Visualising metagenome differences

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Last updated: 2023-11-21

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Knit directory: Amphibolis_Posidonia_Comparison/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/metagenome.Rmd) and HTML (docs/metagenome.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	9229165	Philipp Bayer	2023-11-21	dada
html	eda5f79	Philipp Bayer	2023-11-21	Build site.
Rmd	3f8f8ba	Philipp Bayer	2023-11-21	yay
Rmd	d151dcf	Philipp Bayer	2023-11-21	Add more t-tests
html	d151dcf	Philipp Bayer	2023-11-21	Add more t-tests
Rmd	19de970	Philipp Bayer	2023-11-21	add consistent color scheme for orders
html	19de970	Philipp Bayer	2023-11-21	add consistent color scheme for orders
Rmd	1adc975	Philipp Bayer	2023-11-21	Add the unclassified MAGs too
html	1adc975	Philipp Bayer	2023-11-21	Add the unclassified MAGs too
Rmd	aa839e6	Philipp Bayer	2023-11-21	add missing ifles
html	aa839e6	Philipp Bayer	2023-11-21	add missing ifles
Rmd	52025b5	Philipp Bayer	2023-11-21	Final figures and changes
html	52025b5	Philipp Bayer	2023-11-21	Final figures and changes
html	1d2eef4	Philipp Bayer	2023-11-20	Build site.
Rmd	3ae555e	Philipp Bayer	2023-11-20	workflowr::wflow_publish(files = c("analysis/index.Rmd", "analysis/metagenome.Rmd"))
Rmd	b5757ac	Philipp Bayer	2023-11-20	Expand metagenome analysis
html	b5757ac	Philipp Bayer	2023-11-20	Expand metagenome analysis
Rmd	bf735ee	Philipp Bayer	2023-11-20	Add reformatted CAT for easier plotting
Rmd	f0e4ba7	Philipp Bayer	2023-11-20	add metagenomics files. add renv.

---

library(tidyverse)
library(DT)
library(microshades)
library(ggtext)
library(scales)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())

trunc_str <- function(latin_name) {
  tmp <- str_split(latin_name, pattern = ' ')[[1]]
  return(paste0(substring(tmp[1], 1, 1), '. ', tmp[2]))
}

Here we compare metagenomes (taxonomy and genes) across Amphibolis vs the others.

metadata <- read_tsv('./data/metagenome/samples.tsv')

Rows: 64 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (3): Sample, Species, Tissue
dbl (1): Replicate

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

metadata <- metadata |> rename(Sample_species = Species,
                               Sample_replicate = Replicate) |> 
  mutate(Tissue = str_to_title(Tissue))

Taxonomies

We have two taxonomies: one based on CAT, one based on GTDB. GTDB is less complete but more accurate. Let’s see what they look like.

GTDB

tax <- read_tsv('./data/metagenome/all_gtdb.summary.tsv.gz')

Rows: 520 Columns: 20
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (17): user_genome, classification, fastani_reference, fastani_reference_...
dbl  (3): msa_percent, translation_table, red_value

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Need to put some extra work in; MAGs with no classification are not in the output file.

all_mag_counts <- read_tsv('./data/metagenome/MAG_counts.tsv') |> mutate(Sample = str_replace(Sample, '_L2', '')) |> 
  rename(TotalMAGs = MAGs)

Rows: 64 Columns: 2
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (1): Sample
dbl (1): MAGs

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

clean_tax <- tax |> 
  separate(user_genome, into = c('sample', 'MAG'), sep='\\.') |> 
  separate(classification, into = c('domain', 'phylum', 
                                    'class', 'order', 
                                    'family', 'genus', 
                                    'species'), sep=';') |> 
  mutate(sample = str_replace_all(sample, 'MEGAHIT-', ''), 
         sample = str_replace_all(sample, '_L2', ''))
         
clean_tax <- clean_tax |> left_join(metadata, by = c('sample'='Sample'))

