Last updated: 2020-12-23

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Knit directory: meta-liver/

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Introduction

Here we analysis a mouse model of CCl4 induced chronic liver disease. The transcriptomic profiles were measured at time point 0, 2, 6, and 12 month. For the time points 2 and 12 month time-matched oil controls are available.

Libraries and sources

These libraries and sources are used for this analysis.

library(tidyverse)
library(tidylog)
library(here)

library(edgeR)
library(biobroom)
library(progeny)
library(dorothea)

library(janitor)
library(msigdf) # remotes::install_github("ToledoEM/msigdf@v7.1")

library(AachenColorPalette)
library(cowplot)
library(lemon)
library(patchwork)
library(VennDiagram)
library(gridExtra)
library(ggpubr)

options("tidylog.display" = list(print))
source(here("code/utils-rnaseq.R"))
source(here("code/utils-utils.R"))
source(here("code/utils-plots.R"))

Definition of global variables that are used throughout this analysis.

# i/o
data_path <- "data/mouse-chronic-ccl4"
output_path <- "output/mouse-chronic-ccl4"

# graphical parameters
# fontsize
fz <- 9

Preliminary exploratory analysis

Library size

Barplot of the library size (total counts) for each of the samples.

count_matrix <- readRDS(here(data_path, "count_matrix.rds"))

plot_libsize(count_matrix) +
  my_theme(fsize = fz)

Version Author Date
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Count distribution

Violin plots of the raw read counts for each of the samples.

count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))

count_matrix %>%
  tdy("gene", "sample", "count", meta) %>%
  arrange(treatment) %>%
  ggplot(aes(
    x = fct_reorder(sample, as.numeric(treatment)), y = log10(count + 1),
    group = sample, fill = treatment
  )) +
  geom_violin() +
  theme(
    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
    legend.position = "top"
  ) +
  labs(x = NULL) +
  my_theme(grid = "no", fsize = fz)
#> gather: reorganized (I00001, I00002, I00003, I00004, I00005, …) into (sample, count) [was 32544x37, now 1171584x3]
#> left_join: added 3 columns (time, treatment, group)
#>            > rows only in x           0
#>            > rows only in y  (        0)
#>            > matched rows     1,171,584
#>            >                 ===========
#>            > rows total       1,171,584

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PCA of raw data

PCA plot of raw read counts contextualized based on the time point and treatment. Before gene with a constant expression across all samples are removed and count values are transformed to log2 scale. Only the top 1000 most variable genes are used as features.

count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))

stopifnot(colnames(count_matrix) == meta$sample)

# remove constant expressed genes and transform to log2 scale
preprocessed_count_matrix <- preprocess_count_matrix(count_matrix)
#> Discarding 7711 genes 
#> Keeping 24833 genes


pca_result <- do_pca(preprocessed_count_matrix, meta, top_n_var_genes = 1000)
#> left_join: added 3 columns (time, treatment, group)
#>            > rows only in x    0
#>            > rows only in y  ( 0)
#>            > matched rows     36
#>            >                 ====
#>            > rows total       36

plot_pca(pca_result, feature = "time") +
  plot_pca(pca_result, feature = "treatment") &
  my_theme(fsize = fz)

Version Author Date
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Data processing

Normalization

Raw read counts are normalized by first filtering out lowly expressed genes, TMM normalization and finally logCPM transformation.

count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))

stopifnot(meta$sample == colnames(count_matrix))

dge_obj <- DGEList(count_matrix, group = meta$group)

# filter low read counts, TMM normalization and logCPM transformation
norm <- voom_normalization(dge_obj)
#> Discarding 17206 genes 
#> Keeping 15338 genes

saveRDS(norm, here(output_path, "normalized_expression.rds"))

PCA of normalized data

PCA plot of normalized expression data contextualized based on the time point and treatment. Only the top 1000 most variable genes are used as features.

expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))

pca_result <- do_pca(expr, meta, top_n_var_genes = 1000)
#> left_join: added 3 columns (time, treatment, group)
#>            > rows only in x    0
#>            > rows only in y  ( 0)
#>            > matched rows     36
#>            >                 ====
#>            > rows total       36

saveRDS(pca_result, here(output_path, "pca_result.rds"))

plot_pca(pca_result, feature = "time") +
  plot_pca(pca_result, feature = "treatment") &
  my_theme(fsize = fz)

Version Author Date
8a9b7bf christianholland 2020-12-20
e22a40b christianholland 2020-12-20
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Differential gene expression analysis

Running limma

Differential gene expression analysis via limma with the aim to identify the effect of CCl4 intoxication while regression out the effect of the oil.

