1 Aim

• Analysis of OSD Mamiellophyceae data for Sci Report paper 2019

2 Initialize.

This file defines all the necessary libraries and variables

  source('OSD_Mamiello_init.R', echo=FALSE)

3 Read data

file_main <- "../Supplementary data/OSD_Mamiello_Tables.xlsx"
file_supp <- "../Supplementary data/OSD_Mamiello_Tables Supplementary.xlsx"
otu <- read_excel(path = file_supp, sheet = "ASVs LGC Mamiello")
lookup_species_italic <- otu %>% select(species = species_ASV, species_italic = species_ASV_italic) %>% 
    filter(!is.na(species_italic))
samples <- read_excel(path = file_supp, sheet = "samples")
metadata <- read_excel(path = file_supp, sheet = "metadata")

fasta_dir <- "C:/daniel.vaulot@gmail.com/Metabarcoding/OSD/2014 seq 18S_workable/fasta"

# get a list of all fastq files in the ngs directory and separate R1 and R2
fns <- sort(list.files(fasta_dir, full.names = TRUE))
fns <- fns[str_detect(basename(fns), ".fasta")]

# Extract sample names, assuming filenames have format: SAMPLENAME_XXX.fastq
sample.names <- str_split(basename(fns), pattern = "_18S", simplify = TRUE)
sample.names <- sample.names[, 1]

df <- data.frame()

for (i in 1:length(fns)) {
    
    # use the dada2 function fastq.geometry
    sequences_length <- Biostrings::fasta.seqlengths(fns[i])
    
    # extract the information on number of sequences and file name
    df_one_row <- data.frame(n_seq = length(sequences_length), file_name = basename(fns[i]), 
        sample_name = sample.names[i])
    
    # add one line to data frame
    df <- bind_rows(df, df_one_row)
}

OSD_sequence_info <- df
write_tsv(OSD_sequence_info, "OSD_sequence_info.tsv", na = "")

4 Create OSD station map with leaflet

df <- dplyr::mutate(metadata, latitude = as.numeric(Latitude), longitude = as.numeric(Longitude), 
    label = as.character(OSD_station))

map <- map_leaflet(df, width = 1000, height = 1000)
map

5 Summarize at genus level

genus_summary_all_ASV <- otu %>% group_by(order, genus) %>% summarise(n_ASVs = n(), 
    n_seq = sum(total))
seq_all_ASV <- sum(genus_summary_all_ASV$n_seq)

genus_summary_top_ASV <- otu %>% filter(!is.na(species_ASV)) %>% group_by(order, 
    genus) %>% summarise(n_ASVs = n(), n_seq = sum(total))
seq_top_ASV <- sum(genus_summary_top_ASV$n_seq)

print(glue::glue("Number of different genera : {nrow(genus_summary_all_ASV)}"))

Number of different genera : 16

# knitr::kable(genus_summary_all_ASV)
genus_summary_all_ASV

# A tibble: 16 x 4
# Groups:   order [?]
   order                        genus                          n_ASVs n_seq
   <chr>                        <chr>                           <int> <dbl>
 1 Dolichomastigales            Crustomastigaceae-AB               30   332
 2 Dolichomastigales            Crustomastigaceae-C                 5    22
 3 Dolichomastigales            Crustomastigaceae_unclassified     30   204
 4 Dolichomastigales            Crustomastix                        1    12
 5 Dolichomastigales            Dolichomastigaceae-A                9    53
 6 Dolichomastigales            Dolichomastigaceae-B               80   577
 7 Dolichomastigales            Dolichomastigales unclassified     60   528
 8 Dolichomastigales            Dolichomastix                      23   102
 9 Mamiellales                  Bathycoccus                      1034 24391
10 Mamiellales                  Mamiella                           95  1455
11 Mamiellales                  Mamiellophyceae_unclassified       24   266
12 Mamiellales                  Mantoniella                       786 11921
13 Mamiellales                  Micromonas                       4285 88991
14 Mamiellales                  Ostreococcus                     4223 89199
15 Mamiellales                  RCC391                             12   321
16 Mamiellophyceae_unclassified Mamiellophyceae_unclassified       18   110

print(glue::glue("Fraction of reads of top ASV : {seq_top_ASV/seq_all_ASV*100}%"))

