Sample IDs and Relative Abundance Misc.

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Last updated: 2021-01-14

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Knit directory: esoph-micro-cancer-workflow/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/test-of-replication.Rmd) and HTML (docs/test-of-replication.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	e6b4c7f	noah-padgett	2021-01-14	meeting update
html	e6b4c7f	noah-padgett	2021-01-14	meeting update
Rmd	1d24c1f	noah-padgett	2020-12-17	make waterfall-plots
html	1d24c1f	noah-padgett	2020-12-17	make waterfall-plots
Rmd	cf91029	noah-padgett	2020-12-02	updated analyses
html	cf91029	noah-padgett	2020-12-02	updated analyses

---

• Goal is to replicate Fisher’s exact test.

Data mung

First, we need to format the data for the analyses.

# transform to relative abundances
phylo.data.nci.umd <- transform_sample_counts(phylo.data.nci.umd, function(x){x / sum(x)})
phylo.data.tcga.RNAseq <- transform_sample_counts(phylo.data.tcga.RNAseq, function(x){x / sum(x)})
phylo.data.tcga.WGS <- transform_sample_counts(phylo.data.tcga.WGS, function(x){x / sum(x)})

# melt data down for use
dat.16s <- psmelt(phylo.data.nci.umd)
dat.rna <- psmelt(phylo.data.tcga.RNAseq)
dat.wgs <- psmelt(phylo.data.tcga.WGS)

# fix otu formatting
dat.rna$otu2 <- "a"
dat.wgs$otu2 <- "a"
i <- 1
for(i in 1:nrow(dat.rna)){
  dat.rna$otu2[i] <- str_split(dat.rna$OTU[i], ";")[[1]][7]
}
for(i in 1:nrow(dat.wgs)){
  dat.wgs$otu2[i] <- str_split(dat.wgs$OTU[i], ";")[[1]][7]
}


# subset to fuso. nuc. only
# Streptococcus sanguinis 
# Campylobacter concisus
# Prevotella spp.

dat.16s <- filter(
  dat.16s,
  OTU %in% c(
    "Fusobacterium_nucleatum",
    unique(dat.16s$OTU[dat.16s$OTU %like% "Streptococcus_"]),
    unique(dat.16s$OTU[dat.16s$OTU %like% "Campylobacter_"]),
    "Prevotella_melaninogenica")
)
dat.rna <- filter(
  dat.rna,
  otu2 %in% c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
      "Streptococcus oralis",
      "Streptococcus mitis", 
      "Streptococcus pneumoniae", 
      "Streptococcus parasanguinis", 
      "Streptococcus salivarius",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)
dat.wgs <- filter(
  dat.wgs,
  otu2 %in% c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
      "Streptococcus oralis",
      "Streptococcus mitis", 
      "Streptococcus pneumoniae", 
      "Streptococcus parasanguinis", 
      "Streptococcus salivarius",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)

# new names
dat.16s$OTU1 <- factor(
  dat.16s$OTU,
  levels = c(
    "Fusobacterium_nucleatum",
    "Streptococcus_dentisani:Streptococcus_infantis:Streptococcus_mitis:Streptococcus_oligofermentans:Streptococcus_oralis:Streptococcus_pneumoniae:Streptococcus_pseudopneumoniae:Streptococcus_sanguinis",
    "Campylobacter_rectus:Campylobacter_showae",
    "Prevotella_melaninogenica"
  ),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica"
  )
)
dat.rna$OTU1 <- factor(
  dat.rna$otu2,
  levels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica"),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)

dat.wgs$OTU1 <- factor(
  dat.wgs$otu2,
  levels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica"),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)

# rename bacteria
dat.16s$OTU <- factor(
  dat.16s$OTU,
  levels = c(
    "Fusobacterium_nucleatum",
    "Streptococcus_dentisani:Streptococcus_infantis:Streptococcus_mitis:Streptococcus_oligofermentans:Streptococcus_oralis:Streptococcus_pneumoniae:Streptococcus_pseudopneumoniae:Streptococcus_sanguinis",
    "Campylobacter_rectus:Campylobacter_showae",
    "Prevotella_melaninogenica"
  ),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus spp.",
    "Campylobacter concisus",
    "Prevotella melaninogenica"
  )
)
dat.rna$OTU <- factor(
  dat.rna$otu2,
  levels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
      "Streptococcus oralis",
      "Streptococcus mitis", 
      "Streptococcus pneumoniae", 
      "Streptococcus parasanguinis", 
      "Streptococcus salivarius",
    "Campylobacter concisus",
    "Prevotella melaninogenica"),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus spp.",
      "Streptococcus spp.",
      "Streptococcus spp.", 
      "Streptococcus spp.", 
      "Streptococcus spp.", 
      "Streptococcus spp.",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)

