Chapter 10 Test Example
10.2 Loading data
dada2_res <- readRDS("DataSet/RawData/dada2_res.rds")
tree <- phyloseq::read_tree("DataSet/RawData/tree.nwk")
metadata <- readxl::read_xlsx("DataSet/RawData/诺禾宏基因组678月-ZH.xlsx", sheet = 3)
metaphlan2_res <- read.table("DataSet/RawData/merged_metaphlan2.tsv",
header = TRUE, stringsAsFactors = FALSE) %>%
tibble::rownames_to_column("ID")10.3 Metaphlan2 result
metaphlan2_res_list <- import_metaphlan_taxa(data_metaphlan2 = metaphlan2_res,
taxa_level = "Species")
tax_tab <- metaphlan2_res_list$tax_tab
otu_tab <- metaphlan2_res_list$abu_tab
colnames(otu_tab) <- gsub("X", "S_", colnames(otu_tab))
sam_tab <- metadata %>% data.frame() %>%
dplyr::mutate(Group=ifelse(SampleType == "粪便", "Stool",
ifelse(SampleType == "QC", "QC", "Product"))) %>%
dplyr::select(SampleTubeID, Group, everything())
rownames(sam_tab) <- paste0("S_", sam_tab$SeqID_MGS)
overlap_samples <- dplyr::intersect(rownames(sam_tab), colnames(otu_tab))
otu_tab_cln <- otu_tab[, match(overlap_samples, colnames(otu_tab))]
sam_tab_cln <- sam_tab[match(overlap_samples, rownames(sam_tab)), ]
rownames(sam_tab_cln) <- overlap_samples
metaphlan2_ps <- get_metaphlan_phyloseq(
otu_tab = otu_tab_cln,
sam_tab = sam_tab_cln,
tax_tab = tax_tab)
metaphlan2_ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 315 taxa and 145 samples ]
## sample_data() Sample Data: [ 145 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 315 taxa by 7 taxonomic ranks ]
10.5 Step2: Convert inputs into phyloseq data
tax_tab_16s <- import_dada2_taxa(dada2_taxa = dada2_res$tax_tab)
otu_tab_16s <- dada2_res$seq_tab
# Shouldn't use the Total Number as SampleID (wrong: 123456; right: X123456)
rownames(otu_tab_16s) <- paste0("S_", rownames(otu_tab_16s))
sam_tab_16s <- metadata %>% data.frame() %>%
dplyr::mutate(Group=ifelse(SampleType == "粪便", "Stool",
ifelse(SampleType == "QC", "QC", "Product"))) %>%
dplyr::filter(SampleTubeID %in% sam_tab_cln$SampleTubeID) %>%
dplyr::select(SampleTubeID, Group, everything())
rownames(sam_tab_16s) <- paste0("S_", sam_tab_16s$SeqID_16s)
overlap_samples_16s <- dplyr::intersect(rownames(sam_tab_16s), rownames(otu_tab_16s))
otu_tab_16s_cln <- otu_tab_16s[match(overlap_samples_16s, rownames(otu_tab_16s)), ]
sam_tab_16s_cln <- sam_tab_16s[match(overlap_samples_16s, rownames(sam_tab_16s)), ]
dada2_ps <- get_dada2_phyloseq(
seq_tab = otu_tab_16s_cln,
tax_tab = tax_tab_16s,
sam_tab = sam_tab_16s_cln,
phy_tree = tree)
dada2_ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1948 taxa and 145 samples ]
## sample_data() Sample Data: [ 145 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 1948 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1948 tips and 1933 internal nodes ]
## refseq() DNAStringSet: [ 1948 reference sequences ]10.6 Step3: BRS checking
dada2_ps <- readRDS("DataSet/Step2/Donor_16s_phyloseq.RDS")
dada2_ps_genus <- summarize_taxa(ps = dada2_ps,
taxa_level = "Genus")
tail(dada2_ps_genus@sam_data %>% data.frame())## SampleTubeID Group Date_Sequencing ProductID SampleType ProductBatch Date_Sampling Date_Receiving SeqID_MGS SeqID_16s
## S_7929 GGM50-210730 Stool 2021-08-03 M50 粪便 CYM50-210735 2021.07.30 2021-08-06 7769 7929
## S_7930 CYM50-210735-0727 Product 2021-08-03 M50 肠菌胶囊 CYM50-210735 2021.07.27 2021-08-06 7770 7930
## S_7931 CYM50-210735-0728 Product 2021-08-03 M50 肠菌胶囊 CYM50-210735 2021.07.28 2021-08-06 7771 7931
## S_7932 CYM50-210735-0729 Product 2021-08-03 M50 肠菌胶囊 CYM50-210735 2021.07.29 2021-08-06 7772 7932
## S_7933 CYM50-210735-0730 Product 2021-08-03 M50 肠菌胶囊 CYM50-210735 2021.07.30 2021-08-06 7773 7933
## S_7327 Community QC <NA> Ref QC <NA> <NA> <NA> 7222 7327
## Pipeline_MGS