Chapter 10 Test Example
10.1 Loading packages
library(XMAS2)
library(dplyr)
library(tibble)
library(phyloseq)
library(ggplot2)
library(ggpubr)
library(readxl)10.2 Loading data
metaphlan2_res <- read.table("DataSet/RawData/merged_metaphlan2.tsv",
header = TRUE, stringsAsFactors = FALSE) %>%
tibble::rownames_to_column("ID")
metadata <- readxl::read_xlsx("DataSet/RawData/诺禾宏基因组678月-ZH.xlsx", sheet = 3)10.3 Step1: Convert inputs into phyloseq data
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 <- 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 ]if (!dir.exists("DataSet/Step1/")) {
dir.create("DataSet/Step1/")
}
saveRDS(metaphlan2_ps, "DataSet/Step1/Donor_MGS_phyloseq.RDS", compress = TRUE)10.4 Step2: Transform limit of detection (LOD) into Zeros
# species
metaphlan2_ps_LOD_species <- aggregate_LOD_taxa(ps = metaphlan2_ps,
taxa_level = "Species",
cutoff = 1e-04)
# genus
metaphlan2_ps_LOD_genus <- aggregate_LOD_taxa(ps = metaphlan2_ps,
taxa_level = "Genus",
cutoff = 1e-04)
# order
metaphlan2_ps_LOD_order <- aggregate_LOD_taxa(ps = metaphlan2_ps,
taxa_level = "Order",
cutoff = 1e-04)
if (!dir.exists("DataSet/Step2/")) {
dir.create("DataSet/Step2/")
}
saveRDS(metaphlan2_ps_LOD_species, "DataSet/Step2/Donor_MGS_phyloseq_LOD_species.RDS", compress = TRUE)
saveRDS(metaphlan2_ps_LOD_genus, "DataSet/Step2/Donor_MGS_phyloseq_LOD_genus.RDS", compress = TRUE)
saveRDS(metaphlan2_ps_LOD_order, "DataSet/Step2/Donor_MGS_phyloseq_LOD_order.RDS", compress = TRUE)10.5 Step3: BRS checking
metaphlan2_ps <- readRDS("DataSet/Step1/Donor_MGS_phyloseq.RDS")
metaphlan2_ps_LOD_species <- aggregate_LOD_taxa(ps = metaphlan2_ps,
taxa_level = "Species",
cutoff = 1e-04)
tail(metaphlan2_ps_LOD_species@sam_data %>% data.frame())## SampleTubeID Group Date_Sequencing ProductID SampleType ProductBatch Date_Sampling Date_Receiving SeqID_MGS SeqID_16s
## S_7769 GGM50-210730 Stool 2021-08-03 M50 粪便 CYM50-210735 2021.07.30 2021-08-06 7769 7929
## S_7770 CYM50-210735-0727 Product 2021-08-03 M50 肠菌胶囊 CYM50-210735 2021.07.27 2021-08-06 7770 7930
## S_7771 CYM50-210735-0728 Product 2021-08-03 M50 肠菌胶囊 CYM50-210735 2021.07.28 2021-08-06 7771 7931
## S_7772 CYM50-210735-0729 Product 2021-08-03 M50 肠菌胶囊 CYM50-210735 2021.07.29 2021-08-06 7772 7932
## S_7773 CYM50-210735-0730 Product 2021-08-03 M50 肠菌胶囊 CYM50-210735 2021.07.30 2021-08-06 7773 7933
## S_7222 Community QC <NA> Ref QC <NA> <NA> <NA> 7222 7327
## Pipeline_MGS
## S_7769 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output
## S_7770 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output
## S_7771 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output
## S_7772 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output
## S_7773 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output
## S_7222 /share/work/HPC/work_tmp/PipelineJob_180_20210923/output
## Pipeline_16s
## S_7769 /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7770 /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7771 /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7772 /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7773 /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203
## S_7222 /share/projects/Engineering/pipeline_output/PipelineJob_304_20211203run_RefCheck(
ps = metaphlan2_ps_LOD_species,
BRS_ID = "S_7222",
Ref_type = "MGS"L)## Noting: the Reference Matrix is for MGS
