Tobacco exposure associated with oral microbiome anaerobiosis in the New York City Health and Nutrition Examination Study (NYC HANES) II
13 February 2019
Contents
1 Table 1: Demographics & Descriptive Statistics
| Never smoker | Cigarette | Former smoker | Alternative smoker | Secondhand | |
|---|---|---|---|---|---|
| n | 43 | 86 | 43 | 49 | 38 |
| GENDER = Female (%) | 28 (65.1) | 45 (52.3) | 25 (58.1) | 22 (44.9) | 22 (57.9) |
| RACE (%) | |||||
| Non-Hispanic White | 13 (30.2) | 24 (27.9) | 25 (58.1) | 19 (38.8) | 10 (26.3) |
| Non-Hispanic Black | 13 (30.2) | 33 (38.4) | 4 ( 9.3) | 9 (18.4) | 11 (28.9) |
| Hispanic | 10 (23.3) | 19 (22.1) | 10 (23.3) | 12 (24.5) | 14 (36.8) |
| Asian | 3 ( 7.0) | 9 (10.5) | 3 ( 7.0) | 3 ( 6.1) | 2 ( 5.3) |
| Other | 4 ( 9.3) | 1 ( 1.2) | 1 ( 2.3) | 6 (12.2) | 1 ( 2.6) |
| EDU4CAT (%) | |||||
| College graduate or more | 16 (37.2) | 18 (20.9) | 21 (48.8) | 17 (34.7) | 9 (23.7) |
| Less than High school diploma | 8 (18.6) | 24 (27.9) | 4 ( 9.3) | 10 (20.4) | 14 (36.8) |
| High school graduate/GED | 7 (16.3) | 24 (27.9) | 8 (18.6) | 11 (22.4) | 10 (26.3) |
| Some College or associate’s degree | 12 (27.9) | 20 (23.3) | 10 (23.3) | 11 (22.4) | 5 (13.2) |
| SPAGE (mean (sd)) | 45.42 (16.50) | 45.85 (13.07) | 55.47 (18.00) | 35.59 (16.44) | 37.76 (14.70) |
| AGEGRP5C (%) | |||||
| 20-29 | 7 (16.3) | 10 (11.6) | 3 ( 7.0) | 26 (53.1) | 14 (36.8) |
| 30-39 | 11 (25.6) | 17 (19.8) | 7 (16.3) | 8 (16.3) | 11 (28.9) |
| 40-49 | 10 (23.3) | 25 (29.1) | 7 (16.3) | 4 ( 8.2) | 3 ( 7.9) |
| 50-59 | 6 (14.0) | 19 (22.1) | 8 (18.6) | 7 (14.3) | 6 (15.8) |
| 60 AND OVER | 9 (20.9) | 15 (17.4) | 18 (41.9) | 4 ( 8.2) | 4 (10.5) |
| DBTS_NEW (%) | |||||
| Yes | 5 (11.6) | 5 ( 5.8) | 7 (16.3) | 3 ( 6.1) | 2 ( 5.3) |
| No | 38 (88.4) | 81 (94.2) | 36 (83.7) | 38 (77.6) | 36 (94.7) |
| NA | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) | 8 (16.3) | 0 ( 0.0) |
| SR_ACTIVE (%) | |||||
| Very active | 15 (34.9) | 27 (31.4) | 11 (25.6) | 16 (32.7) | 17 (44.7) |
| Somewhat active | 20 (46.5) | 37 (43.0) | 20 (46.5) | 25 (51.0) | 15 (39.5) |
| Not very active/not active at all | 8 (18.6) | 22 (25.6) | 12 (27.9) | 8 (16.3) | 6 (15.8) |
| INC25KMOD (%) | |||||
| Less Than $20,000 | 5 (11.6) | 31 (36.0) | 8 (18.6) | 20 (40.8) | 14 (36.8) |
| $20,000-$49,999 | 15 (34.9) | 20 (23.3) | 9 (20.9) | 14 (28.6) | 9 (23.7) |
| $50,000-$74,999 | 6 (14.0) | 11 (12.8) | 3 ( 7.0) | 6 (12.2) | 4 (10.5) |