Next, we keep only Posidonia australis and Amphibolis antarctica samples

amph_pos_tax <- clean_tax |> filter(Sample_species %in% c('Amphibolis antarctica', 'Posidonia australis')) |> 
  left_join(all_mag_counts, by = c('sample'='Sample'))

GTDB by order

colors <-c(microshades_palette("micro_blue", 4, lightest = FALSE), 
           microshades_palette("micro_purple", 4, lightest = FALSE),
           microshades_palette("micro_green", 4, lightest = FALSE),
           microshades_palette("micro_orange", 4, lightest = FALSE),
          microshades_palette("micro_brown", 4, lightest = FALSE),
           microshades_palette("micro_gray", 4, lightest = FALSE))
names(colors) <- c("Rhizobiales", "Clostridiales", "Desulfobacterales", 'Desulfarculales', "Chitinophagales", "SBR1031", "Chromatiales", "Solirubrobacterales", "Xanthomonadales", "Acidimicrobiales", "Arenicellales","Granulosicoccales",
"Rhodobacterales", "Corallinales", "Ceramiales", "Alismatales", 
"Thiotrichales", "Flavobacteriales", "UBA4575", "Remainder", "Unclassified")

plot_orders_gtdb <- function(amph_pos_tax, group_remainder = TRUE, add_up=TRUE) {
  temp <- amph_pos_tax |> 
    group_by(sample, Tissue, Sample_species,TotalMAGs) |> 
    mutate(Sample_species = trunc_str(Sample_species)) |> 
    mutate(Sample_species = paste0('*', Sample_species, '*')) |> 
    mutate(order = str_replace_all(order, 'o__','')) |> 
    count(order, .drop = FALSE) 
  if(group_remainder) {
    temp <- temp |> 
    mutate(counted_orders = case_when(n == 1 ~ 'Remainder',
                                      TRUE ~ order))
  } else {
      temp <- temp |> 
        mutate(counted_orders = order)
  }
  temp2 <- temp |> 
    select(-order) |> 
    group_by(sample, Tissue, Sample_species, n, counted_orders) |> 
    summarise(n = sum(n)) |> 
    filter(counted_orders != '' )
  # we need to count the unclassified MAGs that are not in the output
  group_presents <- temp2 |> group_by(sample) |> summarise(classifiedMAGs = sum(n))
  missings <- group_presents |> 
    left_join(temp |> 
                select(sample, TotalMAGs)) |> 
    unique() |> 
    mutate(unclassifiedMAGs = TotalMAGs - classifiedMAGs) |> 
    select(-c(classifiedMAGs,TotalMAGs)) |>
    rename(Unclassified = unclassifiedMAGs) |>
    pivot_longer(-c(sample, Tissue, Sample_species)) |> 
    rename(counted_orders=name, n=value)

  if(add_up){
    # add hte missing numbers ot the main table
    temp2 <-rbind(temp2, missings)
  }
  temp2 <- temp2 |>  mutate(counted_orders = replace_na(counted_orders, 'Unclassified'))
  uniques <- c(setdiff(unique(temp2$counted_orders), c('Unclassified', 'Remainder')), 'Remainder', 'Unclassified')
  temp2 |> 
    mutate(counted_orders = factor(counted_orders, levels=uniques)) |> 
    mutate(sample = str_remove(sample, "[A-Z]+")) |> 
    mutate(sample = str_replace(sample, '31', '3')) |> 
    ggplot(aes(x = factor(sample, levels=rev(unique(sample))), y = n, fill=counted_orders)) +
    geom_col() + 
    theme_minimal() + 
    facet_wrap(~Tissue+Sample_species, scales = 'free_y', ncol=2) + 
    coord_flip() + 
    ylab('Number of MAGs') + xlab('Replicate') +
    labs(fill = 'Order') +
    scale_y_continuous(breaks=pretty_breaks())  +
    # move legend position to bottom
    theme(legend.position = "bottom",
          strip.text.x = element_markdown()) +
    scale_fill_manual(values = colors)
}
plot_orders_gtdb(amph_pos_tax)

