- Beta-Diversity
- General Ordination Plot
- PERMANOVA
- Analysis by Week
- Analysis by Intervention
- Analysis by Intervention
Beta Diversity Analysis
Last updated: 2020-04-16
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Knit directory: Fiber_Intervention_Study/
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Beta-Diversity
General Ordination Plot
# Ordinate
pcoa <- ordinate(
physeq = phylo_data,
method = "PCoA",
distance = "bray"
)
# Plot
p <- plot_ordination(
physeq = phylo_data,
ordination = pcoa,
color = "Week",
shape = "Intervention",
title = "PCoA plot of community differences over time"
)
p +
geom_point(aes(color = Week), alpha = 0.7, size = 4) +
scale_color_manual(values = c("#a65628", "red", "#ffae19","#4daf4a")) +
geom_point(colour = "grey90", size = 1.5) 
PERMANOVA
Below, we present the permutation based ANOVA results for community differences. We conducted these analyses with three models
- Using Week
- Using Intervention
- Using Week × Intervention interaction
Compute the distances matrices and get ordination objects.
# Calculate distance matrices
dist_bray <- phyloseq::distance(phylo_data, method = "bray")
dist_unwt <- phyloseq::distance(phylo_data, method="unifrac", weighted=F)
dist_wt <- phyloseq::distance(phylo_data, method="unifrac", weighted=T)
# plot ordination
ord_bray = ordinate(phylo_data, method="PCoA", distance=dist_bray)
ord_unwt = ordinate(phylo_data, method="PCoA", distance=dist_unwt)
ord_wt = ordinate(phylo_data, method="PCoA", distance=dist_wt)
# sample data
df <- data.frame(sample_data(phylo_data))
# colors
cols <- c("#fe9700","#00a2f2", "#662a00", "#c91acb","grey60","#858c69", "#a8863a", "#737373", "#d43f1f", "#5dd047", "#ffff59")Analysis by Week
p1 <- plot_ordination(phylo_data, ord_bray, color="Week") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Bray-Curtis distance, by Week")
p2 <- plot_ordination(phylo_data, ord_unwt, color="Week") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Unweighted Unifrac distance, by Week")
p3 <- plot_ordination(phylo_data, ord_wt, color="Week") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Weight Unifrac distance, by Week")
p1 + p2 + p3 + plot_layout(ncol=1)
# Bray-Curtis
adonis(dist_bray ~ Week, data = df)
Call:
adonis(formula = dist_bray ~ Week, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Week 3 0.0734 0.024472 0.17948 0.01605 1
Residuals 33 4.4996 0.136350 0.98395
Total 36 4.5730 1.00000 beta <- betadisper(dist_bray, df$Week)
permutest(beta)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 3 0.01554 0.0051788 0.4485 999 0.721
Residuals 33 0.38103 0.0115463 # Unweighted Unifrac
adonis(dist_unwt ~ Week, data = df)
Call:
adonis(formula = dist_unwt ~ Week, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Week 3 0.07589 0.025297 0.30569 0.02704 1
Residuals 33 2.73090 0.082755 0.97296
Total 36 2.80679 1.00000 beta <- betadisper(dist_unwt, df$Week)
permutest(beta)
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 3 0.00130 0.0004334 0.1045 999 0.948
Residuals 33 0.13689 0.0041481 # Weighted Unifrac
adonis(dist_wt ~ Week, data = df)
Call:
adonis(formula = dist_wt ~ Week, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Week 3 0.01240 0.0041349 0.14031 0.0126 1
Residuals 33 0.97247 0.0294688 0.9874
Total 36 0.98487 1.0000 beta <- betadisper(dist_wt, df$Week)
permutest(beta)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 3 0.003706 0.0012353 0.18 999 0.92
Residuals 33 0.226414 0.0068610 Analysis by Intervention
p1 <- plot_ordination(phylo_data, ord_bray, color="Intervention") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Bray-Curtis distance, by Intervention")
p2 <- plot_ordination(phylo_data, ord_unwt, color="Intervention") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Unweighted Unifrac distance, by Intervention")
p3 <- plot_ordination(phylo_data, ord_wt, color="Intervention") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Weight Unifrac distance, by Intervention")
