ancombc2: Analysis of Compositions of Microbiomes with Bias Correction...
In FrederickHuangLin/ANCOMBC: Microbiome differential abudance and correlation analyses with bias correction
ancombc2 R Documentation
Analysis of Compositions of Microbiomes with Bias Correction 2
(ANCOM-BC2)
Description
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
ancombc2 function implements Analysis of Compositions of Microbiomes
with Bias Correction (ANCOM-BC2) in cross-sectional and repeated measurements
data. In addition to the two-group comparison, ANCOM-BC2 also supports
testing for continuous covariates and multi-group comparisons,
including the global test, pairwise directional test, Dunnett's type of
test, and trend test.
Usage
ancombc2(
data,
assay_name = "counts",
tax_level = NULL,
fix_formula,
rand_formula = NULL,
p_adj_method = "holm",
pseudo = 0,
pseudo_sens = TRUE,
prv_cut = 0.1,
lib_cut = 0,
s0_perc = 0.05,
group = NULL,
struc_zero = FALSE,
neg_lb = FALSE,
alpha = 0.05,
n_cl = 1,
verbose = FALSE,
global = FALSE,
pairwise = FALSE,
dunnet = FALSE,
trend = FALSE,
iter_control = list(tol = 0.01, max_iter = 20, verbose = FALSE),
em_control = list(tol = 1e-05, max_iter = 100),
lme_control = lme4::lmerControl(),
mdfdr_control = list(fwer_ctrl_method = "holm", B = 100),
trend_control = list(contrast = NULL, node = NULL, solver = "ECOS", B = 100)
)
Arguments
data
the input data. A
phyloseq, SummarizedExperiment, or
TreeSummarizedExperiment object, which consists of
a feature table (microbial count table), a sample metadata, a
taxonomy table (optional), and a phylogenetic tree (optional). The row names
of the metadata must match the sample names of the feature table, and the
row names of the taxonomy table must match the taxon (feature) names of the
feature table. See ?phyloseq::phyloseq,
?SummarizedExperiment::SummarizedExperiment, or
?TreeSummarizedExperiment::TreeSummarizedExperiment for more details.
It is highly recommended that the input data
are in low taxonomic levels, such as OTU or species level, as the estimation
of sampling fractions requires a large number of taxa.
assay_name
character. Name of the count table in the data object
(only applicable if data object is a (Tree)SummarizedExperiment).
Default is "counts".
See ?SummarizedExperiment::assay for more details.
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-BC2 anlysis will be performed at the lowest taxonomic level of the
input data.
fix_formula
the character string expresses how the microbial absolute
abundances for each taxon depend on the fixed effects in metadata.
rand_formula
the character string expresses how the microbial absolute
abundances for each taxon depend on the random effects in metadata. ANCOM-BC2
follows the lmerTest package in formulating the random effects. See
?lmerTest::lmer for more details. Default is NULL.
p_adj_method
character. method to adjust p-values. Default is "holm".
Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY",
"fdr", "none". See ?stats::p.adjust for more details.
pseudo
numeric. Add pseudo-counts to the data.
Default is 0 (no pseudo-count addition).
pseudo_sens
logical. Whether to perform the sensitivity analysis to
the pseudo-count addition. Default is TRUE. See Details for
a more comprehensive discussion on this sensitivity analysis.
prv_cut
a numerical fraction between 0 and 1. Taxa with prevalences
less than prv_cut will be excluded in the analysis. For instance,
suppose there are 100 samples, if a taxon has nonzero counts presented in
less than 10 samples, it will not be further analyzed. Default is 0.10.
lib_cut
a numerical threshold for filtering samples based on library
sizes. Samples with library sizes less than lib_cut will be
excluded in the analysis. Default is 0, i.e. do not discard any sample.
s0_perc
a numerical fraction between 0 and 1. Inspired by
Significance
Analysis of Microarrays (SAM) methodology, a small positive constant is
added to the denominator of ANCOM-BC2 test statistic corresponding to
each taxon to avoid the significance due to extremely small standard errors,
especially for rare taxa. This small positive constant is chosen as
s0_perc-th percentile of standard error values for each fixed effect.
