Metabarcoding Singapore
OTUs at 98%

Initialize. This file defines all the necessary libraries and variables

  source('Metabarcoding Singapore_init.R', echo=FALSE)

1 Aim

  • Assign and analyze eukaryotes for Singapore metabarcoding data (98%). Do some analyzes with the prokaryotes too…

2 Analysis

2.1 Assignment of 98% OTUs based on PR2 database

2.1.1 Read the data

# Read the sample table
sample_table <- read_excel("../qiime/Singapore otu_table_w_tax_0.98.xlsx", sheet = "samples") %>% 
    mutate(sample_label = str_c(strait, site_M, sprintf("%03d", day), sep = "_"))

# Read the otu table
otu_table <- read_excel("../qiime/Singapore otu_table_w_tax_0.98.xlsx", sheet = "otu_table")

# Clean up the taxonomy
otu_table <- otu_table %>% mutate(taxo_clean = str_replace_all(taxonomy, "D_[0-9]+__", 
    "")) %>% separate(col = taxo_clean, into = str_c("taxo", c(1:7)), sep = "; ")

# Read the sequences
otu_sequences <- readAAStringSet("../qiime/otu_rep_98.fna")
otu_sequences.df <- data.frame(names = names(otu_sequences), sequence = as.character(otu_sequences))

# Split the otu sequences name into the otu id given by qiime and the name
# of the representative sequence Remove the primers
fwd_length = 20
rev_length = 15
otu_sequences.df <- otu_sequences.df %>% separate(col = names, into = c("otu_id_qiime", 
    "otu_rep_seq"), sep = " ") %>% mutate(sequence = str_sub(sequence, start = fwd_length + 
    1, end = -rev_length - 1))
otu_table <- left_join(otu_table, otu_sequences.df)

# Write a fasta file for blast with all taxonomy roups
otu_sequences <- otu_table %>% transmute(sequence = sequence, seq_name = otu_id)
fasta_write(otu_sequences, file_name = "../qiime/otu_rep_98_all.fasta", compress = FALSE, 
    taxo_include = FALSE)
[1] TRUE

2.1.2 Only keep the eukaryotes in the OTU file

otu_table_euk <- otu_table %>% filter(str_detect(taxonomy, "Eukaryota|Unassigned"))

# Write the fasta file file
otu_sequences_euk <- otu_table_euk %>% transmute(sequence = sequence, seq_name = otu_id)
fasta_write(otu_sequences_euk, file_name = "../qiime/otu_rep_98_euk.fasta", 
    compress = FALSE, taxo_include = FALSE)
[1] TRUE

2.1.3 Use dada2 to reassign to PR2

dada2_assign(seq_file_name = "../qiime/otu_rep_98_euk.fasta")

2.1.4 Read the PR2 assignement and merge with initial otu table

otu_euk_pr2 <- read_tsv("../qiime/otu_rep_98_euk.dada2.taxo")
otu_euk_pr2_boot <- read_tsv("../qiime/otu_rep_98_euk.dada2.boot") %>% rename_all(funs(str_c(., 
    "_boot"))) %>% rename(seq_name = seq_name_boot)
otu_euk_pr2 <- left_join(otu_euk_pr2, otu_euk_pr2_boot) %>% rename(otu_id = seq_name)
otu_table_final <- left_join(otu_table, otu_euk_pr2) %>% select(otu_id, otu_id_qiime, 
    taxo1:taxo7, kingdom:species_boot, nseq, STJ11.S23:PR06.S10, otu_rep_seq, 
    sequence)

write_tsv(otu_table_final, "../qiime/otu_table_98_final.tsv")

2.2 Phyloseq analysis

2.2.1 Create phyloseq files for euk after filtering the data

Filter the euk data to remove the low bootstraps values (threshold : bootstrap > 90% at the supergroup level) and create a phyloseq file

