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Introduction

We are exploring the clustering of samples into vaginotype groups and comparing the best approach.

Load mre

## mre init
mre <- readr::read_rds(here::here("data", "preprocessed", "filt_mre.rds"))

## sub mre init & filter
mre_sub <- 
  metar::get_meta(mre) %>%
  dplyr::filter(Timepoint %in% c(0, 14, 26),
                !PatientID %in% c("p_1004","p_1024","p_1037","p_1093","p_1095","p_1108","p_1117","p_1144","p_2006","p_2053","p_2059","p_2096","p_2105","p_2124","p_3021","p_3025")) %>%
  dplyr::pull(SampleID) %>%
  metar::filter_samples(mre, sample_ids = .)

phy <- metar::get_phyloseq(mre_sub, type = "virgo")

mre_sub <- metar::remove_cat(mre_sub, "Plasm_IgG_3_A244_D11")

mre_sub@taxa@virgo@phyloseq <- phy

Load and represent Sam data

sam_vag <- readr::read_csv("~/Documents/RV306/vaginal/Samantha data 08_10_24/RV306_CVM_MBGroups_n97_Pearson4Branches_MSdata.csv") %>%
  dplyr::select(-...1, -freq)
colnames(sam_vag)[1] <- "PatientID"
colnames(sam_vag)[2] <- "V1_Sam"
colnames(sam_vag)[3] <- "V4_Sam"
colnames(sam_vag)[4] <- "V6_Sam"
colnames(sam_vag)[5] <- "Long_changes_Sam"

all_vag <- sam_vag %>% 
  dplyr::mutate(PatientID = paste("p_", PatientID, sep = "")) %>%
  dplyr::select(-5)

# generate cluster data
sam_ordered <- all_vag %>%
  tidyr::pivot_longer(cols = starts_with("V"), names_to = "Sample", values_to = "Value") %>% 
  dplyr::select(-2)
sam_ordered$SampleID <- mre_sub@metadata@metadata_df$SampleID
sam_ordered <- sam_ordered %>% 
  dplyr::arrange(SampleID)
clusters <- as.vector(sam_ordered$Value)
# Plot NMDS
distance_matrix <- vegan::vegdist(as.data.frame(phyloseq::otu_table(mre_sub@taxa@virgo@phyloseq@otu_table)) %>% 
  t(.) %>% as.data.frame(), method = "bray")

nmds <- vegan::metaMDS(distance_matrix, k = 2, trymax = 100, trace = 0)

nmds_data <- as.data.frame(nmds$points)
nmds_data$Cluster <- as.factor(clusters)
nmds_data$SampleID <- rownames(nmds_data)

ggplot(nmds_data, aes(x = MDS1, y = MDS2, color = Cluster, label = SampleID)) +
  geom_point(size = 4) +
  geom_text(vjust = -0.5, hjust = 0.5) +
  theme_minimal() +
  labs(title = "NMDS Plot Colored by Sam Generated Clusters", x = "NMDS1", y = "NMDS2") +
  scale_color_manual(values = c("red", "blue", "green", "purple"))

ps <- metar::get_phyloseq(mre_sub, type = "virgo")
ps_RA <- phyloseq::transform_sample_counts(ps, function(x) x / sum(x))
bray_pcoa  <-
  phyloseq::ordinate(physeq = ps_RA, method = "PCoA", distance = "bray")

ps@sam_data$Cluster <- as.factor(clusters)

phyloseq::plot_ordination(
  physeq = ps_RA,
  ordination = bray_pcoa,
  axes = c(1,2)
  ) +
  geom_point(aes(
    color = clusters
  ), size = 1, stroke = 1) +
  theme_classic() +
  labs(title = "PCo1 vs PCo2", color = "Cluster")

## run barplots 
mre_sub <- metar::virgo_barplots(mre_sub, top_n = 30, save_files = FALSE)

met <- metar::get_meta(mre_sub) %>%
  dplyr::right_join(sam_ordered, met, by = NULL) %>% 
  dplyr::right_join(mre_sub@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot$data, met, by = "SampleID")

plot <- mre_sub@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot

plot$data <- met

plot +
  theme(axis.text.x = element_blank()) +
  facet_wrap(Value ~ ., scales = "free") +
  theme_classic() +
  theme(axis.text.x = element_blank())

Using metar

We’ll first perform the PAM (Partitioning Around Medoids) clustering analysis in order to broadly detect these vagintoypes and be able to uniquely assign each sample to a vaginotype, while ensuring that this assignment does not change in downstream analysis.

