SCZ - Brain Hypothalamus

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Last updated: 2022-03-05

Checks: 5 2

Knit directory: cTWAS_analysis/

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/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/	data
/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config.R	code/ctwas_config.R

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Unstaged changes:
    Modified:   analysis/BMI_Brain_Putamen_basal_ganglia.Rmd
    Modified:   analysis/SCZ_Brain_Amygdala.Rmd
    Modified:   analysis/SCZ_Brain_Anterior_cingulate_cortex_BA24.Rmd
    Modified:   analysis/SCZ_Brain_Caudate_basal_ganglia.Rmd
    Modified:   analysis/SCZ_Brain_Cerebellar_Hemisphere.Rmd
    Modified:   analysis/SCZ_Brain_Cerebellum.Rmd
    Modified:   analysis/SCZ_Brain_Cortex.Rmd
    Modified:   analysis/SCZ_Brain_Frontal_Cortex_BA9.Rmd
    Modified:   analysis/SCZ_Brain_Hippocampus.Rmd
    Modified:   analysis/SCZ_Brain_Hypothalamus.Rmd
    Modified:   analysis/SCZ_Brain_Nucleus_accumbens_basal_ganglia.Rmd
    Modified:   analysis/SCZ_Brain_Spinal_cord_cervical_c-1.Rmd
    Modified:   analysis/SCZ_Brain_Substantia_nigra.Rmd

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Weight QC

#number of imputed weights
nrow(qclist_all)

[1] 11152

#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 
1097  778  665  440  568  568  537  435  425  444  658  638  212  359  377  502 
  17   18   19   20   21   22 
 679  169  849  331  133  288 

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

[1] 8929

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

[1] 0.8007

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 
0.0148454 0.0002505 

#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 
11.589  8.483 

#report sample size
print(sample_size)

[1] 82315

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

[1]   11152 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 
0.02331 0.19553 

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

[1] 0.1358 1.4127

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
3376       CRHR1      17_27    1.0000 3865.12 0.0469552  3.362        1
3993      SPECC1      17_16    0.9949   32.10 0.0003879  5.624        2
5324       FURIN      15_42    0.9892   46.47 0.0005584 -7.000        1
10843     ZNF823      19_10    0.9867   29.93 0.0003588  5.479        2
6111     ARFGAP2      11_29    0.9408   24.98 0.0002856  4.740        1
1089        RRN3      16_15    0.9222   24.23 0.0002714 -4.560        2
11997 AC012074.2       2_15    0.9186   21.83 0.0002436  4.620        2
7382       THOC7       3_43    0.8943   31.42 0.0003414 -5.578        1
2970       SF3B1      2_117    0.8932   44.56 0.0004835  6.725        1
11948  HIST1H2BN       6_21    0.8616   93.98 0.0009837 10.773        1
6802      CDC25C       5_82    0.8496   25.55 0.0002637 -5.591        1
3381       CAAP1       9_20    0.8063   24.10 0.0002361  4.751        2
9203      DIRAS1       19_3    0.8016   24.23 0.0002360  4.765        2
8764        FUT9       6_65    0.7872   29.99 0.0002868  5.427        1
11985 AC073283.4       2_30    0.7857   20.57 0.0001964 -3.881        3
11817  LINC00242      6_112    0.7772   20.55 0.0001940  3.921        2
10218    TMEM222       1_19    0.7456   22.33 0.0002023  3.902        1
7356    SERPINI1      3_103    0.7420   20.10 0.0001812 -4.038        1
3112      MAP7D1       1_22    0.7364   24.37 0.0002180  4.907        1
434       ARID1B      6_102    0.7239   21.68 0.0001906 -3.907        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
3376      CRHR1      17_27 1.000e+00 3865.12 4.696e-02  3.3623        1
10942  HLA-DRB5       6_26 0.000e+00  921.30 0.000e+00  2.9680        1
11166      MSH5       6_26 5.429e-09  839.87 5.539e-11  8.8255        2
10534  HLA-DRB1       6_26 0.000e+00  677.09 0.000e+00  5.1518        1
10645  HLA-DQA1       6_26 0.000e+00  376.40 0.000e+00 -1.6175        1
11162    HSPA1A       6_26 0.000e+00  359.84 0.000e+00  7.1259        1
11158   SLC44A4       6_26 0.000e+00  313.06 0.000e+00  6.1896        1
12073  HLA-DQA2       6_26 0.000e+00  306.49 0.000e+00  0.2164        1
11418     CLIC1       6_26 0.000e+00  262.34 0.000e+00 -0.4634        1
9485   HLA-DQB1       6_26 0.000e+00  217.87 0.000e+00 -1.4137        1
9661      ACBD4      17_27 0.000e+00  196.79 0.000e+00  1.6219        2
11620       C4B       6_26 0.000e+00  158.66 0.000e+00 -4.9282        1
11888   CYP21A2       6_26 0.000e+00  157.05 0.000e+00  3.7459        1
10008     FMNL1      17_27 0.000e+00  152.89 0.000e+00  0.6638        1
11948 HIST1H2BN       6_21 8.616e-01   93.98 9.837e-04 10.7729        1
11799    SAPCD1       6_26 0.000e+00   87.82 0.000e+00 -2.7814        1
2335      GOSR2      17_27 0.000e+00   76.11 0.000e+00 -2.5096        1
2736     PRSS16       6_21 1.002e-01   64.78 7.883e-05 -8.5674        1
11150     STK19       6_26 0.000e+00   56.67 0.000e+00 -2.0635        1
9592  HIST1H2BC       6_20 2.545e-02   54.50 1.685e-05 -8.0277        1