# load expression and meta data
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))

stopifnot(colnames(expr) == meta$sample)

# build design matrix
design <- model.matrix(~ 0 + group, data = meta)
rownames(design) <- meta$sample
colnames(design) <- levels(meta$group)

# define contrasts
contrasts <- makeContrasts(
  # effect of olive oil
  oil_2m_vs_0m = oil.2 - wt,
  oil_12m_vs_0m = oil.12 - wt,
  oil_12m_vs_2m = oil.12 - oil.2,

  # treatment vs control ignoring the effect of oil
  ccl_2m_vs_0m = ccl4.2 - wt,
  ccl_6m_vs_0m = ccl4.6 - wt,
  ccl_12m_vs_0m = ccl4.12 - wt,

  # treatment vs control regressing out the effect of oil
  pure_ccl_2m_vs_0m = (ccl4.2 - wt) - (oil.2 - wt),
  pure_ccl_6m_vs_0m = (ccl4.6 - wt) - ((oil.2 + oil.12) / 2 - wt),
  pure_ccl_12m_vs_0m = (ccl4.12 - wt) - (oil.12 - wt),

  # consecutive time point comparison
  consec_12m_vs_6m = ccl4.12 - ccl4.6,
  consec_12m_vs_2m = ccl4.12 - ccl4.2,
  # consec_48w_vs_8w_2 = (ccl4.48 - oil.48) - (ccl4.8 - oil.8),
  consec_6m_vs_2m = ccl4.6 - ccl4.2,
  levels = design
)

limma_result <- run_limma(expr, design, contrasts) %>%
  assign_deg()
#> Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
#> Please use `tibble::as_tibble()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> select: renamed 3 variables (contrast, logFC, pval) and dropped one variable
#> ungroup: no grouping variables

deg_df <- limma_result %>%
  mutate(contrast = factor(contrast, levels = c(
    "ccl_2m_vs_0m", "ccl_6m_vs_0m",
    "ccl_12m_vs_0m",
    "pure_ccl_2m_vs_0m",
    "pure_ccl_6m_vs_0m",
    "pure_ccl_12m_vs_0m",
    "consec_6m_vs_2m",
    "consec_12m_vs_2m",
    "consec_12m_vs_6m",
    "oil_2m_vs_0m", "oil_12m_vs_0m",
    "oil_12m_vs_2m"
  ))) %>%
  mutate(contrast_reference = case_when(
    str_detect(contrast, "oil") ~ "oil",
    str_detect(contrast, "^pure_ccl") ~ "pure_ccl4",
    str_detect(contrast, "^ccl") ~ "ccl4",
    str_detect(contrast, "consec") ~ "consec"
  ))

saveRDS(deg_df, here(output_path, "limma_result.rds"))

Volcano plots

Volcano plots visualizing the effect of CCl4 on gene expression.

df <- readRDS(here(output_path, "limma_result.rds"))

df %>%
  filter(contrast_reference == "pure_ccl4") %>%
  plot_volcano() +
  my_theme(grid = "y", fsize = fz)
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> rename: renamed one variable (p)

Version Author Date
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e22a40b christianholland 2020-12-20
409ba7d christianholland 2020-12-19

Overlap of genes

df <- readRDS(here(output_path, "limma_result.rds"))

t = df %>%
  filter(contrast_reference == "pure_ccl4") %>%
  mutate(class = str_c("Month ", parse_number(as.character(contrast)))) %>%
  select(-contrast_reference, -contrast) %>%
  mutate(class = factor(class, levels = c("Month 2", "Month 6", "Month 12"))) %>%
  group_split(class)
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> select: dropped 2 variables (contrast, contrast_reference)

plot_venn_diagram(t)
#> distinct: removed 15,337 rows (>99%), one row remaining
#> distinct: removed 15,337 rows (>99%), one row remaining
#> distinct: removed 15,337 rows (>99%), one row remaining
#> count: now 3 rows and 2 columns, ungrouped
#> count: now 3 rows and 2 columns, ungrouped
#> count: now 3 rows and 2 columns, ungrouped
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 15,060 rows (98%), 278 rows remaining
#> filter: removed 15,078 rows (98%), 260 rows remaining
#> filter: removed 15,078 rows (98%), 260 rows remaining
#> filter: removed 13,713 rows (89%), 1,625 rows remaining
#> filter: removed 15,060 rows (98%), 278 rows remaining
#> filter: removed 13,713 rows (89%), 1,625 rows remaining
#> filter: removed 15,060 rows (98%), 278 rows remaining
#> filter: removed 15,078 rows (98%), 260 rows remaining
#> filter: removed 13,713 rows (89%), 1,625 rows remaining