`summarise()` has grouped output by 'sample', 'Tissue', 'Sample_species', 'n'.
You can override using the `.groups` argument.
Adding missing grouping variables: `Tissue`, `Sample_species`
Joining with `by = join_by(sample)`

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
19de970	Philipp Bayer	2023-11-21
1adc975	Philipp Bayer	2023-11-21

GTDB by family

amph_pos_tax |> 
  group_by(sample, Tissue) |> 
  mutate(family = str_replace_all(family, 'f__','')) |> 
  count(family) |> 
  filter ( n > 1) |> 
  filter(family != '' ) |> 
  ggplot(aes(x = sample, y = n, fill=family)) +
  geom_col() + 
  theme_minimal() + 
  #theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
  facet_wrap(~Tissue) + coord_flip() + 
  ylab('Number of contigs') + xlab('Sample')

Past versions of unnamed-chunk-9-1.png

Version	Author	Date
1adc975	Philipp Bayer	2023-11-21
b5757ac	Philipp Bayer	2023-11-20

amph_pos_tax |> group_by(sample, species) |> filter(species != 's__') |> 
  count(species)

# A tibble: 0 × 3
# Groups:   sample, species [0]
# ℹ 3 variables: sample <chr>, species <chr>, n <int>

CAT

Let’s do the same using CAT

tax_cat <- read_tsv('./data/metagenome/all_cat.summary.reformatted.tsv.gz')

Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)

Rows: 1454 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (10): # bin, classification, lineage, lineage scores, Superkingdom, Clas...
dbl  (2): number of ORFs in bin, number of ORFs classification is based on

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

clean_tax_cat <- tax_cat |> 
  separate(`# bin`, into = c('sample', 'MAG', 'fa'), sep='\\.') |> 
  mutate(sample = str_replace_all(sample, 'MEGAHIT-', ''), 
         sample = str_replace_all(sample, '_L2', '')) |> 
  select(-fa)
         
clean_tax_cat <- clean_tax_cat |> left_join(metadata, by = c('sample'='Sample')) |> left_join(all_mag_counts, by =c('sample'='Sample'))

Next, we keep only Posidonia australis and Amphibolis antarctica samples

amph_pos_tax_cat <- clean_tax_cat |> filter(Sample_species %in% c('Amphibolis antarctica', 'Posidonia australis'))

CAT by order

plot_orders_cat <-
  function(amph_pos_tax_cat, group_remainder = TRUE) {
    temp <- amph_pos_tax_cat |>
      separate(Order, into = c('Order', 'Order_score')) |>
      rename(order = Order) |>
      group_by(sample, Tissue, Sample_species, TotalMAGs) |>
      mutate(Sample_species = trunc_str(Sample_species)) |>
      mutate(Sample_species = paste0('*', Sample_species, '*')) |>
      count(order)
    if (group_remainder) {
      temp <- temp |>
        mutate(counted_orders = case_when(n == 1 ~ 'Remainder',
                                          TRUE ~ order))
    } else {
      temp <- temp |>
        mutate(counted_orders = order)
    }
    temp2 <- temp |>
      select(-order) |>
      group_by(sample, Tissue, Sample_species, n, counted_orders,TotalMAGs) |>
      summarise(n = sum(n)) |>
      filter(counted_orders != '')
      # we need to count the unclassified MAGs that are not in the output
    group_presents <- temp2 |> group_by(sample) |> summarise(classifiedMAGs = sum(n))
    missings <- group_presents |> 
      left_join(temp2) |> 
      select(classifiedMAGs, Tissue, Sample_species, sample, TotalMAGs)|> 
      unique() |> 
      mutate(unclassifiedMAGs = TotalMAGs - classifiedMAGs) |> 
      select(-c(classifiedMAGs,TotalMAGs)) |>
      rename(Unclassified = unclassifiedMAGs) |>
      pivot_longer(-c(sample, Tissue, Sample_species)) |> 
      rename(counted_orders=name, n=value)

    temp2 <-rbind(temp2, missings)

