Modif analysis
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3938
analysis_benchmark_lbm_seq.html
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3938
analysis_benchmark_lbm_seq.html
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@ -11,7 +11,7 @@ library(tidyverse)
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library(phyloseq)
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library(biomformat)
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source("utils.R")
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mach_data <- import_biom("data/mach/kinetic.biom")
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the_data <- import_biom("data/chaillou/chaillou.biom")
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per_taxa_networks <- collapse_otu_at_taxo(the_data)
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otu_df <- sapply(per_taxa_networks, nrow) %>%
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@ -20,7 +20,7 @@ otu_df <- sapply(per_taxa_networks, nrow) %>%
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rename(Nb_OTU = ".", Rank = "rowname")
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flist <- list.files(here("results", "lbm-seq"), full.names = TRUE, pattern = ".Rds")
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flist <- list.files(here("results", "lbm-seq", "chaillou"), full.names = TRUE, pattern = ".Rds")
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para_flist <- grepv(pattern = "para.Rds", flist)
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seq_flist <- grepv(pattern = "seq.Rds", flist)
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@ -107,7 +107,7 @@ Tous les Z sont concordants entre les répétitions, je vais donc sélectionner
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Ci-après on extrait les premiers modèles.
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```{r}
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seq_model <- purrr::transpose(seq_results)[[2]]
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seq_model <- purrr::transpose(seq_results)[[1]]
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para_model <- purrr::transpose(para_results)[[1]]
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notrans_model <- purrr::transpose(notrans_results)[[1]]
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```
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@ -178,11 +178,12 @@ seq_df_otu_lodes <- to_lodes_form(seq_df_otu, key = "x", axes = 6:9)
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```{r}
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mach_metadata <- read.table(file = "data/mach/kinetic_sample_metadata.tsv") %>% rownames_to_column(var = "Rank1")
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chaillou_metadata <- read.table(file = "data/chaillou/sample_metadata.tsv") %>% rownames_to_column(var = "Rank1")
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seq_df_sample_lodes <- left_join(seq_df_sample_lodes, mach_metadata)
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seq_df_sample_lodes <- left_join(seq_df_sample_lodes, chaillou_metadata)
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ggplot(seq_df_sample_lodes, aes(x = x, alluvium = alluvium, stratum = stratum, label = stratum)) +
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geom_alluvium(aes(fill = Weaned)) +
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geom_alluvium(aes(fill = EnvType)) +
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geom_stratum() +
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geom_text(stat = "stratum")
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