Pipeline_16s
## S_7929 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7930 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7931 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7932 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7933 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7327 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203## Noting: the Reference Matrix is for 16s
##
## ############Matched baterica of the BRS sample#############
## The number of BRS' bacteria matched the Reference Matrix is [3]
## g__Lactobacillus
## g__Escherichia_Shigella
## g__Enterococcus
## The number of bacteria unmatched the Reference Matrix is [12]
## g__Bifidobacterium
## g__Bacteroides
## g__Faecalibacterium
## g__Parabacteroides
## g__Collinsella
## g__Coprococcus_3
## g__Dorea
## g__Streptococcus
## g__Roseburia
## g__Anaerostipes
## g__Prevotella_9
## g__Eggerthella
## The number of the additional bacteria compared to the Reference Matrix is [6]
## ###########################################################
##
## ##################Status of the BRS sample##################
## Whether the BRS has the all bateria of Reference Matrix: FALSE
## Correlation Coefficient of the BRS is: 6
## Bray Curtis of the BRS is: 0.2265
## Impurity of Max additional genus (g__Listeria) of the BRS is: 16.94
## ###########################################################
## #####Final Evaluation Results of the BRS #######
## The BRS of sequencing dataset didn't pass the cutoff of the Reference Matrix
## ###########################################################
## Gold_Cutoff BRS
## Coef 0.9067 6.0000
## Bray 0.1597 0.2265
## Impurity(max) 1.0000 16.9400dada2_ps_remove_BRS <- get_GroupPhyloseq(
ps = dada2_ps,
group = "Group",
group_names = "QC",
discard = TRUE)
dada2_ps_remove_BRS
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1948 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 1948 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1948 tips and 1933 internal nodes ]
## refseq() DNAStringSet: [ 1948 reference sequences ]
if (!dir.exists("DataSet/Step3/")) {
dir.create("DataSet/Step3/")
}
saveRDS(dada2_ps_remove_BRS, "DataSet/Step3/Donor_16s_phyloseq_remove_BRS.RDS", compress = TRUE)10.7 Step4: Rarefaction curves
dada2_ps_remove_BRS <- readRDS("DataSet/Step3/Donor_16s_phyloseq_remove_BRS.RDS")
plot_RarefCurve(ps = dada2_ps_remove_BRS,
taxa_level = "OTU",
step = 400,
label = "Group",
color = "Group")## rarefying sample S_7271
## rarefying sample S_7272
## rarefying sample S_7273
## rarefying sample S_7274
## rarefying sample S_7275
## rarefying sample S_7276
## rarefying sample S_7277
## rarefying sample S_7278
## rarefying sample S_7279
## rarefying sample S_7280
## rarefying sample S_7281
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## rarefying sample S_7283
## rarefying sample S_7284
## rarefying sample S_7285
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## rarefying sample S_7287
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## rarefying sample S_7289
## rarefying sample S_7290
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## rarefying sample S_7293
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## rarefying sample S_7295
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## rarefying sample S_7297