Fraction of reads of top ASV : 68.268156935977%

# knitr::kable(genus_summary_top_ASV)
genus_summary_top_ASV

# A tibble: 4 x 4
# Groups:   order [?]
  order       genus        n_ASVs n_seq
  <chr>       <chr>         <int> <dbl>
1 Mamiellales Bathycoccus       1 16750
2 Mamiellales Mantoniella       2  8570
3 Mamiellales Micromonas       10 62988
4 Mamiellales Ostreococcus     10 60847

6 Summarize at species and ASV level

There are two types of species assignement

• species_wang is the the species assigned by the Wang classifier to all ASV
• species_ASV is the species assigned to the major ASV using careful phylogenetic analysis

samples_surface <- samples %>% filter(surface_sample >= 0)
sample_number <- nrow(samples_surface)
station_number <- nrow(metadata)
glue::glue("Number of samples: {sample_number}")

Number of samples: 157

glue::glue("Number of stations: {station_number}")

Number of stations: 143

otu_long <- otu %>% select(ASV_code, genus, species_wang, species_ASV, species_ASV_italic, 
    contains("OSD")) %>% gather(sample, n_seq, contains("OSD")) %>% filter(sample %in% 
    samples_surface$sample)
otu_long$n_seq <- otu_long$n_seq %>% replace_na(0)

otu_seq <- otu_long %>% group_by(ASV_code) %>% summarise(n_seq_otu = sum(n_seq)) %>% 
    filter(n_seq_otu > 0)

glue::glue("Number of ASVs for all samples: {nrow(otu_seq)}")

Number of ASVs for all samples: 10715

glue::glue("Number of reads for all samples: {sum(otu_seq$n_seq_otu)}")

Number of reads for all samples: 218484

# write_tsv(species_seq, 'OSD Mamiello sequences.tsv')

sample_seq <- otu_long %>% group_by(sample) %>% summarise(n_seq_sample = sum(n_seq))

samples_with_seq <- left_join(samples, sample_seq)
# write_tsv(samples_updated, 'OSD Mamiello sequences.tsv', na='')

sample_number_above100 <- nrow(filter(sample_seq, n_seq_sample >= 100))
glue::glue("Number of samples with more than 100 Mamiellophyceae reads: {sample_number_above100}")

Number of samples with more than 100 Mamiellophyceae reads: 92

otu_long <- otu_long %>% left_join(sample_seq)

summarise_species <- function(otu_long) {
    
    sample_number <- nrow(distinct(as.tibble(otu_long$sample)))
    print(glue::glue("Number of samples taken into account : {sample_number}"))
    
    species_seq <- otu_long %>% group_by(genus, species) %>% summarise(n_seq_species = sum(n_seq))
    
    
    species_samples <- otu_long %>% group_by(species, sample, n_seq_sample) %>% 
        summarise(n_seq = sum(n_seq)) %>% mutate(pct_seq = n_seq/n_seq_sample * 
        100)
    
    species_samples_metadata <- species_samples %>% left_join(select(samples, 
        sample, OSD_station, sample_label)) %>% left_join(metadata) %>% mutate(Latitude = as.numeric(Latitude), 
        Longitude = as.numeric(Longitude))
    