Note the bootstrap threshold had to be higher for 98% compared to 97% (90% vs 65%)

otu_table_euk_final <- otu_table_final %>% filter(supergroup_boot > 90)
str_c("Number of euk otus : ", nrow(otu_table_euk_final))
[1] "Number of euk otus : 587"
otu_mat <- otu_table_euk_final %>% select(otu = otu_id, STJ11.S23:PR06.S10)
tax_mat <- otu_table_euk_final %>% select(otu = otu_id, kingdom:species)
samples_df <- sample_table %>% rename(sample = sample_id)

row.names(otu_mat) <- otu_mat$otu
otu_mat <- otu_mat %>% select(-otu)
row.names(tax_mat) <- tax_mat$otu
tax_mat <- tax_mat %>% select(-otu)
row.names(samples_df) <- samples_df$sample
samples_df <- samples_df %>% select(-sample)
otu_mat <- as.matrix(otu_mat)
tax_mat <- as.matrix(tax_mat)

OTU = otu_table(otu_mat, taxa_are_rows = TRUE)
TAX = tax_table(tax_mat)
samples = sample_data(samples_df)

singa_euk <- phyloseq(OTU, TAX, samples)
singa_euk
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 587 taxa and 64 samples ]
sample_data() Sample Data:       [ 64 samples by 6 sample variables ]
tax_table()   Taxonomy Table:    [ 587 taxa by 8 taxonomic ranks ]

Break up into photosynthetic and non-photosynthetic (Metazoa are removed)

singa_photo <- subset_taxa(singa_euk, (division %in% c("Chlorophyta", "Dinoflagellata", 
    "Cryptophyta", "Rhodophyta", "Haptophyta", "Ochrophyta", "Cercozoa")) & 
    !(class %in% c("Syndiniales", "Sarcomonadea")))
singa_photo
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 178 taxa and 64 samples ]
sample_data() Sample Data:       [ 64 samples by 6 sample variables ]
tax_table()   Taxonomy Table:    [ 178 taxa by 8 taxonomic ranks ]
singa_hetero <- subset_taxa(singa_euk, !(division %in% c("Chlorophyta", "Dinoflagellata", 
    "Cryptophyta", "Rhodophyta", "Haptophyta", "Ochrophyta", "Cercozoa", "Metazoa")) | 
    (class %in% c("Syndiniales", "Sarcomonadea")))

singa_hetero
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 260 taxa and 64 samples ]
sample_data() Sample Data:       [ 64 samples by 6 sample variables ]
tax_table()   Taxonomy Table:    [ 260 taxa by 8 taxonomic ranks ]

Normalize number of reads in each sample using median sequencing depth.

total_photo = median(sample_sums(singa_photo))
sprintf("The number of reads used for normalization of autotrophs is  %.0f", 
    total_photo)
[1] "The number of reads used for normalization of autotrophs is  383"
standf = function(x, t = total_photo) (t * (x/sum(x)))
singa_photo = transform_sample_counts(singa_photo, standf)

total_hetero = median(sample_sums(singa_hetero))
sprintf("The number of reads used for normalization of heterotrophs is  %.0f", 
    total_hetero)
[1] "The number of reads used for normalization of heterotrophs is  100"
standf = function(x, t = total_hetero) (t * (x/sum(x)))
singa_hetero = transform_sample_counts(singa_hetero, standf)