All samples

## run nmds
mre <- metar::virgo_nmds(mre, top_n = 30, save_files = FALSE)

## create dataframe
metar_all_vag <- 
  mre %>% 
  metar::get_taxa("virgo", "nmds") %>% 
  purrr::pluck("ta1", "top_30", "clustering_results", "sample_cluster_labels") %>% 
  dplyr::mutate(
    vaginotype = dplyr::case_when(
      cluster == 1 ~ "L.crispatus", 
      cluster == 2 ~ "G.vaginalis", 
      cluster == 3 ~ "L.iners"
    ),
    vaginotype_condition = dplyr::if_else(cluster == 2, "nLD", "LD") 
  ) %>% 
  dplyr::select(SampleID, vaginotype, vaginotype_condition) %>% 
  dplyr::left_join(mre@metadata@metadata_df %>% dplyr::select(SampleID, PatientID, Timepoint)) %>% 
  dplyr::arrange(PatientID, Timepoint) %>%
  dplyr::group_by(PatientID) %>%
  dplyr::mutate(Timepoint_label = paste0("V", dplyr::row_number(), "_Metar")) %>%
  dplyr::select(PatientID, Timepoint_label, vaginotype) %>%
  tidyr::spread(key = Timepoint_label, value = vaginotype) %>% 
  dplyr::select(-5, -6)
  

colnames(metar_all_vag)[2] <- "V1_metar_all"
colnames(metar_all_vag)[3] <- "V4_metar_all"
colnames(metar_all_vag)[4] <- "V6_metar_all"

# join
all_vag  <- merge(all_vag, metar_all_vag, all=TRUE)
# Plot NMDS
clusters <- 
  mre %>% 
  metar::get_taxa("virgo", "nmds") %>% 
  purrr::pluck("ta1", "top_30", "clustering_results", "sample_cluster_labels") %>% 
  dplyr::mutate(
    vaginotype = dplyr::case_when(
      cluster == 1 ~ "L.crispatus", 
      cluster == 2 ~ "G.vaginalis", 
      cluster == 3 ~ "L.iners"
    )) %>% 
  dplyr::select(cluster) %>%
  dplyr::pull()

distance_matrix <- vegan::vegdist(as.data.frame(phyloseq::otu_table(mre@taxa@virgo@phyloseq@otu_table)) %>% 
  t(.) %>% as.data.frame(), method = "bray")

nmds <- vegan::metaMDS(distance_matrix, k = 2, trymax = 100, trace = 0)

nmds_data <- as.data.frame(nmds$points)
nmds_data$Cluster <- as.factor(clusters)
nmds_data$SampleID <- rownames(nmds_data)

ggplot(nmds_data, aes(x = MDS1, y = MDS2, color = Cluster, label = SampleID)) +
  geom_point(size = 4) +
  geom_text(vjust = -0.5, hjust = 0.5) +
  theme_minimal() +
  labs(title = "NMDS Plot Colored by metar_all samples Clusters", x = "NMDS1", y = "NMDS2") +
  scale_color_manual(values = c("red", "blue", "green", "purple"))

ps <- metar::get_phyloseq(mre, type = "virgo")
ps_RA <- phyloseq::transform_sample_counts(ps, function(x) x / sum(x))
bray_pcoa  <-
  phyloseq::ordinate(physeq = ps_RA, method = "PCoA", distance = "bray")

ps@sam_data$Cluster <- as.factor(clusters)

phyloseq::plot_ordination(
  physeq = ps_RA,
  ordination = bray_pcoa,
  axes = c(1,2)
  ) +
  geom_point(aes(
    color = as.factor(clusters)
  ), size = 1, stroke = 1) +
  theme_classic() +
  labs(title = "PCo1 vs PCo2", color = "Cluster")

# Create dataframe to merge
metar_all_vag <- 
  mre %>% 
  metar::get_taxa("virgo", "nmds") %>% 
  purrr::pluck("ta1", "top_30", "clustering_results", "sample_cluster_labels") %>% 
  dplyr::mutate(
    vaginotype = dplyr::case_when(
      cluster == 1 ~ "L.crispatus", 
      cluster == 2 ~ "G.vaginalis", 
      cluster == 3 ~ "L.iners"
    ),
    vaginotype_condition = dplyr::if_else(cluster == 2, "nLD", "LD") 
  ) %>% 
  dplyr::select(SampleID, vaginotype)

## run barplots 
mre <- metar::virgo_barplots(mre, top_n = 30, save_files = FALSE)

met <- metar::get_meta(mre) %>%
  dplyr::right_join(metar_all_vag, met, by = NULL) %>% 
  dplyr::right_join(mre@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot$data, met, by = "SampleID")

plot <- mre@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot

plot$data <- met

plot +
  theme(axis.text.x = element_blank()) +
  facet_wrap(vaginotype ~ ., scales = "free") +
  theme_classic() +
  theme(axis.text.x = element_blank())

