Designing three-species subcommunities

Author

Shane Hogle

Published

May 24, 2025

Abstract

This notebook creates and lays out the steps for constructing the experimental species trios for this experiment. It is inspired by the method outlined in this paper: Full factorial construction of synthetic microbial communities, eLife 13:RP101906

1 Setup

1.1 Libraries

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library(here)
library(tidyverse)
library(ggplate)

1.2 Global variables

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data <- here::here("data", "communities", "experiment_design")

# make processed data directory if it doesn't exist
fs::dir_create(data)

1.3 Functions and vars

Species color vector

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my_colors <- c(
  "ANC_0403_10" = "#ffaaaa", "ANC_0403_70" = "#aa0000", "ANC_0403_80" = "#aa0000", "ANC_0403_90" = "#aa0000", 
  "ANC_1287_10" = "#ffeeaa", "ANC_1287_70" = "#d4aa00", "ANC_1287_80" = "#d4aa00", "ANC_1287_90" = "#d4aa00", 
  "ANC_1896_10" = "#ccffaa", "ANC_1896_70" = "#44aa00", "ANC_1896_80" = "#44aa00", "ANC_1896_90" = "#44aa00", 
  "ANC_1977_10" = "#aaccff", "ANC_1977_70" = "#0055d4", "ANC_1977_80" = "#0055d4", "ANC_1977_90" = "#0055d4",
  "EVO_0403_10" = "#ffaaee", "EVO_0403_70" = "#ff00cc", "EVO_0403_80" = "#ff00cc", "EVO_0403_90" = "#ff00cc", 
  "EVO_1287_10" = "#ffccaa", "EVO_1287_70" = "#ff6600", "EVO_1287_80" = "#ff6600", "EVO_1287_90" = "#ff6600", 
  "EVO_1896_10" = "#aaffee", "EVO_1896_70" = "#00ffcc", "EVO_1896_80" = "#00ffcc", "EVO_1896_90" = "#00ffcc", 
  "EVO_1977_10" = "#ccaaff", "EVO_1977_70" = "#7f2aff", "EVO_1977_80" = "#7f2aff", "EVO_1977_90" = "#7f2aff"
  )

For plotting plates

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plate_plot_steps <- function(df, well_colmun, val_column, step, colr_vec){
  ggplate::plate_plot(df,
  position = {{ well_colmun }},
  value = {{ val_column }},
  colour = colr_vec,
  plate_size = 96,
  plate_type = "round"
) + ggtitle(step)
}

2 Construct trio combinations

Get all possible trios while excluding combinations of ANC/EVO of the same species

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combos03 <- t(combn(c("ANC_0403", "EVO_0403", "ANC_1287", "EVO_1287", "ANC_1977", "EVO_1977", "ANC_1896", "EVO_1896"),  3))
combos03 <- tibble("a" = combos03[, 1], "b" = combos03[, 2], "c" = combos03[, 3])
combos03_filt <- combos03 %>%
  filter(str_extract(a, "\\d+") != str_extract(b, "\\d+")) %>% 
  filter(str_extract(a, "\\d+") != str_extract(c, "\\d+")) %>% 
  filter(str_extract(b, "\\d+") != str_extract(c, "\\d+"))

Make plate layouts for the 3-species combos

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combos03_filt_wells <- combos03_filt %>% 
  slice(rep(1:n(), each = 3)) %>%
  mutate(a = paste(a, c(80, 10, 10), sep = "_"),
         b = paste(b, c(10, 80, 10), sep = "_"),
         c = paste(c, c(10, 10, 80), sep = "_")) %>% 
  arrange(a, b, c) %>% 
  # this is a kludge to arrange the species in the most continuous blocks
  mutate(well = paste0(rep(c(LETTERS[c(c(1:8)[1:8%%2 == 1], 
                                         c(1:8)[1:8%%2 == 0])]), 
                             each = n()/8),
                         str_pad(rep(1:12, times = n()/12), 2, pad = "0")))