Remove taxa which are in low abundace (< 0.10 in any given sample) and normalize again…

contrib_min <- 0.1

# Photo abundant
singa_photo_abundant <- filter_taxa(singa_photo, function(x) sum(x > total_photo * 
    contrib_min) > 0, TRUE)

total = median(sample_sums(singa_photo_abundant))
sprintf("The number of reads used for normalization of abundant autotrophs is  %.0f", 
    total)
[1] "The number of reads used for normalization of abundant autotrophs is  290"
standf = function(x, t = total) (t * (x/sum(x)))
singa_photo_abundant = transform_sample_counts(singa_photo_abundant, standf)

singa_photo_abundant
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 26 taxa and 64 samples ]
sample_data() Sample Data:       [ 64 samples by 6 sample variables ]
tax_table()   Taxonomy Table:    [ 26 taxa by 8 taxonomic ranks ]
# Hetero abundant
singa_hetero_abundant <- filter_taxa(singa_hetero, function(x) sum(x > total_hetero * 
    contrib_min) > 0, TRUE)

total = median(sample_sums(singa_hetero_abundant))
sprintf("The number of reads used for normalization of abundant heterotrophs is  %.0f", 
    total)
[1] "The number of reads used for normalization of abundant heterotrophs is  52"
standf = function(x, t = total) (t * (x/sum(x)))
singa_hetero_abundant = transform_sample_counts(singa_hetero_abundant, standf)

singa_hetero_abundant
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 46 taxa and 64 samples ]
sample_data() Sample Data:       [ 64 samples by 6 sample variables ]
tax_table()   Taxonomy Table:    [ 46 taxa by 8 taxonomic ranks ]

2.2.2 Create phyloseq files for proks

otu_table_prok <- otu_table %>% filter(taxo1 %in% c("Bacteria", "Archaea"))

otu_mat <- otu_table_prok %>% select(otu = otu_id, STJ11.S23:PR06.S10)

tax_mat <- otu_table_prok %>% select(otu = otu_id, taxo1:taxo7) %>% rename(kingdom = taxo1, 
    supergroup = taxo2, division = taxo3, class = taxo4, order = taxo5, family = taxo6, 
    genus = taxo7) %>% mutate(species = NA)

samples_df <- sample_table %>% rename(sample = sample_id)

row.names(otu_mat) <- otu_mat$otu
otu_mat <- otu_mat %>% select(-otu)
row.names(tax_mat) <- tax_mat$otu
tax_mat <- tax_mat %>% select(-otu)
row.names(samples_df) <- samples_df$sample
samples_df <- samples_df %>% select(-sample)
otu_mat <- as.matrix(otu_mat)
tax_mat <- as.matrix(tax_mat)

OTU = otu_table(otu_mat, taxa_are_rows = TRUE)
TAX = tax_table(tax_mat)
samples = sample_data(samples_df)

singa_prok <- phyloseq(OTU, TAX, samples)

Normalize number of reads in each sample using median sequencing depth.

total_prok = median(sample_sums(singa_prok))
sprintf("The number of reads used for normalization of prokaryotes is  %.0f", 
    total_prok)
[1] "The number of reads used for normalization of prokaryotes is  7334"
standf = function(x, t = total_prok) (t * (x/sum(x)))
singa_prok = transform_sample_counts(singa_prok, standf)

singa_prok
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 1476 taxa and 64 samples ]
sample_data() Sample Data:       [ 64 samples by 6 sample variables ]
tax_table()   Taxonomy Table:    [ 1476 taxa by 8 taxonomic ranks ]

Remove otus which are in low abundace (< 0.05 in any given sample) and normalize again

contrib_min <- 0.05
singa_prok_abundant <- filter_taxa(singa_prok, function(x) sum(x > total_prok * 
    contrib_min) > 0, TRUE)
total = median(sample_sums(singa_prok_abundant))
sprintf("The number of reads used for normalization of abundant prokaryotes is  %.0f", 
    total)
[1] "The number of reads used for normalization of abundant prokaryotes is  3187"
standf = function(x, t = total) (t * (x/sum(x)))
singa_prok_abundant = transform_sample_counts(singa_prok_abundant, standf)

singa_prok_abundant
phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 42 taxa and 64 samples ]
sample_data() Sample Data:       [ 64 samples by 6 sample variables ]
tax_table()   Taxonomy Table:    [ 42 taxa by 8 taxonomic ranks ]