Selected samples

## run nmds
mre_sub <- metar::virgo_nmds(mre_sub, top_n = 30, save_files = FALSE)

## create dataframe
metar_sel_vag <- 
  mre_sub %>% 
  metar::get_taxa("virgo", "nmds") %>% 
  purrr::pluck("ta1", "top_30", "clustering_results", "sample_cluster_labels") %>% 
  dplyr::mutate(
    vaginotype = dplyr::case_when(
      cluster == 1 ~ "L.iners", 
      cluster == 2 ~ "L.crispatus", 
      cluster == 3 ~ "G.vaginalis"
    ),
    vaginotype_condition = dplyr::if_else(cluster == 2, "nLD", "LD") 
  ) %>% 
  dplyr::select(SampleID, vaginotype, vaginotype_condition) %>% 
  dplyr::left_join(mre_sub@metadata@metadata_df %>% dplyr::select(SampleID, PatientID, Timepoint)) %>% 
  dplyr::arrange(PatientID, Timepoint) %>%
  dplyr::group_by(PatientID) %>%
  dplyr::mutate(Timepoint_label = paste0("V", dplyr::row_number(), "_Metar")) %>%
  dplyr::select(PatientID, Timepoint_label, vaginotype) %>%
  tidyr::spread(key = Timepoint_label, value = vaginotype)
  

colnames(metar_sel_vag)[2] <- "V1_metar_selected"
colnames(metar_sel_vag)[3] <- "V4_metar_selected"
colnames(metar_sel_vag)[4] <- "V6_metar_selected"

# join
all_vag  <- merge(all_vag, metar_sel_vag, all=TRUE)
# Plot NMDS
clusters <- 
  mre_sub %>% 
  metar::get_taxa("virgo", "nmds") %>% 
  purrr::pluck("ta1", "top_30", "clustering_results", "sample_cluster_labels") %>% 
  dplyr::mutate(
    vaginotype = dplyr::case_when(
      cluster == 1 ~ "L.iners", 
      cluster == 2 ~ "L.crispatus", 
      cluster == 3 ~ "G.vaginalis"
    )) %>% 
  dplyr::select(cluster) %>%
  dplyr::pull()

distance_matrix <- vegan::vegdist(as.data.frame(phyloseq::otu_table(mre_sub@taxa@virgo@phyloseq@otu_table)) %>% 
  t(.) %>% as.data.frame(), method = "bray")

nmds <- vegan::metaMDS(distance_matrix, k = 2, trymax = 100, trace = 0)

nmds_data <- as.data.frame(nmds$points)
nmds_data$Cluster <- as.factor(clusters)
nmds_data$SampleID <- rownames(nmds_data)

ggplot(nmds_data, aes(x = MDS1, y = MDS2, color = Cluster, label = SampleID)) +
  geom_point(size = 4) +
  geom_text(vjust = -0.5, hjust = 0.5) +
  theme_minimal() +
  labs(title = "NMDS Plot Colored by metar_selected samples Clusters", x = "NMDS1", y = "NMDS2") +
  scale_color_manual(values = c("red", "blue", "green", "purple"))

ps <- metar::get_phyloseq(mre_sub, type = "virgo")
ps_RA <- phyloseq::transform_sample_counts(ps, function(x) x / sum(x))
bray_pcoa  <-
  phyloseq::ordinate(physeq = ps_RA, method = "PCoA", distance = "bray")

ps@sam_data$Cluster <- as.factor(clusters)

phyloseq::plot_ordination(
  physeq = ps_RA,
  ordination = bray_pcoa,
  axes = c(1,2)
  ) +
  geom_point(aes(
    color = as.factor(clusters)
  ), size = 1, stroke = 1) +
  theme_classic() +
  labs(title = "PCo1 vs PCo2", color = "Cluster")

# Create dataframe to merge
metar_sel_vag <- 
  mre_sub %>% 
  metar::get_taxa("virgo", "nmds") %>% 
  purrr::pluck("ta1", "top_30", "clustering_results", "sample_cluster_labels") %>% 
  dplyr::mutate(
    vaginotype = dplyr::case_when(
      cluster == 1 ~ "L.iners", 
      cluster == 2 ~ "L.crispatus", 
      cluster == 3 ~ "G.vaginalis"
    ),
    vaginotype_condition = dplyr::if_else(cluster == 2, "nLD", "LD") 
  ) %>% 
  dplyr::select(SampleID, vaginotype)

## run barplots 
mre <- metar::virgo_barplots(mre, top_n = 30, save_files = FALSE)

met <- metar::get_meta(mre_sub) %>%
  dplyr::right_join(metar_sel_vag, met, by = NULL) %>% 
  dplyr::right_join(mre_sub@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot$data, met, by = "SampleID")

plot <- mre_sub@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot

plot$data <- met

plot +
  theme(axis.text.x = element_blank()) +
  facet_wrap(vaginotype ~ ., scales = "free") +
  theme_classic() +
  theme(axis.text.x = element_blank())