    # Summaries at species level for all species
    
    species_pct <- species_samples %>% group_by(species) %>% summarise(mean_pct_seq_species = mean(pct_seq), 
        max_pct_seq_species = max(pct_seq))
    
    species_presence <- species_samples %>% filter(n_seq > 0) %>% group_by(species) %>% 
        summarise(n_samples_present = n(), pct_samples_present = 100 * n()/sample_number)
    
    species_more_1pct <- species_samples %>% filter(pct_seq > 1) %>% group_by(species) %>% 
        summarise(n_samples_more_1pct = n(), pct_samples_more_1pct = 100 * n()/sample_number)
    
    species_summary <- species_seq %>% left_join(species_pct) %>% left_join(species_presence) %>% 
        left_join(species_more_1pct) %>% filter(!is.na(n_samples_present))  # This to remove the species not present among the selected OTUs
    
    species_stats <- list(summary = species_summary, metadata = species_samples_metadata)
}

otu_long1 <- otu_long %>% rename(species = species_wang) %>% select(-species_ASV)
species_stats_1 <- summarise_species(otu_long1)

Number of samples taken into account : 152

species_summary_1 <- species_stats_1[["summary"]]

otu_long2 <- otu_long %>% filter(n_seq_sample >= 100 & !is.na(species_ASV)) %>% 
    rename(species = species_ASV) %>% select(-species_wang)
species_stats_2 <- summarise_species(otu_long2)

Number of samples taken into account : 92

species_summary_2 <- species_stats_2[["summary"]]

plot_species_summary <- function(species_summary, file_suffix = "1") {
    
    theme_chlorophyta_map <- theme(legend.position = c(0.1, 0.2), legend.background = element_rect(fill = "transparent"), 
        legend.text = element_text(size = 9), legend.title = element_text(size = 9), 
        plot.tag.position = "topright", plot.tag = element_text(size = 24, face = "bold"), 
        plot.title = element_text(size = 16))
    # Number of sequences per species
    g <- ggplot(species_summary, aes(x = reorder(species, n_seq_species), y = n_seq_species)) + 
        geom_col() + coord_flip() + xlab("Species") + ylab("Total of sequences - all samples")
    print(g)
    
    g_treemap <- ggplot(species_summary, aes(area = n_seq_species, fill = genus, 
        label = species, subgroup = genus)) + ggtitle("OSD - number of sequences") + 
        scale_fill_discrete(name = "Genus") + geom_treemap() + geom_treemap_subgroup_border() + 
        geom_treemap_text(place = "topleft", reflow = F, padding.x = grid::unit(3, 
            "mm"), padding.y = grid::unit(3, "mm"), min.size = 7) + scale_fill_viridis_d()
    print(g_treemap)
    
    ggsave(plot = g_treemap, filename = str_c("pdf/Treemap_sequences_", file_suffix, 
        ".pdf"), width = 10, height = 7, scale = 2, units = "cm", useDingbats = FALSE)
    
    ggsave(plot = g_treemap, filename = str_c("png/Treemap_sequences_", file_suffix, 
        ".png"), dpi = "print", scale = 2)
    
    treemap_dv(species_summary, c("genus", "species"), "n_seq_species", "OSD - number of sequences")
    
    
    # Mean pct of sequences per species
    
    g <- ggplot(species_summary, aes(area = mean_pct_seq_species, fill = genus, 
        label = species, subgroup = genus)) + ggtitle("OSD - mean of contribution", 
        subtitle = "") + geom_treemap() + geom_treemap_subgroup_border() + geom_treemap_text(colour = "white", 
        place = "topleft", reflow = T) + scale_fill_viridis_d()
    print(g)
    
    treemap_dv(species_summary, c("genus", "species"), "mean_pct_seq_species", 
        "OSD - mean of contribution")
    