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#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 2 rows (67%), one row remaining
#> filter: removed 15,210 rows (99%), 128 rows remaining
#> filter: removed 15,196 rows (99%), 142 rows remaining
#> filter: removed 15,196 rows (99%), 142 rows remaining
#> filter: removed 14,764 rows (96%), 574 rows remaining
#> filter: removed 15,210 rows (99%), 128 rows remaining
#> filter: removed 14,764 rows (96%), 574 rows remaining
#> filter: removed 15,210 rows (99%), 128 rows remaining
#> filter: removed 15,196 rows (99%), 142 rows remaining
#> filter: removed 14,764 rows (96%), 574 rows remaining

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Version Author Date
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Top genes of the overlap

df <- readRDS(here(output_path, "limma_result.rds")) %>%
  filter(contrast_reference == "pure_ccl4") %>%
  filter(regulation != "ns")
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> filter: removed 43,007 rows (93%), 3,007 rows remaining
  
top_genes_ranked = df %>%
  # filter for genes that are deregulated at all time points
  group_by(gene, regulation) %>%
  filter(n() == 3) %>%
  summarise(mean_logfc = mean(logFC)) %>%
  group_by(regulation) %>%
  mutate(rank = row_number(-abs(mean_logfc))) %>%
  ungroup()
#> filter (grouped): removed 2,449 rows (81%), 558 rows remaining
#> summarise: now 186 rows and 3 columns, one group variable remaining (gene)
#> ungroup: no grouping variables

top_genes = df %>%
  inner_join(top_genes_ranked, by=c("gene", "regulation"))
#> inner_join: added 2 columns (mean_logfc, rank)
#>             > rows only in x  (2,449)
#>             > rows only in y  (    0)
#>             > matched rows       558
#>             >                 =======
#>             > rows total         558

top_genes %>%
  filter(rank <= 5) %>%
  ggplot(aes(x=fct_reorder(gene, mean_logfc), y=logFC, group = contrast, fill = contrast)) +
  geom_col(position = "dodge") +
  facet_rep_wrap(~regulation, ncol = 1, scales = "free") +
  my_theme(grid = "y", fsize = fz) +
  labs(x="Gene", y="logFC")
#> filter: removed 528 rows (95%), 30 rows remaining

Version Author Date
1e747d9 christianholland 2020-12-21

Time series clustering

Gene expression trajectories are clustered using the STEM software. The cluster algorithm is described here.

Prepare input

# prepare input for stem analysis
df <- readRDS(here(output_path, "limma_result.rds"))

stem_inputs <- df %>%
  filter(contrast_reference %in% c("pure_ccl4")) %>%
  mutate(class = str_c("Month ", parse_number(as.character(contrast)))) %>%
  mutate(class = factor(class, levels = c("Month 2", "Month 6", "Month 12"))) %>%
  select(gene, class, logFC, contrast_reference)
#> filter: removed 138,042 rows (75%), 46,014 rows remaining
#> select: dropped 5 variables (contrast, statistic, pval, fdr, regulation)

stem_inputs %>%
  select(-contrast_reference) %>%
  pivot_wider(names_from = class, values_from = logFC) %>%
  write_delim(here(output_path, "stem/input/pure_ccl4.txt"), delim = "\t")
#> select: dropped one variable (contrast_reference)
#> pivot_wider: reorganized (class, logFC) into (Month 2, Month 6, Month 12) [was 46014x3, now 15338x4]

Run STEM

STEM is implemented in Java. The .jar file is called from R. Only significant time series clusters are visualized.