Using functions

However, we will try to split the samples as the Burgener lab did, determining MB Groups based on complete hierarchical clustering using pearson distance, and split into 4 branches, using same samples as Sam:

All samples

# Obtain abundance data dataframe
abundance_data <- as.data.frame(phyloseq::otu_table(mre@taxa@virgo@phyloseq@otu_table)) %>% 
  t(.) %>% as.data.frame()

# calculate Pearson distance matrix
pearson_distance <- as.dist(1 - cor(t(abundance_data), method = "pearson"))

# Perform hierarchical clustering using complete linkage
hc <- hclust(pearson_distance, method = "complete")

# Cut the dendrogram to create 4 clusters
clusters <- cutree(hc, k = 4)

# Add the cluster assignments to the original data
abundance_data$Cluster <- clusters
abundance_data$SampleID <- rownames(abundance_data)

# Optional: Plot the dendrogram
plot(hc, main = "Hierarchical Clustering Dendrogram", sub = "", xlab = "",
     ylab = "Height")
rect.hclust(hc, k = 4, border = "red")

oriol_all_vag <- 
  abundance_data %>%
  dplyr::select(SampleID, Cluster) %>% 
  dplyr::left_join(mre@metadata@metadata_df %>% dplyr::select(SampleID, PatientID, Timepoint)) %>% 
  dplyr::arrange(PatientID, Timepoint) %>%
  dplyr::group_by(PatientID) %>%
  dplyr::mutate(Timepoint_label = paste0("V", dplyr::row_number(), "_Metar")) %>%
  dplyr::select(PatientID, Timepoint_label, Cluster) %>%
  tidyr::spread(key = Timepoint_label, value = Cluster) %>% 
  dplyr::select(-5, -6)

colnames(oriol_all_vag)[2] <- "V1_oriol_all"
colnames(oriol_all_vag)[3] <- "V4_oriol_all"
colnames(oriol_all_vag)[4] <- "V6_oriol_all"

# join
all_vag  <- merge(all_vag, oriol_all_vag, all=TRUE)
# Plot NMDS
distance_matrix <- vegan::vegdist(as.data.frame(phyloseq::otu_table(mre@taxa@virgo@phyloseq@otu_table)) %>% 
  t(.) %>% as.data.frame(), method = "bray")

nmds <- vegan::metaMDS(distance_matrix, k = 2, trymax = 100, trace = 0)

nmds_data <- as.data.frame(nmds$points)
nmds_data$Cluster <- as.factor(clusters)
nmds_data$SampleID <- rownames(nmds_data)

ggplot(nmds_data, aes(x = MDS1, y = MDS2, color = Cluster, label = SampleID)) +
  geom_point(size = 4) +
  geom_text(vjust = -0.5, hjust = 0.5) +
  theme_minimal() +
  labs(title = "NMDS Plot Colored by hierarchical clustering_all samples Clusters", x = "NMDS1", y = "NMDS2") +
  scale_color_manual(values = c("red", "blue", "green", "purple"))

ps <- metar::get_phyloseq(mre, type = "virgo")
ps_RA <- phyloseq::transform_sample_counts(ps, function(x) x / sum(x))
bray_pcoa  <-
  phyloseq::ordinate(physeq = ps_RA, method = "PCoA", distance = "bray")

ps@sam_data$Cluster <- as.factor(clusters)

phyloseq::plot_ordination(
  physeq = ps_RA,
  ordination = bray_pcoa,
  axes = c(1,2)
  ) +
  geom_point(aes(
    color = as.factor(clusters)
  ), size = 1, stroke = 1) +
  theme_classic() +
  labs(title = "PCo1 vs PCo2", color = "Cluster")

# Create dataframe to merge
oriol_all_vag <- 
  abundance_data %>%
  dplyr::select(SampleID, Cluster) 

met <- metar::get_meta(mre) %>%
  dplyr::right_join(oriol_all_vag, met, by = NULL) %>% 
  dplyr::right_join(mre@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot$data, met, by = "SampleID")

plot <- mre@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot

plot$data <- met

plot +
  theme(axis.text.x = element_blank()) +
  facet_wrap(Cluster ~ ., scales = "free") +
  theme_classic() +
  theme(axis.text.x = element_blank())

Selected samples

# Obtain abundance data dataframe
abundance_data <- as.data.frame(phyloseq::otu_table(mre_sub@taxa@virgo@phyloseq@otu_table)) %>% 
  t(.) %>% as.data.frame()

# calculate Pearson distance matrix
pearson_distance <- as.dist(1 - cor(t(abundance_data), method = "pearson"))

# Perform hierarchical clustering using complete linkage
hc <- hclust(pearson_distance, method = "complete")

# Cut the dendrogram to create 4 clusters
clusters <- cutree(hc, k = 4)

# Add the cluster assignments to the original data
abundance_data$Cluster <- clusters
abundance_data$SampleID <- rownames(abundance_data)