    # Graph
    
    graph_samples_present <- ggplot(species_summary, aes(x = reorder(species, 
        pct_samples_present), y = pct_samples_present)) + geom_col() + geom_text(aes(label = n_samples_present), 
        hjust = -0.25) + coord_flip() + xlab("Species") + ylab("% of samples where species detected") + 
        ylim(0, 100) + theme_dviz_grid() + theme_chlorophyta_map + labs(title = "", 
        tag = "A")
    
    
    print(graph_samples_present)
    
    graph_samples_more_1pct <- ggplot(species_summary, aes(x = reorder(species, 
        pct_samples_present), y = pct_samples_more_1pct)) + geom_col() + geom_text(aes(label = n_samples_more_1pct), 
        hjust = -0.25) + coord_flip() + xlab("Species") + ylab("% of samples where species represents \n more than 1% of Mamiellophyceae") + 
        ylim(0, 100) + theme_dviz_grid() + theme_chlorophyta_map + labs(title = "", 
        tag = "B")
    
    print(graph_samples_more_1pct)
    
    grid_graphs <- gridExtra::grid.arrange(grobs = list(graph_samples_present, 
        graph_samples_more_1pct), ncol = 1, nrow = 2, clip = FALSE, padding = unit(0, 
        "line"))
    
    ggsave(plot = grid_graphs, filename = str_c("pdf/Presence_", file_suffix, 
        ".pdf"), width = 15, height = 20, scale = 1.8, units = "cm", useDingbats = FALSE)
    
    ggsave(plot = grid_graphs, filename = str_c("png/Presence_", file_suffix, 
        ".png"), dpi = "print", scale = 1.8)
    
    # Relation between samples present and samples more than 1 pct
    g <- ggplot(species_summary, aes(x = pct_samples_more_1pct, y = pct_samples_present, 
        label = species)) + geom_point() + geom_text(size = 2, check_overlap = TRUE, 
        vjust = 1) + xlim(0, 100)
    print(g)
    
    # Relation between sequence per species and samples present
    g <- ggplot(species_summary, aes(x = mean_pct_seq_species, y = pct_samples_present, 
        label = species)) + geom_point() + geom_text(size = 2, check_overlap = TRUE, 
        vjust = 1)
    print(g)
}

plot_species_summary(species_stats_1[["summary"]], file_suffix = "all_ASV")

plot_species_summary(species_stats_2[["summary"]], file_suffix = "selected_ASV")

7 Metadata

metadata_summarized <- metadata %>% transmute(Temperature = as.numeric(Temperature), 
    Salinity = as.numeric(Salinity), Nitrates = as.numeric(Nitrates), Phosphates = as.numeric(Phosphates), 
    Chlorophylle_a = as.numeric(Chlorophylle_a)) %>% summarise_all(funs(min, 
    max, mean, median, sd), na.rm = TRUE)
knitr::kable(metadata_summarized)

8 World Maps for each species

# Define constants for the map
size_factor = 1
size_points <- 2.5
size_cross <- 1
color_ice <- "lightslateblue"
color_water <- "red"
color_cultures <- "green"
color_not_present <- "blue"
color_morpho <- "brown"

species_samples_metadata <- species_stats_2[["metadata"]]
species_summary <- species_stats_2[["summary"]]
readr::write_tsv(species_samples_metadata, "Mamiello species station.tsv")
readr::write_tsv(species_summary, "Mamiello species summary.tsv")

for (one_genus in c("Bathycoccus", "Ostreococcus", "Micromonas", "Mantoniella")) {
    
    species_selected <- species_summary$species[species_summary$genus == one_genus]
    map_array_main_fig <- list()  # to store the maps to do tiled graph
    map_array_main_fig_eu <- list()  # to store the EU maps to do tiled graph
    
    for (one_species in species_selected) {
        
        one_species_summary <- species_summary %>% filter(species == one_species)
        one_species_italic <- lookup_species_italic$species_italic[lookup_species_italic$species == 
            one_species]
        
        if (one_species_summary$max_pct_seq_species > 20) {
            species_limits = c(0, 80)
            species_breaks = c(10, 20, 40, 80)
        } else {
            species_limits = c(0, 20)
            species_breaks = c(2, 5, 10, 20)
        }
        
        species.absent <- species_samples_metadata %>% filter(n_seq_sample >= 
            100 & pct_seq < 1 & species == one_species)
        
