SCZ - Brain Spinal cord cervical c-1

workflowr

• Summary
• Checks
• Past versions

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.

---

R Markdown file: up-to-date

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.

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: 1c69dd2

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 1c69dd2. 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

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_Spinal_cord_cervical_c-1.Rmd) and HTML (docs/SCZ_Brain_Spinal_cord_cervical_c-1.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	1c69dd2	sq-96	2022-02-27	update
html	ff6403a	sq-96	2022-02-27	Build site.
Rmd	3dd5b4c	sq-96	2022-02-27	update

---

Weight QC

#number of imputed weights
nrow(qclist_all)

[1] 10567

#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 
1038  754  605  411  517  537  511  406  401  410  617  617  227  356  367  479 
  17   18   19   20   21   22 
 622  168  801  320  127  276 

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

[1] 8626

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

[1] 0.8163

Check convergence of parameters

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

#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 
0.012559 0.000254 

#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 
10.743  8.561 

#report sample size
print(sample_size)

[1] 82315

#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)

[1]   10567 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 
0.01732 0.20011 

#compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))

[1] 0.1174 1.4749

Genes with highest PIPs

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

           genename region_tag susie_pip     mu2       PVE      z num_eqtl
3283          CRHR1      17_27    0.9975 3588.90 0.0434918  3.362        1
10447        ZNF823      19_10    0.9813   29.51 0.0003518  5.455        1
8557        MAP3K11      11_36    0.8742   23.79 0.0002526 -4.544        1
2890          SF3B1      2_117    0.8616   44.16 0.0004622  6.725        1
104           ELAC2      17_11    0.8479   21.96 0.0002262  4.542        1
3886         SPECC1      17_16    0.8395   25.83 0.0002634 -4.889        1
11504    AC012074.2       2_15    0.8272   21.80 0.0002191  4.447        2
11457     HIST1H2BN       6_21    0.7955   93.49 0.0009035 10.773        1
5935        ARFGAP2      11_29    0.7864   24.28 0.0002320  4.740        1
2497           GPN3      12_67    0.7808   24.83 0.0002355  5.009        1
6055          PLBD2      12_68    0.7752   20.28 0.0001910  3.986        1
5485           RIT1       1_76    0.7722   21.23 0.0001992 -3.496        1
12471 RP11-247A12.7       9_66    0.7587   22.39 0.0002063  4.370        2
3216          HSDL2       9_57    0.7581   22.24 0.0002048  4.322        1
8453           FUT9       6_65    0.7445   29.74 0.0002690  5.427        1
9840        TMEM222       1_19    0.7298   23.01 0.0002040  3.936        2
419          ARID1B      6_102    0.7124   22.04 0.0001908  3.907        1
1568       KIAA0391       14_9    0.7060   26.17 0.0002245 -5.150        2
2760         PDCD10      3_103    0.6947   20.40 0.0001722 -4.038        1
3025         MAP7D1       1_22    0.6914   24.16 0.0002029  4.907        1

Genes with largest effect sizes

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

           genename region_tag susie_pip     mu2       PVE        z num_eqtl
3283          CRHR1      17_27 9.975e-01 3588.90 4.349e-02  3.36232        1
6658       ARHGAP27      17_27 0.000e+00 2655.82 0.000e+00 -2.09345        1
4998          PRDM5       4_78 3.307e-13 1816.66 7.299e-15 -2.24071        1
11575 RP11-325F22.2       7_65 0.000e+00  966.29 0.000e+00  4.64948        2
9135       HLA-DQB1       6_26 1.065e-13  867.87 1.123e-15  4.11762        1
11416      HLA-DQB2       6_26 1.030e-13  829.87 1.039e-15 -4.14865        1
11576      HLA-DQA2       6_26 1.030e-13  829.87 1.039e-15 -4.14865        1
10158      HLA-DRB1       6_26 2.827e-13  515.72 1.771e-15  5.15185        1
10266      HLA-DQA1       6_26 4.804e-13  284.17 1.658e-15 -1.06154        2
66            KMT2E       7_65 0.000e+00  227.32 0.000e+00 -2.22870        1
10753          MSH5       6_26 1.508e-12  221.11 4.051e-15  7.25864        1
10740        SKIV2L       6_26 1.756e-12  173.25 3.695e-15 -0.01504        1
4695           NMT1      17_27 0.000e+00  148.07 0.000e+00  2.72086        1
8517          DCAKD      17_27 0.000e+00  116.93 0.000e+00 -2.99967        1
11457     HIST1H2BN       6_21 7.955e-01   93.49 9.035e-04 10.77288        1
10988         CLIC1       6_26 5.148e-13   83.86 5.245e-16  0.46344        1
4529          RINT1       7_65 0.000e+00   74.80 0.000e+00  0.56463        2
9299          ACBD4      17_27 0.000e+00   70.30 0.000e+00  1.73582        1
10270       ZSCAN16       6_22 1.548e-02   67.84 1.276e-05 -8.50932        1
9836         BTN3A2       6_20 2.272e-02   67.49 1.863e-05  9.09444        2

