---
title: "Stats at Taxo"
format: html
---
```{r}
library(here)
library(stringr)
library(tidyverse)
library(phyloseq)
library(biomformat)
source("utils.R")
the_data <- import_biom("data/mach/kinetic.biom")
per_taxa_networks <- collapse_otu_at_taxo(the_data)
otu_df <- sapply(per_taxa_networks, nrow) %>%
data.frame() %>%
rownames_to_column() %>%
rename(Nb_OTU = ".", Rank = "rowname")
flist <- list.files(here("results", "lbm-seq"), full.names = TRUE, pattern = ".Rds")
para_flist <- grepv(pattern = "para.Rds", flist)
seq_flist <- grepv(pattern = "seq.Rds", flist)
notrans_flist <- grepv(pattern = "notrans.Rds", flist)
```
```{r}
bench_df <- do.call("rbind", lapply(flist, function(file) readRDS(file)$benchmark))
bench_df <- bench_df %>%
mutate(expr = as.character(expr)) %>%
separate_wider_regex(cols = "expr", patterns = c(Rank = "Rank[0-9]", type = "para|seq|notrans")) %>%
mutate(Rank = as.factor(Rank), type = as.factor(type)) %>%
left_join(otu_df, by = "Rank") %>%
mutate(Rank = as.factor(Rank), type = as.factor(type))
levels(bench_df$Rank) <- c("Phylum", "Class", "Order", "Family", "Genus")
```
```{r}
library(ggplot2)
coeff <- 220000000000
ggplot(bench_df, aes(x = Rank, col = type)) +
geom_boxplot(aes(y = time)) +
geom_point(aes(y = coeff * Nb_OTU, size = Nb_OTU), shape = 13) +
scale_color_manual(values = c("#363634", "#009E73", "#CC79A7"), labels = c("No transfer", "Parallelized", "Sequential")) +
scale_y_continuous(sec.axis = sec_axis(~ . / coeff, name = "Number of OTUs")) +
labs(size = "Number of OTUs", color = "Algorithm Type", y = "Time (seconds)") +
theme_minimal()
```
```{r}
load_result_list <- function(flist) {
results <- lapply(flist, function(file) readRDS(file)$models)
names(results) <- paste0("Rep", seq_along(results))
results <- purrr::transpose(results)
return(results)
}
library(aricode)
ARI_in_repet_Z1 <- function(result_list) {
lapply(result_list, function(rank) {
Z1_list <- lapply(rank, function(repet) {
Z11 <- apply(repet$memberships[[which.max(repet$ICL)]]$Z1, 1, which.max)
})
outer(X = Z1_list, Y = Z1_list, Vectorize(ARI))
})
}
ARI_in_repet_Z2 <- function(result_list) {
lapply(result_list, function(rank) {
Z2_list <- lapply(rank, function(repet) {
Z2 <- apply(repet$memberships[[which.max(repet$ICL)]]$Z2, 1, which.max)
})
outer(X = Z2_list, Y = Z2_list, Vectorize(ARI))
})
}
```
```{r}
seq_results <- load_result_list(seq_flist)
para_results <- load_result_list(para_flist)
notrans_results <- load_result_list(notrans_flist)
```
```{r}
ARI_in_repet_Z1(seq_results)
ARI_in_repet_Z1(para_results)
ARI_in_repet_Z1(notrans_results)
```
```{r}
ARI_in_repet_Z2(seq_results)
ARI_in_repet_Z2(para_results)
ARI_in_repet_Z2(notrans_results)
```
Tous les Z sont concordants entre les répétitions, je vais donc sélectionner une seule répétition de chaque pour l'analyse.
# Analyse des résultats des LBM
Ci-après on extrait les premiers modèles.