        species.present <- species_samples_metadata %>% filter(n_seq_sample >= 
            100 & pct_seq >= 1 & species == one_species)
        
        one_species_map <- map_world(color_borders = "grey85") + theme_light(base_size = 14) + 
            geom_point(data = species.absent, aes(x = Longitude, y = Latitude), 
                color = color_not_present, size = size_cross, shape = 3) + geom_point(data = species.present, 
            aes(x = Longitude, y = Latitude, size = pct_seq, color = pct_seq, 
                alpha = pct_seq)) + theme(legend.position = c(0.15, 0.25), legend.background = element_rect(fill = "transparent"), 
            legend.text = element_text(size = 12), legend.title = element_text(size = 12)) + 
            scale_size(name = "% of Mamiellophyceae", range = c(0, 8), limits = species_limits, 
                breaks = species_breaks) + scale_alpha_continuous(name = "% of Mamiellophyceae", 
            range = c(0.5, 0.9), limits = species_limits, breaks = species_breaks) + 
            viridis::scale_color_viridis(option = "magma", name = "% of Mamiellophyceae", 
                limits = species_limits, breaks = species_breaks) + guides(colour = guide_legend()) + 
            theme(plot.title = element_text(margin = margin(t = 10, b = 5), 
                size = 18), panel.grid.minor = element_blank()) + ggtitle(parse(text = str_c(one_species_italic, 
            "~-~", one_species_summary$n_seq_species, "~reads~-~", one_species_summary$n_samples_more_1pct, 
            "~samples")))
        
        # range gives maximum and minimum size of symbols, limits the extent of the
        # scale replace guide = 'legend' by guide=FALSE to remove the legend....
        # NOT USED : guides(color = guide_legend(override.aes = list(size=5))) +
        
        one_species_map_eu <- one_species_map + coord_fixed(1.3, xlim = c(-40, 
            40), ylim = c(30, 70)) + scale_x_continuous(breaks = (-4:4) * 10) + 
            scale_y_continuous(breaks = (3:7) * 10)
        
        print(one_species_map)
        print(one_species_map_eu)
        
        map_array_main_fig[[one_species]] <- one_species_map
        map_array_main_fig_eu[[one_species]] <- one_species_map_eu
    }
    
    grid_map_main_fig <- gridExtra::grid.arrange(grobs = map_array_main_fig, 
        ncol = 2, nrow = 5, clip = FALSE, padding = unit(0, "line"))
    grid_map_main_fig_eu <- gridExtra::grid.arrange(grobs = map_array_main_fig_eu, 
        ncol = 2, nrow = 5, clip = FALSE, padding = unit(0, "line"))
    
    # Save pdf
    ggsave(plot = grid_map_main_fig, filename = str_c("pdf/Map_", one_genus, 
        ".pdf"), width = 20, height = 35, scale = 2, units = "cm", useDingbats = FALSE)
    ggsave(plot = grid_map_main_fig_eu, filename = str_c("pdf/Map_", one_genus, 
        "_EU.pdf"), width = 20, height = 35, scale = 2, units = "cm", useDingbats = FALSE)
    
    # Save png
    ggsave(plot = grid_map_main_fig, filename = str_c("png/Map_", one_genus, 
        ".png"), dpi = "print", scale = 1.8)
    
    ggsave(plot = grid_map_main_fig_eu, filename = str_c("png/Map_", one_genus, 
        "_EU.png"), dpi = "print", scale = 1.8)
    
}

9 Heatmaps

# species_heatmap_data <- species_samples_metadata %>% ungroup() %>%
# mutate(sample_code = str_c(Ocean_code,'_',sample_code)) %>%
# select(sample_label, species, pct_seq) %>% spread(species, pct_seq) %>%
# tibble::column_to_rownames(var='sample_label') %>% t()

species_heatmap_data_vertical <- species_samples_metadata %>% ungroup() %>% 
    mutate(sample_label = str_c(Ocean_code, "_", sample_label)) %>% select(sample_label, 
    species, pct_seq) %>% spread(species, pct_seq) %>% tibble::column_to_rownames(var = "sample_label")

species_heatmap_data <- t(species_heatmap_data_vertical)

library(ComplexHeatmap)