# execute stem
stem_res <- run_stem(here(output_path, "stem"), clear_output = T)
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   gene = col_character(),
#>   `Month 2` = col_double(),
#>   `Month 6` = col_double(),
#>   `Month 12` = col_double()
#> )
#> distinct: no rows removed
#> distinct: no rows removed
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   gene = col_character(),
#>   SPOT = col_character(),
#>   Profile = col_double(),
#>   `0` = col_double(),
#>   `Month 2` = col_double(),
#>   `Month 6` = col_double(),
#>   `Month 12` = col_double()
#> )
#> select: dropped one variable (spot)
#> gather: reorganized (x0, month_2, month_6, month_12) into (time, value) [was 3193x7, now 12772x5]
#> 
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#>   `Profile ID` = col_double(),
#>   `Profile Model` = col_character(),
#>   `Cluster (-1 non-significant)` = col_double(),
#>   `# Genes Assigned` = col_double(),
#>   `# Gene Expected` = col_double(),
#>   `p-value` = col_double()
#> )
#> select: renamed 4 variables (profile, y_coords, size, p) and dropped 2 variables
#> Joining, by = c("profile", "key")
#> inner_join: added 3 columns (y_coords, size, p)
#>             > rows only in x  (     0)
#>             > rows only in y  (     0)
#>             > matched rows     12,772
#>             >                 ========
#>             > rows total       12,772
#> inner_join: added one column (symbol)
#>             > rows only in x  (     0)
#>             > rows only in y  (12,145)
#>             > matched rows     12,772
#>             >                 ========
#>             > rows total       12,772
#> transmute: dropped one variable (symbol)
#>            changed 12,644 values (99%) of 'gene' (0 new NA)

saveRDS(stem_res, here(output_path, "stem_result.rds"))

stem_res %>%
  filter(p <= 0.05) %>%
  filter(key == "pure_ccl4") %>%
  distinct() %>%
  plot_stem_profiles(model_profile = F) +
  labs(x = "Time in Month", y = "logFC") +
  my_theme(grid = "y", fsize = fz)
#> filter: removed 3,080 rows (24%), 9,692 rows remaining
#> filter: no rows removed
#> distinct: no rows removed
#> `summarise()` regrouping output by 'key', 'profile', 'p' (override with `.groups` argument)
#> ungroup: no grouping variables

Version Author Date
8a9b7bf christianholland 2020-12-20
e22a40b christianholland 2020-12-20
409ba7d christianholland 2020-12-19

Cluster characterization

STEM clusters are characterized by GO terms, PROGENy’s pathways and DoRothEA’s TFs. As statistic over-representation analysis is used.

stem_res = readRDS(here(output_path, "stem_result.rds"))

signatures = stem_res %>%
  filter(p <= 0.05) %>%
  distinct(profile, gene, p_profile = p)
#> filter: removed 3,080 rows (24%), 9,692 rows remaining
#> distinct: removed 7,269 rows (75%), 2,423 rows remaining

genesets = load_genesets() %>%
  filter(confidence %in% c(NA,"A", "B", "C")) 
#> filter: removed 2,340,732 rows (80%), 597,560 rows remaining
#> select: renamed one variable (gene) and dropped 2 variables
#> gather: reorganized (Androgen, EGFR, Estrogen, Hypoxia, JAK-STAT, …) into (geneset, weight) [was 1299x15, now 18186x3]
#> filter: removed 16,785 rows (92%), 1,401 rows remaining
#> select: dropped one variable (weight)
#> select: renamed 2 variables (geneset, gene) and dropped one variable
#> filter: removed 396,818 rows (39%), 612,694 rows remaining

ora_res = signatures %>%
  nest(sig = c(-profile)) %>%
  dplyr::mutate(ora = sig %>% map(run_ora,  sets = genesets, min_size = 10,
                           options = list(alternative = "greater"), 
                           background_n = 20000)) %>%
  select(-sig) %>%
  unnest(ora)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> add_count: new variable 'n' (integer) with 643 unique values and 0% NA
#> filter: removed 13,996 rows (2%), 598,698 rows remaining
#> select: dropped one variable (n)
#> ungroup: no grouping variables
#> select: dropped 4 variables (set, conf.low, conf.high, method)
#> select: dropped one variable (sig)

saveRDS(ora_res, here(output_path, "stem_characterization.rds"))

Translation to HGNC symbols

For later comparisons to human data the mouse gene symbols are mapped to their human orthologs.

