ancom  R Documentation 
Determine taxa whose absolute abundances, per unit volume, of
the ecosystem (e.g. gut) are significantly different with changes in the
covariate of interest (e.g. group). The current version of
ancom
function implements ANCOM in crosssectional and repeated
measurements data while allowing for covariate adjustment.
ancom( data = NULL, assay_name = "counts", tax_level = NULL, phyloseq = NULL, p_adj_method = "holm", prv_cut = 0.1, lib_cut = 0, main_var, adj_formula = NULL, rand_formula = NULL, lme_control = lme4::lmerControl(), struc_zero = FALSE, neg_lb = FALSE, alpha = 0.05, n_cl = 1 )
data 
the input data. A

assay_name 
character. Name of the count table in the data object
(only applicable if data object is a 
tax_level 
character. The taxonomic level of interest. The input data
can be agglomerated at different taxonomic levels based on your research
interest. Default is NULL, i.e., do not perform agglomeration, and the
ANCOM anlysis will be performed at the lowest taxonomic level of the
input 
phyloseq 
a 
p_adj_method 
character. method to adjust pvalues. Default is "holm".
Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY",
"fdr", "none". See 
prv_cut 
a numerical fraction between 0 and 1. Taxa with prevalences
less than 
lib_cut 
a numerical threshold for filtering samples based on library
sizes. Samples with library sizes less than 
main_var 
character. The name of the main variable of interest. 
adj_formula 
character string representing the formula for
covariate adjustment. Default is 
rand_formula 
the character string expresses how the microbial absolute
abundances for each taxon depend on the random effects in metadata. ANCOM
follows the 
lme_control 
a list of control parameters for mixed model fitting.
See 
struc_zero 
logical. whether to detect structural zeros based on

neg_lb 
logical. whether to classify a taxon as a structural zero using its asymptotic lower bound. Default is FALSE. 
alpha 
numeric. level of significance. Default is 0.05. 
n_cl 
numeric. The number of nodes to be forked. For details, see

A taxon is considered to have structural zeros in some (>=1)
groups if it is completely (or nearly completely) missing in these groups.
For instance, suppose there are three groups: g1, g2, and g3.
If the counts of taxon A in g1 are 0 but nonzero in g2 and g3,
then taxon A will be considered to contain structural zeros in g1.
In this example, taxon A is declared to be differentially abundant between
g1 and g2, g1 and g3, and consequently, it is globally differentially
abundant with respect to this group variable.
Such taxa are not further analyzed using ANCOMBC, but the results are
summarized in the overall summary. For more details about the structural
zeros, please go to the
ANCOMII paper.
Setting neg_lb = TRUE
indicates that you are using both criteria
stated in section 3.2 of
ANCOMII
to detect structural zeros; otherwise, the algorithm will only use the
equation 1 in section 3.2 for declaring structural zeros. Generally, it is
recommended to set neg_lb = TRUE
when the sample size per group is
relatively large (e.g. > 30).
a list
with components:
res
, a data.frame
containing ANCOM
result for the variable specified in main_var
,
each column is:
W
, test statistics.
detected_0.9, detected_0.8, detected_0.7, detected_0.6
,
logical vectors representing whether a taxon is differentially
abundant under a series of cutoffs. For example, TRUE in
detected_0.7
means the number of ALR transformed models where
the taxon is differentially abundant with regard to the main variable
outnumbers 0.7 * (n_tax  1)
. detected_0.7
is commonly
used. Choose detected_0.8
or detected_0.9
for more
conservative results, or choose detected_0.6
for more liberal
results.
zero_ind
, a logical data.frame
with TRUE
indicating the taxon is detected to contain structural zeros in
some specific groups.
beta_data
, a numeric matrix
containing pairwise
coefficients for the main variable of interest in ALR transformed
regression models.
p_data
, a numeric matrix
containing pairwise
pvalues for the main variable of interest in ALR transformed
regression models.
q_data
, a numeric matrix
containing adjusted
pvalues by applying the p_adj_method
to the p_data
matrix.
Huang Lin
mandal2015analysisANCOMBC
\insertRefkaul2017analysisANCOMBC
ancombc
ancombc2
library(ANCOMBC) library(mia) data(hitchip1006) # subset to baseline tse = hitchip1006[, hitchip1006$time == 0] # run ancom function set.seed(123) out = ancom(data = tse, assay_name = "counts", tax_level = "Family", phyloseq = NULL, p_adj_method = "holm", prv_cut = 0.10, lib_cut = 1000, main_var = "bmi_group", adj_formula = "age + nationality", rand_formula = NULL, lme_control = NULL, struc_zero = TRUE, neg_lb = TRUE, alpha = 0.05, n_cl = 1) res = out$res # to run ancom using the phyloseq object tse_alt = agglomerateByRank(tse, "Family") pseq = makePhyloseqFromTreeSummarizedExperiment(tse_alt) set.seed(123) 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_group", adj_formula = "age + nationality", rand_formula = NULL, lme_control = NULL, struc_zero = TRUE, neg_lb = TRUE, alpha = 0.05, n_cl = 1)
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