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added PCoA toturial for R+Python method
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‎docs/beta_diversity_analysis.md

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@@ -191,6 +191,34 @@ $multi_variable_pcoa_plot.py --abundance_table mvpp_mpa_species_relab.tsv --meta
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![Multiple variable PCoA plot](../images/mvpp_pcoa.png)
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As optional ouputs, `multi_variable_pcoa_plot.py` also generates non-adjustment PERMANOVA test (e.g. [mvpp_permanova.tsv](../example_data/mvpp_permanova.tsv)) and coordinates of PC1 and PC2 (e.g. [mvpp_coordinates.tsv](../example_data/mvpp_coordinates.tsv)) which can be used in visualization in other ways we will discuss shortly below.
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## A method mixing R and Python
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As optional ouputs, `get_palette(palette = "default", k)` also generates non-adjustment PERMANOVA test (e.g. [mvpp_permanova.tsv](../example_data/mvpp_permanova.tsv)) and coordinates of PC1 and PC2 (e.g. [mvpp_coordinates.tsv](../example_data/mvpp_coordinates.tsv)) which can be used in visualization in other ways we will discuss shortly below.
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## A method mixing R and Python
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If you wish to enhance the aesthecity of PCoA plot using R but with the pre-calculated coordinates from [multi_variable_pcoa_plot.py](../scripts/multi_variable_pcoa_plot.py) with the flag `--df_opt`, you are suggested to use our R function `pcoa_sideplot()` on coordinate table from `multi_variable_pcoa_plot.py` with arguments:
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* `coordinate_df`: the coordinate table generated from python script multi_variable_pcoa_plot.py --df_opt, for example [coordinate_table.tsv](../example_data/coordinate_table.tsv).
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* `variable`: specify the variable you want to inspect on PCoA.
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* `color_palettes`: give a named vector to pair color palettes with variable group names. default: [ggpubr default palette]
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* `coordinate_1`: specify the column header of the 1st coordinate. default: [*PC1*]
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* `coordinate_2`: specify the column header of the 2nd coordinate. default: [*PC2*]
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* `marker_size`: specify the marker size of the PCoA plot. default: [3]
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* `font_size`: specify the font size of PCoA labels and tick labels. default: [20]
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* `font_style`: specify the font style of PCoA labels and tick labels. default: [*Arial*]
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For example:
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```{r}
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>source(file = "path_to_the_package/KunDH-2023-CRM-MSM_metagenomics/scripts/functions/beta_diversity_funcs.R")
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>coordinate_df <- data.frame(read.csv("path_to_the_package/KunDH-2023-CRM-MSM_metagenomics/example_data/coordinate_table.tsv",
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header = TRUE,
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sep = "\t"))
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>pcoa_sideplot(coordinate_df = coordinate,
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coordinate_1 = "PC1",
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coordinate_2 = "PC2",
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variable = "sexual_orientation",
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color_palettes = c("Non-MSM" = "#888888", "MSM" = "#eb2525"))
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```
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[PCoA Side plot](../images/pcoa_w_sideplot.png)
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**Note:** The figure displayed above had been edited using [inkscape](https://inkscape.org/) on the base of the crude output in order to enhance the readability and aesthetic sense.

‎example_data/coordinate_table.tsv

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PC1 PC2 sexual_orientation
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-0.2628051359128226 -0.15605007858766573 MSM
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0.06365888677175648 0.06778275169584252 MSM
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-0.077356170706838 -0.19385953698160924 Non-MSM
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0.13554557110942725 0.2825494016619917 MSM
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0.37243377344879547 -0.004938847701093791 Non-MSM
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-0.168899331145202 0.31448129015497645 Non-MSM
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-0.20704651572822963 0.01935676754118731 Non-MSM
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0.09962412472581383 -0.05954836155096865 Non-MSM
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-0.13769457780524466 -0.000646348202553923 MSM
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0.23337142086116236 0.10700421418215182 Non-MSM
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-0.13390077085490965 -0.11786180771596756 Non-MSM
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-0.10748700301527406 0.35286795889519224 Non-MSM
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-0.3143329483543445 -0.010426730685141739 MSM
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-0.31761576313682177 -0.22242901200710127 MSM
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-0.31409460863 -0.26905032498012255 Non-MSM
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0.02905069375032977 0.2892589057683008 MSM
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0.03621156681354462 0.3321279458816928 MSM
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0.43065553120847716 -0.09724234758363742 Non-MSM
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0.33100242738444474 0.054094101189757764 Non-MSM
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0.10933860444600554 -0.22517778332196398 Non-MSM
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0.34606308825340554 0.02848018400734919 Non-MSM
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0.3369415090034524 -0.05788677494864451 Non-MSM
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0.13014996661823097 0.09360548773818418 MSM
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0.33425913531034 0.08251047149315524 MSM
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-0.29370231455039514 -0.11448324441243958 MSM
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0.1684365641450756 -0.0870274991147484 MSM
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0.31109698127805974 0.02516007670727285 Non-MSM
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0.3986153456052948 -0.1267373077533661 MSM
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0.046255536309669554 0.2810082298244458 MSM
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0.44245103659096474 -0.1199519770522526 MSM
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0.38479091848409924 -0.15957994126105673 Non-MSM
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0.09345439824515817 -0.2546946277371098 MSM
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0.4357367201331367 -0.15375985445597007 Non-MSM
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0.042134034448124934 0.3253140941844303 MSM
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0.43355550913789936 -0.09489197383147234 MSM
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0.4670348497729969 -0.15508790200823316 Non-MSM
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0.28633308170873556 -0.051094516485459894 Non-MSM
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0.022472017377469258 0.2973332193558033 MSM
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0.000290920583021966 -0.3049162316639377 Non-MSM
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0.35569909828860063 -0.08418795503581224 MSM
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0.42132318212487585 -0.09455376061977355 Non-MSM
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0.2155796686771958 0.08431100213709204 MSM
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0.38070630831272706 -0.06548253255198717 Non-MSM
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0.40668882908433607 -0.14660763825334322 Non-MSM
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0.30280178593804397 -0.0367574847161416 Non-MSM
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-0.1442266836782678 -0.07531906579551187 MSM
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0.13054961923390396 -0.12386766385809217 Non-MSM
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0.38664654306938556 -0.10737000121129817 MSM
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0.28818136797042965 -0.10342621350551943 MSM
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0.30622508939841 0.051103617912361586 Non-MSM
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0.4176342761841033 -0.07057117984723121 MSM
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0.27908158191717863 0.0788783508452007 Non-MSM
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0.0044515314467062465 0.3392047985031592 MSM
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-0.15292430978445407 0.1639421993179468 Non-MSM
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0.16681913824786065 -0.14798783506901328 Non-MSM
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0.3069504767103925 -0.12391018739777997 Non-MSM
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0.013058989422652635 0.3954154942285945 MSM
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0.20762354944056213 -0.058787765264924394 Non-MSM
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0.0408868410498254 0.26834889836329806 MSM
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-0.16356614056338756 0.02576815336069973 MSM

