Genus level microbiome: Prevotella and Bacteroides
Last updated: 2020-06-17
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Knit directory: Fiber_Intervention_Study/
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Calculate Prevotella/Bacteroides ratio for all samples Calculate the change in P/B ratio for all samples (line plot before v after by group) Determine if there is a significant change over time in the P/B ratio and if the intervention had an effect
Ratio of Prevotella to Bacteroides
# genera to keep:
keepGenus <- c("__Bacteroides", "__Prevotella", "__Prevotella_2", "__Prevotella_6")
mphyseq = psmelt(phylo_data)
mphyseq2 <- mphyseq %>%
filter(Genus %in% keepGenus) %>%
mutate(Genus = substring(Genus, 3))
# subset to smaller dataset for easier use
varKeep <- c("Genus", "Abundance", "SubjectID", "Week", "Ethnicity", "Age", "Gender","Intervention")
mphyseq2 <- mphyseq2[, varKeep]
# make wide format wrt genus abundance
mphyseq2 <- mphyseq2 %>%
pivot_wider(names_from = "Genus",
values_from = "Abundance")
# compute ratio
mphyseq2 <- mphyseq2 %>%
mutate(Prev_Bact_ratio = Prevotella/Bacteroides,
Prev2_Bact_ratio = Prevotella_2/Bacteroides,
Prev6_Bact_ratio = Prevotella_6/Bacteroides)
mphyseq2 %>%
dplyr::group_by(Week)%>%
dplyr::summarise(Prev_Bact_ratio = mean(Prev_Bact_ratio),
Prev2_Bact_ratio = mean(Prev2_Bact_ratio),
Prev6_Bact_ratio = mean(Prev6_Bact_ratio))# A tibble: 4 x 4
Week Prev_Bact_ratio Prev2_Bact_ratio Prev6_Bact_ratio
<fct> <dbl> <dbl> <dbl>
1 1 0.00148 0.0830 0.000494
2 4 0.000441 0.0379 0.0000358
3 8 0.00191 0.0459 0.00171
4 12 0.00231 0.0687 0.00222 mphyseq2 %>%
dplyr::group_by(Week, Intervention)%>%
dplyr::summarise(Prev_Bact_ratio = mean(Prev_Bact_ratio),
Prev2_Bact_ratio = mean(Prev2_Bact_ratio),
Prev6_Bact_ratio = mean(Prev6_Bact_ratio))# A tibble: 8 x 5
# Groups: Week [4]
Week Intervention Prev_Bact_ratio Prev2_Bact_ratio Prev6_Bact_ratio
<fct> <chr> <dbl> <dbl> <dbl>
1 1 A 0 0.152 0
2 1 B 0.00326 0 0.00109
3 4 A 0.000437 0.0632 0
4 4 B 0.000447 0 0.0000895
5 8 A 0 0.107 0
6 8 B 0.00333 0 0.00299
7 12 A 0 0.124 0
8 12 B 0.00521 0 0.00500 keepVar <- c("SubjectID", "Week", "Intervention", "Prevotella", "Prevotella_2", "Prevotella_6", "Bacteroides", "Prev_Bact_ratio", "Prev2_Bact_ratio", "Prev6_Bact_ratio")
kable(mphyseq2[,keepVar], format="html", digits=3,
caption="Raw data of Prevotella* and Bacteroides") %>%
kable_styling(full_width = T) %>%
scroll_box(width="100%", height="5in")| SubjectID | Week | Intervention | Prevotella | Prevotella_2 | Prevotella_6 | Bacteroides | Prev_Bact_ratio | Prev2_Bact_ratio | Prev6_Bact_ratio |
|---|---|---|---|---|---|---|---|---|---|
| 1013 | 1 | A | 0 | 0 | 0 | 5016 | 0.000 | 0.000 | 0.000 |
| 1002 | 8 | B | 0 | 0 | 0 | 4701 | 0.000 | 0.000 | 0.000 |
| 1015 | 8 | B | 0 | 0 | 0 | 3910 | 0.000 | 0.000 | 0.000 |
| 1013 | 4 | A | 0 | 0 | 0 | 3793 | 0.000 | 0.000 | 0.000 |
| 1002 | 12 | B | 0 | 0 | 0 | 3667 | 0.000 | 0.000 | 0.000 |
| 1001 | 4 | A | 3 | 0 | 0 | 3469 | 0.001 | 0.000 | 0.000 |
| 1009 | 12 | A | 0 | 0 | 0 | 2954 | 0.000 | 0.000 | 0.000 |
| 1015 | 12 | B | 1 | 0 | 4 | 2927 | 0.000 | 0.000 | 0.001 |
| 1009 | 1 | A | 0 | 0 | 0 | 2923 | 0.000 | 0.000 | 0.000 |
| 1009 | 8 | A | 0 | 0 | 0 | 2918 | 0.000 | 0.000 | 0.000 |
| 1015 | 1 | B | 0 | 0 | 0 | 2853 | 0.000 | 0.000 | 0.000 |
| 1013 | 12 | A | 0 | 0 | 0 | 2849 | 0.000 | 0.000 | 0.000 |
| 1015 | 4 | B | 5 | 0 | 1 | 2794 | 0.002 | 0.000 | 0.000 |
| 1008 | 4 | A | 0 | 0 | 0 | 2686 | 0.000 | 0.000 | 0.000 |
| 1007 | 1 | B | 0 | 0 | 0 | 2662 | 0.000 | 0.000 | 0.000 |
| 1005 | 4 | A | 0 | 954 | 0 | 2614 | 0.000 | 0.365 | 0.000 |
| 1005 | 1 | A | 0 | 2271 | 0 | 2486 | 0.000 | 0.914 | 0.000 |
| 1002 | 4 | B | 0 | 0 | 0 | 2470 | 0.000 | 0.000 | 0.000 |
| 1002 | 1 | B | 0 | 0 | 0 | 2464 | 0.000 | 0.000 | 0.000 |