Default is 0.05 (5th percentile).
group
character. The name of the group variable in metadata.
group should be discrete. Specifying group is required for
detecting structural zeros and performing multi-group comparisons (global
test, pairwise directional test, Dunnett's type of test, and trend test).
Default is NULL. If the group of interest contains only two
categories, leave it as NULL.
struc_zero
logical. Whether to detect structural zeros based on
group. Default is FALSE. See Details for
a more comprehensive discussion on structural zeros.
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
?parallel::makeCluster. Default is 1 (no parallel computing).
verbose
logical. Whether to generate verbose output during the
ANCOM-BC2 fitting process. Default is FALSE.
global
logical. Whether to perform the global test. Default is FALSE.
pairwise
logical. Whether to perform the pairwise directional test.
Default is FALSE.
dunnet
logical. Whether to perform the Dunnett's type of test.
Default is FALSE.
trend
logical. Whether to perform trend test. Default is FALSE.
iter_control
a named list of control parameters for the iterative
MLE or RMEL algorithm, including 1) tol: the iteration convergence
tolerance (default is 1e-02), 2) max_iter: the maximum number of
iterations (default is 20), and 3)verbose: whether to show the verbose
output (default is FALSE).
em_control
a named list of control parameters for the E-M algorithm,
including 1) tol: the iteration convergence tolerance
(default is 1e-05) and 2) max_iter: the maximum number of iterations
(default is 100).
lme_control
a list of control parameters for mixed model fitting.
See ?lme4::lmerControl for details.
mdfdr_control
a named list of control parameters for mixed directional
false discover rate (mdFDR), including 1) fwer_ctrl_method: family
wise error (FWER) controlling procedure, such as "holm", "hochberg",
"bonferroni", etc (default is "holm") and 2) B: the number of
bootstrap samples (default is 100). Increase B will lead to a more
accurate p-values. See Details for a more comprehensive discussion on
mdFDR.
trend_control
a named list of control parameters for the trend test,
including 1) contrast: the list of contrast matrices for
constructing inequalities, 2) node: the list of positions for the
nodal parameter, 3) solver: a string indicating the solver to use
(default is "ECOS"), and 4) B: the number of bootstrap samples
(default is 100). Increase B will lead to a more accurate p-values.
See vignette for the corresponding trend test examples.
Details
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 ANCOM-BC2, but the results are
summarized in the overall summary. For more details about the structural
zeros, please go to the
ANCOM-II paper.
Setting neg_lb = TRUE indicates that you are using both criteria
stated in section 3.2 of
ANCOM-II
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).
Like other differential abundance analysis methods, ANCOM-BC2 log transforms
the observed counts. However, to deal with zero counts, a pseudo-count is
added before the log transformation. Several studies have shown that
differential abundance results could be sensitive to the choice of
pseudo-count
(Costea et al. (2014);
Paulson, Bravo, and Pop (2014)),
resulting in an inflated false positive rate. To avoid such false positives,
we conduct a sensitivity analysis and provide a sensitivity score for
each taxon to determine if a particular taxon is sensitive to the choice of
pseudo-count. The larger the score, the more likely the significant
result is a false positive.
When performning pairwise directional (or Dunnett's type of) test, the mixed
directional false discover rate (mdFDR) should be taken into account.
The mdFDR is the combination of false discovery rate due to multiple testing,
multiple pairwise comparisons, and directional tests within each pairwise
comparison. For example, suppose we have five taxa and three experimental
groups: g1, g2, and g3. Thus, we are performing five tests corresponding to
five taxa. For each taxon, we are also conducting three pairwise comparisons
(g1 vs. g2, g2 vs. g3, and g1 vs. g3). Within each pairwise comparison,
we wish to determine if the abundance has increased or decreased or did not
change (direction of the effect size). Errors could occur in each step.