## rarefying sample S_7298
## rarefying sample S_7299
## rarefying sample S_7300
## rarefying sample S_7301
## rarefying sample S_7302
## rarefying sample S_7303
## rarefying sample S_7304
## rarefying sample S_7305
## rarefying sample S_7306
## rarefying sample S_7307
## rarefying sample S_7308
## rarefying sample S_7309
## rarefying sample S_7310
## rarefying sample S_7311
## rarefying sample S_7312
## rarefying sample S_7313
## rarefying sample S_7314
## rarefying sample S_7315
## rarefying sample S_7316
## rarefying sample S_7317
## rarefying sample S_7318
## rarefying sample S_7319
## rarefying sample S_7320
## rarefying sample S_7321
## rarefying sample S_7322
## rarefying sample S_7323
## rarefying sample S_7324
## rarefying sample S_7325
## rarefying sample S_7326
## rarefying sample S_7846
## rarefying sample S_7847
## rarefying sample S_7848
## rarefying sample S_7849
## rarefying sample S_7850
## rarefying sample S_7851
## rarefying sample S_7852
## rarefying sample S_7853
## rarefying sample S_7854
## rarefying sample S_7855
## rarefying sample S_7856
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## rarefying sample S_7858
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## rarefying sample S_7860
## rarefying sample S_7861
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## rarefying sample S_7863
## rarefying sample S_7864
## rarefying sample S_7865
## rarefying sample S_7866
## rarefying sample S_7867
## rarefying sample S_7868
## rarefying sample S_7869
## rarefying sample S_7870
## rarefying sample S_7871
## rarefying sample S_7872
## rarefying sample S_7873
## rarefying sample S_7874
## rarefying sample S_7875
## rarefying sample S_7876
## rarefying sample S_7877
## rarefying sample S_7878
## rarefying sample S_7879
## rarefying sample S_7880
## rarefying sample S_7881
## rarefying sample S_7882
## rarefying sample S_7883
## rarefying sample S_7884
## rarefying sample S_7885
## rarefying sample S_7886
## rarefying sample S_7887
## rarefying sample S_7888
## rarefying sample S_7889
## rarefying sample S_7890
## rarefying sample S_7891
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## rarefying sample S_7894
## rarefying sample S_7895
## rarefying sample S_7896
## rarefying sample S_7897
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## rarefying sample S_7899
## rarefying sample S_7900
## rarefying sample S_7901
## rarefying sample S_7902
## rarefying sample S_7903
## rarefying sample S_7904
## rarefying sample S_7905
## rarefying sample S_7906
## rarefying sample S_7907
## rarefying sample S_7908
## rarefying sample S_7909
## rarefying sample S_7910
## rarefying sample S_7911
## rarefying sample S_7912
## rarefying sample S_7913
## rarefying sample S_7914
## rarefying sample S_7915
## rarefying sample S_7916
## rarefying sample S_7917
## rarefying sample S_7918
## rarefying sample S_7919
## rarefying sample S_7920
## rarefying sample S_7921
## rarefying sample S_7922
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## rarefying sample S_7925
## rarefying sample S_7926
## rarefying sample S_7927
## rarefying sample S_7928
## rarefying sample S_7929
## rarefying sample S_7930
## rarefying sample S_7931
## rarefying sample S_7932
## rarefying sample S_7933
Figure 10.2: Rarefaction curves (Example)
10.8 Step5: Rarefy otu counts
dada2_ps_remove_BRS <- readRDS("DataSet/Step3/Donor_16s_phyloseq_remove_BRS.RDS")
summarize_phyloseq(ps = dada2_ps_remove_BRS)## [[1]]