| 1009 | 4 | A | 0 | 0 | 0 | 2458 | 0.000 | 0.000 | 0.000 |
| 1008 | 12 | A | 0 | 0 | 0 | 2453 | 0.000 | 0.000 | 0.000 |
| 1008 | 1 | A | 0 | 0 | 0 | 2330 | 0.000 | 0.000 | 0.000 |
| 1007 | 4 | B | 0 | 0 | 0 | 2314 | 0.000 | 0.000 | 0.000 |
| 1007 | 12 | B | 0 | 0 | 0 | 2310 | 0.000 | 0.000 | 0.000 |
| 1001 | 1 | A | 0 | 0 | 0 | 2249 | 0.000 | 0.000 | 0.000 |
| 1007 | 8 | B | 1 | 0 | 2 | 2243 | 0.000 | 0.000 | 0.001 |
| 1005 | 12 | A | 0 | 1237 | 0 | 2046 | 0.000 | 0.605 | 0.000 |
| 1008 | 8 | A | 0 | 0 | 0 | 1723 | 0.000 | 0.000 | 0.000 |
| 1003 | 8 | A | 0 | 509 | 0 | 1585 | 0.000 | 0.321 | 0.000 |
| 1012 | 1 | B | 0 | 0 | 0 | 1351 | 0.000 | 0.000 | 0.000 |
| 1003 | 1 | A | 0 | 0 | 0 | 1174 | 0.000 | 0.000 | 0.000 |
| 1003 | 4 | A | 2 | 16 | 0 | 1138 | 0.002 | 0.014 | 0.000 |
| 1003 | 12 | A | 0 | 12 | 0 | 859 | 0.000 | 0.014 | 0.000 |
| 1010 | 1 | B | 9 | 0 | 3 | 552 | 0.016 | 0.000 | 0.005 |
| 1010 | 8 | B | 7 | 0 | 6 | 543 | 0.013 | 0.000 | 0.011 |
| 1010 | 12 | B | 11 | 0 | 10 | 537 | 0.020 | 0.000 | 0.019 |
| 1010 | 4 | B | 0 | 0 | 0 | 381 | 0.000 | 0.000 | 0.000 |
Line Plot of Ratio
# recode
plot_data <- mphyseq2
so <- distinct(mphyseq2, SubjectID, .keep_all = T)
subjectorder <- so$SubjectID[order(so$Intervention, decreasing = F)]
plot_data$SubjectID10 <- factor(plot_data$SubjectID,
levels = subjectorder,
labels=c(1:11))
ids <- 1:6
plot_data$Intervention2 <- ifelse(plot_data$SubjectID10 %in% ids, "Group A", "Group B")
# plot
p <- ggplot(plot_data, aes(x=Week, y=Prev_Bact_ratio, group=SubjectID10, color=Intervention))+
geom_line()+
geom_point()+
facet_wrap(.~SubjectID10, ncol=6)+
labs(x="Week", y= "Ratio",
title="Prevotella / Bacteroides Ratio across subjects")+
guides(colour= guide_legend(title = "Intervention"))+
scale_colour_manual(values = cols)+
theme(panel.grid = element_blank(),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 10),
legend.position = c(0.95, 0.1))
pgeom_path: Each group consists of only one observation. Do you need to adjust
the group aesthetic?
# plot
p <- ggplot(plot_data, aes(x=Week, y=Prev2_Bact_ratio, group=SubjectID10, color=Intervention))+
geom_line()+
geom_point()+
facet_wrap(.~SubjectID10, ncol=6)+
labs(x="Week", y= "Ratio",
title="Prevotella_2 / Bacteroides Ratio across subjects")+
guides(colour= guide_legend(title = "Intervention"))+
scale_colour_manual(values = cols)+
theme(panel.grid = element_blank(),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 10),
legend.position = c(0.95, 0.1))
pgeom_path: Each group consists of only one observation. Do you need to adjust
the group aesthetic?
p <- ggplot(plot_data, aes(x=Week, y=Prev6_Bact_ratio, group=SubjectID10, color=Intervention))+
geom_line()+
geom_point()+
facet_wrap(.~SubjectID10, ncol=6)+
labs(x="Week", y= "Ratio",
title="Prevotella_6 / Bacteroides Ratio across subjects")+
guides(colour= guide_legend(title = "Intervention"))+
scale_colour_manual(values = cols)+
theme(panel.grid = element_blank(),
axis.text.y = element_text(size = 10),
axis.title = element_text(size = 10),
legend.position = c(0.95, 0.1))
pgeom_path: Each group consists of only one observation. Do you need to adjust
the group aesthetic?
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 highr_0.8 S4Vectors_0.24.4
[64] foreach_1.5.0 BiocGenerics_0.32.0 zip_2.0.4
[67] boot_1.3-24 rlang_0.4.6 pkgconfig_2.0.3
[70] evaluate_0.14 Rhdf5lib_1.8.0 labeling_0.3
[73] tidyselect_1.1.0 magrittr_1.5 R6_2.4.1
[76] IRanges_2.20.2 generics_0.0.2 DBI_1.1.0
[79] foreign_0.8-75 pillar_1.4.4 haven_2.3.0
[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] utf8_1.1.4 rmarkdown_2.1 grid_3.6.3
[91] blob_1.2.1 git2r_0.27.1 reprex_0.3.0
[94] digest_0.6.25 webshot_0.5.2 httpuv_1.5.2
[97] numDeriv_2016.8-1.1 stats4_3.6.3 munsell_0.5.0