p1 + p2 + p3 + plot_layout(ncol=1)
# Bray-Curtis
adonis(dist_bray ~ Intervention, data = df)
Call:
adonis(formula = dist_bray ~ Intervention, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Intervention 1 0.3307 0.33065 2.7279 0.07231 0.018 *
Residuals 35 4.2423 0.12121 0.92769
Total 36 4.5730 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1beta <- betadisper(dist_bray, df$Intervention)
permutest(beta)
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 0.00152 0.001520 0.1361 999 0.704
Residuals 35 0.39088 0.011168 # Unweighted Unifrac
adonis(dist_unwt ~ Intervention, data = df)
Call:
adonis(formula = dist_unwt ~ Intervention, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Intervention 1 0.11464 0.114642 1.4904 0.04084 0.132
Residuals 35 2.69215 0.076919 0.95916
Total 36 2.80679 1.00000 beta <- betadisper(dist_unwt, df$Intervention)
permutest(beta)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.000033 0.0000330 0.0092 999 0.929
Residuals 35 0.125144 0.0035756 # Weighted Unifrac
adonis(dist_wt ~ Intervention, data = df)
Call:
adonis(formula = dist_wt ~ Intervention, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Intervention 1 0.06784 0.067841 2.5892 0.06888 0.071 .
Residuals 35 0.91703 0.026201 0.93112
Total 36 0.98487 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1beta <- betadisper(dist_wt, df$Intervention)
permutest(beta)
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 0.003775 0.0037751 0.6447 999 0.441
Residuals 35 0.204930 0.0058551 Analysis by Intervention
p1 <- plot_ordination(phylo_data, ord_bray,
color="Week", shape="Intervention") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
geom_point(colour = "grey90", size = 1.5) +
labs(title="PCoA on Bray-Curtis distance, by Week & Intervention")+
theme(legend.position = "none")
p2 <- plot_ordination(phylo_data, ord_unwt,
color="Week", shape="Intervention") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
geom_point(colour = "grey90", size = 1.5) +
labs(title="PCoA on Unweighted Unifrac distance, by Week & Intervention")+
theme(legend.position = "none")
p3 <- plot_ordination(phylo_data, ord_wt,
color="Week", shape="Intervention") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
geom_point(colour = "grey90", size = 1.5) +
labs(title="PCoA on Weight Unifrac distance, by Week & Intervention")+
theme(legend.position = "bottom")
p1 + p2 + p3 + plot_layout(ncol=1)
# Bray-Curtis
adonis(dist_bray ~ Intervention*Week, data = df)
Call:
adonis(formula = dist_bray ~ Intervention * Week, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Intervention 1 0.3307 0.33065 2.39689 0.07231 0.033 *
Week 3 0.0759 0.02529 0.18334 0.01659 1.000
Intervention:Week 3 0.1659 0.05529 0.40081 0.03627 0.997
Residuals 29 4.0006 0.13795 0.87483
Total 36 4.5730 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1beta <- betadisper(dist_bray, interaction(df$Intervention, df$Week))
permutest(beta)
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 7 0.02611 0.0037305 0.2873 999 0.959
Residuals 29 0.37661 0.0129867 # Unweighted Unifrac
adonis(dist_unwt ~ Intervention*Week, data = df)
Call:
adonis(formula = dist_unwt ~ Intervention * Week, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Intervention 1 0.11464 0.114642 1.32572 0.04084 0.215
Week 3 0.07498 0.024993 0.28901 0.02671 1.000
Intervention:Week 3 0.10939 0.036463 0.42166 0.03897 0.999
Residuals 29 2.50778 0.086475 0.89347
Total 36 2.80679 1.00000 beta <- betadisper(dist_unwt, interaction(df$Intervention, df$Week))
permutest(beta)
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 7 0.011228 0.0016039 0.3121 999 0.941
Residuals 29 0.149012 0.0051384 # Weighted Unifrac
adonis(dist_wt ~ Intervention*Week, data = df)
Call:
adonis(formula = dist_wt ~ Intervention * Week, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Intervention 1 0.06784 0.067841 2.29626 0.06888 0.092 .