| $75,000-$99,999 | 8 (18.6) | 4 ( 4.7) | 6 (14.0) | 4 ( 8.2) | 2 ( 5.3) |
| $100,000 or More | 6 (14.0) | 11 (12.8) | 13 (30.2) | 2 ( 4.1) | 6 (15.8) |
| NA | 3 ( 7.0) | 9 (10.5) | 4 ( 9.3) | 3 ( 6.1) | 3 ( 7.9) |
| COTININE (median [IQR]) | 0.04 [0.04, 0.04] | 271.49 [189.99, 360.99] | 0.04 [0.04, 0.04] | 10.54 [0.28, 55.36] | 3.01 [1.39, 5.48] |
| OHQ_3 (%) | |||||
| Yes | 4 ( 9.3) | 9 (10.5) | 5 (11.6) | 4 ( 8.2) | 4 (10.5) |
| No | 39 (90.7) | 76 (88.4) | 38 (88.4) | 45 (91.8) | 34 (89.5) |
| NA | 0 ( 0.0) | 1 ( 1.2) | 0 ( 0.0) | 0 ( 0.0) | 0 ( 0.0) |
2 Microbial composition (8 most abundant genera)
3 Alpha diversity
## Df Sum Sq Mean Sq F value Pr(>F)
## smokingstatus 4 0.09 0.02162 0.168 0.954
## Residuals 254 32.62 0.12841## Df Sum Sq Mean Sq F value Pr(>F)
## smokingstatus 4 34597 8649 2.12 0.0788 .
## Residuals 254 1036314 4080
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## smokingstatus 4 74507 18627 1.33 0.259
## Residuals 254 3556471 140024 Serum cotinine vs Smoking status
5 Beta Diversity
5.1 Cigarette smokers vs Never smokers
5.2 Cigarette smokers + Never smokers + Former smokers
5.3 Cigarette smokers + Never smokers + Former smokers + Secondhand smokers
5.4 All smoking statuses
5.5 Discrete serum blood Cotinine levels
5.5.1 Test group with PERMANOVA (adonis, vegan package)
| contrast | r2 | pvalue |
|---|---|---|
| Never smoker vs Cigarette | 0.0564604 | 0.001 |
| Cigarette vs Former smoker | 0.0381748 | 0.001 |
| Cigarette vs Secondhand | 0.0355005 | 0.004 |
| Cigarette vs Alternative smoker | 0.0292579 | 0.011 |
| Never smoker vs Secondhand | 0.0254668 | 0.093 |
| Never smoker vs Former smoker | 0.0185571 | 0.183 |
| Former smoker vs Alternative smoker | 0.0143877 | 0.233 |
| Former smoker vs Secondhand | 0.0125519 | 0.400 |
| Never smoker vs Alternative smoker | 0.0089308 | 0.512 |
| Alternative smoker vs Secondhand | 0.0089586 | 0.512 |
##
## Call:
## vegan::adonis(formula = distwu_2cat ~ COTININE, data = metadata_2cat)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## COTININE 1 0.04880 0.048796 6.7095 0.05018 0.001 ***
## Residuals 127 0.92364 0.007273 0.94982
## Total 128 0.97244 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.5.1.1 Three categories
##
## Call:
## vegan::adonis(formula = distwu_3cat ~ smokingstatus + RACE + GENDER + AGEGRP4C + SR_ACTIVE + EDU3CAT + DBTS_NEW, data = metadata_3cat)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## smokingstatus 2 0.15426 0.077132 5.2045 0.05658 0.001 ***
