--- title: "ANCOM Tutorial" author: - Huang Lin$^1$ - $^1$NIEHS, Research Triangle Park, NC 27709, USA date: '`r format(Sys.Date(), "%B %d, %Y")`' output: rmarkdown::html_vignette bibliography: bibliography.bib vignette: > %\VignetteIndexEntry{ANCOM Tutorial} %\VignetteEngine{knitr::rmarkdown} \usepackage[utf8]{inputenc} --- ```{r setup, message = FALSE, warning = FALSE, comment = NA} knitr::opts_chunk$set(message = FALSE, warning = FALSE, comment = NA, fig.width = 6.25, fig.height = 5) library(ANCOMBC) library(tidyverse) ``` # 1. Introduction Analysis of Composition of Microbiomes (ANCOM) [@mandal2015analysis] is a differential abundance (DA) analysis for microbial absolute abundances. It accounts for the compositionality of microbiome data by performing the additive log ratio (ALR) transformation. ANCOM employs a heuristic strategy to declare taxa that are significantly differentially abundant. For a given taxon, the output W statistic represents the number ALR transformed models where the taxon is differentially abundant with regard to the variable of interest. The larger the value of W, the more likely the taxon is differentially abundant. For more details, please refer to the [ANCOM](https://www.tandfonline.com/doi/full/10.3402/mehd.v26.27663) paper. # 2. Installation Download package. ```{r getPackage, eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("ANCOMBC") ``` Load the package. ```{r load, eval=FALSE} library(ANCOMBC) ``` # 3. Run ANCOM on a real cross-sectional dataset {.tabset} ## 3.1 Import example data The HITChip Atlas dataset contains genus-level microbiota profiling with HITChip for 1006 western adults with no reported health complications, reported in [@lahti2014tipping]. The dataset is available via the microbiome R package [@lahti2017tools] in phyloseq [@mcmurdie2013phyloseq] format. In this tutorial, we consider the following covariates: * Continuous covariates: "age" * Categorical covariates: "region", "bmi" * The group variable of interest: "bmi" + Three groups: "lean", "overweight", "obese" + The reference group: "obese" ```{r} data(atlas1006, package = "microbiome") tse = mia::makeTreeSummarizedExperimentFromPhyloseq(atlas1006) # Subset to baseline tse = tse[, tse$time == 0] # Re-code the bmi group tse$bmi = recode(tse$bmi_group, obese = "obese", severeobese = "obese", morbidobese = "obese") # Subset to lean, overweight, and obese subjects tse = tse[, tse$bmi %in% c("lean", "overweight", "obese")] # Note that by default, levels of a categorical variable in R are sorted # alphabetically. In this case, the reference level for `bmi` will be # `lean`. To manually change the reference level, for instance, setting `obese` # as the reference level, use: tse$bmi = factor(tse$bmi, levels = c("obese", "overweight", "lean")) # You can verify the change by checking: # levels(sample_data(tse)$bmi) # Create the region variable tse$region = recode(as.character(tse$nationality), Scandinavia = "NE", UKIE = "NE", SouthEurope = "SE", CentralEurope = "CE", EasternEurope = "EE", .missing = "unknown") # Discard "EE" as it contains only 1 subject # Discard subjects with missing values of region tse = tse[, ! tse$region %in% c("EE", "unknown")] print(tse) ``` ## 3.2 Run ancom function ```{r} set.seed(123) out = ancom(data = tse, assay_name = "counts", tax_level = "Family", phyloseq = NULL, p_adj_method = "holm", prv_cut = 0.10, lib_cut = 1000, main_var = "bmi", adj_formula = "age + region", rand_formula = NULL, lme_control = NULL, struc_zero = TRUE, neg_lb = TRUE, alpha = 0.05, n_cl = 2) res = out$res # Similarly, if the main variable of interest is continuous, such as age, the # ancom model can be specified as # out = ancom(data = tse, assay_name = "counts", # tax_level = "Family", phyloseq = NULL, # p_adj_method = "holm", prv_cut = 0.10, lib_cut = 1000, # main_var = "age", adj_formula = "bmi + region", # rand_formula = NULL, lme_control = NULL, struc_zero = FALSE, # neg_lb = FALSE, alpha = 0.05, n_cl = 2) # ancom also supports importing data in phyloseq format # tse_alt = agglomerateByRank(tse, "Family") # pseq = makePhyloseqFromTreeSummarizedExperiment(tse_alt) # out = ancom(data = NULL, assay_name = NULL, # tax_level = "Family", phyloseq = pseq, # p_adj_method = "holm", prv_cut = 0.10, lib_cut = 1000, # main_var = "bmi", adj_formula = "age + region", # rand_formula = NULL, lme_control = NULL, struc_zero = TRUE, # neg_lb = TRUE, alpha = 0.05, n_cl = 2) ``` ## 3.3 Scatter plot for W statistics ```{r} q_val = out$q_data beta_val = out$beta_data # Only consider the effect sizes with the corresponding q-value less than alpha beta_val = beta_val * (q_val < 0.05) # Choose the maximum of beta's as the effect size beta_pos = apply(abs(beta_val), 2, which.max) beta_max = vapply(seq_along(beta_pos), function(i) beta_val[beta_pos[i], i], FUN.VALUE = double(1)) # Number of taxa except structural zeros n_taxa = ifelse(is.null(out$zero_ind), nrow(tse), sum(apply(out$zero_ind[, -1], 1, sum) == 0)) # Cutoff values for declaring differentially abundant taxa cut_off = 0.7 * (n_taxa - 1) df_fig_w = res %>% dplyr::mutate(beta = beta_max, direct = case_when( detected_0.7 == TRUE & beta > 0 ~ "Positive", detected_0.7 == TRUE & beta <= 0 ~ "Negative", TRUE ~ "Not Significant" )) %>% dplyr::arrange(W) df_fig_w$taxon = factor(df_fig_w$taxon, levels = df_fig_w$taxon) df_fig_w$W = replace(df_fig_w$W, is.infinite(df_fig_w$W), n_taxa - 1) df_fig_w$direct = factor(df_fig_w$direct, levels = c("Negative", "Positive", "Not Significant")) p_w = df_fig_w %>% ggplot(aes(x = taxon, y = W, color = direct)) + geom_point(size = 2, alpha = 0.6) + labs(x = "Taxon", y = "W") + scale_color_discrete(name = NULL) + geom_hline(yintercept = cut_off, linetype = "dotted", color = "blue", size = 1.5) + geom_text(aes(x = 2, y = cut_off + 0.5, label = "W[0.7]"), size = 5, vjust = -0.5, hjust = 0, color = "orange", parse = TRUE) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), panel.grid.major = element_blank()) p_w ``` # 4. Run ANCOM on a real longitudinal dataset {.tabset} ## 4.1 Import example data A two-week diet swap study between western (USA) and traditional (rural Africa) diets [@lahti2014tipping]. The dataset is available via the microbiome R package [@lahti2017tools] in phyloseq [@mcmurdie2013phyloseq] format. In this tutorial, we consider the following fixed effects: * Continuous covariates: "timepoint" * Categorical covariates: "nationality" * The group variable of interest: "group" + Three groups: "DI", "ED", "HE" + The reference group: "DI" and the following random effects: * A random intercept * A random slope: "timepoint" ```{r} data(dietswap, package = "microbiome") tse = mia::makeTreeSummarizedExperimentFromPhyloseq(dietswap) print(tse) ``` ## 4.2 Run ancom function ```{r} set.seed(123) out = ancom(data = tse, assay_name = "counts", tax_level = "Family", phyloseq = NULL, p_adj_method = "holm", prv_cut = 0.10, lib_cut = 1000, main_var = "group", adj_formula = "nationality + timepoint", rand_formula = "(timepoint | subject)", lme_control = lme4::lmerControl(), struc_zero = TRUE, neg_lb = TRUE, alpha = 0.05, n_cl = 2) res = out$res ``` ## 4.3 Visualization for W statistics ```{r} q_val = out$q_data beta_val = out$beta_data # Only consider the effect sizes with the corresponding q-value less than alpha beta_val = beta_val * (q_val < 0.05) # Choose the maximum of beta's as the effect size beta_pos = apply(abs(beta_val), 2, which.max) beta_max = vapply(seq_along(beta_pos), function(i) beta_val[beta_pos[i], i], FUN.VALUE = double(1)) # Number of taxa except structural zeros n_taxa = ifelse(is.null(out$zero_ind), nrow(tse), sum(apply(out$zero_ind[, -1], 1, sum) == 0)) # Cutoff values for declaring differentially abundant taxa cut_off = 0.7 * (n_taxa - 1) df_fig_w = res %>% dplyr::mutate(beta = beta_max, direct = case_when( detected_0.7 == TRUE & beta > 0 ~ "Positive", detected_0.7 == TRUE & beta <= 0 ~ "Negative", TRUE ~ "Not Significant" )) %>% dplyr::arrange(W) df_fig_w$taxon = factor(df_fig_w$taxon, levels = df_fig_w$taxon) df_fig_w$W = replace(df_fig_w$W, is.infinite(df_fig_w$W), n_taxa - 1) df_fig_w$direct = factor(df_fig_w$direct, levels = c("Negative", "Positive", "Not Significant")) p_w = df_fig_w %>% ggplot(aes(x = taxon, y = W, color = direct)) + geom_point(size = 2, alpha = 0.6) + labs(x = "Taxon", y = "W") + scale_color_discrete(name = NULL) + geom_hline(yintercept = cut_off, linetype = "dotted", color = "blue", size = 1.5) + geom_text(aes(x = 2, y = cut_off + 0.5, label = "W[0.7]"), size = 5, vjust = -0.5, hjust = 0, color = "orange", parse = TRUE) + theme_bw() + theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), panel.grid.major = element_blank()) p_w ``` # Session information ```{r sessionInfo, message = FALSE, warning = FALSE, comment = NA} sessionInfo() ``` # References