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 be614ed. 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 | 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)
#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
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] 0.009055 1.070497
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3237 LPCAT4 15_10 0.9052 25.64 2.029e-04 4.892 3 3
749 BUB1B-PAK6 15_14 0.8895 30.73 2.317e-04 5.588 2 2
3166 LINC00320 21_6 0.8675 29.45 2.164e-04 5.336 3 3
6184 TPGS2 18_20 0.7685 28.26 1.602e-04 -4.088 4 4
1835 DPYSL3 5_86 0.7392 22.59 1.172e-04 -4.157 1 1
5307 SF3B1 2_117 0.7296 46.51 2.374e-04 -7.053 2 2
6674 ZDHHC20 13_2 0.7181 23.83 1.177e-04 -4.615 2 2
1714 DHPS 19_10 0.7106 24.82 1.190e-04 -4.396 1 1
2151 FAM177A1 14_9 0.7085 24.42 1.469e-04 -4.872 12 14
2429 GIGYF1 7_62 0.7004 34.53 1.998e-04 5.266 3 3
325 ANAPC7 12_67 0.6295 38.23 1.595e-04 6.385 4 4
621 B9D1 17_16 0.5939 28.83 1.024e-04 5.282 2 2
3324 MAD1L1 7_3 0.5904 69.62 2.470e-04 8.182 6 7
992 CASP2 7_89 0.5743 21.16 6.628e-05 -3.889 1 1
565 ATP2B2 3_8 0.5679 26.51 8.118e-05 4.229 1 1
613 B3GAT1 11_84 0.5295 23.77 9.786e-05 -4.448 8 12
1620 DBF4B 17_26 0.5257 21.62 5.710e-05 -3.890 2 2
2772 ICE1 5_5 0.5167 26.91 6.996e-05 -3.766 2 2
5577 SNRPA1 15_50 0.5078 22.99 8.584e-05 -3.934 5 7
3252 LRP8 1_33 0.5022 33.65 1.350e-04 4.820 5 5
genename region_tag susie_pip mu2 PVE z num_intron num_sqtl
3324 MAD1L1 7_3 0.5904 69.62 2.470e-04 8.182 6 7
5307 SF3B1 2_117 0.7296 46.51 2.374e-04 -7.053 2 2
749 BUB1B-PAK6 15_14 0.8895 30.73 2.317e-04 5.588 2 2
3166 LINC00320 21_6 0.8675 29.45 2.164e-04 5.336 3 3
3237 LPCAT4 15_10 0.9052 25.64 2.029e-04 4.892 3 3
2429 GIGYF1 7_62 0.7004 34.53 1.998e-04 5.266 3 3
416 APOPT1 14_54 0.5005 46.02 1.645e-04 -7.407 7 10
1707 DGKZ 11_28 0.4211 48.30 1.626e-04 7.216 2 2
6184 TPGS2 18_20 0.7685 28.26 1.602e-04 -4.088 4 4
325 ANAPC7 12_67 0.6295 38.23 1.595e-04 6.385 4 4
2151 FAM177A1 14_9 0.7085 24.42 1.469e-04 -4.872 12 14
4000 NT5C2 10_66 0.4284 48.83 1.410e-04 -8.541 11 13
3252 LRP8 1_33 0.5022 33.65 1.350e-04 4.820 5 5
1714 DHPS 19_10 0.7106 24.82 1.190e-04 -4.396 1 1
6674 ZDHHC20 13_2 0.7181 23.83 1.177e-04 -4.615 2 2
1835 DPYSL3 5_86 0.7392 22.59 1.172e-04 -4.157 1 1
621 B9D1 17_16 0.5939 28.83 1.024e-04 5.282 2 2
671 BDNF 11_19 0.4914 23.62 9.879e-05 4.348 3 4
613 B3GAT1 11_84 0.5295 23.77 9.786e-05 -4.448 8 12
4171 PCBP2 12_33 0.4213 26.30 8.863e-05 -4.953 2 2
[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 1.124e-04 221.94 2.676e-11 -11.590 5
1657 DDR1 6_25 1.717e-01 105.59 3.080e-05 11.175 2
879 C6orf136 6_24 5.073e-02 82.59 4.036e-06 -11.031 2
2295 FLOT1 6_24 4.039e-02 81.23 6.519e-06 -10.981 6
745 BTN3A2 6_20 7.395e-02 92.71 4.606e-06 -10.665 5
4528 PPT2 6_26 3.481e-05 152.97 1.782e-12 -10.061 5
1923 EGFL8 6_26 2.741e-05 142.24 1.027e-12 -9.625 4
1072 CCHCR1 6_25 1.143e-02 68.64 1.231e-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 3.701e-02 69.70 1.370e-06 8.727 6
4000 NT5C2 10_66 4.284e-01 48.83 1.410e-04 -8.541 11
3324 MAD1L1 7_3 5.904e-01 69.62 2.470e-04 8.182 6
6271 TSNARE1 8_93 1.806e-02 53.87 2.032e-07 7.961 4