## [1] "1] Min. number of reads = 33267"
##
## [[2]]
## [1] "2] Max. number of reads = 153367"
##
## [[3]]
## [1] "3] Total number of reads = 10909876"
##
## [[4]]
## [1] "4] Average number of reads = 75763.0277777778"
##
## [[5]]
## [1] "5] Median number of reads = 71985.5"
##
## [[6]]
## [1] "7] Sparsity = 0.912599104494638"
##
## [[7]]
## [1] "6] Any OTU sum to 1 or less? YES"
##
## [[8]]
## [1] "8] Number of singletons = 837"
##
## [[9]]
## [1] "9] Percent of OTUs that are singletons\n (i.e. exactly one read detected across all samples)0"
##
## [[10]]
## [1] "10] Number of sample variables are: 12"
##
## [[11]]
## [1] "SampleTubeID" "Group" "Date_Sequencing" "ProductID" "SampleType" "ProductBatch" "Date_Sampling"
## [8] "Date_Receiving" "SeqID_MGS" "SeqID_16s" "Pipeline_MGS" "Pipeline_16s"## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1091 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 1091 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1091 tips and 1086 internal nodes ]
## refseq() DNAStringSet: [ 1091 reference sequences ]10.9 Step6: Extracting specific taxonomic level
dada2_ps_rare <- readRDS("DataSet/Step5/Donor_16s_phyloseq_remove_BRS_rare.RDS")
dada2_ps_rare_genus <- summarize_taxa(ps = dada2_ps_rare,
taxa_level = "Genus")
dada2_ps_rare_genus## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 225 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 225 taxa by 6 taxonomic ranks ]## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 37 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 37 taxa by 4 taxonomic ranks ]dada2_ps_rare_phylum <- summarize_taxa(ps = dada2_ps_rare,
taxa_level = "Phylum")
dada2_ps_rare_phylum## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 15 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 15 taxa by 2 taxonomic ranks ]if (!dir.exists("DataSet/Step6/")) {
dir.create("DataSet/Step6/")
}
saveRDS(dada2_ps_rare_genus, "DataSet/Step6/Donor_16s_phyloseq_remove_BRS_rare_genus.RDS", compress = TRUE)
saveRDS(dada2_ps_rare_order, "DataSet/Step6/Donor_16s_phyloseq_remove_BRS_rare_order.RDS", compress = TRUE)
saveRDS(dada2_ps_rare_phylum, "DataSet/Step6/Donor_16s_phyloseq_remove_BRS_rare_phylum.RDS", compress = TRUE)10.10 Step7: GlobalView
dada2_ps_rare_genus <- readRDS("DataSet/Step6/Donor_16s_phyloseq_remove_BRS_rare_genus.RDS")
# alpha
dada2_ps_rare_genus_alpha <- run_alpha_diversity(ps = dada2_ps_rare_genus,
measures = c("Shannon", "Chao1", "Observed"))
plot_boxplot(data = dada2_ps_rare_genus_alpha,
y_index = c("Shannon", "Chao1", "Observed"),
group = "Group",
group_names = c("Stool", "Product"),
group_color = c("red", "blue"))
Figure 10.3: diversity and ordination and composition(Example)
# beta
dada2_ps_beta <- run_beta_diversity(ps = dada2_ps_rare_genus,
method = "bray")
plot_distance_corrplot(datMatrix = dada2_ps_beta$BetaDistance)
Figure 10.4: diversity and ordination and composition(Example)
# permanova
dada2_ps_per <- run_permanova(ps = dada2_ps_rare_genus,
method = "bray",
columns = "Group")
print(dada2_ps_per)## SumsOfSample Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) AdjustedPvalue
## Group 144 1 1.335187 1.335187 9.669403 0.06375315 0.001 0.001# beta dispersion
beta_df <- run_beta_diversity(ps = dada2_ps_rare_genus,
method = "bray",
group = "Group")##
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
##
## Response: Distances
## Df Sum Sq Mean Sq F N.Perm Pr(>F)
## Groups 1 0.04375 0.043749 5.6785 999 0.016 *
## Residuals 142 1.09402 0.007704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Product Stool
## Product 0.017
## Stool 0.018497# ordination
dada2_ps_ordination <- run_ordination(
ps = dada2_ps_rare_genus,
group = "Group",
method = "PCoA")## [1] "Pvalue of beta dispersion less than 0.05"plot_Ordination(ResultList = dada2_ps_ordination,
group = "Group",
group_names = c("Stool", "Product"),
group_color = c("blue", "red"))
Figure 10.5: diversity and ordination and composition(Example)
# Microbial composition
plot_stacked_bar_XIVZ(
phyloseq = dada2_ps_rare_genus,
level = "Phylum",
feature = "Group")
Figure 10.6: diversity and ordination and composition(Example)
10.11 Step8: Differential Analysis