2.1 Format and save

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combos03_filt_wells %>%
  group_by(a, b, c) %>% 
  mutate(microcosm_id = cur_group_id()) %>% 
  ungroup() %>% 
  mutate(a_sp = paste0(str_split_i(a, "_", 2), stringr::str_extract(str_split_i(a, "_", 1), "^.{1}")),
         b_sp = paste0(str_split_i(b, "_", 2), stringr::str_extract(str_split_i(b, "_", 1), "^.{1}")),
         c_sp = paste0(str_split_i(c, "_", 2), stringr::str_extract(str_split_i(c, "_", 1), "^.{1}")),
         a_f = str_split_i(a, "_", 3),
         b_f = str_split_i(b, "_", 3),
         c_f = str_split_i(c, "_", 3)) %>% 
  arrange(well) %>% 
  dplyr::select(microcosm_id, well, a, a_sp, a_f, b, b_sp, b_f, c, c_sp, c_f) %>% 
  readr::write_tsv(here::here(data, "trios_sample_composition_wide.tsv"))

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combos03_filt_wells %>%
  group_by(a, b, c) %>% 
  mutate(microcosm_id = cur_group_id()) %>% 
  ungroup() %>% 
  dplyr::select(a:c, well, microcosm_id) %>% 
  tidyr::pivot_longer(c(-well, -microcosm_id)) %>% 
  tidyr::separate(value, c("evo_hist", "strainID", "target_f")) %>% 
  dplyr::mutate(evo_hist = stringr::str_to_lower(evo_hist),
                strainID = paste0("HAMBI_", strainID),
                target_f = as.numeric(target_f)/100,
                n_species = 3) %>% 
  dplyr::select(-name) %>% 
  dplyr::relocate(microcosm_id, n_species) %>% 
  readr::write_tsv(here::here(data, "trios_sample_composition_long.tsv"))

3 Pipetting

Proceed in the order of steps below to construct master plates used to inoculate different conditions

3.1 First pipetting step

Figure 1: Layout for the first pipetting step for constructing trios Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 80% (e.g., `_80`) or 10% (e.g., `_10`).

3.2 Second pipetting step

Figure 2: Layout for the second pipetting step for constructing trios Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 80% (e.g., `_80`) or 10% (e.g., `_10`).

3.3 Third pipetting step

Figure 3: Layout for the third pipetting step for constructing trios Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 80% (e.g., `_80`) or 10% (e.g., `_10`).