dat.wgs$OTU <- factor(
  dat.wgs$otu2,
  levels = c(
    "Fusobacterium nucleatum",
    "Streptococcus sanguinis",
      "Streptococcus oralis",
      "Streptococcus mitis", 
      "Streptococcus pneumoniae", 
      "Streptococcus parasanguinis", 
      "Streptococcus salivarius",
    "Campylobacter concisus",
    "Prevotella melaninogenica"),
  labels = c(
    "Fusobacterium nucleatum",
    "Streptococcus spp.",
      "Streptococcus spp.",
      "Streptococcus spp.", 
      "Streptococcus spp.", 
      "Streptococcus spp.", 
      "Streptococcus spp.",
    "Campylobacter concisus",
    "Prevotella melaninogenica")
)



# make tumor vs normal variable
dat.16s$tumor.cat <- factor(dat.16s$tissue, levels=c("BO", "N", "T"), labels = c("Non-Tumor", "Non-Tumor", "Tumor"))
dat.rna$tumor.cat <- factor(dat.rna$SampleType_Level2, levels=c("Normal", "Tumor"), labels = c("Non-Tumor", "Tumor"))
dat.wgs$tumor.cat <- factor(dat.wgs$SampleType_Level2, levels=c("Normal", "Tumor"), labels = c("Non-Tumor", "Tumor"))

# dataset id
dat.16s$source <- "16s"
dat.rna$source <- "rna"
dat.wgs$source <- "wgs"

# plotting ids
dat.16s$X <- paste0(dat.16s$source, "-", dat.16s$tumor.cat)
dat.rna$X <- paste0(dat.rna$source, "-", dat.rna$tumor.cat)
dat.wgs$X <- paste0(dat.wgs$source, "-", dat.wgs$tumor.cat)

# relabel as (0/1) for analysis
dat.16s$tumor <- as.numeric(factor(dat.16s$tissue, levels=c("BO", "N", "T"), labels = c("Non-Tumor", "Non-Tumor", "Tumor"))) - 1
dat.rna$tumor <- as.numeric(factor(dat.rna$SampleType_Level2, levels=c("Normal", "Tumor"), labels = c("Non-Tumor", "Tumor"))) - 1
dat.wgs$tumor <- as.numeric(factor(dat.wgs$SampleType_Level2, levels=c("Normal", "Tumor"), labels = c("Non-Tumor", "Tumor"))) - 1

# presence- absence
dat.16s$pres <- ifelse(dat.16s$Abundance > 0, 1, 0)
dat.16s$pres[is.na(dat.16s$pres)] <- 0
dat.rna$pres <- ifelse(dat.rna$Abundance > 0, 1, 0)
dat.rna$pres[is.na(dat.rna$pres)] <- 0
dat.wgs$pres <- ifelse(dat.wgs$Abundance > 0, 1, 0)
dat.wgs$pres[is.na(dat.wgs$pres)] <- 0

# blood exclusions
dat.16s$bloodWGS <- NA
dat.rna$bloodWGS <- ifelse(dat.rna$SampleType_Level1 %in% c("Blood.Normal"),1,0)
dat.wgs$bloodWGS <- ifelse(dat.wgs$SampleType_Level1 %in% c("Blood.Normal"),1,0)

# merge data
cls <- c("OTU", "OTU1", "Sample", "Abundance", "tumor.cat", "tumor", "source", "X", "pres", "bloodWGS")
mydata <- full_join(dat.16s[,cls], dat.rna[,cls])

Joining, by = c("OTU", "OTU1", "Sample", "Abundance", "tumor.cat", "tumor", "source", "X", "pres", "bloodWGS")

mydata <- full_join(mydata, dat.wgs[,cls])

Joining, by = c("OTU", "OTU1", "Sample", "Abundance", "tumor.cat", "tumor", "source", "X", "pres", "bloodWGS")

Replicating the Analysis

Plot

p <- ggplot(mydata, aes(x=X, y=Abundance)) +
  geom_violin() +
  geom_jitter(alpha=0.5) +
  facet_wrap(.~OTU)+
  scale_y_continuous(
    trans = "sqrt",
    breaks=c(0.002, 0.01, 0.05, 0.1, 0.25, 0.5, 0.8)) +
  labs(x=NULL, y="Relative Abundance")+
  theme_classic() +
  theme(
    axis.text.x = element_text(angle = 30,vjust=0.95, hjust=0.95)
  )
p

Warning: Removed 1107 rows containing non-finite values (stat_ydensity).