2.2.3 Create a list for the auto and hetero phyloseq files

ps_list <- list(ps = c(singa_photo, singa_hetero, singa_prok), title = c("Eukaryotes - Autotrophs - all OTUs", 
    "Eukaryotes - Heterotrophs - all OTUs", "Prokaryotes - all OTUs"))
ps_list_abundant <- list(ps = c(singa_photo_abundant, singa_hetero_abundant, 
    singa_prok_abundant), title = c("Eukaryotes - Autotrophs - abundant OTUs (> 10%)", 
    "Eukaryotes - Heterotrophs - abundant OTUs (> 10%)", "Prokaryotes - abundant OTUs (> 5%)"))

2.2.4 Bar plot of divisions per station for auto and hetero

for (i in 1:3) {
    p <- plot_bar(ps_list$ps[[i]], x = "sample_label", fill = "division") + 
        geom_bar(aes(color = division, fill = division), stat = "identity", 
            position = "stack") + ggtitle(str_c("Division level - ", ps_list$title[[i]])) + 
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
    print(p)
}

2.2.5 Bar plot of class per station for auto and hetero

Only consider the abundant taxa

for (i in 1:3) {
    p <- plot_bar(ps_list_abundant$ps[[i]], x = "sample_label", fill = "class") + 
        geom_bar(aes(color = class, fill = class), stat = "identity", position = "stack") + 
        ggtitle(str_c("Class level - ", ps_list_abundant$title[[i]])) + theme(axis.text.x = element_text(angle = 90, 
        vjust = 0.5, hjust = 1))
    print(p)
}

2.2.6 Compare by Straight, Site, Moonsoon (abundant OTUs only)

for (factor in c("strait", "site", "moonsoon")) {
    for (i in 1:3) {
        ps_aggregate <- merge_samples(ps_list_abundant$ps[[i]], factor)
        p <- plot_bar(ps_aggregate, fill = "division") + geom_bar(aes(color = division, 
            fill = division), stat = "identity", position = "stack") + ggtitle(str_c(ps_list_abundant$title[[i]], 
            " - ", factor)) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, 
            hjust = 1))
        print(p)
    }
}

2.2.7 Main genera for different division of Eukaryotes (Autotrophs)

for (one_division in c("Chlorophyta", "Dinoflagellata", "Ochrophyta")) {
    ps_subset <- subset_taxa(singa_photo_abundant, division %in% one_division)
    p <- plot_bar(ps_subset, x = "genus", fill = "genus", facet_grid = strait ~ 
        moonsoon) + geom_bar(aes(color = genus, fill = genus), stat = "identity", 
        position = "stack") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, 
        hjust = 1)) + ggtitle(str_c(one_division, " - Abundant OTUs"))
    print(p)
}

2.2.8 Main species of Mamiellophyceae and Diatoms (Eukaryotes - Autrotrophs)

for (one_class in c("Mamiellophyceae", "Bacillariophyta")) {
    ps_subset <- subset_taxa(singa_photo_abundant, class %in% one_class)
    p <- plot_bar(ps_subset, x = "species", fill = "species", facet_grid = strait ~ 
        moonsoon) + geom_bar(aes(color = species, fill = species), stat = "identity", 
        position = "stack") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, 
        hjust = 1)) + ggtitle(str_c(one_class, " - Abundant OTUs"))
    print(p)
}

2.2.9 Heatmaps (abundant OTUs)

for (i in 1:2) {
    
    p <- plot_heatmap(ps_list_abundant$ps[[i]], method = "NMDS", distance = "bray", 
        taxa.label = "species", taxa.order = "division", sample.label = "sample_label", 
        sample.order = "sample_label", low = "beige", high = "red", na.value = "beige", 
        title = ps_list_abundant$title[[i]])
    print(p)
}

for (i in c(3)) {
    
    p <- plot_heatmap(ps_list_abundant$ps[[i]], method = "NMDS", distance = "bray", 
        taxa.label = "family", taxa.order = "division", sample.label = "sample_label", 
        sample.order = "sample_label", low = "beige", high = "red", na.value = "beige", 
        title = ps_list_abundant$title[[i]])
    print(p)
}