The overall false discovery rate is controlled by the mdFDR methodology we
adopted from
Guo, Sarkar, and Peddada (2010) and
Grandhi, Guo, and Peddada (2016).
Value
a list with components:
-
feature_table, a data.frame of pre-processed
(based on prv_cut and lib_cut) microbial count table.
-
zero_ind, a logical data.frame with TRUE
indicating the taxon is detected to contain structural zeros in
some specific groups.
-
samp_frac, a numeric vector of estimated sampling
fractions in log scale (natural log).
-
delta_em, estimated sample-specific biases
through E-M algorithm.
-
delta_wls, estimated sample-specific biases through
weighted least squares (WLS) algorithm.
-
pseudo_sens_tab, the results of sensitivity analysis
for the pseudo-count addition.
-
res, a data.frame containing ANCOM-BC2 primary
result:
columns started with lfc: log fold changes
obtained from the ANCOM-BC2 log-linear (natural log) model.
columns started with se: standard errors (SEs) of
lfc.
columns started with W: test statistics.
W = lfc/se.
columns started with p: p-values. P-values are
obtained from two-sided Z-test using the test statistic W.
columns started with q: adjusted p-values.
Adjusted p-values are obtained by applying p_adj_method
to p.
columns started with diff: TRUE if the
taxon is significant (has q less than alpha).
-
res_global, a data.frame containing ANCOM-BC2
global test result for the variable specified in group,
each column is:
-
W, test statistics.
-
p_val, p-values, which are obtained from two-sided
Chi-square test using W.
-
q_val, adjusted p-values. Adjusted p-values are
obtained by applying p_adj_method to p_val.
-
diff_abn, A logical vector. TRUE if the taxon has
q_val less than alpha.
-
res_pair, a data.frame containing ANCOM-BC2
pairwise directional test result for the variable specified in
group:
columns started with lfc: log fold changes.
columns started with se: standard errors (SEs).
columns started with W: test statistics.
columns started with p: p-values.
columns started with q: adjusted p-values.
columns started with diff: TRUE if the
taxon is significant (has q less than alpha).
-
res_dunn, a data.frame containing ANCOM-BC2
Dunnett's type of test result for the variable specified in
group:
columns started with lfc: log fold changes.
columns started with se: standard errors (SEs).
columns started with W: test statistics.
columns started with p: p-values.
columns started with q: adjusted p-values.
columns started with diff: TRUE if the
taxon is significant (has q less than alpha).
-
res_trend, a data.frame containing ANCOM-BC2
trend test result for the variable specified in
group:
columns started with lfc: log fold changes.
columns started with se: standard errors (SEs).
-
W: test statistics.
-
p_val: p-values.
-
q_val: adjusted p-values.
-
diff_abn: TRUE if the
taxon is significant (has q less than alpha).