Genes with highest PVE

        genename region_tag susie_pip     mu2       PVE      z num_eqtl
3376       CRHR1      17_27    1.0000 3865.12 0.0469552  3.362        1
11948  HIST1H2BN       6_21    0.8616   93.98 0.0009837 10.773        1
5324       FURIN      15_42    0.9892   46.47 0.0005584 -7.000        1
2970       SF3B1      2_117    0.8932   44.56 0.0004835  6.725        1
3993      SPECC1      17_16    0.9949   32.10 0.0003879  5.624        2
10843     ZNF823      19_10    0.9867   29.93 0.0003588  5.479        2
7382       THOC7       3_43    0.8943   31.42 0.0003414 -5.578        1
2829        PCCB       3_84    0.7124   35.08 0.0003036 -6.358        1
8764        FUT9       6_65    0.7872   29.99 0.0002868  5.427        1
6111     ARFGAP2      11_29    0.9408   24.98 0.0002856  4.740        1
1089        RRN3      16_15    0.9222   24.23 0.0002714 -4.560        2
6802      CDC25C       5_82    0.8496   25.55 0.0002637 -5.591        1
1685    PPP1R16B      20_23    0.6083   35.39 0.0002615  6.091        1
11997 AC012074.2       2_15    0.9186   21.83 0.0002436  4.620        2
3381       CAAP1       9_20    0.8063   24.10 0.0002361  4.751        2
9203      DIRAS1       19_3    0.8016   24.23 0.0002360  4.765        2
2505         MDK      11_28    0.4635   38.86 0.0002188 -6.357        1
3112      MAP7D1       1_22    0.7364   24.37 0.0002180  4.907        1
6178       TAOK2      16_24    0.4564   37.80 0.0002096  6.189        1
3041       ALMS1       2_48    0.6430   26.52 0.0002072 -5.154        1

Genes with largest z scores

       genename region_tag susie_pip    mu2       PVE      z num_eqtl
11948 HIST1H2BN       6_21 8.616e-01  93.98 9.837e-04 10.773        1
11166      MSH5       6_26 5.429e-09 839.87 5.539e-11  8.825        2
2736     PRSS16       6_21 1.002e-01  64.78 7.883e-05 -8.567        1
10214    BTN3A2       6_20 1.908e-02  53.75 1.246e-05  8.047        3
9592  HIST1H2BC       6_20 2.545e-02  54.50 1.685e-05 -8.028        1
2696     TRIM38       6_20 1.994e-02  49.73 1.205e-05 -7.769        2
11162    HSPA1A       6_26 0.000e+00 359.84 0.000e+00  7.126        1
5324      FURIN      15_42 9.892e-01  46.47 5.584e-04 -7.000        1
2970      SF3B1      2_117 8.932e-01  44.56 4.835e-04  6.725        1
10360   ZSCAN23       6_22 1.101e-01  46.18 6.177e-05 -6.717        2
1571    ZFYVE21      14_54 1.435e-01  40.59 7.079e-05 -6.708        1
3855      XRCC3      14_54 1.179e-01  42.60 6.100e-05  6.690        1
9715    ARL6IP4      12_75 1.257e-02  40.62 6.201e-06  6.491        1
10694   ZSCAN26       6_22 2.479e-02  28.09 8.460e-06  6.389        2
2829       PCCB       3_84 7.124e-01  35.08 3.036e-04 -6.358        1
2505        MDK      11_28 4.635e-01  38.86 2.188e-04 -6.357        1
11158   SLC44A4       6_26 0.000e+00 313.06 0.000e+00  6.190        1
6178      TAOK2      16_24 4.564e-01  37.80 2.096e-04  6.189        1
12000   ZSCAN31       6_22 1.972e-02  33.00 7.908e-06 -6.182        3
9577     HARBI1      11_28 1.681e-01  36.17 7.388e-05  6.169        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] 0.007443