   uniques <- c(setdiff(unique(temp2$counted_orders), c('Unclassified', 'Remainder')), 'Remainder', 'Unclassified')
 
    temp2 |>
      mutate(counted_orders = factor(counted_orders, levels = uniques)) |>
      mutate(sample = str_remove(sample, "[A-Z]+")) |>
      mutate(sample = str_replace(sample, '31', '3')) |>
      ggplot(aes(
        x = factor(sample, levels = rev(unique(sample))),
        y = n,
        fill = counted_orders
      )) +
      geom_col() +
      theme_minimal() +
      facet_wrap( ~ Sample_species + Tissue, ncol = 2, scales = 'free_y') +
      coord_flip() +
      ylab('Number of MAGs') + xlab('Replicate') +
      labs(fill = 'Order') +
      scale_y_continuous(breaks = pretty_breaks())  +
      # move legend position to bottom
      theme(legend.position = "bottom",
            strip.text.x = element_markdown()) +
      scale_fill_manual(values = colors)
  }
plot_orders_cat(amph_pos_tax_cat)

Warning: Expected 2 pieces. Additional pieces discarded in 345 rows [1, 2, 3, 4, 5, 7,
8, 11, 13, 14, 15, 17, 22, 25, 26, 27, 29, 30, 33, 34, ...].

`summarise()` has grouped output by 'sample', 'Tissue', 'Sample_species', 'n',
'counted_orders'. You can override using the `.groups` argument.
Joining with `by = join_by(sample)`

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
1adc975	Philipp Bayer	2023-11-21

CAT by family

amph_pos_tax_cat |> 
  separate(Family, into = c('Family', 'Family_score')) |> 
  group_by(sample, Tissue) |> 
  count(Family) |> 
  filter ( n > 1) |> 
  filter(Family != '' ) |> 
  ggplot(aes(x = sample, y = n, fill=Family)) +
  geom_col() + 
  theme_minimal() + 
  #theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
  facet_wrap(~Tissue) + coord_flip() + 
  ylab('Number of contigs') + xlab('Sample')

Warning: Expected 2 pieces. Additional pieces discarded in 178 rows [3, 4, 5, 26, 29,
30, 33, 47, 56, 75, 79, 81, 82, 84, 88, 92, 93, 95, 97, 101, ...].

Past versions of unnamed-chunk-15-1.png

Version	Author	Date
1adc975	Philipp Bayer	2023-11-21
b5757ac	Philipp Bayer	2023-11-20

amph_pos_tax_cat |> separate(Species, into = c('Species', 'Species_score'), sep =':') |> filter(!is.na(Species)) |>  group_by(sample, Species) |> 
  count(Species)

# A tibble: 68 × 3
# Groups:   sample, Species [68]
   sample Species                                n
   <chr>  <chr>                              <int>
 1 AR1    Acidihalobacter ferrooxydans           1
 2 AR1    Deltaproteobacteria bacterium          1
 3 AR1    Desulfobacterales bacterium            1
 4 AR1    Gammaproteobacteria bacterium          1
 5 AR1    Solirubrobacterales bacterium 70-9     1
 6 AR1    Spirochaetaceae bacterium 4572_59      1
 7 AR2    Acidihalobacter ferrooxydans           1
 8 AR2    Desulfobacterales bacterium            1
 9 AR2    Flammeovirgaceae bacterium             1
10 AR2    Gammaproteobacteria bacterium          2
# ℹ 58 more rows

amph_pos_tax_cat |> separate(Species, into = c('Species', 'Species_score'), sep =':') |> filter(!is.na(Species)) |>  group_by(sample, Species) |> 
  count(Species) |> filter(str_detect(Species, 'Acidihalobacter'))

# A tibble: 4 × 3
# Groups:   sample, Species [4]
  sample Species                          n
  <chr>  <chr>                        <int>
1 AR1    Acidihalobacter ferrooxydans     1
2 AR2    Acidihalobacter ferrooxydans     1
3 AR3    Acidihalobacter ferrooxydans     1
4 AR4    Acidihalobacter ferrooxydans     1

There we go :) Which MAGs are those?

amph_pos_tax_cat |> filter(str_detect(Species, 'Acidihalobacter')) |> select(-c(lineage, `lineage scores`)) |> datatable()

amph_pos_tax_cat |> filter(str_detect(Family, 'Ectothiorhodospiraceae'))|> select(-c(lineage, `lineage scores`)) |>  datatable()

Are these in the GTDB classification?

amph_pos_tax |> filter(str_detect(family, 'Ectothiorhodospiraceae'))|> nrow()

[1] 0

Nope!