Warning: Removed 1107 rows containing missing values (geom_point).

Statistical Tests

Fusobacterium nucleatum

# No additional subsetting/exclusions
d <- mydata %>%
  filter(OTU == "Fusobacterium nucleatum",
         source == "16s")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.1487
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.8126368 3.2144776
sample estimates:
odds ratio 
  1.610386 

d <- mydata %>%
  filter(OTU == "Fusobacterium nucleatum",
         source == "wgs")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.02533
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 1.065529 4.759083
sample estimates:
odds ratio 
  2.232631 

d <- mydata %>%
  filter(OTU == "Fusobacterium nucleatum",
         source == "rna")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.04707
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.05593801 1.12690374
sample estimates:
odds ratio 
 0.2723903 

# Excluding blood != 0
d <- mydata %>%
  filter(OTU == "Fusobacterium nucleatum",
         source == "wgs",
         bloodWGS != 1)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.8134
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.4333645 3.4530814
sample estimates:
odds ratio 
  1.214012 

d <- mydata %>%
  filter(OTU == "Fusobacterium nucleatum",
         source == "rna",
         bloodWGS != 1)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.04707
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.05593801 1.12690374
sample estimates:
odds ratio 
 0.2723903 

Streptococcus spp.

difficult to determine which species were used

# No additional subsetting/exclusions
d <- mydata %>%
  filter(OTU == "Streptococcus spp.",
         source == "16s")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.2189448 7.8326544
sample estimates:
odds ratio 
  1.173049 

d <- mydata %>%
  filter(OTU == "Streptococcus spp.",
         source == "wgs")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 1.413e-06
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 1.487867 2.643073
sample estimates:
odds ratio 
  1.981098 

d <- mydata %>%
  filter(OTU == "Streptococcus spp.",
         source == "rna")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 8.651e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.1589282 0.4818080
sample estimates:
odds ratio 
 0.2795264 

# Excluding blood != 0
d <- mydata %>%
  filter(OTU == "Streptococcus spp.",
         source == "wgs",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.6295
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.7401289 1.6390874
sample estimates:
odds ratio 
  1.101331 

d <- mydata %>%
  filter(OTU == "Streptococcus spp.",
         source == "rna",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 8.651e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.1589282 0.4818080
sample estimates:
odds ratio 
 0.2795264 

Streptococcus sanguinis

difficult to determine which species were used

# No additional subsetting/exclusions
d <- mydata %>%
  filter(OTU1 == "Streptococcus sanguinis",
         source == "16s")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.2189448 7.8326544
sample estimates:
odds ratio 
  1.173049 

d <- mydata %>%
  filter(OTU1 == "Streptococcus sanguinis",
         source == "wgs")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.4639
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.6098689 2.9683346
sample estimates:
odds ratio 
  1.339246 

d <- mydata %>%
  filter(OTU1 == "Streptococcus sanguinis",
         source == "rna")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.04205
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.05280152 1.06581611
sample estimates:
odds ratio 
 0.2573433 

# Excluding blood != 0
d <- mydata %>%
  filter(OTU1 == "Streptococcus sanguinis",
         source == "wgs",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.3084
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.580749 7.303673
sample estimates:
odds ratio 
  1.887736 

d <- mydata %>%
  filter(OTU1 == "Streptococcus sanguinis",
         source == "rna",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.04205
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.05280152 1.06581611
sample estimates:
odds ratio 
 0.2573433 

Campylobacter concisus

# No additional subsetting/exclusions
d <- mydata %>%
  filter(OTU == "Campylobacter concisus",
         source == "16s")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.2325
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.7306314 3.9724225
sample estimates:
odds ratio 
  1.698461 

d <- mydata %>%
  filter(OTU == "Campylobacter concisus",
         source == "wgs")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.008873
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 1.225362 6.656564
sample estimates:
odds ratio 
  2.797326 

d <- mydata %>%
  filter(OTU == "Campylobacter concisus",
         source == "rna")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.04188
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.05364598 1.20067358
sample estimates:
odds ratio 
  0.235494 

# Excluding blood != 0
d <- mydata %>%
  filter(OTU == "Campylobacter concisus",
         source == "wgs",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.3750782 3.1937453
sample estimates:
odds ratio 
  1.070662 

d <- mydata %>%
  filter(OTU == "Campylobacter concisus",
         source == "rna",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.04188
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.05364598 1.20067358
sample estimates:
odds ratio 
  0.235494 

