Beta Diversity Analysis
Last updated: 2020-06-16
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Knit directory: Fiber_Intervention_Study/
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| Rmd | 2d97c53 | noah-padgett | 2020-04-16 | updated figure heights |
| html | 2d97c53 | noah-padgett | 2020-04-16 | updated figure heights |
| Rmd | 774a55f | noah-padgett | 2020-04-16 | updated beta diversity and permanova |
| html | 774a55f | noah-padgett | 2020-04-16 | updated beta diversity and permanova |
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 \(\times\) 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 0.0737 0.024579 0.18048 0.01614 1
Residuals 33 4.4941 0.136186 0.98386
Total 36 4.5679 1.00000 beta <- betadisper(dist_bray, df$Week)
permutest(beta)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 3 0.01379 0.0045955 0.3485 999 0.799
Residuals 33 0.43519 0.0131877 # Unweighted Unifrac
adonis(dist_unwt ~ Week, data = df)
Call:
adonis(formula = dist_unwt ~ Week, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Week 3 0.1176 0.039214 0.42432 0.03714 1
Residuals 33 3.0497 0.092415 0.96286
Total 36 3.1674 1.00000 beta <- betadisper(dist_unwt, df$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 0.002658 0.0008861 0.1983 999 0.893
Residuals 33 0.147440 0.0044679 # 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 0.01289 0.0042983 0.14493 0.013 1
Residuals 33 0.97871 0.0296578 0.987
Total 36 0.99160 1.000 beta <- betadisper(dist_wt, df$Week)
permutest(beta)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 3 0.003299 0.0010998 0.1571 999 0.922
Residuals 33 0.231081 0.0070025 Analysis by Intervention
p1 <- plot_ordination(phylo_data, ord_bray, color="Intervention") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Bray-Curtis distance, by Intervention")
p2 <- plot_ordination(phylo_data, ord_unwt, color="Intervention") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Unweighted Unifrac distance, by Intervention")
p3 <- plot_ordination(phylo_data, ord_wt, color="Intervention") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Weight Unifrac distance, by Intervention")
p1 + p2 + p3 + plot_layout(ncol=1)
# Bray-Curtis
adonis(dist_bray ~ Intervention, data = df)
Call:
adonis(formula = dist_bray ~ Intervention, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Intervention 1 0.3446 0.34458 2.8556 0.07544 0.015 *
Residuals 35 4.2233 0.12067 0.92456
Total 36 4.5679 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1beta <- betadisper(dist_bray, df$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 0.00598 0.0059801 0.5103 999 0.442
Residuals 35 0.41017 0.0117192 # 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 0.1786 0.178624 2.0918 0.0564 0.025 *
Residuals 35 2.9887 0.085392 0.9436
Total 36 3.1674 1.0000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1beta <- betadisper(dist_unwt, df$Intervention)
permutest(beta)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.002663 0.0026630 0.7857 999 0.404
Residuals 35 0.118624 0.0033893 # Weighted Unifrac
adonis(dist_wt ~ Intervention, data = df)
Call:
adonis(formula = dist_wt ~ Intervention, data = df)
Permutation: free
Number of permutations: 999
Terms added sequentially (first to last)
Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
Intervention 1 0.06784 0.067841 2.5704 0.06842 0.068 .
Residuals 35 0.92376 0.026393 0.93158
Total 36 0.99160 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1beta <- betadisper(dist_wt, df$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 0.003642 0.0036417 0.6068 999 0.447
Residuals 35 0.210051 0.0060015 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 0.3446 0.34458 2.50480 0.07544 0.027 *
Week 3 0.0757 0.02523 0.18336 0.01657 1.000
Intervention:Week 3 0.1582 0.05272 0.38324 0.03463 0.996
Residuals 29 3.9895 0.13757 0.87337
Total 36 4.5679 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 0.02637 0.0037678 0.2664 999 0.972
Residuals 29 0.41020 0.0141449 # 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 0.1786 0.178624 1.86888 0.05640 0.046 *
Week 3 0.1121 0.037379 0.39108 0.03540 0.999
Intervention:Week 3 0.1048 0.034937 0.36553 0.03309 1.000
Residuals 29 2.7718 0.095579 0.87511
Total 36 3.1674 1.00000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1beta <- 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 0.021912 0.0031302 0.6816 999 0.708
Residuals 29 0.133173 0.0045922 # 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 2.28504 0.06842 0.098 .
