PVE      z num_eqtl
3165         SF3B1      2_117    0.8406 43.74 0.0004467  6.725        1
11314       ZNF823      19_10    0.9894 30.41 0.0003655  5.576        2
3085         SPCS1       3_36    0.8248 35.15 0.0003522 -6.504        1
11969        AS3MT      10_66    0.7577 38.26 0.0003522  6.688        3
958          NT5C2      10_66    0.5084 51.68 0.0003192 -8.066        1
13679 RP11-230C9.4      6_102    0.9655 24.38 0.0002859 -4.864        2
376           CUL3      2_132    0.6584 29.41 0.0002352 -5.491        1
6435       ARFGAP2      11_29    0.7533 24.88 0.0002277  4.740        1
2682           MDK      11_29    0.4345 39.31 0.0002075 -6.357        1
5055        RCBTB1      13_21    0.7808 21.04 0.0001996 -4.143        2
421          TRIT1       1_25    0.7795 21.04 0.0001992 -4.073        3
11176        PCBP2      12_33    0.7873 20.60 0.0001970  4.202        1
6035      METTL21A      2_122    0.7032 22.51 0.0001923 -4.406        1
13958        CWC25      17_23    0.6607 22.97 0.0001843 -3.926        2
13401        CORO7       16_4    0.5211 28.97 0.0001834 -5.016        2
3183        CNPPD1      2_129    0.6333 23.74 0.0001827 -4.678        2
6507       TMEM219      16_24    0.4011 37.11 0.0001808  6.243        1
4002         ARMC7      17_42    0.5918 23.73 0.0001706  4.133        2
5958        CEP170      1_128    0.5671 24.30 0.0001674  4.678        1
752        PPP2R5B      11_36    0.5634 24.18 0.0001655 -4.577        1

Genes with largest z scores

       genename region_tag susie_pip    mu2       PVE      z num_eqtl
11907     CLIC1       6_26  0.000000 949.93 0.000e+00  9.312        2
11645    LY6G6C       6_26  0.000000 960.79 0.000e+00  8.872        1
2927     PRSS16       6_21  0.112056  62.33 8.485e-05 -8.564        2
958       NT5C2      10_66  0.508364  51.68 3.192e-04 -8.066        1
6413      CNNM2      10_66  0.047944  45.59 2.655e-05 -7.691        1
10662    BTN3A2       6_20  0.016326  46.66 9.254e-06  7.313        3
6404        INA      10_66  0.023665  48.25 1.387e-05 -7.264        1
13518 LINC01415      18_30  0.027640  32.82 1.102e-05 -7.188        2
11640    HSPA1L       6_26  0.000000 401.95 0.000e+00 -7.126        1
12511   ZSCAN31       6_22  0.029845  37.53 1.361e-05 -6.820        3
3165      SF3B1      2_117  0.840602  43.74 4.467e-04  6.725        1
11969     AS3MT      10_66  0.757723  38.26 3.522e-04  6.688        3
11171   ZSCAN26       6_22  0.016156  37.52 7.365e-06  6.645        3
2756     OGFOD2      12_75  0.010145  39.52 4.870e-06  6.518        1
3085      SPCS1       3_36  0.824822  35.15 3.522e-04 -6.504        1
3616      SNX19      11_81  0.134870  41.83 6.853e-05  6.459        2
10809   ZSCAN23       6_22  0.050592  38.63 2.374e-05 -6.415        1
6550      ABCB9      12_75  0.007503  37.92 3.457e-06  6.404        1
2682        MDK      11_29  0.434514  39.31 2.075e-04 -6.357        1
3159     KCNJ13      2_137  0.209359  34.40 8.748e-05  6.333        1

Comparing z scores and PIPs

[1] 0.006607
       genename region_tag susie_pip    mu2       PVE      z num_eqtl
11907     CLIC1       6_26  0.000000 949.93 0.000e+00  9.312        2
11645    LY6G6C       6_26  0.000000 960.79 0.000e+00  8.872        1
2927     PRSS16       6_21  0.112056  62.33 8.485e-05 -8.564        2
958       NT5C2      10_66  0.508364  51.68 3.192e-04 -8.066        1
6413      CNNM2      10_66  0.047944  45.59 2.655e-05 -7.691        1
10662    BTN3A2       6_20  0.016326  46.66 9.254e-06  7.313        3
6404        INA      10_66  0.023665  48.25 1.387e-05 -7.264        1
13518 LINC01415      18_30  0.027640  32.82 1.102e-05 -7.188        2
11640    HSPA1L       6_26  0.000000 401.95 0.000e+00 -7.126        1
12511   ZSCAN31       6_22  0.029845  37.53 1.361e-05 -6.820        3
3165      SF3B1      2_117  0.840602  43.74 4.467e-04  6.725        1
11969     AS3MT      10_66  0.757723  38.26 3.522e-04  6.688        3
11171   ZSCAN26       6_22  0.016156  37.52 7.365e-06  6.645        3
2756     OGFOD2      12_75  0.010145  39.52 4.870e-06  6.518        1
3085      SPCS1       3_36  0.824822  35.15 3.522e-04 -6.504        1
3616      SNX19      11_81  0.134870  41.83 6.853e-05  6.459        2
10809   ZSCAN23       6_22  0.050592  38.63 2.374e-05 -6.415        1
6550      ABCB9      12_75  0.007503  37.92 3.457e-06  6.404        1
2682        MDK      11_29  0.434514  39.31 2.075e-04 -6.357        1
3159     KCNJ13      2_137  0.209359  34.40 8.748e-05  6.333        1