# Palette de couleurs

reds = colorRampPalette(c("grey95", "orange", "red3"))
couleurs = reds(10)

pdf(file = "pdf/Heatmap horizontal no clustering.pdf", width = 15, height = 6)
# png(file='png/Heatmap_horizontal.png', width =1500, height = 600)
Heatmap(as.matrix(species_heatmap_data), col = couleurs, clustering_distance_columns = function(m) vegan::vegdist(m), 
    cluster_rows = FALSE, cluster_columns = TRUE, show_column_dend = TRUE, show_row_dend = FALSE, 
    column_dend_height = unit(30, "mm"), column_title = "Sample", row_title = "", 
    column_title_side = "top", row_title_side = "left", row_title_gp = gpar(fontsize = 12, 
        fontface = "plain"), column_title_gp = gpar(fontsize = 12, fontface = "plain"), 
    column_names_gp = gpar(fontsize = 8, fontface = "plain"), column_names_side = "top", 
    column_dend_side = "bottom", name = "%", heatmap_legend_param = list(title_gp = gpar(fontface = "plain")))
while (!is.null(dev.list())) dev.off()


# pdf(file='pdf/Heatmap vertical.pdf', width =6, height = 12)
# png(file='png/Heatmap vertical.png', width =600, height = 1200)
Heatmap(as.matrix(species_heatmap_data_vertical), col = couleurs, split = 7, 
    combined_name_fun = NULL, km_title = "", clustering_distance_rows = function(m) vegan::vegdist(m), 
    clustering_distance_columns = function(m) vegan::vegdist(m), cluster_rows = TRUE, 
    cluster_columns = TRUE, show_column_dend = FALSE, show_row_dend = TRUE, 
    row_dend_width = unit(30, "mm"), column_title = "", row_title = "Sample", 
    column_title_side = "bottom", row_title_side = "right", row_title_gp = gpar(fontsize = 0, 
        fontface = "plain"), column_title_gp = gpar(fontsize = 15, fontface = "plain"), 
    row_names_gp = gpar(fontsize = 8, fontface = "plain"), name = "%", heatmap_legend_param = list(title_gp = gpar(fontface = "plain")))
while (!is.null(dev.list())) dev.off()

10 Generate tables for the paper from the xtable package

Note : italics are done by encosing between {}

sanitize.italics <- function(str) {
    str_replace_all(str, c(`_` = "\\\\_", `\\{` = "\\\\textit{", `°` = "\\\\degree", 
        X = "\\\\cellcolor{gray}"))
}


path_table <- function(file_table) {
    str_c("../version_latex_3/tables/", file_table)
}

11 Table of ASVs (Table 1)

table_ASV <- read_excel(path = file_main, sheet = "Table 1")
table_ASV <- xtable::xtable(table_ASV, label = "table:ASV", caption = "Major Mamiellophyceae amplicon single variants (ASVs) from the LGC and LW datasets: taxonomic assignation, total abundance and representative sequences name.", 
    digits = 0)
print(table_ASV, scalebox = 0.75, caption.placement = "top", include.rownames = FALSE, 
    file = path_table("table_ASV.tex"), sanitize.text.function = sanitize.italics)

12 Table summary (Table 2)

table_ASV <- read_excel(path = file_main, sheet = "Table 2")
table_ASV <- xtable::xtable(table_ASV, label = "table:summary", caption = "Summary of the coastal distribution of Mamiellophyceae species and clades.  The column indicate the number of samples where the species/clade represented more than 1\\% of Mamiellophyceae reads in the LGC dataset.  For \\textit{Mantoniella} clade A was only oberved in the LW dataset.", 
    align = "lllcccccccc", digits = 0)

print(table_ASV, scalebox = 0.75, caption.placement = "top", include.rownames = FALSE, 
    file = path_table("table_summary.tex"), sanitize.text.function = sanitize.italics)