df <- readRDS(here(output_path, "limma_result.rds"))

mapped_df <- df %>%
  translate_gene_ids(from = "symbol_mgi", to = "symbol_hgnc") %>%
  drop_na() %>%
  # for duplicated genes, keep the one with the highest absolute logFC
  group_by(contrast_reference, contrast, gene) %>%
  slice_max(order_by = abs(logFC), n = 1, with_ties = F) %>%
  ungroup()
#> select: dropped 6 variables (ensembl_mgi, ensembl_v_mgi, entrez_mgi, ensembl_hgnc, ensembl_v_hgnc, …)
#> drop_na: removed 1,905 rows (6%), 30,461 rows remaining
#> rename: renamed one variable (symbol_mgi)
#> left_join: added one column (symbol_hgnc)
#>            > rows only in x    22,992
#>            > rows only in y  ( 16,094)
#>            > matched rows     172,404    (includes duplicates)
#>            >                 =========
#>            > rows total       195,396
#> select: renamed one variable (gene) and dropped one variable
#> drop_na: removed 22,992 rows (12%), 172,404 rows remaining
#> slice_max (grouped): removed 11,328 rows (7%), 161,076 rows remaining
#> ungroup: no grouping variables

saveRDS(mapped_df, here(output_path, "limma_result_hs.rds"))

Time spend to execute this analysis: 01:09 minutes.


sessionInfo()
#> R version 4.0.2 (2020-06-22)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Mojave 10.14.5
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] grid      stats     graphics  grDevices datasets  utils     methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] ggpubr_0.4.0             gridExtra_2.3            VennDiagram_1.6.20      
#>  [4] futile.logger_1.4.3      patchwork_1.1.1          lemon_0.4.5             
#>  [7] cowplot_1.1.0            AachenColorPalette_1.1.2 msigdf_7.1              
#> [10] janitor_2.0.1            dorothea_1.0.1           progeny_1.10.0          
#> [13] biobroom_1.20.0          broom_0.7.3              edgeR_3.30.3            
#> [16] limma_3.44.3             here_1.0.1               tidylog_1.0.2           
#> [19] forcats_0.5.0            stringr_1.4.0            dplyr_1.0.2             
#> [22] purrr_0.3.4              readr_1.4.0              tidyr_1.1.2             
#> [25] tibble_3.0.4             ggplot2_3.3.2            tidyverse_1.3.0         
#> [28] workflowr_1.6.2         
#> 
#> loaded via a namespace (and not attached):
#>  [1] colorspace_2.0-0     ggsignif_0.6.0       ellipsis_0.3.1      
#>  [4] rio_0.5.16           rprojroot_2.0.2      snakecase_0.11.0    
#>  [7] fs_1.5.0             rstudioapi_0.13      farver_2.0.3        
#> [10] ggrepel_0.9.0        fansi_0.4.1          lubridate_1.7.9.2   
#> [13] xml2_1.3.2           codetools_0.2-16     knitr_1.30          
#> [16] jsonlite_1.7.2       bcellViper_1.24.0    dbplyr_2.0.0        
#> [19] compiler_4.0.2       httr_1.4.2           backports_1.2.1     
#> [22] assertthat_0.2.1     cli_2.2.0            later_1.1.0.1       
#> [25] formatR_1.7          htmltools_0.5.0      tools_4.0.2         
#> [28] gtable_0.3.0         glue_1.4.2           Rcpp_1.0.5          
#> [31] carData_3.0-4        Biobase_2.48.0       cellranger_1.1.0    
#> [34] vctrs_0.3.6          xfun_0.19            openxlsx_4.2.3      
#> [37] rvest_0.3.6          lifecycle_0.2.0      renv_0.12.3         
#> [40] rstatix_0.6.0        scales_1.1.1         clisymbols_1.2.0    
#> [43] hms_0.5.3            promises_1.1.1       parallel_4.0.2      
#> [46] lambda.r_1.2.4       yaml_2.2.1           curl_4.3            
#> [49] stringi_1.5.3        BiocGenerics_0.34.0  zip_2.1.1           
#> [52] rlang_0.4.9          pkgconfig_2.0.3      evaluate_0.14       
#> [55] lattice_0.20-41      labeling_0.4.2       tidyselect_1.1.0    
#> [58] plyr_1.8.6           magrittr_2.0.1       R6_2.5.0            
#> [61] generics_0.1.0       DBI_1.1.0            pillar_1.4.7        
#> [64] haven_2.3.1          whisker_0.4          foreign_0.8-80      
#> [67] withr_2.3.0          abind_1.4-5          modelr_0.1.8        
#> [70] crayon_1.3.4         car_3.0-10           futile.options_1.0.1
#> [73] rmarkdown_2.6        locfit_1.5-9.4       readxl_1.3.1        
#> [76] data.table_1.13.4    git2r_0.27.1         reprex_0.3.0        
#> [79] digest_0.6.27        httpuv_1.5.4         munsell_0.5.0       
#> [82] viridisLite_0.3.0