## RACE 4 0.08734 0.021835 1.4733 0.03204 0.106
## GENDER 1 0.02819 0.028195 1.9024 0.01034 0.095 .
## AGEGRP4C 1 0.03677 0.036774 2.4813 0.01349 0.045 *
## SR_ACTIVE 2 0.03089 0.015445 1.0421 0.01133 0.394
## EDU3CAT 2 0.03429 0.017143 1.1567 0.01258 0.290
## DBTS_NEW 1 0.01293 0.012933 0.8727 0.00474 0.479
## Residuals 158 2.34162 0.014820 0.85890
## Total 171 2.72630 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.5.1.2 All categories
##
## Call:
## vegan::adonis(formula = distwu ~ smokingstatus, data = metadata)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## smokingstatus 4 0.2913 0.072835 3.2575 0.0488 0.001 ***
## Residuals 254 5.6793 0.022360 0.9512
## Total 258 5.9707 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16 Differential analysis
6.1 DESeq2
DESeq2 is presented as sensitivity analysis. Main results are calculated by edgeR.
6.1.1 Current smokers vs. never smokers crude
6.1.2 Current smokers vs. never smokers adjusting for confounding
6.1.3 Plot crude vs. adjusted from DESeq2
6.1.4 Secondhand vs Cigarette/Never
6.2 edgeR
6.2.1 Cigarette smoker vs Never smoker: crude
6.3 Cigarette smoker vs Never smoker: crude phylum level
6.3.1 Cigarette smoker vs Never smoker: Adjusted
6.3.2 Plot crude vs adjusted beta coefficients
6.3.3 Secondhand vs Cigarette/Never: only significant OTUs
A function for merging smoking and cotinine edgeR results:
And a plotting function:
Two thresholds for serum cotinine:
## [1] 1.0022 1.0394 1.0418 1.0715 1.0915 1.2015 1.2282 1.2718 1.3822
## [10] 1.3822 1.4094 1.5315 1.6918 2.0094 2.5994 2.6759 2.8020 2.9394
## [19] 2.9720 3.0459 3.1082 3.1722 3.4715 3.8394 4.3418 4.5294 4.5394
## [28] 4.7522 5.7220 6.3118 7.1315 7.7382 9.1118 9.6415 10.2922 10.6920
## [37] 10.7915 13.7894## Mode FALSE TRUE
## logical 34 4Number of OTUs with FDR < 0.05 and total that passed non-specific screens in both analysis:
## Mode FALSE TRUE
## logical 93 28## Mode FALSE TRUE
## logical 87 27Correlation between crude smoking and cotinine coefficients, all OTUs:
Strict definition of second-hand smoke:
Correlation between crude smoking and cotinine coefficients, FDR < 0.05 in smoking:
All OTUs, black for FDR < 0.05 on smoking coefficients, grey for FDR >= 0.05 
Repeat, using the strict definition of second-hand smokers (cotinine < 10) 
6.3.4 Adjusted Secondhand vs Adjusted Cigarette/Never: only significant OTUs
Number of differentially abundant OTUs, and total
## [1] 121## Mode FALSE TRUE
## logical 63 58## Mode FALSE TRUE
## logical 38 83Correlation between adjusted smoking and cotinine coefficients, all OTUs:
As above, using strict serum cotinine cutoff (<10)
Correlation between adjusted smoking and cotinine coefficients, FDR < 0.05 in smoking:
##
## Pearson's product-moment correlation
##
## data: logFC_smoking and logFC_cotinine
## t = 3.8131, df = 17, p-value = 0.001391
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3249855 0.8660843
## sample estimates:
## cor
## 0.6789709Sensitivity analysis:
## , , FDRcutoff = no
##
## counfounding
## secondhand crude adjusted
## cot14 0.4 0.28
## cot10 0.4 0.34
##
## , , FDRcutoff = yes
##
## counfounding
## secondhand crude adjusted
## cot14 NA 0.34
## cot10 0 NA## , , FDRcutoff = no
##
## counfounding
## secondhand crude adjusted
## cot14 5e-06 2e-03
## cot10 9e-06 3e-04
##
## , , FDRcutoff = yes
##
## counfounding
## secondhand crude adjusted
## cot14 NA 3e-04
## cot10 0.6 NA
Repeat, using the strict definition of second-hand smokers (cotinine < 10) 
