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a/vignettes/articles/Ravel_2011_16S_BV_whole.Rmd b/vignettes/articles/Ravel_2011_16S_BV_whole.Rmd index ad47d9f..0213282 100644 --- a/vignettes/articles/Ravel_2011_16S_BV_whole.Rmd +++ b/vignettes/articles/Ravel_2011_16S_BV_whole.Rmd @@ -31,54 +31,44 @@ conditions_col <- 'study_condition' conditions <- c(condB = 'healthy', condA = 'bacterial_vaginosis') tse <- getBenchmarkData(dat_name, dryrun = FALSE)[[1]] -col_data <- tse |> - colData() |> - as.data.frame() |> - dplyr::filter(study_condition %in% conditions) -col_data |> - summarise( - .by = c( - "ethnicity", "nugent_score_category", "study_condition" - ), - range = paste0(min(nugent_score), "-", max(nugent_score)), - n = n() - ) |> - arrange(ethnicity, study_condition, n) +tse ``` -```{r subset data} -select_samples <- col_data |> - {\(y) split(y, factor(y$study_condition))}() |> - map(rownames) |> - flatten_chr() +Select samples: + +```{r} +select_samples <- which(colData(tse)$study_condition %in% conditions) tse_subset <- tse[, select_samples] +tse_subset +``` +Agglomerate by genus: + +```{r subset data} tse_genus <- agglomerateByRank( tse_subset, rank = 'genus', na.rm = FALSE, onRankOnly = FALSE ) |> filterTaxa(min_ab = 1, min_per = 0.2) |> {\(y) magrittr::set_rownames(y, editMiaTaxaNames(y))}() - colData(tse_genus)$study_condition <- factor(colData(tse_genus)$study_condition, levels = conditions) - tse_genus ``` -```{r sample counts} -tse_genus |> - colData() |> - as_tibble() |> - summarise( +Samples metadata: + +```{r} +col_data <- as_tibble(colData(tse_genus)) +col_data |> + summarise( .by = c( - # "ethnicity", "nugent_score_category", "study_condition" "nugent_score_category", "study_condition" ), range = paste0(min(nugent_score), "-", max(nugent_score)), n = n() ) |> arrange(study_condition, n) |> - relocate(study_condition) + relocate(study_condition, n) ``` ## Prior info (biological annotations) @@ -104,16 +94,14 @@ Convert to phyloseq: ```{r convert to phyloseq, warning=FALSE} ps <- convertToPhyloseq(tse_genus) -sample_data(ps)[[conditions_col]] <- factor( - sample_data(ps)[[conditions_col]], levels = conditions -) +sample_data(ps)[[conditions_col]] <- + factor(sample_data(ps)[[conditions_col]], levels = conditions) ``` Set method parameters: ```{r method parameters, weights, DA methods, warning=FALSE} norm_methods <- set_norm_list() - ps <- runNormalizations(norm_methods, ps, verbose = FALSE) zw <- weights_ZINB(ps, design = conditions_col) DA_methods <- set_DA_methods_list(conditions_col, conditions) @@ -150,8 +138,7 @@ tim ## Enrichment -We need a threshold for DA for lefse-CLR -(It can't be the same as when using lefse-TSS): +We need a threshold for DA for lefse-CLR: ```{r median lefser} c( @@ -160,14 +147,11 @@ c( ) ``` -Create some variables for selecting and ranking differentially abundant -features: +Create some variables for selecting and ranking differentially abundant features: ```{r variables for enrichment funs} direction <- get_direction_cols(DA_output, conditions_col, conditions) -## The methods based on lefse have artificial p-values because -## the lefser output doesn't provide such information adjThr<- rep(0.1, length(DA_output)) names(adjThr) <- names(DA_output) @@ -178,7 +162,6 @@ esThr <- case_when( ) |> set_names(names(DA_output)) -## Use effect size for lefser and adjusted p-value for all of the other methods slotV <- ifelse(grepl("lefse", names(DA_output)), "statInfo", "pValMat") colNameV <- ifelse(grepl("lefse", names(DA_output)), "LDA_scores", "adjP") typeV <- ifelse(grepl("lefse", names(DA_output)), "logfc", "pvalue")