Gene comparison

Let’s compare gene presence/absence by names first

genes <- read_tsv('././data/metagenome/no_hypothetical.all_genes.tsv.gz')

Rows: 1362907 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (8): filename, locus_tag, ftype, length_bp, gene, EC_number, COG, product

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

genes <- genes |> 
  separate(filename, into = c('sample', 'MAG'), sep='\\.', extra = 'drop') |>  # get rid of extra filenaming stuff
  mutate(sample = str_replace(sample, 'MEGAHIT-', '')) |> 
  separate(MAG, into = c('MAG', 'rest'), sep ='/') |> 
  select(-rest)

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 1443 rows [2106, 4306,
7036, 7144, 7539, 10011, 12436, 12584, 13618, 17164, 17303, 19074, 21967,
22288, 28457, 30064, 32221, 32315, 34439, 34480, ...].

genessplit <- genes |> separate(gene, into = c('gene', 'gene_copy_number'), sep='_')

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 692502 rows [2, 3, 5, 6,
7, 10, 12, 13, 14, 15, 17, 18, 25, 26, 30, 31, 33, 35, 36, 37, ...].

genessplit <- genessplit |> mutate(sample = str_replace(sample, '_L2', '')) |> 
  left_join(metadata, by = c('sample'='Sample')) |> 
  filter(Sample_species %in% c('Amphibolis antarctica', 'Posidonia australis'))

count_diffs <- genessplit |> 
  group_by(Sample_species, gene) |> 
  count(gene) |> 
  pivot_wider(names_from = Sample_species, values_from = n) |> 
  mutate(difference = `Amphibolis antarctica` - `Posidonia australis`) |> 
  arrange(desc(difference))

count_diffs |> filter(`Posidonia australis` <100) |> head() |> datatable()

Eeeh this is too unspecific. Let’s look into specific differences.

ACC deaminase

We hypothesise that the ACC precursor-existing genes in Amphibolis lead to different microbiomes. ACC is broken down by ACC deaminases, which are present in Amphibolis but not Posidonia. Let’s see if we can find ACC deaminases in the metagenomes (acdS).

genessplit |> filter(gene == 'acdS') |> 
  group_by(Sample_species, Tissue, Sample_replicate) |> 
  count(gene) |> 
  mutate(Sample_species = trunc_str(Sample_species)) |> 
  mutate(Sample_species = paste0('*', Sample_species, '*')) |> 
  ggplot(aes(x= interaction(factor(Sample_replicate, levels=4:1), Tissue), y = n, fill = Sample_species)) + 
  geom_col() + 
  facet_wrap(~Sample_species) +
  coord_flip()+ theme_minimal() + 
  scale_y_continuous(breaks=pretty_breaks()) +
  ylab('Total number of *acdS*-containing MAGs') +
  xlab('Sample species') + 
  theme(legend.position = 'none',
        strip.text.x =  element_markdown(),
        axis.title.x = element_markdown())

Past versions of unnamed-chunk-26-1.png

Version	Author	Date
1adc975	Philipp Bayer	2023-11-21

What are those acdS-containing MAGs?

genessplit |> filter(gene == 'acdS') |> left_join(amph_pos_tax, by=c('sample', 'MAG'), multiple = 'any') |> datatable()

Lots of unknown taxonomies :(

TODO: The unknowns should have a color!!