--- title: "Designing three-species subcommunities" author: "Shane Hogle" date: today link-citations: true abstract: "This notebook creates and lays out the steps for constructing the experimental species trios for this experiment. It is inspired by the method outlined in this paper: [Full factorial construction of synthetic microbial communities, eLife 13:RP101906](https://doi.org/10.7554/eLife.101906.1)" --- # Setup ## Libraries ```{r} #| output: false library(here) library(tidyverse) library(ggplate) ``` ## Global variables ```{r} data <- here::here("data", "communities", "experiment_design") # make processed data directory if it doesn't exist fs::dir_create(data) ``` ## Functions and vars Species color vector ```{r} my_colors <- c( "ANC_0403_10" = "#ffaaaa", "ANC_0403_70" = "#aa0000", "ANC_0403_80" = "#aa0000", "ANC_0403_90" = "#aa0000", "ANC_1287_10" = "#ffeeaa", "ANC_1287_70" = "#d4aa00", "ANC_1287_80" = "#d4aa00", "ANC_1287_90" = "#d4aa00", "ANC_1896_10" = "#ccffaa", "ANC_1896_70" = "#44aa00", "ANC_1896_80" = "#44aa00", "ANC_1896_90" = "#44aa00", "ANC_1977_10" = "#aaccff", "ANC_1977_70" = "#0055d4", "ANC_1977_80" = "#0055d4", "ANC_1977_90" = "#0055d4", "EVO_0403_10" = "#ffaaee", "EVO_0403_70" = "#ff00cc", "EVO_0403_80" = "#ff00cc", "EVO_0403_90" = "#ff00cc", "EVO_1287_10" = "#ffccaa", "EVO_1287_70" = "#ff6600", "EVO_1287_80" = "#ff6600", "EVO_1287_90" = "#ff6600", "EVO_1896_10" = "#aaffee", "EVO_1896_70" = "#00ffcc", "EVO_1896_80" = "#00ffcc", "EVO_1896_90" = "#00ffcc", "EVO_1977_10" = "#ccaaff", "EVO_1977_70" = "#7f2aff", "EVO_1977_80" = "#7f2aff", "EVO_1977_90" = "#7f2aff" ) ``` For plotting plates ```{r} plate_plot_steps <- function(df, well_colmun, val_column, step, colr_vec){ ggplate::plate_plot(df, position = {{ well_colmun }}, value = {{ val_column }}, colour = colr_vec, plate_size = 96, plate_type = "round" ) + ggtitle(step) } ``` # Construct trio combinations Get all possible trios while excluding combinations of ANC/EVO of the same species ```{r} combos03 <- t(combn(c("ANC_0403", "EVO_0403", "ANC_1287", "EVO_1287", "ANC_1977", "EVO_1977", "ANC_1896", "EVO_1896"), 3)) combos03 <- tibble("a" = combos03[, 1], "b" = combos03[, 2], "c" = combos03[, 3]) combos03_filt <- combos03 %>% filter(str_extract(a, "\\d+") != str_extract(b, "\\d+")) %>% filter(str_extract(a, "\\d+") != str_extract(c, "\\d+")) %>% filter(str_extract(b, "\\d+") != str_extract(c, "\\d+")) ``` Make plate layouts for the 3-species combos ```{r} combos03_filt_wells <- combos03_filt %>% slice(rep(1:n(), each = 3)) %>% mutate(a = paste(a, c(80, 10, 10), sep = "_"), b = paste(b, c(10, 80, 10), sep = "_"), c = paste(c, c(10, 10, 80), sep = "_")) %>% arrange(a, b, c) %>% # this is a kludge to arrange the species in the most continuous blocks mutate(well = paste0(rep(c(LETTERS[c(c(1:8)[1:8%%2 == 1], c(1:8)[1:8%%2 == 0])]), each = n()/8), str_pad(rep(1:12, times = n()/12), 2, pad = "0"))) ``` ## Format and save ```{r} combos03_filt_wells %>% group_by(a, b, c) %>% mutate(microcosm_id = cur_group_id()) %>% ungroup() %>% mutate(a_sp = paste0(str_split_i(a, "_", 2), stringr::str_extract(str_split_i(a, "_", 1), "^.{1}")), b_sp = paste0(str_split_i(b, "_", 2), stringr::str_extract(str_split_i(b, "_", 1), "^.{1}")), c_sp = paste0(str_split_i(c, "_", 2), stringr::str_extract(str_split_i(c, "_", 1), "^.{1}")), a_f = str_split_i(a, "_", 3), b_f = str_split_i(b, "_", 3), c_f = str_split_i(c, "_", 3)) %>% arrange(well) %>% dplyr::select(microcosm_id, well, a, a_sp, a_f, b, b_sp, b_f, c, c_sp, c_f) %>% readr::write_tsv(here::here(data, "trios_sample_composition_wide.tsv")) ``` ```{r} combos03_filt_wells %>% group_by(a, b, c) %>% mutate(microcosm_id = cur_group_id()) %>% ungroup() %>% dplyr::select(a:c, well, microcosm_id) %>% tidyr::pivot_longer(c(-well, -microcosm_id)) %>% tidyr::separate(value, c("evo_hist", "strainID", "target_f")) %>% dplyr::mutate(evo_hist = stringr::str_to_lower(evo_hist), strainID = paste0("HAMBI_", strainID), target_f = as.numeric(target_f)/100, n_species = 3) %>% dplyr::select(-name) %>% dplyr::relocate(microcosm_id, n_species) %>% readr::write_tsv(here::here(data, "trios_sample_composition_long.tsv")) ``` # Pipetting Proceed in the order of steps below to construct master plates used to inoculate different conditions ## First pipetting step ::: {#fig-01} ```{r} #| fig.width: 8 #| fig.height: 5 #| echo: false plate_plot_steps(combos03_filt_wells, well, a, "Trios Step 1", my_colors) ``` Layout for the first pipetting step for constructing trios Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 80% (e.g., `_80`) or 10% (e.g., `_10`). ::: ## Second pipetting step ::: {#fig-02} ```{r} #| fig.width: 8 #| fig.height: 5 #| echo: false plate_plot_steps(combos03_filt_wells, well, b, "Trios Step 2", my_colors) ``` Layout for the second pipetting step for constructing trios Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 80% (e.g., `_80`) or 10% (e.g., `_10`). ::: ## Third pipetting step ::: {#fig-03} ```{r} #| fig.width: 8 #| fig.height: 5 #| echo: false plate_plot_steps(combos03_filt_wells, well, c, "Trios Step 3", my_colors) ``` Layout for the third pipetting step for constructing trios Different colors show different species/evolution combinations. The darkness of the color indicates whether a species/evolution combo is added at 80% (e.g., `_80`) or 10% (e.g., `_10`). :::