##
## ############Matched baterica of the BRS sample#############
## The number of BRS' bacteria matched the Reference Matrix is [2]
## s__Enterococcus_faecalis
## s__Escherichia_coli
## The number of bacteria unmatched the Reference Matrix is [25]
## s__Bacteroides_fragilis
## s__Parabacteroides_goldsteinii
## s__Lactobacillus_salivarius
## s__Enterococcus_faecium
## s__Bifidobacterium_longum
## s__Bacteroides_ovatus
## s__Coprococcus_comes
## s__Bacteroides_vulgatus
## s__Bifidobacterium_adolescentis
## s__Bacteroides_thetaiotaomicron
## s__Streptococcus_salivarius
## s__Dorea_formicigenerans
## s__Bifidobacterium_pseudocatenulatum
## s__Bacteroides_uniformis
## s__Bacteroides_xylanisolvens
## s__Prevotella_copri
## s__Bifidobacterium_bifidum
## s__Lactobacillus_pentosus
## s__Eggerthella_unclassified
## s__Faecalibacterium_prausnitzii
## s__Collinsella_aerofaciens
## s__Propionibacterium_acnes
## s__Lachnospiraceae_bacterium_5_1_63FAA
## s__Bacteroides_intestinalis
## s__Roseburia_hominis
## The number of the additional bacteria compared to the Reference Matrix is [2]
## ###########################################################
##
## ##################Status of the BRS sample##################
## Whether the BRS has the all bateria of Reference Matrix: FALSE
## Correlation Coefficient of the BRS is: 2.22e-16
## Bray Curtis of the BRS is: 0.08567
## Impurity of the BRS is: 72.95
## ###########################################################
## #####Final Evaluation Results of the BRS #######
## The BRS of sequencing dataset didn't pass the cutoff of the Reference Matrix
## ###########################################################
## 10471 10636 10637 10639 10640 10769 11115 11708 12592 13164 13331 13837
## Enterococcus_faecalis 12.22545 12.95765 13.26370 16.90821 15.50012 11.14008 10.80865 11.15036 16.95287 13.54415 11.91148 12.42703
## Escherichia_coli 10.45990 9.62134 9.25891 9.45073 8.59647 8.01447 8.62655 8.29413 7.91170 8.38669 8.77327 9.16687
## Impurity_level 4.70413 4.52902 4.60861 1.87475 1.92536 1.77555 3.36663 3.85245 0.03527 3.47337 3.73338 4.44354
## 13864 13883 14294 14353 14514 14692 15043 15377 15378 15919 16048 16316
## Enterococcus_faecalis 11.73824 15.02487 12.04265 13.30646 13.04880 17.03368 18.19633 16.87963 13.65086 12.75575 10.71452 13.58463
## Escherichia_coli 7.65961 8.07574 7.18094 9.02693 8.01771 9.24078 7.50739 7.81145 8.85383 7.89352 7.46987 7.83391
## Impurity_level 2.38221 2.32083 2.82561 4.48127 6.06169 1.42752 0.82386 2.80354 3.66638 2.44231 3.65887 2.15969
## 16319 16347 16379 16416 16643 17346 17358 17367 17447 17574 17614 17907
## Enterococcus_faecalis 13.51058 14.39433 15.84027 14.32948 13.60631 13.57461 11.97870 11.20007 2.89096 12.52717 15.21587 13.87337
## Escherichia_coli 7.78559 9.97867 8.76792 8.12718 8.24203 8.37464 8.52090 8.02366 14.61317 3.25451 5.65047 11.56616
## Impurity_level 2.12420 4.66298 1.74884 2.89559 3.16093 1.91433 2.22895 3.95869 1.63455 1.26012 2.52024 5.35382
## 18003 18123 18158 18254 18652 18748 18883 19025 19151 19175 7682 7683
## Enterococcus_faecalis 14.26032 14.59397 20.35865 16.85781 14.74892 14.89502 21.71030 15.03847 15.11212 15.87920 11.92169 12.99330
## Escherichia_coli 10.65107 10.69954 9.75794 11.92552 9.79362 11.73026 6.23209 9.48271 9.97228 9.95575 8.44917 7.97261
## Impurity_level 5.03729 4.67436 0.71954 2.20140 3.52704 4.37791 0.69343 3.11022 3.96834 3.24676 2.67618 2.24664
## 7684 7685 7842 7843 7844 7845 8108 8635 8952 9456 9474 S_7222
## Enterococcus_faecalis 12.58464 12.26114 13.80341 13.53297 13.30763 13.29233 14.05273 17.27500 18.58891 18.13412 7.62186 17.19286