6.3.5 Alternative smokers vs Never smoker: crude
6.3.5.1 E-cigarette vs never smokers
6.3.5.2 Hookah pipe vs never smokers
6.3.5.3 Cigars and cigarillos vs never smokers
## [1] "No results"7 Analysis on biosis of bacteria
7.1 Odds ratio smokers vs never smokers
## $tab
##
## Enriched in smokers p0 Depleted in smokers
## OTU is aerobic 23 0.0362776 55
## OTU is not aerobic 611 0.9637224 457
##
## p1 oddsratio lower upper p.value
## OTU is aerobic 0.1074219 1.0000000 NA NA NA
## OTU is not aerobic 0.8925781 0.3127808 0.1894164 0.5164911 2.682452e-06
##
## $measure
## [1] "wald"
##
## $conf.level
## [1] 0.95
##
## $pvalue
## [1] "fisher.exact"## $tab
##
## Enriched in smokers p0 Depleted in smokers
## OTU is anaerobic 407 0.6419558 266
## OTU is not anaerobic 227 0.3580442 246
##
## p1 oddsratio lower upper p.value
## OTU is anaerobic 0.5195312 1.000000 NA NA NA
## OTU is not anaerobic 0.4804688 1.658143 1.307573 2.102704 3.107853e-05
##
## $measure
## [1] "wald"
##
## $conf.level
## [1] 0.95
##
## $pvalue
## [1] "fisher.exact"## $tab
##
## Enriched in smokers p0 Depleted in smokers
## OTU is F Anaerobic 204 0.3217666 191
## OTU is not F Anaerobic 430 0.6782334 321
##
## p1 oddsratio lower upper p.value
## OTU is F Anaerobic 0.3730469 1.0000000 NA NA NA
## OTU is not F Anaerobic 0.6269531 0.7973213 0.624298 1.018298 0.07029177
##
## $measure
## [1] "wald"
##
## $conf.level
## [1] 0.95
##
## $pvalue
## [1] "fisher.exact"7.2 Cigarette smokers vs Never smokers
7.2.1 GSEA
## DataFrame with 3 rows and 4 columns
## GENE.SET ES NES PVAL
## <character> <numeric> <numeric> <numeric>
## 1 aero -0.825 -1.72 0.00436
## 2 anae 0.333 0.887 0.569
## 3 fana -0.24 -0.77 0.742
7.2.2 ORA
## 100 permutations completed
## 200 permutations completed
## 300 permutations completed
## 400 permutations completed
## 500 permutations completed
## 600 permutations completed
## 700 permutations completed
## 800 permutations completed
## 900 permutations completed
## 1000 permutations completed## DataFrame with 3 rows and 4 columns
## GENE.SET GLOB.STAT NGLOB.STAT PVAL
## <character> <numeric> <numeric> <numeric>
## 1 aero 7 0.636 0.003
## 2 fana 8 0.154 0.567
## 3 anae 11 0.126 0.9087.3 Secondhand smokers
7.3.1 GSEA
## DataFrame with 3 rows and 4 columns
## GENE.SET ES NES PVAL
## <character> <numeric> <numeric> <numeric>
## 1 anae 0.576 1.42 0.115
## 2 fana -0.405 -1.29 0.205
## 3 aero 0.429 0.932 0.5387.3.2 ORA
## 100 permutations completed
## 200 permutations completed
## 300 permutations completed
## 400 permutations completed
## 500 permutations completed
## 600 permutations completed
## 700 permutations completed
## 800 permutations completed
## 900 permutations completed
## 1000 permutations completed## DataFrame with 3 rows and 4 columns
## GENE.SET GLOB.STAT NGLOB.STAT PVAL
## <character> <numeric> <numeric> <numeric>
## 1 aero 0 0 0.001
## 2 anae 0 0 0.001
## 3 fana 0 0 0.0017.4 Continuous cotinine
7.4.1 GSVA continuous cotinine on secondhand smokers