Week 3 0.01366 0.004553 0.15337 0.01378 0.999
Intervention:Week 3 0.04912 0.016372 0.55144 0.04953 0.846
Residuals 29 0.86099 0.029689 0.86828
Total 36 0.99160 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 0.018049 0.0025784 0.3502 999 0.918
Residuals 29 0.213534 0.0073632 Beta diversity plot (Figure 2)
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_wt, color="Week") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Weight Unifrac distance, by Week")
dat.bray <- p1$data
dat.wt <- p2$data
so <- distinct(microbiome_data$meta.dat, SubjectID, .keep_all = T)
subjectorder <- so$SubjectID[order(so$Intervention, decreasing = F)]
dat.bray$SubjectID10 <- factor(dat.bray$SubjectID,
levels = subjectorder,
labels=c(1:11))
dat.wt$SubjectID10 <- factor(dat.wt$SubjectID,
levels = subjectorder,
labels=c(1:11))
ids <- 1:6
dat.bray$Intervention2 <- ifelse(dat.bray$SubjectID10 %in% ids, "Group A", "Group B")
dat.wt$Intervention2 <- ifelse(dat.wt$SubjectID10 %in% ids, "Group A", "Group B")
p1 <- ggplot(dat.wt, aes(x=Week, y=Axis.1, color=Intervention2)) +
geom_point(size=3)+
facet_wrap(.~SubjectID10,ncol=6) +
scale_color_manual(values=cols)+
labs(y="PC1",
title="Individualized beta-diversity over study duration")+
guides(colour= guide_legend(title = "Intervention"))+
theme(panel.grid = element_blank(),
strip.text.x = element_text(angle = 0, size = 11, face = "italic"),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 10),
plot.title = element_text(hjust = 0.5),
#axis.title.x = element_blank(),
strip.background = element_blank(),
legend.position = c(0.92, 0.25),
#legend.text = element_text(size = 7),
#legend.title = element_blank(),
panel.spacing.x=unit(0.001, "lines"))
p1
library(cowplot)
#p1.leg <- get_legend(p1)
#p1.nl <- p1 + theme(legend.position = "none")
ggsave("fig/figure2.pdf", p1, width=7.9,height=4.5, units="in")
#bigplotlegend <- plot_grid(p1.leg, nrow =1)
#save_plot("fig/figure2_legend.pdf", bigplotlegend,
# base_width = 7.9, base_height = 4.5)Beta diversity plot (Figure 3)
p1 <- plot_ordination(phylo_data, ord_bray,
color="Intervention", shape="Week") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Bray-Curtis distance",
tag="A")+
theme(panel.grid = element_blank(),
axis.title = element_text(size = 10),
plot.title = element_text(hjust = 0.5))
p2 <- plot_ordination(phylo_data, ord_unwt,
color="Intervention", shape="Week") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Unweighted Unifrac distance",
tag="B")+
theme(panel.grid = element_blank(),
axis.title = element_text(size = 10),
plot.title = element_text(hjust = 0.5))
p3 <- plot_ordination(phylo_data, ord_wt,
color="Intervention", shape="Week") +
geom_point(size=3) +
scale_colour_manual(values=cols)+