## Escherichia_coli 7.82872 7.39019 10.55559 10.31875 10.30313 11.16387 8.98268 10.86530 7.08166 8.03494 6.22940 9.85431
## Impurity_level 2.35311 1.45627 2.83764 3.70535 3.55981 4.66901 3.32214 1.31017 0.99781 0.53936 1.74945 72.95000
## mean Evaluation
## Enterococcus_faecalis 13.995421 S_7222 didn't pass the threshold (2023-04-12 14:12:03).
## Escherichia_coli 8.854478 S_7222 didn't pass the threshold (2023-04-12 14:12:03).
## Impurity_level 4.046139 S_7222 didn't pass the threshold (2023-04-12 14:12:03).metaphlan2_ps_LOD_species_remove_BRS <- get_GroupPhyloseq(
ps = metaphlan2_ps_LOD_species,
group = "Group",
group_names = "QC",
discard = TRUE)
metaphlan2_ps_LOD_species_remove_BRS## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 180 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 180 taxa by 7 taxonomic ranks ]if (!dir.exists("DataSet/Step3/")) {
dir.create("DataSet/Step3/")
}
saveRDS(metaphlan2_ps_LOD_species_remove_BRS, "DataSet/Step3/Donor_MGS_phyloseq_LOD_species_remove_BRS.RDS", compress = TRUE)10.6 Step4: Extracting specific taxonomic level
- Removing spike-in sample (BRS)
metaphlan2_ps <- readRDS("DataSet/Step1/Donor_MGS_phyloseq.RDS")
metaphlan2_ps_remove_BRS <- get_GroupPhyloseq(
ps = metaphlan2_ps,
group = "Group",
group_names = "QC",
discard = TRUE)
metaphlan2_ps_LOD_species_remove_BRS## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 180 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 180 taxa by 7 taxonomic ranks ]- Species
metaphlan2_ps_remove_BRS_LOD_species <- aggregate_LOD_taxa(
ps = metaphlan2_ps_remove_BRS,
taxa_level = "Species",
cutoff = 1e-04)
metaphlan2_ps_remove_BRS_LOD_species## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 180 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 180 taxa by 7 taxonomic ranks ]- Genus
metaphlan2_ps_remove_BRS_LOD_genus <- aggregate_LOD_taxa(
ps = metaphlan2_ps_remove_BRS,
taxa_level = "Genus",
cutoff = 1e-04)
metaphlan2_ps_remove_BRS_LOD_genus## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 67 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 67 taxa by 6 taxonomic ranks ]- Phylum
metaphlan2_ps_remove_BRS_LOD_phylum <- aggregate_LOD_taxa(
ps = metaphlan2_ps_remove_BRS,
taxa_level = "Phylum",
cutoff = 1e-04)
metaphlan2_ps_remove_BRS_LOD_phylum## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 7 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 7 taxa by 2 taxonomic ranks ]- output
if (!dir.exists("DataSet/Step4/")) {
dir.create("DataSet/Step4/")
}
saveRDS(metaphlan2_ps_remove_BRS_LOD_species, "DataSet/Step4/Donor_MGS_phyloseq_remove_BRS_LOD_species.RDS", compress = TRUE)
saveRDS(metaphlan2_ps_remove_BRS_LOD_genus, "DataSet/Step4/Donor_MGS_phyloseq_remove_BRS_LOD_genus.RDS", compress = TRUE)
saveRDS(metaphlan2_ps_remove_BRS_LOD_phylum, "DataSet/Step4/Donor_MGS_phyloseq_remove_BRS_LOD_phylum.RDS", compress = TRUE)10.7 Step5: GlobalView
metaphlan2_ps_remove_BRS_LOD_species <- readRDS("DataSet/Step4/Donor_MGS_phyloseq_remove_BRS_LOD_species.RDS")
# alpha
metaphlan2_ps_remove_BRS_species_alpha <- run_alpha_diversity(
ps = metaphlan2_ps_remove_BRS_LOD_species,
measures = c("Shannon", "Simpson", "InvSimpson"))
