This tutorial explore a phyloseq version of the dataset from Tengeler
et al. (2020) available in the mia package.
Load library
library("MicrobiotaProcess")
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) :
#> object 'type_sum.accel' not found
library("MiscMetabar")
library("ggplot2")
library("patchwork")
library("iNEXT")
?Tengeler2020
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.
#> ℹ 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
#> Permutation: free
#> Number of permutations: 999
#>
#> vegan::adonis2(formula = .formula, data = metadata)
#> Df SumOfSqs R2 F Pr(>F)
#> Model 3 1.2425 0.18483 1.7383 0.028 *
#> 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 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Debian GNU/Linux 12 (bookworm)
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
#>
#> 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] iNEXT_3.0.1 patchwork_1.3.0 MiscMetabar_0.12.0
#> [4] purrr_1.0.2 dplyr_1.1.4 dada2_1.34.0
#> [7] Rcpp_1.0.13-1 ggplot2_3.5.1 phyloseq_1.50.0
#> [10] MicrobiotaProcess_1.16.1
#>
#> loaded via a namespace (and not attached):
#> [1] libcoin_1.0-10 RColorBrewer_1.1-3
#> [3] shape_1.4.6.1 jsonlite_1.8.9
#> [5] magrittr_2.0.3 TH.data_1.1-2
#> [7] modeltools_0.2-23 farver_2.1.2
#> [9] rmarkdown_2.29 GlobalOptions_0.1.2
#> [11] fs_1.6.5 zlibbioc_1.50.0
#> [13] ragg_1.3.3 vctrs_0.6.5
#> [15] multtest_2.60.0 Rsamtools_2.20.0
#> [17] ggtree_3.12.0 htmltools_0.5.8.1
#> [19] S4Arrays_1.4.1 ComplexUpset_1.3.3
#> [21] Rhdf5lib_1.26.0 SparseArray_1.4.8
#> [23] rhdf5_2.48.0 gridGraphics_0.5-1
#> [25] sass_0.4.9 bslib_0.8.0
#> [27] htmlwidgets_1.6.4 desc_1.4.3
#> [29] plyr_1.8.9 sandwich_3.1-1
#> [31] zoo_1.8-12 cachem_1.1.0
#> [33] ggfittext_0.10.2 GenomicAlignments_1.40.0
#> [35] igraph_2.1.2 lifecycle_1.0.4
#> [37] iterators_1.0.14 pkgconfig_2.0.3
#> [39] Matrix_1.7-1 R6_2.5.1
#> [41] fastmap_1.2.0 GenomeInfoDbData_1.2.12
#> [43] MatrixGenerics_1.16.0 digest_0.6.37
#> [45] aplot_0.2.3 colorspace_2.1-1
#> [47] ggnewscale_0.5.0 ShortRead_1.62.0
#> [49] S4Vectors_0.42.1 DESeq2_1.44.0
#> [51] textshaping_0.4.1 GenomicRanges_1.56.2
#> [53] hwriter_1.3.2.1 vegan_2.6-8
#> [55] labeling_0.4.3 httr_1.4.7
#> [57] abind_1.4-8 mgcv_1.9-1
#> [59] compiler_4.4.2 withr_3.0.2
#> [61] BiocParallel_1.38.0 ggsignif_0.6.4
#> [63] MASS_7.3-61 DelayedArray_0.30.1
#> [65] biomformat_1.32.0 permute_0.9-7
#> [67] tools_4.4.2 ape_5.8-1
#> [69] glue_1.8.0 treemapify_2.5.6
#> [71] nlme_3.1-166 rhdf5filters_1.16.0
#> [73] grid_4.4.2 cluster_2.1.6
#> [75] reshape2_1.4.4 ade4_1.7-22
#> [77] generics_0.1.3 gtable_0.3.6
#> [79] tidyr_1.3.1 ggVennDiagram_1.5.2
#> [81] data.table_1.16.4 coin_1.4-3
#> [83] XVector_0.44.0 BiocGenerics_0.50.0
#> [85] ggrepel_0.9.6 foreach_1.5.2
#> [87] pillar_1.10.0 stringr_1.5.1
#> [89] yulab.utils_0.1.8 circlize_0.4.16
#> [91] splines_4.4.2 treeio_1.28.0
#> [93] lattice_0.22-6 survival_3.7-0
#> [95] deldir_2.0-4 tidyselect_1.2.1
#> [97] locfit_1.5-9.10 pbapply_1.7-2
#> [99] Biostrings_2.72.1 knitr_1.49
#> [101] gridExtra_2.3 IRanges_2.38.1
#> [103] SummarizedExperiment_1.34.0 ggtreeExtra_1.14.0
#> [105] stats4_4.4.2 xfun_0.49
#> [107] Biobase_2.64.0 matrixStats_1.4.1
#> [109] stringi_1.8.4 UCSC.utils_1.0.0
#> [111] lazyeval_0.2.2 ggfun_0.1.8
#> [113] yaml_2.3.10 evaluate_1.0.1
#> [115] codetools_0.2-20 interp_1.1-6
#> [117] tibble_3.2.1 ggplotify_0.1.2
#> [119] cli_3.6.3 RcppParallel_5.1.9
#> [121] systemfonts_1.1.0 munsell_0.5.1
#> [123] jquerylib_0.1.4 GenomeInfoDb_1.40.1
#> [125] png_0.1-8 parallel_4.4.2
#> [127] ggh4x_0.2.8 pkgdown_2.1.1
#> [129] latticeExtra_0.6-30 jpeg_0.1-10
#> [131] bitops_1.0-9 ggstar_1.0.4
#> [133] pwalign_1.0.0 mvtnorm_1.3-2
#> [135] tidytree_0.4.6 scales_1.3.0
#> [137] crayon_1.5.3 rlang_1.1.4
#> [139] multcomp_1.4-26References
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