## Estimating GSVA scores for 3 gene sets.
## Computing observed enrichment scores
## Estimating ECDFs with Gaussian kernels
## Using parallel with 1 cores
##
|
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|====================== | 33%
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|===================================================================| 100%## logFC AveExpr t P.Value adj.P.Val B
## aero 0.0049390950 0.063396833 0.8947032 0.3756454 0.6151923 -7.392942
## anae 0.0029017486 -0.003614764 0.8313163 0.4101282 0.6151923 -7.447853
## fana 0.0007153343 0.034927979 0.1784040 0.8591962 0.8591962 -7.7815137.4.2 GSVA continuous cotinine on cigarette vs never smokers
## Estimating GSVA scores for 3 gene sets.
## Computing observed enrichment scores
## Estimating ECDFs with Gaussian kernels
## Using parallel with 1 cores
##
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|====================== | 33%
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|===================================================================| 100%## logFC AveExpr t P.Value adj.P.Val B
## anae 9.312388e-05 0.01061843 2.635005 0.009409142 0.02519808 -8.951131
## fana -1.031180e-04 -0.02637825 -2.421534 0.016798721 0.02519808 -9.469995
## aero 9.331210e-05 0.10299623 1.397472 0.164590432 0.16459043 -11.3841737.5 Hookah smokers vs Never smokers
7.5.1 GSEA
## DataFrame with 3 rows and 4 columns
## GENE.SET ES NES PVAL
## <character> <numeric> <numeric> <numeric>
## 1 fana -0.489 -1.65 0.0359
## 2 anae 0.404 1.12 0.331
## 3 aero 0.311 0.726 0.7997.5.2 ORA
## DataFrame with 3 rows and 4 columns
## GENE.SET NR.GENES NR.SIG.GENES PVAL
## <character> <numeric> <numeric> <numeric>
## 1 anae 87 4 0.281
## 2 fana 53 1 0.885
## 3 aero 13 0 17.6 E-cigarette smokers vs Never smokers
7.6.1 GSEA
## DataFrame with 3 rows and 4 columns
## GENE.SET ES NES PVAL
## <character> <numeric> <numeric> <numeric>
## 1 fana -0.514 -1.71 0.0253
## 2 aero 0.541 1.18 0.267
## 3 anae 0.351 1.03 0.4187.6.2 ORA
## DataFrame with 3 rows and 4 columns
## GENE.SET NR.GENES NR.SIG.GENES PVAL
## <character> <numeric> <numeric> <numeric>
## 1 fana 50 1 0.566
## 2 anae 84 1 0.818
## 3 aero 13 0 17.7 Cigar/cigarillos smokers vs Never smokers
7.7.1 GSEA
## DataFrame with 3 rows and 4 columns
## GENE.SET ES NES PVAL
## <character> <numeric> <numeric> <numeric>
## 1 aero -0.697 -1.56 0.0405
## 2 fana -0.219 -0.747 0.813
## 3 anae 0.241 0.678 0.8517.7.2 ORA
## DataFrame with 3 rows and 4 columns
## GENE.SET NR.GENES NR.SIG.GENES PVAL
## <character> <numeric> <numeric> <numeric>
## 1 anae 86 0 1
## 2 fana 53 0 1
## 3 aero 13 0 1## R version 3.5.2 (2018-12-20)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 9 (stretch)
##
## Matrix products: default
## BLAS: /usr/lib/openblas-base/libblas.so.3
## LAPACK: /usr/lib/libopenblasp-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=C