plot_boxplot(data = metaphlan2_ps_remove_BRS_species_alpha,
y_index = c("Shannon", "Simpson", "InvSimpson"),
group = "Group",
group_names = c("Stool", "Product"),
group_color = c("red", "blue"))
Figure 10.1: diversity and ordination and composition(Example)
# beta
metaphlan2_ps_remove_BRS_species_beta <- run_beta_diversity(
ps = metaphlan2_ps_remove_BRS_LOD_species,
method = "bray")
plot_distance_corrplot(datMatrix = metaphlan2_ps_remove_BRS_species_beta$BetaDistance)
Figure 10.2: diversity and ordination and composition(Example)
# permanova
run_permanova(ps = metaphlan2_ps_remove_BRS_LOD_species,
method = "bray",
columns = "Group")## SumsOfSample Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) AdjustedPvalue
## Group 144 1 1.328635 1.328635 6.307557 0.04253025 0.001 0.001# beta dispersion
beta_df <- run_beta_diversity(ps = metaphlan2_ps_remove_BRS_LOD_species,
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 0.01652 0.016518 1.1697 999 0.282
## Residuals 142 2.00532 0.014122
##
## Pairwise comparisons:
## (Observed p-value below diagonal, permuted p-value above diagonal)
## Product Stool
## Product 0.281
## Stool 0.28129# ordination
metaphlan2_ps_ordination <- run_ordination(
ps = metaphlan2_ps_remove_BRS_LOD_species,
group = "Group",
method = "PCoA")
plot_Ordination(ResultList = metaphlan2_ps_ordination,
group = "Group",
group_names = c("Stool", "Product"),
group_color = c("blue", "red"))
Figure 10.3: diversity and ordination and composition(Example)
# Microbial composition
plot_stacked_bar_XIVZ(
phyloseq = metaphlan2_ps_remove_BRS_LOD_species,
level = "Phylum",
feature = "Group")
Figure 10.4: diversity and ordination and composition(Example)
10.8 Step6: Differential Analysis
metaphlan2_ps_remove_BRS_LOD_species <- readRDS("DataSet/Step4/Donor_MGS_phyloseq_remove_BRS_LOD_species.RDS")
# filter & trim
metaphlan2_ps_remove_BRS_species_filter <- run_filter(ps = metaphlan2_ps_remove_BRS_LOD_species,
cutoff = 1e-4,
unclass = TRUE)
metaphlan2_ps_remove_BRS_species_filter_trim <- run_trim(object = metaphlan2_ps_remove_BRS_species_filter,
cutoff = 0.1, trim = "feature")
metaphlan2_ps_remove_BRS_species_filter_trim## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 129 taxa and 144 samples ]
## sample_data() Sample Data: [ 144 samples by 12 sample variables ]
## tax_table() Taxonomy Table: [ 129 taxa by 7 taxonomic ranks ]# lefse
metaphlan2_ps_lefse <- run_lefse(
ps = metaphlan2_ps_remove_BRS_species_filter_trim,
group = "Group",
group_names = c("Stool", "Product"),
norm = "CPM",
Lda = 2)
# # don't run this code when you do lefse in reality
# metaphlan2_ps_lefse$LDA_Score <- metaphlan2_ps_lefse$LDA_Score * 1000
plot_lefse(
da_res = metaphlan2_ps_lefse,
x_index = "LDA_Score",
x_index_cutoff = 2,
group_color = c("green", "red"))
Figure 10.5: Differential Analysis (Example)
metaphlan2_ps_wilcox <- run_wilcox(
ps = metaphlan2_ps_remove_BRS_species_filter_trim,
group = "Group",
group_names = c("Stool", "Product"))
plot_volcano(
da_res = metaphlan2_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 10.6: Differential Analysis (Example)
if (!dir.exists("DataSet/Step8/")) {
dir.create("DataSet/Step8/")
}
