SCZ 2018 - Brain_Cerebellar_Hemisphere
sheng Qian
2021-2-6
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.
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 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_Cerebellar_Hemisphere_S.Rmd) and HTML (docs/SCZ_2018_Brain_Cerebellar_Hemisphere_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] 26564#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2520 1814 1594 973 1137 1377 1526 911 1106 1166 1579 1419 520 921 928 1179
17 18 19 20 21 22
1880 325 1895 891 51 852 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 23201#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8734finish
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionCheck convergence of parameters
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
gene snp
0.0103642 0.0002912 gene snp
12.00 10.12 [1] 105318[1] 8010 6309950 gene snp
0.00946 0.17654 [1] 0.03556 1.05335Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
6205 SLC8B1 12_68 1.5477 28.59 5.738e-04 -4.047 11 12
3664 LRP8 1_33 1.2607 32.55 3.761e-04 4.820 11 11
7510 WDR27 6_111 1.1795 17.37 1.185e-04 2.338 30 41
2370 FAM177A1 14_9 1.1089 24.30 2.444e-04 -4.872 10 11
2694 GIGYF2 2_137 1.0940 56.96 5.633e-04 -8.128 6 6
5370 R3HDM2 12_36 1.0731 43.83 4.526e-04 6.634 9 11
3648 LPCAT4 15_10 0.9950 25.36 2.297e-04 4.892 3 4
2693 GIGYF1 7_62 0.9927 26.42 2.425e-04 -5.266 5 5
1109 CCDC57 17_47 0.9837 18.98 1.239e-04 -3.061 34 44
3494 LAMA5 20_37 0.9462 28.70 1.979e-04 -4.211 25 32
118 ACTR1B 2_57 0.9438 19.16 1.576e-04 3.978 9 9
4732 PATJ 1_39 0.9400 22.53 1.571e-04 2.798 15 17
987 CAMKK2 12_74 0.9338 35.78 2.086e-04 4.159 6 8
4808 PDXDC1 16_15 0.9292 29.62 1.423e-04 3.879 17 18
4700 PAK6 15_14 0.9242 29.86 2.422e-04 -5.588 1 1
5975 SF3B1 2_117 0.9204 45.85 3.612e-04 -7.053 3 3
4121 MRPS33 7_87 0.9200 20.70 1.602e-04 -4.304 5 5
1453 CNOT1 16_31 0.9167 35.98 2.567e-04 6.282 10 11
6804 THAP8 19_25 0.9103 19.03 1.497e-04 3.847 2 2
5358 PYROXD2 10_62 0.9087 21.98 1.517e-04 -3.852 9 10
4480 NPIPB14P 16_37 0.8947 18.17 1.248e-04 3.742 16 19
4586 NUP50 22_20 0.8804 18.64 1.329e-04 -3.850 5 5
5991 SGCE 7_58 0.8780 20.72 1.455e-04 4.413 6 6
2494 FGFR1 8_34 0.8758 36.56 2.276e-04 -6.046 10 11
1230 CECR2 22_2 0.8629 18.61 1.295e-04 -3.928 4 4
263 AKT3 1_128 0.8604 35.12 2.284e-04 6.266 5 5
4550 NTRK3 15_41 0.8602 24.09 1.557e-04 4.457 3 3
2899 GUSBP11 22_6 0.8598 21.55 9.607e-05 -2.922 21 34
1683 CUL9 6_33 0.8557 31.85 1.747e-04 4.961 11 12
6995 TNK2 3_120 0.8483 27.71 1.251e-04 3.409 16 16
7146 TSNARE1 8_93 0.8348 34.12 1.825e-04 6.364 10 10
654 B3GAT1 11_84 0.8323 23.80 1.477e-04 4.394 4 6
7650 ZDHHC20 13_2 0.8099 25.00 1.495e-04 -4.832 3 4
1919 DNAJB1 19_12 0.8080 19.58 1.102e-04 3.972 5 6
1612 CRTAP 3_24 0.8018 20.88 1.249e-04 3.929 2 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
