Last updated: 2022-05-19

Checks: 5 2

Knit directory: cTWAS_analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). 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 bcaadf3. 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:  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_54.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/ttt.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 bcaadf3 sq-96 2022-05-19 update
html bcaadf3 sq-96 2022-05-19 update
Rmd be614ed sq-96 2022-05-19 update
html be614ed sq-96 2022-05-19 update
Rmd 7d08c9b sq-96 2022-05-18 update
html 7d08c9b sq-96 2022-05-18 update
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
INFO:numexpr.utils:Note: NumExpr detected 56 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
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

Version Author Date
2749be9 sq-96 2022-05-12
     gene       snp 
0.0079441 0.0003125 
 gene   snp 
13.20 10.19 
[1] 105318
[1]    6870 6309950
   gene     snp 
0.00684 0.19087 
[1] 0.01537 1.07050

Genes with highest PIPs

Version Author Date
bcaadf3 sq-96 2022-05-19
be614ed sq-96 2022-05-19
2749be9 sq-96 2022-05-12
       genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
3252       LRP8       1_33    1.1226 33.65 0.0003019  4.820          5        5
2151   FAM177A1       14_9    1.0331 24.42 0.0002142 -4.872         12       14
2429     GIGYF1       7_62    1.0221 34.53 0.0002916  5.266          3        3
3237     LPCAT4      15_10    0.9460 25.64 0.0002120  4.892          3        3
3166  LINC00320       21_6    0.9267 29.45 0.0002312  5.336          3        3
671        BDNF      11_19    0.9135 23.62 0.0001837  4.348          3        4
749  BUB1B-PAK6      15_14    0.9025 30.73 0.0002351  5.588          2        2
613      B3GAT1      11_84    0.8969 23.77 0.0001658 -4.448          8       12
1471      CRTAP       3_24    0.8847 20.12 0.0001488  3.929          2        2
3324     MAD1L1        7_3    0.8812 69.62 0.0003687  8.182          6        7
5965      THAP8      19_25    0.8507 19.76 0.0001358 -3.847          2        2
4171      PCBP2      12_33    0.8426 26.30 0.0001773 -4.953          2        2
1707       DGKZ      11_28    0.8422 48.30 0.0003253  7.216          2        2
5577     SNRPA1      15_50    0.8264 22.99 0.0001397 -3.934          5        7
6184      TPGS2      18_20    0.8198 28.26 0.0001709 -4.088          4        4
416      APOPT1      14_54    0.7858 46.02 0.0002582 -7.407          7       10
4000      NT5C2      10_66    0.7796 48.83 0.0002566 -8.541         11       13
141      ACTR1B       2_57    0.7780 20.17 0.0001136  3.978          5        5
325      ANAPC7      12_67    0.7579 38.23 0.0001921  6.385          4        4
5307      SF3B1      2_117    0.7509 46.51 0.0002443 -7.053          2        2

Genes with highest PVE

       genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
3324     MAD1L1        7_3    0.8812 69.62 0.0003687  8.182          6        7
1707       DGKZ      11_28    0.8422 48.30 0.0003253  7.216          2        2
3252       LRP8       1_33    1.1226 33.65 0.0003019  4.820          5        5
2429     GIGYF1       7_62    1.0221 34.53 0.0002916  5.266          3        3
416      APOPT1      14_54    0.7858 46.02 0.0002582 -7.407          7       10
4000      NT5C2      10_66    0.7796 48.83 0.0002566 -8.541         11       13
5307      SF3B1      2_117    0.7509 46.51 0.0002443 -7.053          2        2
749  BUB1B-PAK6      15_14    0.9025 30.73 0.0002351  5.588          2        2
3166  LINC00320       21_6    0.9267 29.45 0.0002312  5.336          3        3
2151   FAM177A1       14_9    1.0331 24.42 0.0002142 -4.872         12       14
3237     LPCAT4      15_10    0.9460 25.64 0.0002120  4.892          3        3
325      ANAPC7      12_67    0.7579 38.23 0.0001921  6.385          4        4
671        BDNF      11_19    0.9135 23.62 0.0001837  4.348          3        4
4171      PCBP2      12_33    0.8426 26.30 0.0001773 -4.953          2        2
6184      TPGS2      18_20    0.8198 28.26 0.0001709 -4.088          4        4
613      B3GAT1      11_84    0.8969 23.77 0.0001658 -4.448          8       12
1471      CRTAP       3_24    0.8847 20.12 0.0001488  3.929          2        2
5577     SNRPA1      15_50    0.8264 22.99 0.0001397 -3.934          5        7
2348       FXR1      3_111    0.5794 44.40 0.0001380  6.837          4        4
5965      THAP8      19_25    0.8507 19.76 0.0001358 -3.847          2        2