Genes with highest PVE

           genename region_tag susie_pip     mu2       PVE      z num_eqtl
3283          CRHR1      17_27    0.9975 3588.90 0.0434918  3.362        1
11457     HIST1H2BN       6_21    0.7955   93.49 0.0009035 10.773        1
2890          SF3B1      2_117    0.8616   44.16 0.0004622  6.725        1
10447        ZNF823      19_10    0.9813   29.51 0.0003518  5.455        1
8068         INO80E      16_24    0.6064   39.54 0.0002913  6.350        1
3741           KLC1      14_54    0.5642   41.29 0.0002830  7.026        1
8453           FUT9       6_65    0.7445   29.74 0.0002690  5.427        1
3886         SPECC1      17_16    0.8395   25.83 0.0002634 -4.889        1
8557        MAP3K11      11_36    0.8742   23.79 0.0002526 -4.544        1
2445            MDK      11_28    0.5321   38.44 0.0002485 -6.357        1
2497           GPN3      12_67    0.7808   24.83 0.0002355  5.009        1
5935        ARFGAP2      11_29    0.7864   24.28 0.0002320  4.740        1
104           ELAC2      17_11    0.8479   21.96 0.0002262  4.542        1
1568       KIAA0391       14_9    0.7060   26.17 0.0002245 -5.150        2
11504    AC012074.2       2_15    0.8272   21.80 0.0002191  4.447        2
12471 RP11-247A12.7       9_66    0.7587   22.39 0.0002063  4.370        2
3216          HSDL2       9_57    0.7581   22.24 0.0002048  4.322        1
9840        TMEM222       1_19    0.7298   23.01 0.0002040  3.936        2
3025         MAP7D1       1_22    0.6914   24.16 0.0002029  4.907        1
5485           RIT1       1_76    0.7722   21.23 0.0001992 -3.496        1

Genes with largest z scores

       genename region_tag susie_pip    mu2       PVE      z num_eqtl
11457 HIST1H2BN       6_21 7.955e-01  93.49 9.035e-04 10.773        1
9836     BTN3A2       6_20 2.272e-02  67.49 1.863e-05  9.094        2
10270   ZSCAN16       6_22 1.548e-02  67.84 1.276e-05 -8.509        1
9594   HIST1H1B       6_21 1.726e-02  53.84 1.129e-05 -8.250        1
4810      PGBD1       6_22 1.331e-02  59.13 9.560e-06 -8.142        2
9231  HIST1H2BC       6_20 2.374e-02  53.30 1.537e-05 -8.028        1
10753      MSH5       6_26 1.508e-12 221.11 4.051e-15  7.259        1
7004       TYW5      2_118 3.162e-01  40.60 1.559e-04 -7.226        1
7005      MAIP1      2_118 3.162e-01  40.60 1.559e-04  7.226        1
440    MPHOSPH9      12_75 1.561e-01  46.28 8.779e-05  7.158        1
12858 HIST1H2BO       6_21 1.711e-02  41.74 8.673e-06 -7.075        1
3741       KLC1      14_54 5.642e-01  41.29 2.830e-04  7.026        1
2623     TRIM38       6_20 1.807e-02  40.40 8.869e-06 -7.012        2
2890      SF3B1      2_117 8.616e-01  44.16 4.622e-04  6.725        1
9981    ZSCAN23       6_22 9.773e-02  45.98 5.459e-05 -6.675        2
9354    ARL6IP4      12_75 9.288e-03  39.92 4.504e-06 -6.491        1
3313      SNX19      11_81 1.556e-01  42.66 8.062e-05  6.484        2
6037      ABCB9      12_75 7.382e-03  38.61 3.462e-06  6.404        1
2511     OGFOD2      12_75 6.986e-03  38.28 3.249e-06  6.374        1
2445        MDK      11_28 5.321e-01  38.44 2.485e-04 -6.357        1