2.2.10 NMDS

All OTUs

for (i in 1:3) {
    
    singa.ord <- ordinate(ps_list$ps[[i]], "NMDS", "bray")
    print(singa.ord)
    
    p <- plot_ordination(ps_list$ps[[i]], singa.ord, type = "samples", color = "moonsoon", 
        shape = "strait", title = ps_list$title[[i]]) + geom_point(size = 3) + 
        scale_colour_manual(values = c("yellow", "red", "blue"))
    
    print(p)
}
Square root transformation
Wisconsin double standardization
Run 0 stress 0.14 
Run 1 stress 0.15 
Run 2 stress 0.15 
Run 3 stress 0.15 
Run 4 stress 0.15 
Run 5 stress 0.15 
Run 6 stress 0.14 
Run 7 stress 0.15 
Run 8 stress 0.16 
Run 9 stress 0.15 
Run 10 stress 0.16 
Run 11 stress 0.16 
Run 12 stress 0.16 
Run 13 stress 0.15 
Run 14 stress 0.15 
Run 15 stress 0.15 
Run 16 stress 0.15 
Run 17 stress 0.16 
Run 18 stress 0.14 
... New best solution
... Procrustes: rmse 0.048  max resid 0.21 
Run 19 stress 0.15 
Run 20 stress 0.14 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.14 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))' 

Square root transformation
Wisconsin double standardization
Run 0 stress 0.18 
Run 1 stress 0.18 
Run 2 stress 0.18 
Run 3 stress 0.18 
Run 4 stress 0.18 
Run 5 stress 0.18 
Run 6 stress 0.18 
Run 7 stress 0.18 
Run 8 stress 0.17 
... New best solution
... Procrustes: rmse 0.032  max resid 0.16 
Run 9 stress 0.19 
Run 10 stress 0.18 
Run 11 stress 0.19 
Run 12 stress 0.18 
Run 13 stress 0.18 
Run 14 stress 0.18 
Run 15 stress 0.2 
Run 16 stress 0.18 
Run 17 stress 0.18 
Run 18 stress 0.18 
Run 19 stress 0.18 
Run 20 stress 0.18 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.17 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))' 

Square root transformation
Wisconsin double standardization
Run 0 stress 0.089 
Run 1 stress 0.1 
Run 2 stress 0.11 
Run 3 stress 0.11 
Run 4 stress 0.1 
Run 5 stress 0.11 
Run 6 stress 0.097 
Run 7 stress 0.11 
Run 8 stress 0.11 
Run 9 stress 0.11 
Run 10 stress 0.1 
Run 11 stress 0.11 
Run 12 stress 0.11 
Run 13 stress 0.11 
Run 14 stress 0.095 
Run 15 stress 0.11 
Run 16 stress 0.11 
Run 17 stress 0.11 
Run 18 stress 0.11 
Run 19 stress 0.11 
Run 20 stress 0.1 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.089 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))'

Abundant OTUs

for (i in 1:3) {
    
    singa.ord <- ordinate(ps_list_abundant$ps[[i]], "NMDS", "bray")
    print(singa.ord)
    
    p <- plot_ordination(ps_list$ps[[i]], singa.ord, type = "samples", color = "moonsoon", 
        shape = "strait", title = ps_list_abundant$title[[i]]) + geom_point(size = 3) + 
        scale_colour_manual(values = c("yellow", "red", "blue"))
    print(p)
    