saveRDS(metaphlan2_ps_remove_BRS_species_filter_trim, "DataSet/Step6/Donor_MGS_phyloseq_remove_BRS_species_filter_trim.RDS", compress = TRUE)10.9 Systematic Information
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.2 (2021-11-01)
## 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 2023-04-12
## rstudio 2022.07.2+576 Spotted Wakerobin (desktop)
## pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [1] CRAN (R 4.1.0)
## ade4 1.7-18 2021-09-16 [1] CRAN (R 4.1.0)
## annotate 1.72.0 2021-10-26 [1] Bioconductor
## AnnotationDbi 1.60.2 2023-03-10 [1] Bioconductor
## ape 5.6-2 2022-03-02 [1] CRAN (R 4.1.2)
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.0)
## backports 1.4.1 2021-12-13 [1] CRAN (R 4.1.0)
## base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.1.0)
## Biobase 2.54.0 2021-10-26 [1] Bioconductor
## BiocGenerics 0.40.0 2021-10-26 [1] Bioconductor
## BiocParallel 1.28.3 2021-12-09 [1] Bioconductor
## biomformat 1.22.0 2021-10-26 [1] Bioconductor
## Biostrings 2.62.0 2021-10-26 [1] Bioconductor
## bit 4.0.4 2020-08-04 [1] CRAN (R 4.1.0)
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## bitops 1.0-7 2021-04-24 [1] CRAN (R 4.1.0)
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## car 3.0-12 2021-11-06 [1] CRAN (R 4.1.0)
## carData 3.0-5 2022-01-06 [1] CRAN (R 4.1.2)
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## DelayedArray 0.20.0 2021-10-26 [1] Bioconductor
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## desc 1.4.1 2022-03-06 [1] CRAN (R 4.1.2)
## DESeq2 1.34.0 2021-10-26 [1] Bioconductor
## devtools 2.4.3 2021-11-30 [1] CRAN (R 4.1.0)
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## dplyr * 1.0.10 2022-09-01 [1] CRAN (R 4.1.2)
## DT 0.21 2022-02-26 [1] CRAN (R 4.1.2)
## dynamicTreeCut 1.63-1 2016-03-11 [1] CRAN (R 4.1.0)
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## fdrtool 1.2.17 2021-11-13 [1] CRAN (R 4.1.0)
## filematrix 1.3 2018-02-27 [1] CRAN (R 4.1.0)
## FNN 1.1.3 2019-02-15 [1] CRAN (R 4.1.0)
## foreach 1.5.2 2022-02-02 [1] CRAN (R 4.1.2)
## foreign 0.8-82 2022-01-13 [1] CRAN (R 4.1.2)
## forestplot 2.0.1 2021-09-03 [1] CRAN (R 4.1.0)
## Formula 1.2-4 2020-10-16 [1] CRAN (R 4.1.0)
## fs 1.5.2 2021-12-08 [1] CRAN (R 4.1.0)
## genefilter 1.76.0 2021-10-26 [1] Bioconductor
## geneplotter 1.72.0 2021-10-26 [1] Bioconductor
## generics 0.1.2 2022-01-31 [1] CRAN (R 4.1.2)
## GenomeInfoDb 1.30.1 2022-01-30 [1] Bioconductor
## GenomeInfoDbData 1.2.7 2022-03-09 [1] Bioconductor
## GenomicRanges 1.46.1 2021-11-18 [1] Bioconductor
## ggiraph 0.8.2 2022-02-22 [1] CRAN (R 4.1.2)
## ggiraphExtra 0.3.0 2020-10-06 [1] CRAN (R 4.1.2)
## ggplot2 * 3.4.0 2022-11-04 [1] CRAN (R 4.1.2)
## ggpubr * 0.4.0 2020-06-27 [1] CRAN (R 4.1.0)
## ggrepel 0.9.1 2021-01-15 [1] CRAN (R 4.1.0)
## ggsignif 0.6.3 2021-09-09 [1] CRAN (R 4.1.0)
## glasso 1.11 2019-10-01 [1] CRAN (R 4.1.0)
## glmnet 4.1-3 2021-11-02 [1] CRAN (R 4.1.0)
## glue * 1.6.2 2022-02-24 [1] CRAN (R 4.1.2)
## Gmisc * 3.0.0 2022-01-03 [1] CRAN (R 4.1.2)
## GO.db 3.14.0 2022-04-11 [1] Bioconductor
## gplots 3.1.1 2020-11-28 [1] CRAN (R 4.1.0)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.1.0)
## gtable 0.3.0 2019-03-25 [1] CRAN (R 