genessplit |> filter(gene == 'acdS') |> left_join(amph_pos_tax, by=c('sample', 'MAG')) |>
  select(-c(Tissue.y, Sample_species.y)) |>
  rename(Tissue = Tissue.x, Sample_species = Sample_species.x) |> 
  mutate(order = replace_na(order, 'Unclassified')) |>
  plot_orders_gtdb(group_remainder = FALSE, add_up = FALSE)

`summarise()` has grouped output by 'sample', 'Tissue', 'Sample_species', 'n'.
You can override using the `.groups` argument.
Adding missing grouping variables: `Tissue`, `Sample_species`
Joining with `by = join_by(sample)`

Past versions of unnamed-chunk-28-1.png

Version	Author	Date
19de970	Philipp Bayer	2023-11-21
1adc975	Philipp Bayer	2023-11-21

  #select(sample, MAG, domain:species)

genessplit |> filter(gene == 'acdS') |> 
  left_join(amph_pos_tax_cat, by=c('sample', 'MAG')) |> 
    select(-c(Tissue.y, Sample_species.y)) |>
  rename(Tissue = Tissue.x, Sample_species = Sample_species.x) |> 
  plot_orders_cat(group_remainder = FALSE) #

Warning: Expected 2 pieces. Additional pieces discarded in 19 rows [1, 7, 8, 12, 16, 17,
18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31].

`summarise()` has grouped output by 'sample', 'Tissue', 'Sample_species', 'n',
'counted_orders'. You can override using the `.groups` argument.
Joining with `by = join_by(sample)`

Past versions of unnamed-chunk-29-1.png

Version	Author	Date
19de970	Philipp Bayer	2023-11-21
1adc975	Philipp Bayer	2023-11-21
b5757ac	Philipp Bayer	2023-11-20

  #select(sample, MAG, Superkingdom:Species) |> 

Let’s chekc for nitrogen fixation genes (nif) - hypothesis is that Amphibolis roots contain more of these

genessplit |> filter(str_detect(gene, 'nifH') | str_detect(gene, 'acdS') | str_detect(gene, 'sctC')) |> 
  mutate(gene_class = case_when(str_detect(gene, 'nifH') ~ '*nifH*',
                                str_detect(gene, 'acdS') ~ '*acdS*',
                                str_detect(gene, 'sctC') ~ '*sctC*')) |>
  #group_by(Sample_species, Tissue, Sample_replicate, MAG) |> 
  #summarise(gene_class = paste0(sort(unique(gene_class)), collapse = ' & ')) |>
  group_by(Sample_species, Tissue, Sample_replicate) |> 
  count(gene_class) |> 
  mutate(Sample_species = trunc_str(Sample_species)) |> 
  mutate(Sample_species = paste0('*', Sample_species, '*')) |> 
  ggplot(aes(x=factor(Sample_species, levels=rev(c(unique(Sample_species)))), y=n, fill=gene_class)) + 
  geom_boxplot(outlier.shape = NA) +
  facet_wrap(~factor(Tissue, levels=rev(c(unique(Tissue))))) + 
  geom_point(position = position_jitterdodge(), aes(color=gene_class)) +
  coord_flip() +
  theme_minimal() +
  labs(color='MAG gene content', fill='MAG gene content') +
  theme(axis.text.y =  element_markdown(),legend.text = element_markdown()) +
  ylab('Count of MAGs') + xlab('Species')

Past versions of unnamed-chunk-30-1.png

Version	Author	Date
1adc975	Philipp Bayer	2023-11-21
b5757ac	Philipp Bayer	2023-11-20