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)
[1] 26
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"

                                                                         Term
1 regulation of nucleobase-containing compound metabolic process (GO:0019219)
  Overlap Adjusted.P.value       Genes
1    2/12          0.02022 PCBP3;PCBP2
[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
55                           Disproportionate tall stature 0.009895   1/9
56       Reticular Dystrophy Of Retinal Pigment Epithelium 0.009895   1/9
60                       PSEUDOHYPOALDOSTERONISM, TYPE IIE 0.009895   1/9
62              SPASTIC PARAPLEGIA 45, AUTOSOMAL RECESSIVE 0.009895   1/9
63 RETINAL DYSTROPHY WITH OR WITHOUT EXTRAOCULAR ANOMALIES 0.009895   1/9
64        COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 35 0.009895   1/9
17                     Neoplasms, Glandular and Epithelial 0.011869   1/9
29                                     Glandular Neoplasms 0.011869   1/9
48              Refractory anemia with ringed sideroblasts 0.011869   1/9
51                                             Epithelioma 0.011869   1/9
   BgRatio
55  1/9703
56  1/9703
60  1/9703
62  1/9703
63  1/9703
64  1/9703
17  2/9703
29  2/9703
48  2/9703
51  2/9703

WebGestalt 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

PIP Manhattan Plot

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$genename))
[1] 25
#significance threshold for TWAS
print(sig_thresh)
[1] 4.599
#number of ctwas genes
length(ctwas_genes)
[1] 4
#number of TWAS genes
length(twas_genes)
[1] 78
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$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     0 
#specificity
print(specificity)
 ctwas   TWAS 
0.9997 0.9934 
#precision / PPV
print(precision)
ctwas  TWAS 
    0     0 

Locus Plots - 3_36

Locus Plots - 15_21

Locus Plots - 12_4

Locus Plots - 16_24

Locus Plots - 11_36


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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] readxl_1.3.1      forcats_0.5.1     stringr_1.4.0     dplyr_1.0.7      
 [5] purrr_0.3.4       readr_2.1.1       tidyr_1.1.4       tidyverse_1.3.1  
 [9] tibble_3.1.6      WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0      
[13] cowplot_1.0.0     ggplot2_3.3.5     workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] fs_1.5.2          lubridate_1.8.0   bit64_4.0.5       doParallel_1.0.17
 [5] httr_1.4.2        rprojroot_2.0.2   tools_3.6.1       backports_1.4.1  
 [9] doRNG_1.8.2       utf8_1.2.2        R6_2.5.1          vipor_0.4.5      
[13] DBI_1.1.2         colorspace_2.0-2  withr_2.4.3       ggrastr_1.0.1    
[17] tidyselect_1.1.1  bit_4.0.4         curl_4.3.2        compiler_3.6.1   
[21] git2r_0.26.1      rvest_1.0.2       cli_3.1.0         Cairo_1.5-12.2   
[25] xml2_1.3.3        labeling_0.4.2    scales_1.1.1      apcluster_1.4.8  
[29] digest_0.6.29     rmarkdown_2.11    svglite_1.2.2     pkgconfig_2.0.3  
[33] htmltools_0.5.2   dbplyr_2.1.1      fastmap_1.1.0     highr_0.9        
[37] rlang_1.0.1       rstudioapi_0.13   RSQLite_2.2.8     jquerylib_0.1.4  
[41] farver_2.1.0      generics_0.1.1    jsonlite_1.7.2    vroom_1.5.7      
[45] magrittr_2.0.2    Matrix_1.2-18     ggbeeswarm_0.6.0  Rcpp_1.0.8       
[49] munsell_0.5.0     fansi_1.0.2       gdtools_0.1.9     lifecycle_1.0.1  
[53] stringi_1.7.6     whisker_0.3-2     yaml_2.2.1        plyr_1.8.6       
[57] grid_3.6.1        blob_1.2.2        ggrepel_0.9.1     parallel_3.6.1   
[61] promises_1.0.1    crayon_1.5.0      lattice_0.20-38   haven_2.4.3      
[65] hms_1.1.1         knitr_1.36        pillar_1.6.4      igraph_1.2.10    
[69] rjson_0.2.20      rngtools_1.5.2    reshape2_1.4.4    codetools_0.2-16 
[73] reprex_2.0.1      glue_1.6.2        evaluate_0.14     data.table_1.14.2
[77] modelr_0.1.8      vctrs_0.3.8       tzdb_0.2.0        httpuv_1.5.1     
[81] foreach_1.5.2     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[85] cachem_1.0.6      xfun_0.29         broom_0.7.10      later_0.8.0      
[89] iterators_1.0.14  beeswarm_0.2.3    memoise_2.0.1     ellipsis_0.3.2