4.1.0)
## gtools 3.9.2 2021-06-06 [1] CRAN (R 4.1.0)
## highr 0.9 2021-04-16 [1] CRAN (R 4.1.0)
## Hmisc 4.6-0 2021-10-07 [1] CRAN (R 4.1.0)
## htmlTable * 2.4.0 2022-01-04 [1] CRAN (R 4.1.2)
## htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.1.2)
## htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.1.0)
## httr 1.4.2 2020-07-20 [1] CRAN (R 4.1.0)
## huge 1.3.5 2021-06-30 [1] CRAN (R 4.1.0)
## igraph 1.2.11 2022-01-04 [1] CRAN (R 4.1.2)
## impute 1.68.0 2021-10-26 [1] Bioconductor
## insight 0.17.0 2022-03-29 [1] CRAN (R 4.1.2)
## IRanges 2.28.0 2021-10-26 [1] Bioconductor
## irlba 2.3.5 2021-12-06 [1] CRAN (R 4.1.0)
## iterators 1.0.14 2022-02-05 [1] CRAN (R 4.1.2)
## jpeg 0.1-9 2021-07-24 [1] CRAN (R 4.1.0)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.1.0)
## jsonlite 1.8.3 2022-10-21 [1] CRAN (R 4.1.2)
## KEGGREST 1.34.0 2021-10-26 [1] Bioconductor
## KernSmooth 2.23-20 2021-05-03 [1] CRAN (R 4.1.2)
## knitr 1.40 2022-08-24 [1] CRAN (R 4.1.2)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.1.0)
## lattice 0.20-45 2021-09-22 [1] CRAN (R 4.1.2)
## latticeExtra 0.6-29 2019-12-19 [1] CRAN (R 4.1.0)
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## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.1.2)
## limma 3.50.1 2022-02-17 [1] Bioconductor
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## lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.1.0)
## magrittr * 2.0.3 2022-03-30 [1] CRAN (R 4.1.2)
## MASS 7.3-55 2022-01-13 [1] CRAN (R 4.1.2)
## Matrix 1.4-0 2021-12-08 [1] CRAN (R 4.1.0)
## MatrixGenerics 1.6.0 2021-10-26 [1] Bioconductor
## matrixStats 0.61.0 2021-09-17 [1] CRAN (R 4.1.0)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.1.0)
## metagenomeSeq 1.36.0 2021-10-26 [1] Bioconductor
## mgcv 1.8-39 2022-02-24 [1] CRAN (R 4.1.2)
## mixedCCA 1.5.2 2022-07-14 [1] Github (irinagain/mixedCCA@c6d41a3)
## mnormt 2.0.2 2020-09-01 [1] CRAN (R 4.1.0)
## modeltools 0.2-23 2020-03-05 [1] CRAN (R 4.1.0)
## multcomp 1.4-18 2022-01-04 [1] CRAN (R 4.1.2)
## multtest 2.50.0 2021-10-26 [1] Bioconductor
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.0)
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## NADA 1.6-1.1 2020-03-22 [1] CRAN (R 4.1.0)
## NetCoMi * 1.0.3 2022-07-14 [1] Github (stefpeschel/NetCoMi@d4d80d3)
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## nnet 7.3-17 2022-01-13 [1] CRAN (R 4.1.2)
## pbapply 1.5-0 2021-09-16 [1] CRAN (R 4.1.0)
## pbivnorm 0.6.0 2015-01-23 [1] CRAN (R 4.1.0)
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## permute 0.9-7 2022-01-27 [1] CRAN (R 4.1.2)
## pheatmap 1.0.12 2019-01-04 [1] CRAN (R 4.1.0)
## phyloseq * 1.38.0 2021-10-26 [1] Bioconductor
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## pkgload 1.2.4 2021-11-30 [1] CRAN (R 4.1.0)
## plyr 1.8.6 2020-03-03 [1] CRAN (R 4.1.0)
## png 0.1-7 2013-12-03 [1] CRAN (R 4.1.0)
## ppcor 1.1 2015-12-03 [1] CRAN (R 4.1.0)
## preprocessCore 1.56.0 2021-10-26 [1] Bioconductor
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## processx 3.5.2 2021-04-30 [1] CRAN (R 4.1.0)