genessplit |> filter(str_detect(gene, 'nifH') | str_detect(gene, 'acdS') | str_detect(gene, 'sctC')) |> 
  mutate(gene_class = case_when(str_detect(gene, 'nifH') ~ '*nifH*',
                                str_detect(gene, 'acdS') ~ '*acdS*',
                                str_detect(gene, 'sctC') ~ '*sctC*')) |>
  #group_by(Sample_species, Tissue, Sample_replicate, MAG) |> 
  #summarise(gene_class = paste0(sort(unique(gene_class)), collapse = ' & ')) |>
  group_by(Sample_species, Tissue, Sample_replicate) |> 
  count(gene_class) |> 
  mutate(Sample_species = trunc_str(Sample_species)) |> 
  mutate(Sample_species = paste0('*', Sample_species, '*')) |> 
  ggplot(aes(x=factor(Tissue, levels=rev(c('Shoot/Leaf', 'Shoot/Leaf Biofilm', 'Rhizome', 'Root'))), y=n, fill=Sample_species)) + 
  geom_boxplot(outlier.shape = NA) +
  facet_wrap(~factor(gene_class, levels=c('*acdS*', '*nifH*', '*sctC*')), scales = 'free_x') + 
  geom_point(position = position_jitterdodge(), aes(color=Sample_species)) +
  coord_flip() +
  theme_minimal() +
  labs(color='Species', fill='Species') +
  theme(axis.text.y =  element_markdown(),legend.text = element_markdown(),
        strip.text.x = element_markdown()) +
  ylab('Count of MAGs') + xlab('Species')

Past versions of unnamed-chunk-31-1.png

Version	Author	Date
d151dcf	Philipp Bayer	2023-11-21
aa839e6	Philipp Bayer	2023-11-21
920282d	Philipp Bayer	2023-11-21

I like this one more. The differences are stronger.

Statistic test

Can we make a simple association test?

First, a general one:

tmp <- genessplit |> filter(str_detect(gene, 'nifH') | str_detect(gene, 'acdS') | str_detect(gene, 'sctC')) |> 
  mutate(gene_class = case_when(str_detect(gene, 'nifH') ~ '*nifH*',
                                str_detect(gene, 'acdS') ~ '*acdS*',
                                str_detect(gene, 'sctC') ~ '*sctC*')) |> 
  group_by(Sample_species, Sample_replicate, Tissue) |> 
  count(gene_class) |> 
  mutate(Sample_species = factor(Sample_species, levels = c('Posidonia australis', 'Amphibolis antarctica'))) 
summary(lm(n ~ gene_class + Sample_species + Sample_replicate + Tissue, data=tmp))

Call:
lm(formula = n ~ gene_class + Sample_species + Sample_replicate + 
    Tissue, data = tmp)

Residuals:
    Min      1Q  Median      3Q     Max 
-25.496  -7.571  -1.483   4.858  44.164 

Coefficients:
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          -7.1173     6.5738  -1.083  0.28343    
gene_class*nifH*                      8.7515     5.1233   1.708  0.09295 .  
gene_class*sctC*                     20.1394     4.1926   4.804 1.14e-05 ***
Sample_speciesAmphibolis antarctica  10.1763     3.5162   2.894  0.00535 ** 
Sample_replicate                     -0.6348     1.5468  -0.410  0.68300    
TissueRoot                           13.5423     4.3568   3.108  0.00291 ** 
TissueShoot/Leaf                     -3.0185     5.0868  -0.593  0.55523    
TissueShoot/Leaf Biofilm              5.2019     5.2435   0.992  0.32528    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 13.59 on 58 degrees of freedom
Multiple R-squared:  0.4475,    Adjusted R-squared:  0.3809 
F-statistic: 6.712 on 7 and 58 DF,  p-value: 7.885e-06

OK! So we have an association with gene_class, which is obvious, but we also have an association with species (great!) and root-tissue (also great!)

Subtests

Now let’s make the specific ones; nifh, acdS, and sctC counts within Tissue=root across both species

nifH

tmp <- genessplit |> filter(str_detect(gene, 'nifH') & str_detect(Tissue, 'Root')) |> 
  mutate(gene_class = case_when(str_detect(gene, 'nifH') ~ 'nifH')) |> 
  group_by(Sample_species, Sample_replicate) |> count(gene_class) 
tmp  |> datatable()

t.test(n ~ Sample_species, data=tmp)