## [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] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] DESeq2_1.22.2 bindrcpp_0.2.2
## [3] nychanesmicrobiome_0.1.2 kableExtra_1.0.1
## [5] sas7bdat_0.5 lsr_0.5
## [7] phyloseq_1.26.1 edgeR_3.24.3
## [9] limma_3.38.3 EnrichmentBrowser_2.12.1
## [11] graph_1.60.0 SummarizedExperiment_1.12.0
## [13] DelayedArray_0.8.0 BiocParallel_1.16.5
## [15] matrixStats_0.54.0 Biobase_2.42.0
## [17] GenomicRanges_1.34.0 GenomeInfoDb_1.18.1
## [19] IRanges_2.16.0 S4Vectors_0.20.1
## [21] BiocGenerics_0.28.0 magrittr_1.5
## [23] tidyr_0.8.2 ggplot2_3.1.0
## [25] dplyr_0.7.8 BiocStyle_2.10.0
## [27] BiocManager_1.30.4
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.5 backports_1.1.3 Hmisc_4.1-1
## [4] plyr_1.8.4 igraph_1.2.3 lazyeval_0.2.1
## [7] GSEABase_1.44.0 splines_3.5.2 digest_0.6.18
## [10] foreach_1.4.4 htmltools_0.3.6 checkmate_1.9.1
## [13] memoise_1.1.0 cluster_2.0.7-1 ComplexHeatmap_1.20.0
## [16] Biostrings_2.50.2 readr_1.3.1 annotate_1.60.0
## [19] R.utils_2.7.0 colorspace_1.4-0 blob_1.1.1
## [22] rvest_0.3.2 rappdirs_0.3.1 haven_2.0.0
## [25] xfun_0.4 crayon_1.3.4 RCurl_1.95-4.11
## [28] jsonlite_1.6 genefilter_1.64.0 bindr_0.1.1
## [31] survival_2.43-3 zoo_1.8-4 iterators_1.0.10
## [34] ape_5.2 glue_1.3.0 gtable_0.2.0
## [37] zlibbioc_1.28.0 XVector_0.22.0 webshot_0.5.1
## [40] GetoptLong_0.1.7 Rhdf5lib_1.4.2 shape_1.4.4
## [43] SparseM_1.77 scales_1.0.0 DBI_1.0.0
## [46] Rcpp_1.0.0 viridisLite_0.3.0 xtable_1.8-3
## [49] htmlTable_1.13.1 foreign_0.8-71 bit_1.1-14
## [52] Formula_1.2-3 GSVA_1.30.0 survey_3.35-1
## [55] htmlwidgets_1.3 httr_1.4.0 RColorBrewer_1.1-2
## [58] acepack_1.4.1 pkgconfig_2.0.2 XML_3.98-1.16
## [61] R.methodsS3_1.7.1 nnet_7.3-12 locfit_1.5-9.1
## [64] later_0.7.5 tidyselect_0.2.5 labeling_0.3
## [67] rlang_0.3.1 reshape2_1.4.3 AnnotationDbi_1.44.0
## [70] munsell_0.5.0 tools_3.5.2 RSQLite_2.1.1
## [73] ade4_1.7-13 evaluate_0.12 biomformat_1.10.1
## [76] stringr_1.3.1 yaml_2.2.0 knitr_1.21
## [79] bit64_0.9-7 purrr_0.2.5 nlme_3.1-137
## [82] mime_0.6 R.oo_1.22.0 KEGGgraph_1.42.0
## [85] xml2_1.2.0 shinythemes_1.1.2 compiler_3.5.2
## [88] rstudioapi_0.9.0 e1071_1.7-0.1 statmod_1.4.30
## [91] tibble_2.0.1 geneplotter_1.60.0 stringi_1.2.4
## [94] highr_0.7 forcats_0.3.0 lattice_0.20-38
## [97] Matrix_1.2-15 vegan_2.5-3 permute_0.9-4
## [100] multtest_2.38.0 pillar_1.3.1 GlobalOptions_0.1.0
## [103] data.table_1.12.0 bitops_1.0-6 httpuv_1.4.5.1
## [106] R6_2.3.0 latticeExtra_0.6-28 promises_1.0.1
## [109] bookdown_0.9 gridExtra_2.3 codetools_0.2-15
## [112] MASS_7.3-51.1 assertthat_0.2.0 rhdf5_2.26.2
## [115] rjson_0.2.20 safe_3.22.0 withr_2.1.2
## [118] GenomeInfoDbData_1.2.0 mgcv_1.8-26 hms_0.4.2
## [121] grid_3.5.2 rpart_4.1-13 labelled_2.0.2
## [124] tableone_0.9.3 class_7.3-14 rmarkdown_1.11
## [127] shiny_1.2.0 base64enc_0.1-3 epitools_0.5-10