# Optional: Plot the dendrogram
plot(hc, main = "Hierarchical Clustering Dendrogram", sub = "", xlab = "",
     ylab = "Height")
rect.hclust(hc, k = 4, border = "red")

oriol_sel_vag <- 
  abundance_data %>%
  dplyr::select(SampleID, Cluster) %>% 
  dplyr::left_join(mre_sub@metadata@metadata_df %>% dplyr::select(SampleID, PatientID, Timepoint)) %>% 
  dplyr::arrange(PatientID, Timepoint) %>%
  dplyr::group_by(PatientID) %>%
  dplyr::mutate(Timepoint_label = paste0("V", dplyr::row_number(), "_Metar")) %>%
  dplyr::select(PatientID, Timepoint_label, Cluster) %>%
  tidyr::spread(key = Timepoint_label, value = Cluster)

colnames(oriol_sel_vag)[2] <- "V1_oriol_selected"
colnames(oriol_sel_vag)[3] <- "V4_oriol_selected"
colnames(oriol_sel_vag)[4] <- "V6_oriol_selected"

# join
all_vag  <- merge(all_vag, oriol_sel_vag, sort = TRUE,  all=TRUE)
# Plot NMDS
distance_matrix <- vegan::vegdist(as.data.frame(phyloseq::otu_table(mre_sub@taxa@virgo@phyloseq@otu_table)) %>% 
  t(.) %>% as.data.frame(), method = "bray")

nmds <- vegan::metaMDS(distance_matrix, k = 2, trymax = 100, trace = 0)

nmds_data <- as.data.frame(nmds$points)
nmds_data$Cluster <- as.factor(clusters)
nmds_data$SampleID <- rownames(nmds_data)

ggplot(nmds_data, aes(x = MDS1, y = MDS2, color = Cluster, label = SampleID)) +
  geom_point(size = 4) +
  geom_text(vjust = -0.5, hjust = 0.5) +
  theme_minimal() +
  labs(title = "NMDS Plot Colored by hierarchical clustering_selected samples Clusters", x = "NMDS1", y = "NMDS2") +
  scale_color_manual(values = c("red", "blue", "green", "purple"))

ps <- metar::get_phyloseq(mre_sub, type = "virgo")
ps_RA <- phyloseq::transform_sample_counts(ps, function(x) x / sum(x))
bray_pcoa  <-
  phyloseq::ordinate(physeq = ps_RA, method = "PCoA", distance = "bray")

ps@sam_data$Cluster <- as.factor(clusters)

phyloseq::plot_ordination(
  physeq = ps_RA,
  ordination = bray_pcoa,
  axes = c(1,2)
  ) +
  geom_point(aes(
    color = as.factor(clusters)
  ), size = 1, stroke = 1) +
  theme_classic() +
  labs(title = "PCo1 vs PCo2", color = "Cluster")

# Create dataframe to merge
oriol_sel_vag <- 
  abundance_data %>%
  dplyr::select(SampleID, Cluster) 

met <- metar::get_meta(mre_sub) %>%
  dplyr::right_join(oriol_sel_vag, met, by = NULL) %>% 
  dplyr::right_join(mre_sub@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot$data, met, by = "SampleID")

plot <- mre_sub@taxa@virgo@barplots$ta1$top_30$rel_abundance$bray_NMDS1_order_barplot

plot$data <- met

plot +
  theme(axis.text.x = element_blank()) +
  facet_wrap(Cluster ~ ., scales = "free") +
  theme_classic() +
  theme(axis.text.x = element_blank())