Comparing z scores and PIPs

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

Past versions of unnamed-chunk-11-2.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

[1] 0.007665

       genename region_tag susie_pip    mu2       PVE      z num_eqtl
11457 HIST1H2BN       6_21 7.955e-01  93.49 9.035e-04 10.773        1
9836     BTN3A2       6_20 2.272e-02  67.49 1.863e-05  9.094        2
10270   ZSCAN16       6_22 1.548e-02  67.84 1.276e-05 -8.509        1
9594   HIST1H1B       6_21 1.726e-02  53.84 1.129e-05 -8.250        1
4810      PGBD1       6_22 1.331e-02  59.13 9.560e-06 -8.142        2
9231  HIST1H2BC       6_20 2.374e-02  53.30 1.537e-05 -8.028        1
10753      MSH5       6_26 1.508e-12 221.11 4.051e-15  7.259        1
7004       TYW5      2_118 3.162e-01  40.60 1.559e-04 -7.226        1
7005      MAIP1      2_118 3.162e-01  40.60 1.559e-04  7.226        1
440    MPHOSPH9      12_75 1.561e-01  46.28 8.779e-05  7.158        1
12858 HIST1H2BO       6_21 1.711e-02  41.74 8.673e-06 -7.075        1
3741       KLC1      14_54 5.642e-01  41.29 2.830e-04  7.026        1
2623     TRIM38       6_20 1.807e-02  40.40 8.869e-06 -7.012        2
2890      SF3B1      2_117 8.616e-01  44.16 4.622e-04  6.725        1
9981    ZSCAN23       6_22 9.773e-02  45.98 5.459e-05 -6.675        2
9354    ARL6IP4      12_75 9.288e-03  39.92 4.504e-06 -6.491        1
3313      SNX19      11_81 1.556e-01  42.66 8.062e-05  6.484        2
6037      ABCB9      12_75 7.382e-03  38.61 3.462e-06  6.404        1
2511     OGFOD2      12_75 6.986e-03  38.28 3.249e-06  6.374        1
2445        MDK      11_28 5.321e-01  38.44 2.485e-04 -6.357        1

GO enrichment analysis for genes with PIP>0.5

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

[1] 31

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-14-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

                                                                        Term
1                    regulation of leukocyte cell-cell adhesion (GO:1903037)
2 regulation of leukocyte adhesion to vascular endothelial cell (GO:1904994)
  Overlap Adjusted.P.value    Genes
1    2/12          0.02772 FUT9;MDK
2    2/13          0.02772 FUT9;MDK
[1] "GO_Cellular_Component_2021"

Past versions of unnamed-chunk-14-2.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"

Past versions of unnamed-chunk-14-3.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

[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
5                                   Anxiety Disorders 0.01361  2/11 44/9703
56                           Anxiety States, Neurotic 0.01361  2/11 44/9703
84                         Anxiety neurosis (finding) 0.01361  2/11 44/9703
92                 Cerebral Cavernous Malformations 3 0.01361  1/11  1/9703
96           Familial cerebral cavernous malformation 0.01361  1/11  1/9703
103                    PROSTATE CANCER, HEREDITARY, 2 0.01361  1/11  1/9703
104                                 NOONAN SYNDROME 8 0.01361  1/11  1/9703
105  COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.01361  1/11  1/9703
107 Very long chain acyl-CoA dehydrogenase deficiency 0.01361  1/11  1/9703
30                                Pain, Postoperative 0.01883  1/11  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

Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

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] 58

#significance threshold for TWAS
print(sig_thresh)

[1] 4.576

#number of ctwas genes
length(ctwas_genes)

[1] 7

#number of TWAS genes
length(twas_genes)

[1] 81

#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_eqtl
11504 AC012074.2       2_15    0.8272   21.80 0.0002191  4.447        2
8557     MAP3K11      11_36    0.8742   23.79 0.0002526 -4.544        1
104        ELAC2      17_11    0.8479   21.96 0.0002262  4.542        1
3283       CRHR1      17_27    0.9975 3588.90 0.0434918  3.362        1

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.03077 0.04615 

#specificity
print(specificity)

 ctwas   TWAS 
0.9997 0.9929 

#precision / PPV
print(precision)

  ctwas    TWAS 
0.57143 0.07407 

Past versions of unnamed-chunk-20-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

Session information

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