Author(s)
Huang Lin
References
\insertRefkaul2017analysisANCOMBC
\insertReflin2020analysisANCOMBC
\insertReftusher2001significanceANCOMBC
\insertRefcostea2014fairANCOMBC
\insertRefpaulson2014replyANCOMBC
\insertRefguo2010controllingANCOMBC
\insertRefgrandhi2016multipleANCOMBC
See Also
ancom ancombc
Examples
#===========Build a TreeSummarizedExperiment Object from Scratch=============
library(mia)
# microbial count table
otu_mat = matrix(sample(1:100, 100, replace = TRUE), nrow = 10, ncol = 10)
rownames(otu_mat) = paste0("taxon", 1:nrow(otu_mat))
colnames(otu_mat) = paste0("sample", 1:ncol(otu_mat))
assays = SimpleList(counts = otu_mat)
# sample metadata
smd = data.frame(group = sample(LETTERS[1:4], size = 10, replace = TRUE),
row.names = paste0("sample", 1:ncol(otu_mat)),
stringsAsFactors = FALSE)
smd = DataFrame(smd)
# taxonomy table
tax_tab = matrix(sample(letters, 70, replace = TRUE),
nrow = nrow(otu_mat), ncol = 7)
rownames(tax_tab) = rownames(otu_mat)
colnames(tax_tab) = c("Kingdom", "Phylum", "Class", "Order",
"Family", "Genus", "Species")
tax_tab = DataFrame(tax_tab)
# create TSE
tse = TreeSummarizedExperiment(assays = assays,
colData = smd,
rowData = tax_tab)
# convert TSE to phyloseq
pseq = makePhyloseqFromTreeSummarizedExperiment(tse)
#=======================Run ANCOMBC2 Using a Real Data=======================
library(ANCOMBC)
data(dietswap)
colData(dietswap)$bmi_group = factor(colData(dietswap)$bmi_group,
levels = c("obese",
"overweight",
"lean"))
set.seed(123)
# Note that setting pseudo_sens = FALSE, max_iter = 1, and B = 1 is
# only for the sake of speed
# Set pseudo_sens = TRUE, and use default or larger values for max_iter and B
# for better performance
out = ancombc2(data = dietswap, assay_name = "counts", tax_level = "Phylum",
fix_formula = "nationality + timepoint + bmi_group",
rand_formula = "(timepoint | subject)",
p_adj_method = "holm", pseudo = 0, pseudo_sens = FALSE,
prv_cut = 0.10, lib_cut = 1000, s0_perc = 0.05,
group = "bmi_group", struc_zero = TRUE, neg_lb = TRUE,
alpha = 0.05, n_cl = 1, verbose = TRUE,
global = TRUE, pairwise = TRUE, dunnet = TRUE, trend = TRUE,
iter_control = list(tol = 1e-2, max_iter = 1, verbose = TRUE),
em_control = list(tol = 1e-5, max_iter = 1),
lme_control = lme4::lmerControl(),
mdfdr_control = list(fwer_ctrl_method = "holm", B = 1),
trend_control = list(contrast =
list(matrix(c(1, 0, -1, 1),
nrow = 2,
byrow = TRUE)),
node = list(2),
solver = "ECOS",
B = 1))
res_prim = out$res
res_global = out$res_global
res_pair = out$res_pair
res_dunn = out$res_dunn
res_trend = out$res_trend
FrederickHuangLin/ANCOMBC documentation built on Feb. 23, 2023, 11:13 p.m.
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| ancombc2 | R Documentation |
Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2)
Description
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
ancombc2 function implements Analysis of Compositions of Microbiomes
with Bias Correction (ANCOM-BC2) in cross-sectional and repeated measurements
data. In addition to the two-group comparison, ANCOM-BC2 also supports
testing for continuous covariates and multi-group comparisons,
including the global test, pairwise directional test, Dunnett's type of
test, and trend test.
Usage
ancombc2( data, assay_name = "counts", tax_level = NULL, fix_formula, rand_formula = NULL, p_adj_method = "holm", pseudo = 0, pseudo_sens = TRUE, prv_cut = 0.1, lib_cut = 0, s0_perc = 0.05, group = NULL, struc_zero = FALSE, neg_lb = FALSE, alpha = 0.05, n_cl = 1, verbose = FALSE, global = FALSE, pairwise = FALSE, dunnet = FALSE, trend = FALSE, iter_control = list(tol = 0.01, max_iter = 20, verbose = FALSE), em_control = list(tol = 1e-05, max_iter = 100), lme_control = lme4::lmerControl(), mdfdr_control = list(fwer_ctrl_method = "holm", B = 100), trend_control = list(contrast = NULL, node = NULL, solver = "ECOS", B = 100) )
Arguments
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-BC2 anlysis will be performed at the lowest taxonomic level of the
input |
fix_formula |
the character string expresses how the microbial absolute abundances for each taxon depend on the fixed effects in metadata. |
rand_formula |
the character string expresses how the microbial absolute
abundances for each taxon depend on the random effects in metadata. ANCOM-BC2
follows the |
p_adj_method |
character. method to adjust p-values. Default is "holm".