Week 3 0.01314 0.004378 0.14820 0.01334 0.999
Intervention:Week 3 0.04712 0.015707 0.53163 0.04784 0.836
Residuals 29 0.85678 0.029544 0.86994
Total 36 0.98487 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1beta <- betadisper(dist_wt, interaction(df$Intervention, df$Week))
permutest(beta)
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 7 0.01848 0.0026401 0.3651 999 0.919
Residuals 29 0.20968 0.0072303
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] car_3.0-5 carData_3.0-3 gvlma_1.0.0.3 patchwork_1.0.0
[5] viridis_0.5.1 viridisLite_0.3.0 gridExtra_2.3 xtable_1.8-4
[9] kableExtra_1.1.0 plyr_1.8.4 data.table_1.12.6 readxl_1.3.1
[13] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
[17] readr_1.3.1 tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1
[21] tidyverse_1.3.0 lmerTest_3.1-1 lme4_1.1-21 Matrix_1.2-18
[25] vegan_2.5-6 lattice_0.20-38 permute_0.9-5 phyloseq_1.30.0
loaded via a namespace (and not attached):
[1] minqa_1.2.4 colorspace_1.4-1 rio_0.5.16
[4] rprojroot_1.3-2 XVector_0.26.0 fs_1.3.1
[7] rstudioapi_0.10 farver_2.0.1 lubridate_1.7.4
[10] xml2_1.2.2 codetools_0.2-16 splines_3.6.3
[13] knitr_1.26 zeallot_0.1.0 ade4_1.7-13
[16] jsonlite_1.6 workflowr_1.5.0 nloptr_1.2.1
[19] broom_0.5.2 cluster_2.1.0 dbplyr_1.4.2
[22] compiler_3.6.3 httr_1.4.1 backports_1.1.5
[25] assertthat_0.2.1 lazyeval_0.2.2 cli_1.1.0
[28] later_1.0.0 htmltools_0.4.0 tools_3.6.3
[31] igraph_1.2.4.2 gtable_0.3.0 glue_1.3.1
[34] reshape2_1.4.3 Rcpp_1.0.3 Biobase_2.46.0
[37] cellranger_1.1.0 vctrs_0.2.0 Biostrings_2.54.0
[40] multtest_2.42.0 ape_5.3 nlme_3.1-144
[43] iterators_1.0.12 xfun_0.11 openxlsx_4.1.3
[46] rvest_0.3.5 lifecycle_0.1.0 zlibbioc_1.32.0
[49] MASS_7.3-51.5 scales_1.1.0 hms_0.5.2
[52] promises_1.1.0 parallel_3.6.3 biomformat_1.14.0
[55] rhdf5_2.30.1 curl_4.3 yaml_2.2.0
[58] stringi_1.4.3 S4Vectors_0.24.1 foreach_1.4.7
[61] BiocGenerics_0.32.0 zip_2.0.4 boot_1.3-24
[64] rlang_0.4.2 pkgconfig_2.0.3 evaluate_0.14
[67] Rhdf5lib_1.8.0 labeling_0.3 tidyselect_0.2.5
[70] magrittr_1.5 R6_2.4.1 IRanges_2.20.1
[73] generics_0.0.2 DBI_1.0.0 foreign_0.8-75
[76] pillar_1.4.2 haven_2.2.0 withr_2.1.2
[79] mgcv_1.8-31 abind_1.4-5 survival_3.1-8
[82] modelr_0.1.5 crayon_1.3.4 rmarkdown_1.18
[85] grid_3.6.3 git2r_0.26.1 reprex_0.3.0
[88] digest_0.6.23 webshot_0.5.2 httpuv_1.5.2
[91] numDeriv_2016.8-1.1 stats4_3.6.3 munsell_0.5.0