‎images/pcoa_w_sideplot.png

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‎scripts/alpha_diversity_analysis.R

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nper = 999,
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to_rm = c("no_receptive_anal_intercourse"),
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by_method = "margin")
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pcoa_df <- data.frame(read.csv("/Users/kunhuang/R_analysis_mirror/msm_analysis/manuscript_05012023/figure1/pcoa_df.tsv",
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header = TRUE,
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sep = "\t"))
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length(unique(pcoa_df[, "sexual_orientation"]))
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pcoa_sideplot(coordinate_df = pcoa_df,
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coordinate_1 = "PC1",
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coordinate_2 = "PC2",
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variable = "sexual_orientation")

‎scripts/functions/beta_diversity_funcs.R

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est <- vegan::adonis2(eval(parse(text = str2)), data = md, permutations = nper, by = by_method)
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}
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est
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}
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pcoa_sideplot <- function(coordinate_df,
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variable,
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color_palettes = ggpubr::get_palette(palette = "default", k = length(unique(coordinate_df[, variable]))),
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coordinate_1 = "PC1",
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coordinate_2 = "PC2",
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marker_size = 3,
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font_size = 20,
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font_style = "Arial"
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){
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# coordinate_df: the coordinate table generated from python script multi_variable_pcoa_plot.py --df_opt
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# variable: specify the variable you want to inspect on PCoA.
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# color_palettes: give a named vector to pair color palettes with variable group names. default: [ggpubr default palette]
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# coordinate_1: specify the column header of the 1st coordinate. default: [PC1]
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# coordinate_2: specify the column header of the 2nd coordinate. default: [PC2]
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# marker_size: specify the marker size of the PCoA plot. default: [3]
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# font_size: specify the font size of PCoA labels and tick labels. default: [20]
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# font_style: specify the font style of PCoA labels and tick labels. default: ["Arial"]
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main_plot <- ggplot2::ggplot(coordinate_df,
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ggplot2::aes(x = .data[[coordinate_1]], y = .data[[coordinate_2]], color = .data[[variable]])) +
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ggplot2::geom_point(size = marker_size) +
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ggplot2::theme_bw() +
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ggplot2::theme(text = ggplot2::element_text(size = font_size, family = font_style)) +
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ggpubr::color_palette(color_palettes)
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xdens <- cowplot::axis_canvas(main_plot, axis = "x") +
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ggplot2::geom_density(data = coordinate_df,
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ggplot2::aes(x = .data[[coordinate_1]], fill = .data[[variable]]),
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alpha = 0.7,
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size = 0.2) +
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ggpubr::fill_palette(color_palettes)
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ydens <- cowplot::axis_canvas(main_plot, axis = "y", coord_flip = TRUE) +
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ggplot2::geom_density(data = coordinate_df,
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ggplot2::aes(x = .data[[coordinate_2]], fill = .data[[variable]]),
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alpha = 0.7,
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size = 0.2) +
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ggplot2::coord_flip() +
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ggpubr::fill_palette(color_palettes)
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plot1 <- cowplot::insert_xaxis_grob(main_plot, xdens, grid::unit(.2, "null"), position = "top")
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plot2 <- cowplot::insert_yaxis_grob(plot1, ydens, grid::unit(.2, "null"), position = "right")
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comb_plot <- cowplot::ggdraw(plot2)
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comb_plot
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}

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