Options include "holm", "hochberg", "hommel", "bonferroni", "BH", "BY",
"fdr", "none". See |
pseudo |
numeric. Add pseudo-counts to the data. Default is 0 (no pseudo-count addition). |
pseudo_sens |
logical. Whether to perform the sensitivity analysis to
the pseudo-count addition. Default is |
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 |
s0_perc |
a numerical fraction between 0 and 1. Inspired by
Significance
Analysis of Microarrays (SAM) methodology, a small positive constant is
added to the denominator of ANCOM-BC2 test statistic corresponding to
each taxon to avoid the significance due to extremely small standard errors,
especially for rare taxa. This small positive constant is chosen as
|
group |
character. The name of the group variable in metadata.
|
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
|
verbose |
logical. Whether to generate verbose output during the ANCOM-BC2 fitting process. Default is FALSE. |
global |
logical. Whether to perform the global test. Default is FALSE. |
pairwise |
logical. Whether to perform the pairwise directional test. Default is FALSE. |
dunnet |
logical. Whether to perform the Dunnett's type of test. Default is FALSE. |
trend |
logical. Whether to perform trend test. Default is FALSE. |
iter_control |
a named list of control parameters for the iterative
MLE or RMEL algorithm, including 1) |
em_control |
a named list of control parameters for the E-M algorithm,
including 1) |
lme_control |
a list of control parameters for mixed model fitting.
See |
mdfdr_control |
a named list of control parameters for mixed directional
false discover rate (mdFDR), including 1) |
trend_control |
a named list of control parameters for the trend test,
including 1) |
Details
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 ANCOM-BC2, but the results are
summarized in the overall summary. For more details about the structural
zeros, please go to the
ANCOM-II paper.
Setting neg_lb = TRUE indicates that you are using both criteria
stated in section 3.2 of
ANCOM-II
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).
Like other differential abundance analysis methods, ANCOM-BC2 log transforms the observed counts. However, to deal with zero counts, a pseudo-count is added before the log transformation. Several studies have shown that differential abundance results could be sensitive to the choice of pseudo-count (Costea et al. (2014); Paulson, Bravo, and Pop (2014)), resulting in an inflated false positive rate. To avoid such false positives, we conduct a sensitivity analysis and provide a sensitivity score for each taxon to determine if a particular taxon is sensitive to the choice of pseudo-count. The larger the score, the more likely the significant result is a false positive.
When performning pairwise directional (or Dunnett's type of) test, the mixed directional false discover rate (mdFDR) should be taken into account. The mdFDR is the combination of false discovery rate due to multiple testing, multiple pairwise comparisons, and directional tests within each pairwise comparison. For example, suppose we have five taxa and three experimental groups: g1, g2, and g3. Thus, we are performing five tests corresponding to five taxa. For each taxon, we are also conducting three pairwise comparisons (g1 vs. g2, g2 vs. g3, and g1 vs. g3). Within each pairwise comparison, we wish to determine if the abundance has increased or decreased or did not change (direction of the effect size). Errors could occur in each step. The overall false discovery rate is controlled by the mdFDR methodology we adopted from Guo, Sarkar, and Peddada (2010) and Grandhi, Guo, and Peddada (2016).
Value
a list with components:
-
feature_table, adata.frameof pre-processed (based onprv_cutandlib_cut) microbial count table. -
zero_ind, a logicaldata.framewith TRUE indicating the taxon is detected to contain structural zeros in some specific groups. -
samp_frac, a numeric vector of estimated sampling fractions in log scale (natural log). -
delta_em, estimated sample-specific biases through E-M algorithm. -
delta_wls, estimated sample-specific biases through weighted least squares (WLS) algorithm. -
pseudo_sens_tab, the results of sensitivity analysis for the pseudo-count addition. -
res, adata.framecontaining ANCOM-BC2 primary result:columns started with
lfc: log fold changes obtained from the ANCOM-BC2 log-linear (natural log) model.columns started with
se: standard errors (SEs) oflfc.columns started with
W: test statistics.W = lfc/se.columns started with
p: p-values. P-values are obtained from two-sided Z-test using the test statisticW.columns started with
q: adjusted p-values. Adjusted p-values are obtained by applyingp_adj_methodtop.columns started with
diff: TRUE if the taxon is significant (hasqless thanalpha).