420 APOM 6_26 0.4404 626.01 0.0011520 11.590 2 2
7415 VARS1 6_26 0.3832 628.56 0.0008763 -11.620 2 2
821 BTN3A1 6_20 0.7245 145.15 0.0006369 13.091 7 8
6205 SLC8B1 12_68 1.5477 28.59 0.0005738 -4.047 11 12
2694 GIGYF2 2_137 1.0940 56.96 0.0005633 -8.128 6 6
5370 R3HDM2 12_36 1.0731 43.83 0.0004526 6.634 9 11
3664 LRP8 1_33 1.2607 32.55 0.0003761 4.820 11 11
5975 SF3B1 2_117 0.9204 45.85 0.0003612 -7.053 3 3
1453 CNOT1 16_31 0.9167 35.98 0.0002567 6.282 10 11
2370 FAM177A1 14_9 1.1089 24.30 0.0002444 -4.872 10 11
2693 GIGYF1 7_62 0.9927 26.42 0.0002425 -5.266 5 5
4700 PAK6 15_14 0.9242 29.86 0.0002422 -5.588 1 1
3648 LPCAT4 15_10 0.9950 25.36 0.0002297 4.892 3 4
263 AKT3 1_128 0.8604 35.12 0.0002284 6.266 5 5
2494 FGFR1 8_34 0.8758 36.56 0.0002276 -6.046 10 11
987 CAMKK2 12_74 0.9338 35.78 0.0002086 4.159 6 8
3494 LAMA5 20_37 0.9462 28.70 0.0001979 -4.211 25 32
7070 TRANK1 3_27 0.7567 38.76 0.0001973 -6.365 6 6
7146 TSNARE1 8_93 0.8348 34.12 0.0001825 6.364 10 10
1683 CUL9 6_33 0.8557 31.85 0.0001747 4.961 11 12Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
[1] 0.02197 genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
7999 ZSCAN31 6_22 3.695e-02 160.21 1.569e-06 -13.135 2 2
821 BTN3A1 6_20 7.245e-01 145.15 6.369e-04 13.091 7 8
4852 PGBD1 6_22 1.027e-01 159.13 7.726e-06 -13.087 5 7
7415 VARS1 6_26 3.832e-01 628.56 8.763e-04 -11.620 2 2
420 APOM 6_26 4.404e-01 626.01 1.152e-03 11.590 2 2
1789 DDR1 6_25 3.515e-01 101.78 1.165e-04 -11.175 4 4
915 C6orf136 6_24 1.205e-01 80.18 1.105e-05 -11.031 2 2
2523 FLOT1 6_24 3.515e-01 78.83 9.198e-05 10.981 8 8
822 BTN3A2 6_20 1.534e-01 94.90 1.064e-05 -10.743 5 7
1712 CYP21A2 6_26 5.976e-06 607.99 2.062e-13 -10.513 1 2
674 BAG6 6_26 1.124e-08 500.57 5.992e-19 10.342 10 11
819 BTN2A1 6_20 1.490e-01 84.19 6.335e-06 10.110 7 7
5179 PPT2 6_26 5.412e-12 466.36 1.297e-25 10.061 7 9
2107 EGFL8 6_26 4.315e-12 465.72 8.227e-26 10.036 5 6
5241 PRRT1 6_26 3.762e-12 464.63 6.243e-26 -10.018 1 1
2820 GPSM3 6_26 2.356e-13 416.63 2.196e-28 -9.377 2 2
1130 CCHCR1 6_25 9.102e-02 59.77 1.948e-06 -9.032 11 18
7007 TNXB 6_26 2.108e-13 454.39 1.918e-28 9.001 4 5
3002 HLA-DMA 6_27 1.797e-01 70.57 1.141e-05 8.860 5 6
7995 ZSCAN23 6_22 1.294e-02 46.07 7.324e-08 -8.541 1 1GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 138Uploading 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 |
|---|---|---|
| 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 |
|---|---|---|
| 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"
| Version | Author | Date |
|---|---|---|
| 2749be9 | sq-96 | 2022-05-12 |
Term Overlap Adjusted.P.value
1 cadherin binding (GO:0045296) 10/322 0.01731
Genes
1 CAST;DNAJB1;DLG1;PDXDC1;SRC;PAK6;LRRFIP1;CD46;GIGYF2;KTN1DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio BgRatio