dada2_ps_rare_genus <- readRDS("DataSet/Step6/Donor_16s_phyloseq_remove_BRS_rare_genus.RDS")
# filter & trim
dada2_ps_rare_genus_filter <- run_filter(ps = dada2_ps_rare_genus,
cutoff = 10,
unclass = TRUE)
dada2_ps_rare_genus_filter_trim <- run_trim(object = dada2_ps_rare_genus_filter,
cutoff = 0.1,
trim = "feature")
dada2_ps_rare_genus_filter_trim## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 84 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 84 taxa by 6 taxonomic ranks ]# lefse
# dada2_ps_lefse <- run_lefse(
# ps = dada2_ps_rare_genus_filter_trim,
# group = "Group",
# group_names = c("Stool", "Product"),
# norm = "CPM",
# Lda = 2)
dada2_ps_lefse <- run_lefse2(
ps = dada2_ps_rare_genus_filter_trim,
group = "Group",
group_names = c("Stool", "Product"),
norm = "CPM",
lda_cutoff = 2)
# # don't run this code when you do lefse in reality
# dada2_ps_lefse$LDA_Score <- dada2_ps_lefse$LDA_Score * 1000
plot_lefse(
da_res = dada2_ps_lefse,
x_index = "LDA_Score",
x_index_cutoff = 2,
group_color = c("green", "red"))
Figure 10.7: Differential Analysis (Example)
dada2_ps_wilcox <- run_wilcox(
ps = dada2_ps_rare_genus_filter_trim,
group = "Group",
group_names = c("Stool", "Product"))
plot_volcano(
da_res = dada2_ps_wilcox,
group_names = c("Stool", "Product"),
x_index = "Log2FoldChange (Rank)\nStool_vs_Product",
x_index_cutoff = 0.5,
y_index = "Pvalue",
y_index_cutoff = 0.05,
group_color = c("red", "grey", "blue"),
topN = 5)
Figure 10.8: Differential Analysis (Example)
10.12 Systematic Information
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.3 (2022-03-10)
## os macOS Monterey 12.2.1
## system x86_64, darwin17.0
## ui RStudio
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Shanghai
## date 2023-10-27
## rstudio 2023.09.0+463 Desert Sunflower (desktop)
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [2] CRAN (R 4.1.0)
## ade4 1.7-22 2023-02-06 [2] CRAN (R 4.1.2)
## ALDEx2 1.30.0 2022-11-01 [2] Bioconductor
## annotate 1.72.0 2021-10-26 [2] Bioconductor
## AnnotationDbi 1.60.2 2023-03-10 [2] Bioconductor
## ape * 5.7-1 2023-03-13 [2] CRAN (R 4.1.2)
## askpass 1.1 2019-01-13 [2] CRAN (R 4.1.0)
## backports 1.4.1 2021-12-13 [2] CRAN (R 4.1.0)
## base64enc 0.1-3 2015-07-28 [2] CRAN (R 4.1.0)
## bayesm 3.1-5 2022-12-02 [2] CRAN (R 4.1.2)
## Biobase * 2.54.0 2021-10-26 [2] Bioconductor
## BiocGenerics * 0.40.0 2021-10-26 [2] Bioconductor
## BiocParallel 1.28.3 2021-12-09 [2] Bioconductor
## biomformat 1.22.0 2021-10-26 [2] Bioconductor
## Biostrings 2.62.0 2021-10-26 [2] Bioconductor
## bit 4.0.5 2022-11-15 [2] CRAN (R 4.1.2)
## bit64 4.0.5 2020-08-30 [2] CRAN (R 4.1.0)
## bitops 1.0-7 2021-04-24 [2] CRAN (R 4.1.0)
## blob 1.2.4 2023-03-17 [2] CRAN (R 4.1.2)
## bookdown 0.34 2023-05-09 [2] CRAN (R 4.1.2)
## broom 1.0.5 2023-06-09 [2] CRAN (R 4.1.3)
## bslib 0.5.0 2023-06-09 [2] CRAN (R 4.1.3)
## cachem 1.0.8 2023-05-01 [2] CRAN (R 4.1.2)
## callr 3.7.3 2022-11-02 [2] CRAN (R 4.1.2)
## car 3.1-2 2023-03-30 [2] CRAN (R 4.1.2)
## carData 3.0-5 2022-01-06 [2] CRAN (R 4.1.2)
## caTools 1.18.2 2021-03-28 [2] CRAN (R 4.1.0)
## cccd 1.6 2022-04-08 [2] CRAN (R 4.1.2)
## cellranger 1.1.0 2016-07-27 [2] CRAN (R 4.1.0)
## checkmate 2.2.0 2023-04-27 [2] CRAN (R 4.1.2)
## class 7.3-22 2023-05-03 [2] CRAN (R 4.1.2)
## classInt 0.4-9 2023-02-28 [2] CRAN (R 4.1.2)
## cli 3.6.1 2023-03-23 [2] CRAN (R 4.1.2)
## cluster 2.1.4 2022-08-22 [2] CRAN (R 4.1.2)
## coda 0.19-4 2020-09-30 [2] CRAN (R 4.1.0)
## codetools 0.2-19 2023-02-01 [2] CRAN (R 4.1.2)
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## [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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