## proxy 0.4-26 2021-06-07 [1] CRAN (R 4.1.0)
## ps 1.6.0 2021-02-28 [1] CRAN (R 4.1.0)
## psych 2.2.5 2022-05-10 [1] CRAN (R 4.1.2)
## pulsar 0.3.7 2020-08-07 [1] CRAN (R 4.1.0)
## purrr 0.3.4 2020-04-17 [1] CRAN (R 4.1.0)
## qgraph 1.9.2 2022-03-04 [1] CRAN (R 4.1.2)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.0)
## rbibutils 2.2.7 2021-12-07 [1] CRAN (R 4.1.0)
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## Rcpp * 1.0.10 2023-01-22 [1] CRAN (R 4.1.2)
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## Rdpack 2.2 2022-03-19 [1] CRAN (R 4.1.2)
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## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.1.0)
## rhdf5 2.38.1 2022-03-10 [1] Bioconductor
## rhdf5filters 1.6.0 2021-10-26 [1] Bioconductor
## Rhdf5lib 1.16.0 2021-10-26 [1] Bioconductor
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## rmarkdown 2.17 2022-10-07 [1] CRAN (R 4.1.2)
## rootSolve 1.8.2.3 2021-09-29 [1] CRAN (R 4.1.0)
## rpart 4.1.16 2022-01-24 [1] CRAN (R 4.1.2)
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## RSQLite 2.2.10 2022-02-17 [1] CRAN (R 4.1.2)
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## S4Vectors 0.32.3 2021-11-21 [1] Bioconductor
## sandwich 3.0-1 2021-05-18 [1] CRAN (R 4.1.0)
## sass 0.4.2 2022-07-16 [1] CRAN (R 4.1.2)
## scales 1.2.1 2022-08-20 [1] CRAN (R 4.1.2)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.0)
## shape 1.4.6 2021-05-19 [1] CRAN (R 4.1.0)
## sjlabelled 1.2.0 2022-04-10 [1] CRAN (R 4.1.2)
## sjmisc 2.8.9 2021-12-03 [1] CRAN (R 4.1.0)
## snow 0.4-4 2021-10-27 [1] CRAN (R 4.1.0)
## SpiecEasi * 1.1.2 2022-07-14 [1] Github (zdk123/SpiecEasi@c463727)
## SPRING 1.0.4 2022-08-03 [1] Github (GraceYoon/SPRING@3d641a4)
## stringi 1.7.8 2022-07-11 [1] CRAN (R 4.1.2)
## stringr 1.4.1 2022-08-20 [1] CRAN (R 4.1.2)
## SummarizedExperiment 1.24.0 2021-10-26 [1] Bioconductor
## survival 3.4-0 2022-08-09 [1] CRAN (R 4.1.2)
## systemfonts 1.0.4 2022-02-11 [1] CRAN (R 4.1.2)
## testthat 3.1.2 2022-01-20 [1] CRAN (R 4.1.2)
## TH.data 1.1-0 2021-09-27 [1] CRAN (R 4.1.0)
## tibble * 3.1.8 2022-07-22 [1] CRAN (R 4.1.2)
## tidyr 1.2.0 2022-02-01 [1] CRAN (R 4.1.2)
## tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.1.2)
## tmvnsim 1.0-2 2016-12-15 [1] CRAN (R 4.1.0)
## truncnorm 1.0-8 2018-02-27 [1] CRAN (R 4.1.0)
## usethis 2.1.5 2021-12-09 [1] CRAN (R 4.1.0)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0)
## uuid 1.0-3 2021-11-01 [1] CRAN (R 4.1.0)
## vctrs 0.5.1 2022-11-16 [1] CRAN (R 4.1.2)
## vegan 2.5-7 2020-11-28 [1] CRAN (R 4.1.0)
## VGAM 1.1-6 2022-02-14 [1] CRAN (R 4.1.2)
## WGCNA 1.71 2022-04-22 [1] CRAN (R 4.1.2)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.2)
## Wrench 1.12.0 2021-10-26 [1] Bioconductor
## xfun 0.34 2022-10-18 [1] CRAN (R 4.1.2)
## XMAS2 * 2.1.8.7 2023-01-06 [1] local
## XML 3.99-0.9 2022-02-24 [1] CRAN (R 4.1.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.1.0)
## XVector 0.34.0 2021-10-26 [1] Bioconductor
## yaml 2.3.6 2022-10-18 [1] CRAN (R 4.1.2)
## zCompositions 1.4.0 2022-01-13 [1] CRAN (R 4.1.2)
## zlibbioc 1.40.0 2021-10-26 [1] Bioconductor
## zoo 1.8-9 2021-03-09 [1] CRAN (R 4.1.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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