Comparing z scores and PIPs

Version Author Date
be614ed sq-96 2022-05-19
2749be9 sq-96 2022-05-12

Version Author Date
bcaadf3 sq-96 2022-05-19
be614ed sq-96 2022-05-19
2749be9 sq-96 2022-05-12
[1] 0.01805
         genename region_tag susie_pip    mu2       PVE       z num_intron
3284         LSM2       6_26 9.745e-05 222.29 2.004e-11 -11.599          1
626          BAG6       6_26 1.158e-04 221.94 2.757e-11 -11.590          5
1657         DDR1       6_25 1.826e-01 105.59 3.276e-05  11.175          2
879      C6orf136       6_24 1.015e-01  82.59 8.072e-06 -11.031          2
2295        FLOT1       6_24 2.106e-01  81.23 3.399e-05 -10.981          6
745        BTN3A2       6_20 1.394e-01  92.71 8.680e-06 -10.665          5
4528         PPT2       6_26 3.691e-05 152.97 1.889e-12 -10.061          5
1923        EGFL8       6_26 2.931e-05 142.24 1.098e-12  -9.625          4
1072       CCHCR1       6_25 3.239e-02  68.64 3.490e-07  -9.508          6
2535        GPSM3       6_26 2.168e-06 124.08 5.539e-15   9.377          1
6706      ZKSCAN3       6_22 9.297e-03  58.35 4.789e-08  -9.321          1
2692      HLA-DMA       6_27 1.319e-01  69.70 4.883e-06   8.727          6
4000        NT5C2      10_66 7.796e-01  48.83 2.566e-04  -8.541         11
3324       MAD1L1        7_3 8.812e-01  69.62 3.687e-04   8.182          6
6271      TSNARE1       8_93 3.299e-02  53.87 3.712e-07   7.961          4
4259        PGBD1       6_22 3.918e-02  40.35 3.080e-07  -7.746          2
743        BTN2A1       6_20 4.151e-02  51.43 3.563e-07  -7.727          3
5073 RP5-874C20.8       6_22 3.226e-02  38.78 2.883e-07   7.631          4
744        BTN3A1       6_20 5.359e-02  47.80 4.827e-07   7.490          4
720          BRD2       6_27 2.136e-01  46.77 1.498e-05   7.455          6
     num_sqtl
3284        1
626         6
1657        2
879         2
2295        6
745         5
4528        5
1923        5
1072        9
2535        1
6706        1
2692       10
4000       13
3324        7
6271        6
4259        2
743         3
5073        4
744         4
720         7

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)
[1] 51
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"

Version Author Date
bcaadf3 sq-96 2022-05-19
be614ed sq-96 2022-05-19
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"

Version Author Date
bcaadf3 sq-96 2022-05-19
be614ed sq-96 2022-05-19
2749be9 sq-96 2022-05-12
                                              Term Overlap Adjusted.P.value
1 protein phosphatase type 2A complex (GO:0000159)    2/17          0.03387
2                            U2 snRNP (GO:0005686)    2/20          0.03387
3            microtubule cytoskeleton (GO:0015630)   5/331          0.03387
                                Genes
1                     PPP2R5B;PPP2R2A
2                        SNRPA1;SF3B1
3 DYNC1I2;ACTR1B;ANAPC7;KIF21B;MAD1L1
[1] "GO_Molecular_Function_2021"

