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Added automatic W threshold #5

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Added automatic W threshold #5

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86 changes: 68 additions & 18 deletions scripts/ancom_v2.1.R
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Original file line number Diff line number Diff line change
Expand Up @@ -123,7 +123,7 @@ feature_table_pre_process = function(feature_table, meta_data, sample_var, group

# ANCOM main function
ANCOM = function(feature_table, meta_data, struc_zero = NULL, main_var, p_adj_method = "BH",
alpha = 0.05, adj_formula = NULL, rand_formula = NULL, ...){
alpha = 0.05, adj_formula = NULL, rand_formula = NULL, autoW = FALSE){
# OTU table transformation:
# (1) Discard taxa with structural zeros (if any); (2) Add pseudocount (1) and take logarithm.
if (!is.null(struc_zero)) {
Expand Down Expand Up @@ -190,24 +190,24 @@ ANCOM = function(feature_table, meta_data, struc_zero = NULL, main_var, p_adj_me
if (is.null(rand_formula) & is.null(adj_formula)) {
p_data[-(1:i), i] = apply(alr_data[, 1:n_lr, drop = FALSE], 2, function(x){
tfun(tformula, data = data.frame(x, alr_data, check.names = FALSE))$p.value
}
}
)
}else if (is.null(rand_formula) & !is.null(adj_formula)) {
p_data[-(1:i), i] = apply(alr_data[, 1:n_lr, drop = FALSE], 2, function(x){
fit = tfun(tformula,
data = data.frame(x, alr_data, check.names = FALSE),
na.action = na.omit)
summary(fit)[[1]][main_var, "Pr(>F)"]
}
}
)
}else if (!is.null(rand_formula)) {
p_data[-(1:i), i] = apply(alr_data[, 1:n_lr, drop = FALSE], 2, function(x){
fit = tfun(fixed = tformula,
data = data.frame(x, alr_data, check.names = FALSE),
random = formula(rand_formula),
na.action = na.omit, ...)
na.action = na.omit)
anova(fit)[main_var, "p-value"]
}
}
)
}
}
Expand All @@ -223,23 +223,71 @@ ANCOM = function(feature_table, meta_data, struc_zero = NULL, main_var, p_adj_me
# For each taxon, count the number of q-values < alpha.
W = apply(q_data, 2, function(x) sum(x < alpha))

# Define W threshold
if (autoW){
theta = 0.1
tau = 0.02
c_start = max(W, na.rm=T) / n_taxa

if (c_start < tau) {
reject = rep(FALSE, n_taxa)
autow = FALSE
} else {
cutoff = c_start - seq(from=0.05, to=0.25, length.out=5)
prop_cut = rep(0, length(cutoff))
for (i in 1:length(cutoff)){
prop_cut[i] <- mean(W>n_taxa*cutoff[i], na.rm=T)
}

dels = abs(prop_cut - c(tail(prop_cut,-1) , head(prop_cut,1)))
dels[length(dels)] = 0
if ((dels[1] < tau) & (dels[2] < tau) & (dels[3] < tau)) { nu = cutoff[2] }
else if ((dels[1] >= tau) & (dels[2] < tau) & (dels[3] < tau)) { nu = cutoff[3] }
else if ((dels[2] >= tau) & (dels[3] < tau) & (dels[4] < tau)) { nu = cutoff[4] }
else {nu = cutoff[5]}

autow = nu*n_taxa
# Define column that declare differentially abundant based on automatic W.
reject = (W >= autow)
}
}

# Organize outputs
out_comp = data.frame(taxa_id, W, row.names = NULL, check.names = FALSE)
# Declare a taxon to be differentially abundant based on the quantile of W statistic.
# We perform (n_taxa - 1) hypothesis testings on each taxon, so the maximum number of rejections is (n_taxa - 1).
out_comp = out_comp%>%mutate(detected_0.9 = ifelse(W > 0.9 * (n_taxa -1), TRUE, FALSE),
detected_0.8 = ifelse(W > 0.8 * (n_taxa -1), TRUE, FALSE),
detected_0.7 = ifelse(W > 0.7 * (n_taxa -1), TRUE, FALSE),
detected_0.6 = ifelse(W > 0.6 * (n_taxa -1), TRUE, FALSE))

# Taxa with structural zeros are automatically declared to be differentially abundant
if (!is.null(struc_zero)){
out = data.frame(taxa_id = rownames(struc_zero), W = Inf, detected_0.9 = TRUE,
detected_0.8 = TRUE, detected_0.7 = TRUE, detected_0.6 = TRUE,
row.names = NULL, check.names = FALSE)
out[match(taxa_id, out$taxa_id), ] = out_comp
if (autoW){
out_comp = out_comp%>%mutate(detected_0.9 = ifelse(W > 0.9 * (n_taxa -1), TRUE, FALSE),
detected_0.8 = ifelse(W > 0.8 * (n_taxa -1), TRUE, FALSE),
detected_0.7 = ifelse(W > 0.7 * (n_taxa -1), TRUE, FALSE),
detected_0.6 = ifelse(W > 0.6 * (n_taxa -1), TRUE, FALSE),
reject = reject)

# Taxa with structural zeros are automatically declared to be differentially abundant
if (!is.null(struc_zero)){
out = data.frame(taxa_id = rownames(struc_zero), W = Inf, detected_0.9 = TRUE,
detected_0.8 = TRUE, detected_0.7 = TRUE, detected_0.6 = TRUE,
row.names = NULL, check.names = FALSE, reject = TRUE)
out[match(taxa_id, out$taxa_id), ] = out_comp
}else{
out = out_comp
}

}else{
out = out_comp
out_comp = out_comp%>%mutate(detected_0.9 = ifelse(W > 0.9 * (n_taxa -1), TRUE, FALSE),
detected_0.8 = ifelse(W > 0.8 * (n_taxa -1), TRUE, FALSE),
detected_0.7 = ifelse(W > 0.7 * (n_taxa -1), TRUE, FALSE),
detected_0.6 = ifelse(W > 0.6 * (n_taxa -1), TRUE, FALSE))

# Taxa with structural zeros are automatically declared to be differentially abundant
if (!is.null(struc_zero)){
out = data.frame(taxa_id = rownames(struc_zero), W = Inf, detected_0.9 = TRUE,
detected_0.8 = TRUE, detected_0.7 = TRUE, detected_0.6 = TRUE,
row.names = NULL, check.names = FALSE)
out[match(taxa_id, out$taxa_id), ] = out_comp
}else{
out = out_comp
}
}

# Draw volcano plot
Expand Down Expand Up @@ -286,7 +334,9 @@ ANCOM = function(feature_table, meta_data, struc_zero = NULL, main_var, p_adj_me
fig
}

res = list(out = out, fig = fig)
# If autoW=TRUE, return automatically defined W, and results with additional column.
if (autoW){ res = list(out = out, fig = fig, autoW=autow) }
else { res = list(out = out, fig = fig) }
return(res)
}

Expand Down