labs(title="PCoA on Weight Unifrac distance",
tag="C")+
theme(panel.grid = element_blank(),
axis.title = element_text(size = 10),
plot.title = element_text(hjust = 0.5))
library(cowplot)
p1.leg <- get_legend(p1)
p1.nl <- p1 + theme(legend.position = "none")
p2.nl <- p2 + theme(legend.position = "none")
p3.nl <- p3 + theme(legend.position = "none")
#p <- p1.nl + p2.nl + p3.nl + plot_layout(ncol=1, guides="collect")
p <- p1 + p2 + p3 + plot_layout(ncol=1, guides="collect")
p
ggsave("fig/figure3.pdf", p, width=5,height=7, units="in")
#bigplotlegend <- plot_grid(p1.leg, nrow =1)
#save_plot("fig/figure3_legend.pdf", bigplotlegend,
# base_width = 7.9, base_height = 4.5)
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] cowplot_1.0.0 microbiome_1.8.0 car_3.0-8 carData_3.0-4
[5] gvlma_1.0.0.3 patchwork_1.0.0 viridis_0.5.1 viridisLite_0.3.0
[9] gridExtra_2.3 xtable_1.8-4 kableExtra_1.1.0 plyr_1.8.6
[13] data.table_1.12.8 readxl_1.3.1 forcats_0.5.0 stringr_1.4.0
[17] dplyr_0.8.5 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[21] tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0 lmerTest_3.1-2
[25] lme4_1.1-23 Matrix_1.2-18 vegan_2.5-6 lattice_0.20-38
[29] permute_0.9-5 phyloseq_1.30.0
loaded via a namespace (and not attached):
[1] Rtsne_0.15 minqa_1.2.4 colorspace_1.4-1
[4] rio_0.5.16 ellipsis_0.3.1 rprojroot_1.3-2
[7] XVector_0.26.0 fs_1.4.1 rstudioapi_0.11
[10] farver_2.0.3 fansi_0.4.1 lubridate_1.7.8
[13] xml2_1.3.2 codetools_0.2-16 splines_3.6.3
[16] knitr_1.28 ade4_1.7-15 jsonlite_1.6.1
[19] workflowr_1.6.2 nloptr_1.2.2.1 broom_0.5.6
[22] cluster_2.1.0 dbplyr_1.4.4 BiocManager_1.30.10
[25] compiler_3.6.3 httr_1.4.1 backports_1.1.7
[28] assertthat_0.2.1 cli_2.0.2 later_1.0.0
[31] htmltools_0.4.0 tools_3.6.3 igraph_1.2.5
[34] gtable_0.3.0 glue_1.4.1 reshape2_1.4.4
[37] Rcpp_1.0.4.6 Biobase_2.46.0 cellranger_1.1.0
[40] vctrs_0.3.0 Biostrings_2.54.0 multtest_2.42.0
[43] ape_5.3 nlme_3.1-144 iterators_1.0.12
[46] xfun_0.14 openxlsx_4.1.5 rvest_0.3.5
[49] lifecycle_0.2.0 statmod_1.4.34 zlibbioc_1.32.0
[52] MASS_7.3-51.5 scales_1.1.1 hms_0.5.3
[55] promises_1.1.0 parallel_3.6.3 biomformat_1.14.0
[58] rhdf5_2.30.1 curl_4.3 yaml_2.2.1
[61] stringi_1.4.6 S4Vectors_0.24.4 foreach_1.5.0
[64] BiocGenerics_0.32.0 zip_2.0.4 boot_1.3-24
[67] rlang_0.4.6 pkgconfig_2.0.3 evaluate_0.14
[70] Rhdf5lib_1.8.0 labeling_0.3 tidyselect_1.1.0
[73] magrittr_1.5 R6_2.4.1 IRanges_2.20.2
[76] generics_0.0.2 DBI_1.1.0 foreign_0.8-75
[79] pillar_1.4.4 haven_2.3.0 whisker_0.4
[82] withr_2.2.0 mgcv_1.8-31 abind_1.4-5
[85] survival_3.1-8 modelr_0.1.8 crayon_1.3.4
[88] rmarkdown_2.1 grid_3.6.3 blob_1.2.1
[91] git2r_0.27.1 reprex_0.3.0 digest_0.6.25
[94] webshot_0.5.2 httpuv_1.5.2 numDeriv_2016.8-1.1
[97] stats4_3.6.3 munsell_0.5.0