Version Author Date
bcaadf3 sq-96 2022-05-19
be614ed sq-96 2022-05-19
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 BgRatio
238                           Abnormality of head or neck 0.01257  2/22  5/9703
17                             Adenocarcinoma of prostate 0.02914  2/22 20/9703
54                                                Measles 0.02914  1/22  1/9703
93                          Electroencephalogram abnormal 0.02914  1/22  1/9703
199                             Sporadic Breast Carcinoma 0.02914  1/22  1/9703
205                          Primary peritoneal carcinoma 0.02914  1/22  1/9703
208                      Osteogenesis Imperfecta Type VII 0.02914  1/22  1/9703
211 BREAST-OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02914  1/22  1/9703
212         BREAST CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02914  1/22  1/9703
213        OVARIAN CANCER, FAMILIAL, SUSCEPTIBILITY TO, 1 0.02914  1/22  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: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Version Author Date
bcaadf3 sq-96 2022-05-19
be614ed sq-96 2022-05-19
7d08c9b sq-96 2022-05-18
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$genename))
[1] 51
#significance threshold for TWAS
print(sig_thresh)
[1] 4.485
#number of ctwas genes
length(ctwas_genes)
[1] 15
#number of TWAS genes
length(twas_genes)
[1] 124
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]
     genename region_tag susie_pip   mu2       PVE      z num_intron num_sqtl
613    B3GAT1      11_84    0.8969 23.77 0.0001658 -4.448          8       12
671      BDNF      11_19    0.9135 23.62 0.0001837  4.348          3        4
1471    CRTAP       3_24    0.8847 20.12 0.0001488  3.929          2        2
5577   SNRPA1      15_50    0.8264 22.99 0.0001397 -3.934          5        7
5965    THAP8      19_25    0.8507 19.76 0.0001358 -3.847          2        2
6184    TPGS2      18_20    0.8198 28.26 0.0001709 -4.088          4        4
#sensitivity / recall
print(sensitivity)
  ctwas    TWAS 
0.02308 0.13077 
#specificity
print(specificity)
 ctwas   TWAS 
0.9982 0.9843 
#precision / PPV
print(precision)
 ctwas   TWAS 
0.2000 0.1371 

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_1.25   workflowr_1.7.0  

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  processx_3.5.2    curl_4.3.2        compiler_4.1.0   
[21] git2r_0.28.0      rvest_1.0.0       cli_3.0.0         Cairo_1.5-15     
[25] xml2_1.3.2        labeling_0.4.2    sass_0.4.0        scales_1.1.1     
[29] callr_3.7.0       systemfonts_1.0.4 apcluster_1.4.9   digest_0.6.27    
[33] rmarkdown_2.9     svglite_2.0.0     pkgconfig_2.0.3   htmltools_0.5.1.1
[37] dbplyr_2.1.1      highr_0.9         rlang_1.0.2       rstudioapi_0.13  
[41] jquerylib_0.1.4   farver_2.1.0      generics_0.1.0    jsonlite_1.7.2   
[45] magrittr_2.0.1    Matrix_1.3-3      ggbeeswarm_0.6.0  Rcpp_1.0.7       
[49] munsell_0.5.0     fansi_0.5.0       lifecycle_1.0.0   stringi_1.6.2    
[53] whisker_0.4       yaml_2.2.1        plyr_1.8.6        grid_4.1.0       
[57] ggrepel_0.9.1     parallel_4.1.0    promises_1.2.0.1  crayon_1.4.1     
[61] lattice_0.20-44   haven_2.4.1       hms_1.1.0         knitr_1.33       
[65] ps_1.6.0          pillar_1.7.0      igraph_1.2.6      rjson_0.2.20     
[69] rngtools_1.5      reshape2_1.4.4    codetools_0.2-18  reprex_2.0.0     
[73] glue_1.4.2        evaluate_0.14     getPass_0.2-2     modelr_0.1.8     
[77] data.table_1.14.0 png_0.1-7         vctrs_0.3.8       httpuv_1.6.1     
[81] foreach_1.5.1     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[85] xfun_0.24         broom_0.7.8       later_1.2.0       iterators_1.0.13 
[89] beeswarm_0.4.0    ellipsis_0.3.2    here_1.0.1