GO enrichment analysis for genes with PIP>0.5

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

[1] 35

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

[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[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
11                                    Involutional Depression 0.01732  2/12
17                                       Hirschsprung Disease 0.01732  2/12
63                                           Alstrom Syndrome 0.01732  1/12
109                                  Involutional paraphrenia 0.01732  2/12
110                                   Psychosis, Involutional 0.01732  2/12
113 Familial encephalopathy with neuroserpin inclusion bodies 0.01732  1/12
114                           Childhood-onset truncal obesity 0.01732  1/12
123                                         NOONAN SYNDROME 8 0.01732  1/12
125                       EPILEPSY, FAMILIAL TEMPORAL LOBE, 8 0.01732  1/12
106                       Cardiomyopathy, Familial Idiopathic 0.02095  2/12
    BgRatio
11  25/9703
17  31/9703
63   1/9703
109 25/9703
110 25/9703
113  1/9703
114  1/9703
123  1/9703
125  1/9703
106 50/9703

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

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] 60

#significance threshold for TWAS
print(sig_thresh)

[1] 4.588

#number of ctwas genes
length(ctwas_genes)

[1] 13

#number of TWAS genes
length(twas_genes)

[1] 83

#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
1089     RRN3      16_15    0.9222   24.23 0.0002714 -4.560        2
3376    CRHR1      17_27    1.0000 3865.12 0.0469552  3.362        1

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.03846 0.07692 

#specificity
print(specificity)

 ctwas   TWAS 
0.9993 0.9934 

#precision / PPV
print(precision)

 ctwas   TWAS 
0.3846 0.1205 

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

Version	Author	Date
75a1466	sq-96	2022-02-27
ff6403a	sq-96	2022-02-27

cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes

#number of genes in known annotations (with imputed expression)
print(length(known_annotations))

[1] 60

#number of bystander genes (with imputed expression)
print(length(unrelated_genes))

[1] 727

#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$genename %in% c(known_annotations, unrelated_genes),]

#assign ctwas and TWAS genes
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]

#significance threshold for TWAS
print(sig_thresh)

[1] 4.588

#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)

[1] 6

#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)

[1] 28

#sensitivity / recall
sensitivity

  ctwas    TWAS 
0.08333 0.16667 

#specificity / (1 - False Positive Rate)
specificity

 ctwas   TWAS 
0.9986 0.9752 

#precision / PPV / (1 - False Discovery Rate)
precision

 ctwas   TWAS 
0.8333 0.3571 

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
addb825	sq-96	2022-02-28

Past versions of unnamed-chunk-25-1.png

Version	Author	Date
addb825	sq-96	2022-02-28

pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))

for (index in 1:length(pip_range)){
  pip <- pip_range[index]
  ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip]
  sensitivity[index] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
  specificity[index] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
}

plot(1-specificity, sensitivity, type="l", xlim=c(0,1), ylim=c(0,1), main="", xlab="1 - Specificity", ylab="Sensitivity")
title(expression("ROC Curve for cTWAS (black) and TWAS (" * phantom("red") * ")"))
title(expression(phantom("ROC Curve for cTWAS (black) and TWAS (") * "red" * phantom(")")), col.main="red")

sig_thresh_range <- seq(from=0, to=max(abs(ctwas_gene_res_subset$z)), length.out=length(pip_range))

for (index in 1:length(sig_thresh_range)){
  sig_thresh_plot <- sig_thresh_range[index]
  twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>=sig_thresh_plot]
  sensitivity[index] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
  specificity[index] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
}

lines(1-specificity, sensitivity, xlim=c(0,1), ylim=c(0,1), col="red", lty=1)

abline(a=0,b=1,lty=3)