Table

all_vag %>%
  kableExtra::kable(caption = "Clustering of samples using different methods") %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  kableExtra::row_spec(0, bold = TRUE) %>%
  kableExtra::column_spec(1, bold = FALSE) %>% 
  kableExtra::add_header_above(c(" ", "Sam" = 3, "Metar package all samples" = 3, "Metar package selected samples" = 3, "Oriol all samples" = 3, "Oriol selected samples" = 3))
Clustering of samples using different methods
Sam
Metar package all samples
Metar package selected samples
Oriol all samples
Oriol selected samples
PatientID V1_Sam V4_Sam V6_Sam V1_metar_all V4_metar_all V6_metar_all V1_metar_selected V4_metar_selected V6_metar_selected V1_oriol_all V4_oriol_all V6_oriol_all V1_oriol_selected V4_oriol_selected V6_oriol_selected
p_1004 NA NA NA L.iners L.iners L.iners NA NA NA 1 2 2 NA NA NA
p_1005 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1008 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_1009 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus G.vaginalis L.crispatus G.vaginalis 1 1 1 1 1 1
p_1019 Polymicrobial L.crispatus L.crispatus G.vaginalis L.crispatus L.crispatus G.vaginalis L.crispatus L.crispatus 2 1 1 3 1 1
p_1024 NA NA NA G.vaginalis G.vaginalis G.vaginalis NA NA NA 2 2 2 NA NA NA
p_1025 L.iners Li-anaerobes Polymicrobial L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis 1 2 2 1 3 3
p_1026 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1030 Polymicrobial Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1033 Polymicrobial Polymicrobial Li-anaerobes G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1037 NA NA NA L.crispatus L.iners NA NA NA NA 1 1 NA NA NA NA
p_1039 Polymicrobial Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1043 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_1044 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1046 Polymicrobial L.crispatus L.crispatus G.vaginalis L.crispatus L.crispatus G.vaginalis L.crispatus L.crispatus 2 1 1 3 1 1
p_1050 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1057 Li-anaerobes Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1058 L.iners L.crispatus Polymicrobial L.iners L.crispatus L.crispatus L.iners L.crispatus G.vaginalis 1 1 1 1 1 1
p_1059 L.crispatus L.crispatus L.crispatus G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1061 Polymicrobial Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1063 L.crispatus L.iners L.iners L.crispatus L.iners L.iners L.crispatus L.iners L.iners 1 1 1 1 1 1
p_1067 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1069 Polymicrobial Polymicrobial L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis L.iners 2 2 1 3 3 1
p_1072 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1073 Polymicrobial L.crispatus L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis L.iners 2 2 1 3 3 1
p_1074 L.crispatus L.crispatus Polymicrobial L.crispatus L.crispatus G.vaginalis L.crispatus L.crispatus G.vaginalis 1 1 2 1 1 3
p_1078 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1081 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_1082 L.crispatus Polymicrobial Polymicrobial L.crispatus G.vaginalis G.vaginalis L.crispatus G.vaginalis G.vaginalis 1 2 2 1 3 3
p_1083 Polymicrobial L.iners Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1086 L.iners L.iners NA L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1088 L.crispatus L.crispatus L.iners L.crispatus L.crispatus L.iners L.crispatus L.crispatus L.iners 1 1 1 1 1 1
p_1090 L.iners L.iners Polymicrobial L.crispatus L.iners G.vaginalis L.iners L.iners G.vaginalis 1 1 2 1 1 3
p_1091 L.iners L.iners L.iners L.iners L.crispatus L.iners L.iners L.crispatus L.iners 1 1 1 1 1 1
p_1093 NA NA NA L.iners L.iners NA NA NA NA 1 1 NA NA NA NA
p_1094 Polymicrobial Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1095 NA NA NA G.vaginalis G.vaginalis G.vaginalis NA NA NA 2 2 2 NA NA NA
p_1096 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1100 L.iners Polymicrobial L.iners G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1103 L.crispatus L.iners Li-anaerobes L.crispatus L.iners G.vaginalis L.crispatus L.iners G.vaginalis 1 1 2 1 1 3
p_1104 L.iners Polymicrobial Polymicrobial L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis 1 2 2 1 3 3
p_1108 NA NA NA L.crispatus L.crispatus NA NA NA NA 1 1 NA NA NA NA
p_1110 L.crispatus L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 4 1 1 4 1 1
p_1112 Polymicrobial L.iners Li-anaerobes G.vaginalis L.iners G.vaginalis G.vaginalis L.iners G.vaginalis 2 2 2 3 1 3
p_1113 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_1117 NA NA NA L.crispatus L.crispatus G.vaginalis NA NA NA 1 1 2 NA NA NA
p_1118 Polymicrobial L.iners L.iners G.vaginalis L.iners L.iners G.vaginalis L.iners L.iners 2 1 1 3 1 1
p_1119 Li-anaerobes Li-anaerobes Li-anaerobes G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1122 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_1124 Polymicrobial Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1125 Polymicrobial L.iners L.iners G.vaginalis L.iners L.iners G.vaginalis L.iners L.iners 2 1 1 3 1 1
p_1129 Polymicrobial Polymicrobial L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis L.iners 2 2 1 3 3 1