13 Balo's Concentric Sclerosis 0.0579 1/86 1/9703
40 Diffuse Cerebral Sclerosis of Schilder 0.0579 1/86 1/9703
90 Profound Mental Retardation 0.0579 5/86 139/9703
100 Acute monocytic leukemia 0.0579 3/86 26/9703
101 Leukemia, Myelocytic, Acute 0.0579 6/86 173/9703
112 Measles 0.0579 1/86 1/9703
116 Mental Retardation, Psychosocial 0.0579 5/86 139/9703
132 Nicotine Dependence 0.0579 2/86 14/9703
153 Schizophrenia 0.0579 17/86 883/9703
157 Status Epilepticus 0.0579 4/86 68/9703WebGestalt 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...
description size overlap FDR database
1 Bipolar Disorder 139 12 0.01063 disease_GLAD4U
2 Schizophrenia 171 12 0.04340 disease_GLAD4U
userId
1 AS3MT;BDNF;CAMKK2;DLG1;GABBR2;NT5C2;NTRK3;SDCCAG8;SYNE1;TCF4;TRANK1;TSNARE1
2 AHI1;AS3MT;BDNF;CAMKK2;DLG1;NT5C2;NTRK3;SDCCAG8;SYNE1;TCF4;TRANK1;TSNARE1PIP Manhattan Plot
Warning: Removed 2 rows containing missing values (geom_point).
Warning: Removed 2 rows containing missing values (geom_point).Warning: Removed 2 rows containing missing values (geom_label_repel).Warning: ggrepel: 106 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| 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] 60#significance threshold for TWAS
print(sig_thresh)[1] 4.518#number of ctwas genes
length(ctwas_genes)[1] 35#number of TWAS genes
length(twas_genes)[1] 176#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
118 ACTR1B 2_57 0.9438 19.16 1.576e-04 3.978 9 9
654 B3GAT1 11_84 0.8323 23.80 1.477e-04 4.394 4 6
987 CAMKK2 12_74 0.9338 35.78 2.086e-04 4.159 6 8
1109 CCDC57 17_47 0.9837 18.98 1.239e-04 -3.061 34 44
1230 CECR2 22_2 0.8629 18.61 1.295e-04 -3.928 4 4
1612 CRTAP 3_24 0.8018 20.88 1.249e-04 3.929 2 2
1919 DNAJB1 19_12 0.8080 19.58 1.102e-04 3.972 5 6
2899 GUSBP11 22_6 0.8598 21.55 9.607e-05 -2.922 21 34
3494 LAMA5 20_37 0.9462 28.70 1.979e-04 -4.211 25 32
4121 MRPS33 7_87 0.9200 20.70 1.602e-04 -4.304 5 5
4480 NPIPB14P 16_37 0.8947 18.17 1.248e-04 3.742 16 19
4550 NTRK3 15_41 0.8602 24.09 1.557e-04 4.457 3 3
4586 NUP50 22_20 0.8804 18.64 1.329e-04 -3.850 5 5
4732 PATJ 1_39 0.9400 22.53 1.571e-04 2.798 15 17
4808 PDXDC1 16_15 0.9292 29.62 1.423e-04 3.879 17 18
5358 PYROXD2 10_62 0.9087 21.98 1.517e-04 -3.852 9 10
5991 SGCE 7_58 0.8780 20.72 1.455e-04 4.413 6 6
6205 SLC8B1 12_68 1.5477 28.59 5.738e-04 -4.047 11 12
6804 THAP8 19_25 0.9103 19.03 1.497e-04 3.847 2 2
6995 TNK2 3_120 0.8483 27.71 1.251e-04 3.409 16 16
7510 WDR27 6_111 1.1795 17.37 1.185e-04 2.338 30 41#sensitivity / recall
print(sensitivity) ctwas TWAS
0.06154 0.13846 #specificity
print(specificity) ctwas TWAS
0.9966 0.9801 #precision / PPV
print(precision) ctwas TWAS
0.2286 0.1023
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