    p <- plot_ordination(ps_list$ps[[i]], singa.ord, type = "taxa", color = "division", 
        shape = "division", title = ps_list_abundant$title[[i]]) + geom_point(size = 3)
    print(p)
}
Square root transformation
Wisconsin double standardization
Run 0 stress 0.14 
Run 1 stress 0.17 
Run 2 stress 0.15 
Run 3 stress 0.17 
Run 4 stress 0.16 
Run 5 stress 0.18 
Run 6 stress 0.17 
Run 7 stress 0.18 
Run 8 stress 0.17 
Run 9 stress 0.17 
Run 10 stress 0.18 
Run 11 stress 0.17 
Run 12 stress 0.18 
Run 13 stress 0.15 
Run 14 stress 0.17 
Run 15 stress 0.18 
Run 16 stress 0.17 
Run 17 stress 0.17 
Run 18 stress 0.17 
Run 19 stress 0.18 
Run 20 stress 0.17 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.14 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))' 

Wisconsin double standardization
Run 0 stress 0.24 
Run 1 stress 0.25 
Run 2 stress 0.25 
Run 3 stress 0.24 
... Procrustes: rmse 0.0063  max resid 0.038 
Run 4 stress 0.26 
Run 5 stress 0.25 
Run 6 stress 0.24 
... Procrustes: rmse 0.012  max resid 0.06 
Run 7 stress 0.25 
Run 8 stress 0.26 
Run 9 stress 0.25 
Run 10 stress 0.25 
Run 11 stress 0.25 
Run 12 stress 0.25 
Run 13 stress 0.24 
... New best solution
... Procrustes: rmse 0.0037  max resid 0.023 
Run 14 stress 0.24 
Run 15 stress 0.26 
Run 16 stress 0.26 
Run 17 stress 0.26 
Run 18 stress 0.26 
Run 19 stress 0.26 
Run 20 stress 0.27 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(veganifyOTU(physeq)) 
Distance: bray 

Dimensions: 2 
Stress:     0.24 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(veganifyOTU(physeq))' 

Square root transformation
Wisconsin double standardization
Run 0 stress 0.12 
Run 1 stress 0.13 
Run 2 stress 0.13 
Run 3 stress 0.13 
Run 4 stress 0.14 
Run 5 stress 0.13 
Run 6 stress 0.13 
Run 7 stress 0.13 
Run 8 stress 0.13 
Run 9 stress 0.13 
Run 10 stress 0.13 
Run 11 stress 0.14 
Run 12 stress 0.14 
Run 13 stress 0.13 
Run 14 stress 0.12 
Run 15 stress 0.13 
Run 16 stress 0.14 
Run 17 stress 0.15 
Run 18 stress 0.13 
Run 19 stress 0.13 
Run 20 stress 0.13 
*** No convergence -- monoMDS stopping criteria:
    20: stress ratio > sratmax

Call:
metaMDS(comm = veganifyOTU(physeq), distance = distance) 

global Multidimensional Scaling using monoMDS

Data:     wisconsin(sqrt(veganifyOTU(physeq))) 
Distance: bray 

Dimensions: 2 
Stress:     0.12 
Stress type 1, weak ties
No convergent solutions - best solution after 20 tries
Scaling: centring, PC rotation, halfchange scaling 
Species: expanded scores based on 'wisconsin(sqrt(veganifyOTU(physeq)))'

2.2.11 Network analysis

for (i in 1:2) {
    
    p <- plot_net(ps_list_abundant$ps[[i]], distance = "(A+B-2*J)/(A+B)", type = "taxa", 
        maxdist = 0.4, color = "class", point_label = "genus") + ggtitle(ps_list_abundant$title[[i]])
    
    print(p)
}

for (i in c(3)) {
    
    p <- plot_net(ps_list_abundant$ps[[i]], distance = "(A+B-2*J)/(A+B)", type = "taxa", 
        maxdist = 0.4, color = "class", point_label = "family") + ggtitle(ps_list_abundant$title[[i]])
    
    print(p)
}

Daniel Vaulot, Adriana Lopes dos Santos

10 07 2018