p_1130 L.iners Polymicrobial L.iners L.iners G.vaginalis L.iners L.iners G.vaginalis L.iners 1 2 1 1 3 1
p_1132 L.iners L.iners Polymicrobial L.iners L.iners G.vaginalis L.iners L.iners G.vaginalis 2 1 2 1 1 3
p_1135 Polymicrobial Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1136 L.iners L.iners Polymicrobial L.iners L.crispatus G.vaginalis L.iners L.crispatus G.vaginalis 1 1 2 1 1 3
p_1137 Li-anaerobes Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1138 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_1141 Li-anaerobes L.iners Li-anaerobes G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1144 NA NA NA G.vaginalis G.vaginalis G.vaginalis NA NA NA 2 2 2 NA NA NA
p_1145 L.iners L.crispatus L.iners L.iners L.crispatus L.crispatus L.iners L.crispatus L.crispatus 1 1 1 1 1 1
p_1147 L.crispatus L.iners L.crispatus L.crispatus L.iners L.crispatus L.crispatus L.iners L.crispatus 1 1 1 1 1 1
p_1149 Li-anaerobes Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1150 Polymicrobial Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_1151 L.iners Polymicrobial Polymicrobial L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis 1 2 2 1 3 3
p_1152 Polymicrobial Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_2001 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_2002 Polymicrobial Polymicrobial Polymicrobial L.iners L.iners L.iners L.iners L.iners L.iners 3 3 3 2 2 2
p_2004 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_2005 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_2006 NA NA NA L.iners G.vaginalis NA NA NA NA 1 2 NA NA NA NA
p_2008 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_2010 NA Polymicrobial L.iners G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_2011 L.iners Li-anaerobes Polymicrobial L.iners G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 1 2 2 1 3 3
p_2012 L.iners L.iners Polymicrobial L.iners L.iners L.iners L.iners L.iners L.iners 1 1 2 1 1 3
p_2017 L.iners Polymicrobial Polymicrobial L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis 2 2 2 1 3 3
p_2018 Polymicrobial L.iners L.iners G.vaginalis L.iners L.iners G.vaginalis L.iners L.iners 2 1 1 3 1 1
p_2019 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_2021 L.iners L.iners L.iners L.iners L.iners L.crispatus L.iners L.iners L.crispatus 1 1 1 1 1 1
p_2025 Polymicrobial Polymicrobial Polymicrobial L.iners L.iners L.iners L.iners L.iners L.iners 3 3 3 2 2 2
p_2044 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 2 1 1 3
p_2050 NA L.iners Li-anaerobes G.vaginalis L.iners G.vaginalis G.vaginalis L.iners G.vaginalis 2 1 2 3 1 3
p_2053 NA NA NA G.vaginalis L.iners L.iners NA NA NA 2 1 1 NA NA NA
p_2055 Polymicrobial Polymicrobial Li-anaerobes G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_2059 NA NA NA L.iners L.crispatus L.crispatus NA NA NA 1 1 1 NA NA NA
p_2071 L.iners L.iners L.iners L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_2074 Li-anaerobes Polymicrobial Polymicrobial L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_2079 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_2094 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_2096 NA NA NA L.iners L.iners L.iners NA NA NA 1 1 2 NA NA NA
p_2099 L.iners L.crispatus Polymicrobial L.iners L.crispatus G.vaginalis L.iners L.crispatus G.vaginalis 1 1 2 1 1 3
p_2101 Polymicrobial L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 4 1 1 4 1 1
p_2102 L.iners Polymicrobial Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_2105 NA NA NA G.vaginalis G.vaginalis L.iners NA NA NA 2 2 1 NA NA NA
p_2106 L.iners L.iners L.iners L.iners G.vaginalis L.iners G.vaginalis G.vaginalis L.iners 2 2 1 3 3 1
p_2109 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_2110 L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_2119 L.crispatus NA L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus L.crispatus 1 1 1 1 1 1
p_2120 Polymicrobial Polymicrobial Polymicrobial L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis 2 2 2 3 3 3
p_2123 L.crispatus L.crispatus L.iners L.crispatus L.crispatus L.iners L.crispatus L.crispatus L.iners 1 1 2 1 1 3
p_2124 NA NA NA L.crispatus L.crispatus L.crispatus NA NA NA 1 1 1 NA NA NA
p_2134 L.iners L.iners L.crispatus L.iners L.iners L.crispatus L.iners L.iners L.crispatus 1 1 1 1 1 1
p_2140 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_2153 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 1 1 1 1 1 1
p_3001 Polymicrobial NA Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_3002 Polymicrobial L.iners L.iners G.vaginalis L.iners L.iners G.vaginalis L.iners L.iners 2 1 1 3 1 1
p_3008 L.iners Li-anaerobes Polymicrobial G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3
p_3012 L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners L.iners 2 2 1 3 3 1
p_3021 NA NA NA L.crispatus L.crispatus L.crispatus NA NA NA 1 1 1 NA NA NA
p_3025 NA NA NA L.iners L.iners L.iners NA NA NA 1 1 1 NA NA NA
p_3046 Polymicrobial Polymicrobial L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis L.iners 2 2 1 3 3 1
p_3048 L.iners Polymicrobial Polymicrobial L.iners G.vaginalis G.vaginalis L.iners G.vaginalis G.vaginalis 1 2 2 1 3 3
p_3056 L.crispatus L.crispatus L.crispatus G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis G.vaginalis 2 2 2 3 3 3 

sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=es_ES.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=es_ES.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Europe/Madrid
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ggplot2_3.5.1  magrittr_2.0.3
#> 
#> loaded via a namespace (and not attached):
#>   [1] ggstatsplot_0.12.4      splines_4.4.1           later_1.3.2            
#>   [4] tibble_3.2.1            polyclip_1.10-7         datawizard_0.13.0      
#>   [7] ggmosaic_0.3.3          janitor_2.2.0           lifecycle_1.0.4        
#>  [10] rstatix_0.7.2           rprojroot_2.0.4         lattice_0.22-6         
#>  [13] vroom_1.6.5             MASS_7.3-61             insight_0.20.5         
#>  [16] backports_1.5.0         plotly_4.10.4           sass_0.4.9             
#>  [19] rmarkdown_2.28          jquerylib_0.1.4         yaml_2.3.10            
#>  [22] wesanderson_0.3.7       httpuv_1.6.15           ggside_0.3.1           
#>  [25] RColorBrewer_1.1-3      ade4_1.7-22             lubridate_1.9.3        
#>  [28] abind_1.4-8             zlibbioc_1.50.0         purrr_1.0.2            
#>  [31] BiocGenerics_0.50.0     phyloseq_1.48.0         tweenr_2.0.3           
#>  [34] git2r_0.33.0            GenomeInfoDbData_1.2.12 IRanges_2.38.1         
#>  [37] S4Vectors_0.42.1        ggrepel_0.9.6           correlation_0.8.5      
#>  [40] vegan_2.6-8             svglite_2.1.3           permute_0.9-7          
#>  [43] codetools_0.2-20        xml2_1.3.6              ggforce_0.4.2          
#>  [46] tidyselect_1.2.1        SuppDists_1.1-9.8       UCSC.utils_1.0.0       
#>  [49] farver_2.1.2            gmp_0.7-5               effectsize_0.8.9       
#>  [52] stats4_4.4.1            jsonlite_1.8.9          multtest_2.60.0        
#>  [55] Formula_1.2-5           survival_3.7-0          iterators_1.0.14       
#>  [58] systemfonts_1.1.0       foreach_1.5.2           BWStest_0.2.3          
#>  [61] tools_4.4.1             PMCMRplus_1.9.12        Rcpp_1.0.13            
#>  [64] glue_1.8.0              xfun_0.48               here_1.0.1             
#>  [67] mgcv_1.9-1              kSamples_1.2-10         metar_0.1.5            
#>  [70] GenomeInfoDb_1.40.1     dplyr_1.1.4             withr_3.0.1            
#>  [73] fastmap_1.2.0           boot_1.3-31             rhdf5filters_1.16.0    
#>  [76] fansi_1.0.6             digest_0.6.37           timechange_0.3.0       
#>  [79] R6_2.5.1                colorspace_2.1-1        utf8_1.2.4             
#>  [82] tidyr_1.3.1             generics_0.1.3          data.table_1.16.2      
#>  [85] httr_1.4.7              htmlwidgets_1.6.4       parameters_0.22.2      
#>  [88] pkgconfig_2.0.3         gtable_0.3.5            Rmpfr_0.9-5            
#>  [91] workflowr_1.7.1         statsExpressions_1.6.0  XVector_0.44.0         
#>  [94] htmltools_0.5.8.1       carData_3.0-5           biomformat_1.32.0      
#>  [97] multcompView_0.1-10     scales_1.3.0            kableExtra_1.4.0       
#> [100] Biobase_2.64.0          snakecase_0.11.1        knitr_1.48             
#> [103] rstudioapi_0.16.0       tzdb_0.4.0              reshape2_1.4.4         
#> [106] nlme_3.1-166            cachem_1.1.0            rhdf5_2.48.0           
#> [109] stringr_1.5.1           parallel_4.4.1          pillar_1.9.0           
#> [112] grid_4.4.1              logger_0.3.0            vctrs_0.6.5            
#> [115] promises_1.3.0          ggpubr_0.6.0            car_3.1-3              
#> [118] cluster_2.1.6           paletteer_1.6.0         evaluate_1.0.1         
#> [121] readr_2.1.5             zeallot_0.1.0           mvtnorm_1.3-1          
#> [124] cli_3.6.3               compiler_4.4.1          rlang_1.1.4            
#> [127] crayon_1.5.3            ggsignif_0.6.4          labeling_0.4.3         
#> [130] rematch2_2.1.2          plyr_1.8.9              forcats_1.0.0          
#> [133] fs_1.6.4                stringi_1.8.4           viridisLite_0.4.2      
#> [136] munsell_0.5.1           Biostrings_2.72.1       lazyeval_0.2.2         
#> [139] bayestestR_0.14.0       Matrix_1.7-0            hms_1.1.3              
#> [142] patchwork_1.3.0         bit64_4.5.2             Rhdf5lib_1.26.0        
#> [145] highr_0.11              igraph_2.0.3            broom_1.0.7            
#> [148] memoise_2.0.1           bslib_0.8.0             bit_4.5.0              
#> [151] ape_5.8