---
title: "ANCOM Tutorial"
author: 
  - Huang Lin$^1$
  - $^1$NICHD, 6710B Rockledge Dr, Bethesda, MD 20892
date: '`r format(Sys.Date(), "%B %d, %Y")`'
output: rmarkdown::html_vignette
bibliography: bibliography.bib
vignette: >
  %\VignetteIndexEntry{ANCOM}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE, comment = NA,
                      fig.width = 6.25, fig.height = 5)
library(ANCOMBC)
library(tidyverse)
library(mia)
```

# 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 also 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)

# Subset to baseline
tse = atlas1006[, atlas1006$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$zero_ind), 
                nrow(feature_table), 
                sum(apply(out$zero_ind, 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$taxon_id = factor(df_fig_w$taxon_id, levels = df_fig_w$taxon_id)
df_fig_w$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_id, 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 also 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)
tse = 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 = list(maxIter = 100, msMaxIter = 100, opt = "optim"), 
            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$zero_ind), 
                nrow(feature_table), 
                sum(apply(out$zero_ind, 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$taxon_id = factor(df_fig_w$taxon_id, levels = df_fig_w$taxon_id)
df_fig_w$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_id, 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