4259 PGBD1 6_22 2.892e-02 40.35 2.273e-07 -7.746 2
743 BTN2A1 6_20 1.458e-02 51.43 1.251e-07 -7.727 3
5073 RP5-874C20.8 6_22 8.659e-03 38.78 7.738e-08 7.631 4
744 BTN3A1 6_20 1.358e-02 47.80 1.223e-07 7.490 4
720 BRD2 6_27 1.523e-01 46.77 1.067e-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
#number of genes for gene set enrichment
length(genes)
[1] 21
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"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
Term Overlap Adjusted.P.value
1 U2 snRNP (GO:0005686) 2/20 0.007298
2 U2-type precatalytic spliceosome (GO:0071005) 2/50 0.012478
3 spliceosomal snRNP complex (GO:0097525) 2/51 0.012478
4 precatalytic spliceosome (GO:0071011) 2/52 0.012478
5 U2-type spliceosomal complex (GO:0005684) 2/89 0.028799
Genes
1 SNRPA1;SF3B1
2 SNRPA1;SF3B1
3 SNRPA1;SF3B1
4 SNRPA1;SF3B1
5 SNRPA1;SF3B1
[1] "GO_Molecular_Function_2021"
Term
1 acyltransferase activity, transferring groups other than amino-acyl groups (GO:0016747)
2 U2 snRNA binding (GO:0030620)
3 1-acylglycerophosphocholine O-acyltransferase activity (GO:0047184)
4 dihydropyrimidinase activity (GO:0004157)
5 lysophospholipid acyltransferase activity (GO:0071617)
6 O-acetyltransferase activity (GO:0016413)
7 P-type calcium transporter activity (GO:0005388)
8 hydrolase activity, acting on carbon-nitrogen (but not peptide) bonds, in cyclic amides (GO:0016812)
9 cysteine-type endopeptidase activity involved in apoptotic signaling pathway (GO:0097199)
10 filamin binding (GO:0031005)
11 cysteine-type endopeptidase activity involved in execution phase of apoptosis (GO:0097200)
12 P-type ion transporter activity (GO:0015662)
13 cysteine-type endopeptidase activity involved in apoptotic process (GO:0097153)
Overlap Adjusted.P.value Genes
1 2/76 0.04070 ZDHHC20;LPCAT4
2 1/5 0.04070 SNRPA1
3 1/6 0.04070 LPCAT4
4 1/6 0.04070 DPYSL3
5 1/8 0.04070 LPCAT4
6 1/8 0.04070 LPCAT4
7 1/9 0.04070 ATP2B2
8 1/10 0.04070 DPYSL3
9 1/10 0.04070 CASP2
10 1/11 0.04070 DPYSL3
11 1/13 0.04070 CASP2
12 1/13 0.04070 ATP2B2
13 1/15 0.04331 CASP2
Description FDR Ratio BgRatio
18 Electroencephalogram abnormal 0.01366 1/8 1/9703
34 Refractory anemia with ringed sideroblasts 0.01366 1/8 2/9703
41 Deafness, Autosomal Recessive 12 0.01366 1/8 2/9703
43 Prostate cancer, familial 0.01366 2/8 69/9703
46 MECKEL SYNDROME, TYPE 9 0.01366 1/8 1/9703
54 JOUBERT SYNDROME 27 0.01366 1/8 1/9703
58 PROSTATE CANCER, HEREDITARY, 1 0.01366 2/8 60/9703
26 Malignant melanoma of iris 0.02388 1/8 5/9703
27 Malignant melanoma of choroid 0.02388 1/8 5/9703
52 Abnormality of head or neck 0.02388 1/8 5/9703
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
#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] 3
#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]
[1] genename region_tag susie_pip mu2 PVE z num_intron
[8] num_sqtl
<0 rows> (or 0-length row.names)
#sensitivity / recall
print(sensitivity)
ctwas TWAS
0.007692 0.130769
#specificity
print(specificity)
ctwas TWAS
0.9997 0.9843
#precision / PPV
print(precision)
ctwas TWAS
0.3333 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