    Welch Two Sample t-test

data:  n by Sample_species
t = 10.614, df = 5.5702, p-value = 6.634e-05
alternative hypothesis: true difference in means between group Amphibolis antarctica and group Posidonia australis is not equal to 0
95 percent confidence interval:
 19.8924 32.1076
sample estimates:
mean in group Amphibolis antarctica   mean in group Posidonia australis 
                                 30                                   4 

P < 0.0005

sctC

tmp <- genessplit |> filter(str_detect(gene, 'sctC') & str_detect(Tissue, 'Root')) |> 
  mutate(gene_class = case_when(str_detect(gene, 'sctC') ~ 'sctC')) |> 
  group_by(Sample_species, Sample_replicate) |> count(gene_class) 
tmp  |> datatable()

t.test(n ~ Sample_species, data=tmp)

    Welch Two Sample t-test

data:  n by Sample_species
t = 7.4878, df = 3.7586, p-value = 0.002169
alternative hypothesis: true difference in means between group Amphibolis antarctica and group Posidonia australis is not equal to 0
95 percent confidence interval:
 35.93797 80.06203
sample estimates:
mean in group Amphibolis antarctica   mean in group Posidonia australis 
                                 67                                   9 

p < 0.005

acdS

tmp <- genessplit |> filter(str_detect(gene, 'acdS') & str_detect(Tissue, 'Root')) |> 
  mutate(gene_class = case_when(str_detect(gene, 'acdS') ~ 'acdS')) |> 
  group_by(Sample_species, Sample_replicate) |> count(gene_class) 
tmp |> datatable()

can’t run t-test, only one group present!

# t.test(n ~ Sample_species, data=tmp)

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Australia/Perth
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] scales_1.2.1     ggtext_0.1.2     microshades_1.11 DT_0.30         
 [5] lubridate_1.9.2  forcats_1.0.0    stringr_1.5.0    dplyr_1.1.2     
 [9] purrr_1.0.1      readr_2.1.4      tidyr_1.3.0      tibble_3.2.1    
[13] ggplot2_3.4.2    tidyverse_2.0.0  workflowr_1.7.1 

loaded via a namespace (and not attached):
 [1] gtable_0.3.3        xfun_0.39           bslib_0.4.2        
 [4] htmlwidgets_1.6.2   processx_3.8.1      callr_3.7.3        
 [7] tzdb_0.4.0          crosstalk_1.2.0     vctrs_0.6.2        
[10] tools_4.3.2         ps_1.7.5            generics_0.1.3     
[13] parallel_4.3.2      fansi_1.0.4         highr_0.10         
[16] pkgconfig_2.0.3     lifecycle_1.0.3     farver_2.1.1       
[19] compiler_4.3.2      git2r_0.32.0        munsell_0.5.0      
[22] getPass_0.2-2       httpuv_1.6.11       htmltools_0.5.5    
[25] sass_0.4.6          yaml_2.3.7          crayon_1.5.2       
[28] later_1.3.1         pillar_1.9.0        jquerylib_0.1.4    
[31] whisker_0.4.1       ellipsis_0.3.2      cachem_1.0.8       
[34] commonmark_1.9.0    tidyselect_1.2.0    digest_0.6.31      
[37] stringi_1.7.12      labeling_0.4.2      cowplot_1.1.1      
[40] rprojroot_2.0.3     fastmap_1.1.1       grid_4.3.2         
[43] colorspace_2.1-0    cli_3.6.1           magrittr_2.0.3     
[46] utf8_1.2.3          withr_2.5.0         promises_1.2.0.1   
[49] bit64_4.0.5         timechange_0.2.0    rmarkdown_2.21     
[52] httr_1.4.6          bit_4.0.5           hms_1.1.3          
[55] evaluate_0.21       knitr_1.42          markdown_1.11      
[58] rlang_1.1.1         gridtext_0.1.5      Rcpp_1.0.10        
[61] glue_1.6.2          BiocManager_1.30.20 xml2_1.3.4         
[64] renv_1.0.2          rstudioapi_0.14     vroom_1.6.3        
[67] jsonlite_1.8.4      R6_2.5.1            fs_1.6.2