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This tutorial explore a phyloseq version of the dataset from Tengeler et al. (2020) available in the mia package.

Import dataset in phyloseq format

data(Tengeler2020_pq)
ten <- Tengeler2020_pq
summary_plot_pq(ten)

Alpha-diversity analysis

hill_pq(ten, "patient_status", one_plot = TRUE)

res_inext <-
  iNEXT_pq(ten,
    datatype = "abundance",
    merge_sample_by = "patient_status_vs_cohort",
    nboot = 5
  )
ggiNEXT(res_inext)

accu_plot(
  ten,
  fact = "sample_name",
  add_nb_seq = TRUE,
  by.fact = TRUE,
  step = 100
) + theme(legend.position = c(.8, .6))
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 9
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 17
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 25
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 7
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 8
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 6
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 14
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 28
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 3
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 7
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 2
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 15
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 5
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 5
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 3
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 6
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 3
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 4
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 6
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 3
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 3
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 21
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 5
#> Warning in vegan::rarefy(as.matrix(unclass(x[i, ])), n, se = TRUE): most
#> observed count data have counts 1, but smallest count is 13
#> Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
#> 3.5.0.
#>  Please use the `legend.position.inside` argument of `theme()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).

Explore taxonomy

library(metacoder)
heat_tree_pq(ten,
  node_size = n_obs,
  node_color = nb_sequences,
  node_label = taxon_names,
  tree_label = taxon_names,
  node_size_trans = "log10 area"
)

treemap_pq(ten, lvl1 = "Order", lvl2 = "Family")

Beta-diversity analysis : effect of patient status and cohort

circle_pq(ten, "patient_status")

upset_pq(ten, "patient_status_vs_cohort")

ggvenn_pq(clean_pq(ten, force_taxa_as_columns = TRUE),
  "cohort",
  rarefy_before_merging = TRUE
) +
  theme(legend.position = "none")

ten_control <- clean_pq(subset_samples(ten, patient_status == "Control"))
p_control <- heat_tree_pq(ten_control,
  node_size = n_obs,
  node_color = nb_sequences,
  node_label = taxon_names,
  tree_label = taxon_names,
  node_size_trans = "log10 area"
)

ten_ADHD <- clean_pq(subset_samples(ten, patient_status == "ADHD"))
p_ADHD <- heat_tree_pq(ten_ADHD,
  node_size = n_obs,
  node_color = nb_sequences,
  node_label = taxon_names,
  tree_label = taxon_names,
  node_size_trans = "log10 area"
)

p_control + ggtitle("Control") + p_ADHD + ggtitle("ADHD")

knitr::kable(track_wkflow(list(
  "All samples" = ten,
  "Control samples" = ten_control,
  "ADHD samples" = ten_ADHD
)))
nb_sequences nb_clusters nb_samples
All samples 485932 151 27
Control samples 239329 130 14
ADHD samples 246603 142 13
adonis_pq(ten, "cohort + patient_status")
#> Permutation test for adonis under reduced model
#> Terms added sequentially (first to last)
#> Permutation: free
#> Number of permutations: 999
#> 
#> vegan::adonis2(formula = .formula, data = metadata)
#>                Df SumOfSqs      R2      F Pr(>F)  
#> cohort          2   0.7922 0.11785 1.6626  0.059 .
#> patient_status  1   0.4503 0.06698 1.8898  0.078 .
#> Residual       23   5.4799 0.81517                
#> Total          26   6.7223 1.00000                
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ten@tax_table <- phyloseq::tax_table(cbind(
  ten@tax_table,
  "Species" = taxa_names(ten)
))

biplot_pq(subset_taxa_pq(ten, taxa_sums(ten) > 3000),
  merge_sample_by = "patient_status",
  fact = "patient_status",
  nudge_y = 0.4
)

multitax_bar_pq(ten, "Phylum", "Class", "Order", "patient_status")

multitax_bar_pq(ten, "Phylum", "Class", "Order", "patient_status",
  nb_seq = FALSE, log10trans = FALSE
)

Differential abundance analysis

plot_deseq2_pq(ten,
  contrast = c("patient_status", "ADHD", "Control"),
  taxolev = "Genus"
)
#> Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
#> design formula are characters, converting to factors

LEfSe <- diff_analysis(
  ten,
  classgroup = "patient_status",
  mlfun = "lda",
  ldascore = 2,
  p.adjust.methods = "bh"
)
library(ggplot2)
ggeffectsize(LEfSe) +
  scale_color_manual(values = c(
    "#00AED7",
    "#FD9347"
  )) +
  theme_bw()