```{r}
seq_model <- purrr::transpose(seq_results)[[2]]
para_model <- purrr::transpose(para_results)[[1]]
notrans_model <- purrr::transpose(notrans_results)[[1]]
```
```{r}
extract_memberships <- function(model) {
memberships <- model$memberships[[which.max(model$ICL)]]
rownames(memberships[["Z1"]]) <- rownames(model$adj)
rownames(memberships[["Z2"]]) <- colnames(model$adj)
memberships
map_memberships <- list(Z1 = apply(memberships[["Z1"]], 1, which.max), Z2 = apply(memberships[["Z2"]], 1, which.max))
map_memberships
}
```
## Comparaison des ARI par rang taxonomique
```{r}
seq_memberships <- lapply(seq_model, extract_memberships)
para_memberships <- lapply(para_model, extract_memberships)
notrans_memberships <- lapply(notrans_model, extract_memberships)
```
```{r}
tibble("Para v No transfer" = map2_dbl(purrr::transpose(para_memberships)[["Z1"]], purrr::transpose(notrans_memberships)[["Z1"]], .f = ARI), "Sequential v No transfer" = map2_dbl(purrr::transpose(seq_memberships)[["Z1"]], purrr::transpose(notrans_memberships)[["Z1"]], .f = ARI), "Para v Sequential" = map2_dbl(purrr::transpose(para_memberships)[["Z1"]], purrr::transpose(seq_memberships)[["Z1"]], .f = ARI)) %>% t() %>%
knitr::kable(col.names = paste0("Rank", seq(2,5)), caption = "ARI for the methods for OTU memberships")
```
```{r}
tibble("Para v No transfer" = map2_dbl(purrr::transpose(para_memberships)[["Z2"]], purrr::transpose(notrans_memberships)[["Z2"]], .f = ARI), "Sequential v No transfer" = map2_dbl(purrr::transpose(seq_memberships)[["Z2"]], purrr::transpose(notrans_memberships)[["Z2"]], .f = ARI), "Para v Sequential" = map2_dbl(purrr::transpose(para_memberships)[["Z2"]], purrr::transpose(seq_memberships)[["Z2"]], .f = ARI)) %>% t() %>%
knitr::kable(col.names = paste0("Rank", seq(2,5)), caption = "ARI for the methods for sample memberships")
```
```{r}
library(ggalluvial)
membership_to_df <- function(membership_list, suffix = 1) {
lapply(membership_list, function(membership_vec) {
df <- data.frame(membership_vec)
colnames(df) <- paste0("Z", suffix)
df %>%
rownames_to_column(var = "Rank") %>%
separate_wider_delim(cols = "Rank", delim = ";_;", names_sep = "")
})
}
seq_df <- lapply(seq_along(seq_memberships), function(idx) {
membership_to_df(seq_memberships[[idx]], suffix = idx + 1)
})
seq_df_otu <- lapply(seq_df, function(df) {
df$Z1
}) %>% # Below we join by the ranks
reduce(left_join) %>%
select(sort(names(.)))
seq_df_sample <- lapply(seq_df, function(df) {
df$Z2
}) %>% reduce(left_join, by = "Rank1")
seq_df_sample_lodes <- to_lodes_form(seq_df_sample, key = "x", axes = 2:5)
seq_df_otu_lodes <- to_lodes_form(seq_df_otu, key = "x", axes = 6:9)
```
```{r}
mach_metadata <- read.table(file = "data/mach/kinetic_sample_metadata.tsv") %>% rownames_to_column(var = "Rank1")
seq_df_sample_lodes <- left_join(seq_df_sample_lodes, mach_metadata)
ggplot(seq_df_sample_lodes, aes(x = x, alluvium = alluvium, stratum = stratum, label = stratum)) +
geom_alluvium(aes(fill = Weaned)) +
geom_stratum() +
geom_text(stat = "stratum")
ggplot(seq_df_otu_lodes, aes(x = x, alluvium = alluvium, stratum = stratum, label = stratum)) +
geom_alluvium(aes(fill = Rank2)) +
geom_stratum() +
geom_text(stat = "stratum")
```