#add previously computed points from the analysis
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]

points(1-specificity_plot["ctwas"], sensitivity_plot["ctwas"], pch=21, bg="black")
points(1-specificity_plot["TWAS"], sensitivity_plot["TWAS"], pch=21, bg="red")

Past versions of unnamed-chunk-26-1.png

Version	Author	Date
4a5db1c	sq-96	2022-03-03

Undetected silver standard genes have low TWAS z-scores or stronger signal from nearby variants

#table of outcomes for silver standard genes
-sort(-table(silver_standard_case))

silver_standard_case
          Not Imputed Insignificant z-score         Nearby SNP(s) 
                   70                    49                     6 
 Detected (PIP > 0.8) 
                    5 

#show inconclusive genes
silver_standard_case[silver_standard_case=="Inconclusive"]

named character(0)

Past versions of unnamed-chunk-30-1.png

Version	Author	Date
4a5db1c	sq-96	2022-03-03

Locus plot for three silver standard genes that cTWAS identifies

Locus 17_27: In TWAS, no SNP or gene passes the threshold. But cTWAS is able to identify gene CRHR1 with high PIP.

locus_plot("17_27", label="TWAS")

Locus 15_42: Known risk gene FURIN has very high PIP in this tissue

locus_plot("15_42", label="TWAS")

Locus 19_10: Both TWAS and cTWAS work well in this locus.

locus_plot("19_10", label="TWAS")

Locus 3_43: The output of cTWAS is very clean in this locus.

locus_plot("3_43", label="TWAS")

Locus 2_117: The output of cTWAS is very clean in this locus.

locus_plot("2_117", label="TWAS")

Some know SCZ risk genes that cTWAS does not find

Locus 19_35: Gene IRF3 is around the threshold in TWAS. There is another gene in the right hand side getting higher PIP.

locus_plot5("19_35", focus="IRF3")

Locus 1_6: The neurodevelopmental disorder gene RERE is not imputed in this tissue.

Locus 17_38: ACE encoding angiotensin converting enzyme, the target of a major class of anti-hypertensive drugs (schizophrenia under expression), has insignificant z-score

locus_plot5("17_38", focus="ACE")

Locus 3_27: DCLK3 encoding a neuroprotective kinase (schizophrenia under-expression), has insignificant z-score. No gene in this locus has high PIP or z-score

locus_plot("3_27", label="TWAS")

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_1.7.0  

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  processx_3.5.2    bit_4.0.4         curl_4.3.2       
[21] compiler_3.6.1    git2r_0.26.1      rvest_1.0.2       cli_3.1.0        
[25] Cairo_1.5-12.2    xml2_1.3.3        labeling_0.4.2    scales_1.1.1     
[29] callr_3.7.0       apcluster_1.4.8   digest_0.6.29     rmarkdown_2.11   
[33] svglite_1.2.2     pkgconfig_2.0.3   htmltools_0.5.2   dbplyr_2.1.1     
[37] fastmap_1.1.0     highr_0.9         rlang_1.0.1       rstudioapi_0.13  
[41] RSQLite_2.2.8     jquerylib_0.1.4   farver_2.1.0      generics_0.1.1   
[45] jsonlite_1.7.2    vroom_1.5.7       magrittr_2.0.2    Matrix_1.2-18    
[49] ggbeeswarm_0.6.0  Rcpp_1.0.8        munsell_0.5.0     fansi_1.0.2      
[53] gdtools_0.1.9     lifecycle_1.0.1   stringi_1.7.6     whisker_0.3-2    
[57] yaml_2.2.1        plyr_1.8.6        grid_3.6.1        blob_1.2.2       
[61] ggrepel_0.9.1     parallel_3.6.1    promises_1.0.1    crayon_1.5.0     
[65] lattice_0.20-38   haven_2.4.3       hms_1.1.1         knitr_1.36       
[69] ps_1.6.0          pillar_1.6.4      igraph_1.2.10     rjson_0.2.20     
[73] rngtools_1.5.2    reshape2_1.4.4    codetools_0.2-16  reprex_2.0.1     
[77] glue_1.6.2        evaluate_0.14     getPass_0.2-2     modelr_0.1.8     
[81] data.table_1.14.2 vctrs_0.3.8       tzdb_0.2.0        httpuv_1.5.1     
[85] foreach_1.5.2     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[89] cachem_1.0.6      xfun_0.29         broom_0.7.10      later_0.8.0      
[93] iterators_1.0.14  beeswarm_0.2.3    memoise_2.0.1     ellipsis_0.3.2