13 Table Micromonas clades (Table 3)

table_ASV <- read_excel(path = file_main, sheet = "Table 3")
table_ASV <- xtable::xtable(table_ASV, label = "table:micromonas", caption = "Equivalence between the clade nomenclature for the genus \\textit{Micromonas} in this paper compared to those of Guillou \\textit{et al.} (2004) \\cite{Guillou2004}, Slapeta \\textit{et al.} (2006) \\cite{Slapeta2006}, Worden 2006 \\cite{Worden2006} and Simon \\textit{et al.} (2017) \\cite{Simon2017c}.", 
    align = "lllccccc", digits = 0)

print(table_ASV, scalebox = 0.85, caption.placement = "top", include.rownames = FALSE, 
    file = path_table("table_micromonas.tex"), sanitize.text.function = sanitize.italics)

14 Table of OSD stations

table_metadata <- left_join(samples, metadata) %>% filter(n_seq_Mamiello_LGC >= 
    100) %>% select(OSD_station_code, water_depth, Station_name, Country, Ocean, 
    n_seq_total_LGC, n_seq_Mamiello_LGC) %>% mutate(`Mamiellophyceae %` = n_seq_Mamiello_LGC/n_seq_total_LGC * 
    100) %>% select(-n_seq_total_LGC) %>% rename(Station = OSD_station_code, 
    Name = Station_name, `Depth (m)` = water_depth, `Mamiellophyceae reads` = n_seq_Mamiello_LGC)

# The align should include the column for the row number
table_metadata <- xtable::xtable(table_metadata, label = "table:OSD_stations", 
    caption = "List of the 92 OSD samples used in this paper. Only samples with more than 100 Mamiellophyceae LGC reads were taken into account.  The percentage of Mamiellophyceae is relative to the total number of LGC reads at each station.", 
    digits = c(0, 0, 0, 0, 0, 0, 0, 1), align = "llclllcc")

print(table_metadata, , size = "\\scriptsize", tabular.environment = "longtable", 
    floating = FALSE, format.args = list(big.mark = ",", decimal.mark = "."), 
    caption.placement = "top", include.rownames = FALSE, file = path_table("table_OSD.tex"))

15 Table of similarities

# Do not use format='latex', done by default

table_simil <- read_excel(path = file_supp, sheet = "simil_Ostreococcus")
table_simil <- xtable::xtable(table_simil, label = "table:simil_ostreo", caption = "Matrix of the pairwise percent identity between \\textit{Ostreococcus} clades. The calculation was done based on the alignment available as Supplementary data S6.")
print(table_simil, scalebox = 0.9, caption.placement = "top", include.rownames = FALSE, 
    file = path_table("table_simil_ostreo.tex"))

table_simil <- read_excel(path = file_supp, sheet = "simil_Micromonas")
table_simil <- xtable::xtable(table_simil, label = "table:simil_micromonas", 
    caption = "Matrix of the pairwise percent identity between \\textit{Micromonas} clades. The calculation was done based on the alignment available as Supplementary data S7.")
print(table_simil, scalebox = 0.65, caption.placement = "top", include.rownames = FALSE, 
    file = path_table("table_simil_micromonas.tex"))

table_simil <- read_excel(path = file_supp, sheet = "simil_Mantoniella")
table_simil <- xtable::xtable(table_simil, label = "table:simil_mantoniella", 
    caption = "Matrix of the pairwise percent identity between \\textit{Mantoniella} clades. The calculation was done based on the alignment available as Supplementary data S8.")
print(table_simil, scalebox = 0.9, caption.placement = "top", include.rownames = FALSE, 
    file = path_table("table_simil_mantoniella.tex"))