Prevotella melaninogenica

# No additional subsetting/exclusions
d <- mydata %>%
  filter(OTU == "Prevotella melaninogenica",
         source == "16s")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.1531
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.790056 3.924226
sample estimates:
odds ratio 
  1.728747 

d <- mydata %>%
  filter(OTU == "Prevotella melaninogenica",
         source == "wgs")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 3.232e-06
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
  2.492672 12.298866
sample estimates:
odds ratio 
  5.430828 

d <- mydata %>%
  filter(OTU == "Prevotella melaninogenica",
         source == "rna")
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.1884
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.09378767 1.68318325
sample estimates:
odds ratio 
 0.4075071 

# Excluding blood != 0
d <- mydata %>%
  filter(OTU == "Prevotella melaninogenica",
         source == "wgs",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.1254
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.7196366 6.4306037
sample estimates:
odds ratio 
  2.165006 

d <- mydata %>%
  filter(OTU == "Prevotella melaninogenica",
         source == "rna",
         bloodWGS==0)
fisher.test(d$pres, d$tumor)

    Fisher's Exact Test for Count Data

data:  d$pres and d$tumor
p-value = 0.1884
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.09378767 1.68318325
sample estimates:
odds ratio 
 0.4075071 

Session information

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] car_3.0-8         carData_3.0-4     gvlma_1.0.0.3     patchwork_1.0.1  
 [5] viridis_0.5.1     viridisLite_0.3.0 gridExtra_2.3     xtable_1.8-4     
 [9] kableExtra_1.1.0  plyr_1.8.6        data.table_1.13.0 readxl_1.3.1     
[13] forcats_0.5.0     stringr_1.4.0     dplyr_1.0.1       purrr_0.3.4      
[17] readr_1.3.1       tidyr_1.1.1       tibble_3.0.3      ggplot2_3.3.2    
[21] tidyverse_1.3.0   lmerTest_3.1-2    lme4_1.1-23       Matrix_1.2-18    
[25] vegan_2.5-6       lattice_0.20-41   permute_0.9-5     phyloseq_1.32.0  
[29] workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] minqa_1.2.4         colorspace_1.4-1    rio_0.5.16         
 [4] ellipsis_0.3.1      rprojroot_1.3-2     XVector_0.28.0     
 [7] fs_1.5.0            rstudioapi_0.11     farver_2.0.3       
[10] fansi_0.4.1         lubridate_1.7.9     xml2_1.3.2         
[13] codetools_0.2-16    splines_4.0.2       knitr_1.29         
[16] ade4_1.7-15         jsonlite_1.7.0      nloptr_1.2.2.2     
[19] broom_0.7.0         cluster_2.1.0       dbplyr_1.4.4       
[22] BiocManager_1.30.10 compiler_4.0.2      httr_1.4.2         
[25] backports_1.1.7     assertthat_0.2.1    cli_2.0.2          
[28] later_1.1.0.1       htmltools_0.5.0     tools_4.0.2        
[31] igraph_1.2.5        gtable_0.3.0        glue_1.4.1         
[34] reshape2_1.4.4      Rcpp_1.0.5          Biobase_2.48.0     
[37] cellranger_1.1.0    vctrs_0.3.2         Biostrings_2.56.0  
[40] multtest_2.44.0     ape_5.4             nlme_3.1-148       
[43] iterators_1.0.12    xfun_0.19           openxlsx_4.1.5     
[46] rvest_0.3.6         lifecycle_0.2.0     statmod_1.4.34     
[49] zlibbioc_1.34.0     MASS_7.3-51.6       scales_1.1.1       
[52] hms_0.5.3           promises_1.1.1      parallel_4.0.2     
[55] biomformat_1.16.0   rhdf5_2.32.2        curl_4.3           
[58] yaml_2.2.1          stringi_1.4.6       S4Vectors_0.26.1   
[61] foreach_1.5.0       BiocGenerics_0.34.0 zip_2.0.4          
[64] boot_1.3-25         rlang_0.4.7         pkgconfig_2.0.3    
[67] evaluate_0.14       Rhdf5lib_1.10.1     tidyselect_1.1.0   
[70] magrittr_1.5        R6_2.4.1            IRanges_2.22.2     
[73] generics_0.0.2      DBI_1.1.0           foreign_0.8-80     
[76] pillar_1.4.6        haven_2.3.1         whisker_0.4        
[79] withr_2.2.0         mgcv_1.8-31         abind_1.4-5        
[82] survival_3.2-3      modelr_0.1.8        crayon_1.3.4       
[85] rmarkdown_2.5       grid_4.0.2          blob_1.2.1         
[88] git2r_0.27.1        reprex_0.3.0        digest_0.6.25      
[91] webshot_0.5.2       httpuv_1.5.4        numDeriv_2016.8-1.1
[94] stats4_4.0.2        munsell_0.5.0