Session information

sessionInfo()
#> R version 4.3.3 (2024-02-29)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Debian GNU/Linux 11 (bullseye)
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.13.so;  LAPACK version 3.9.0
#> 
#> locale:
#>  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
#>  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
#>  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Europe/Paris
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] metacoder_0.3.7          iNEXT_3.0.1              patchwork_1.2.0         
#>  [4] MiscMetabar_0.9.1        purrr_1.0.2              dplyr_1.1.4             
#>  [7] dada2_1.30.0             Rcpp_1.0.12              ggplot2_3.5.0           
#> [10] phyloseq_1.46.0          MicrobiotaProcess_1.14.1
#> 
#> loaded via a namespace (and not attached):
#>   [1] libcoin_1.0-10              RColorBrewer_1.1-3         
#>   [3] shape_1.4.6.1               rstudioapi_0.16.0          
#>   [5] jsonlite_1.8.8              magrittr_2.0.3             
#>   [7] TH.data_1.1-2               modeltools_0.2-23          
#>   [9] farver_2.1.1                rmarkdown_2.26             
#>  [11] GlobalOptions_0.1.2         fs_1.6.3                   
#>  [13] zlibbioc_1.48.2             ragg_1.3.0                 
#>  [15] vctrs_0.6.5                 multtest_2.58.0            
#>  [17] memoise_2.0.1               Rsamtools_2.18.0           
#>  [19] RCurl_1.98-1.14             ggtree_3.10.1              
#>  [21] htmltools_0.5.8.1           S4Arrays_1.2.1             
#>  [23] ComplexUpset_1.3.3          Rhdf5lib_1.24.2            
#>  [25] SparseArray_1.2.4           rhdf5_2.46.1               
#>  [27] gridGraphics_0.5-1          sass_0.4.9                 
#>  [29] bslib_0.7.0                 desc_1.4.3                 
#>  [31] plyr_1.8.9                  sandwich_3.1-0             
#>  [33] zoo_1.8-12                  cachem_1.0.8               
#>  [35] ggfittext_0.10.2            GenomicAlignments_1.38.2   
#>  [37] igraph_2.0.3                lifecycle_1.0.4            
#>  [39] iterators_1.0.14            pkgconfig_2.0.3            
#>  [41] Matrix_1.6-5                R6_2.5.1                   
#>  [43] fastmap_1.1.1               GenomeInfoDbData_1.2.11    
#>  [45] MatrixGenerics_1.14.0       digest_0.6.35              
#>  [47] aplot_0.2.2                 colorspace_2.1-0           
#>  [49] ggnewscale_0.4.10           ShortRead_1.60.0           
#>  [51] S4Vectors_0.40.2            DESeq2_1.42.1              
#>  [53] textshaping_0.3.7           GenomicRanges_1.54.1       
#>  [55] hwriter_1.3.2.1             vegan_2.6-4                
#>  [57] labeling_0.4.3              fansi_1.0.6                
#>  [59] abind_1.4-5                 mgcv_1.9-1                 
#>  [61] compiler_4.3.3              withr_3.0.0                
#>  [63] BiocParallel_1.36.0         highr_0.10                 
#>  [65] ggsignif_0.6.4              MASS_7.3-60.0.1            
#>  [67] DelayedArray_0.28.0         biomformat_1.30.0          
#>  [69] permute_0.9-7               tools_4.3.3                
#>  [71] ape_5.8                     glue_1.7.0                 
#>  [73] treemapify_2.5.6            nlme_3.1-164               
#>  [75] rhdf5filters_1.14.1         grid_4.3.3                 
#>  [77] cluster_2.1.6               reshape2_1.4.4             
#>  [79] ade4_1.7-22                 generics_0.1.3             
#>  [81] gtable_0.3.4                tidyr_1.3.1                
#>  [83] ggVennDiagram_1.5.2         data.table_1.15.4          
#>  [85] coin_1.4-3                  utf8_1.2.4                 
#>  [87] XVector_0.42.0              BiocGenerics_0.48.1        
#>  [89] ggrepel_0.9.5               foreach_1.5.2              
#>  [91] pillar_1.9.0                stringr_1.5.1              
#>  [93] yulab.utils_0.1.4           circlize_0.4.16            
#>  [95] splines_4.3.3               treeio_1.26.0              
#>  [97] lattice_0.22-6              deldir_2.0-4               
#>  [99] survival_3.5-8              tidyselect_1.2.1           
#> [101] locfit_1.5-9.9              pbapply_1.7-2              
#> [103] Biostrings_2.70.3           knitr_1.46                 
#> [105] gridExtra_2.3               IRanges_2.36.0             
#> [107] SummarizedExperiment_1.32.0 ggtreeExtra_1.12.0         
#> [109] stats4_4.3.3                xfun_0.43                  
#> [111] Biobase_2.62.0              matrixStats_1.3.0          
#> [113] stringi_1.8.3               lazyeval_0.2.2             
#> [115] ggfun_0.1.4                 yaml_2.3.8                 
#> [117] evaluate_0.23               codetools_0.2-19           
#> [119] interp_1.1-6                tibble_3.2.1               
#> [121] ggplotify_0.1.2             cli_3.6.2                  
#> [123] RcppParallel_5.1.7          systemfonts_1.0.6          
#> [125] munsell_0.5.1               jquerylib_0.1.4            
#> [127] GenomeInfoDb_1.38.8         png_0.1-8                  
#> [129] parallel_4.3.3              ggh4x_0.2.8                
#> [131] pkgdown_2.0.7               latticeExtra_0.6-30        
#> [133] jpeg_0.1-10                 bitops_1.0-7               
#> [135] ggstar_1.0.4                mvtnorm_1.2-4              
#> [137] tidytree_0.4.6              GA_3.2.4                   
#> [139] scales_1.3.0                crayon_1.5.2               
#> [141] rlang_1.1.3                 multcomp_1.4-25

References

Tengeler, A.C., Dam, S.A., Wiesmann, M. et al. Gut microbiota from persons with attention-deficit/hyperactivity disorder affects the brain in mice. Microbiome 8, 44 (2020). https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-020-00816-x