-
res_global, adata.framecontaining ANCOM-BC2 global test result for the variable specified ingroup, each column is:-
W, test statistics. -
p_val, p-values, which are obtained from two-sided Chi-square test usingW. -
q_val, adjusted p-values. Adjusted p-values are obtained by applyingp_adj_methodtop_val. -
diff_abn, A logical vector. TRUE if the taxon hasq_valless thanalpha.
-
-
res_pair, adata.framecontaining ANCOM-BC2 pairwise directional test result for the variable specified ingroup:columns started with
lfc: log fold changes.columns started with
se: standard errors (SEs).columns started with
W: test statistics.columns started with
p: p-values.columns started with
q: adjusted p-values.columns started with
diff: TRUE if the taxon is significant (hasqless thanalpha).
-
res_dunn, adata.framecontaining ANCOM-BC2 Dunnett's type of test result for the variable specified ingroup:columns started with
lfc: log fold changes.columns started with
se: standard errors (SEs).columns started with
W: test statistics.columns started with
p: p-values.columns started with
q: adjusted p-values.columns started with
diff: TRUE if the taxon is significant (hasqless thanalpha).
-
res_trend, adata.framecontaining ANCOM-BC2 trend test result for the variable specified ingroup:columns started with
lfc: log fold changes.columns started with
se: standard errors (SEs).-
W: test statistics. -
p_val: p-values. -
q_val: adjusted p-values. -
diff_abn: TRUE if the taxon is significant (hasqless thanalpha).
Author(s)
Huang Lin
References
\insertRefkaul2017analysisANCOMBC
\insertReflin2020analysisANCOMBC
\insertReftusher2001significanceANCOMBC
\insertRefcostea2014fairANCOMBC
\insertRefpaulson2014replyANCOMBC
\insertRefguo2010controllingANCOMBC
\insertRefgrandhi2016multipleANCOMBC
See Also
ancom ancombc
Examples
#===========Build a TreeSummarizedExperiment Object from Scratch=============
library(mia)
# microbial count table
otu_mat = matrix(sample(1:100, 100, replace = TRUE), nrow = 10, ncol = 10)
rownames(otu_mat) = paste0("taxon", 1:nrow(otu_mat))
colnames(otu_mat) = paste0("sample", 1:ncol(otu_mat))
assays = SimpleList(counts = otu_mat)
# sample metadata
smd = data.frame(group = sample(LETTERS[1:4], size = 10, replace = TRUE),
row.names = paste0("sample", 1:ncol(otu_mat)),
stringsAsFactors = FALSE)
smd = DataFrame(smd)
# taxonomy table
tax_tab = matrix(sample(letters, 70, replace = TRUE),
nrow = nrow(otu_mat), ncol = 7)
rownames(tax_tab) = rownames(otu_mat)
colnames(tax_tab) = c("Kingdom", "Phylum", "Class", "Order",
"Family", "Genus", "Species")
tax_tab = DataFrame(tax_tab)
# create TSE
tse = TreeSummarizedExperiment(assays = assays,
colData = smd,
rowData = tax_tab)
# convert TSE to phyloseq
pseq = makePhyloseqFromTreeSummarizedExperiment(tse)
#=======================Run ANCOMBC2 Using a Real Data=======================
library(ANCOMBC)
data(dietswap)
colData(dietswap)$bmi_group = factor(colData(dietswap)$bmi_group,
levels = c("obese",
"overweight",
"lean"))
set.seed(123)
# Note that setting pseudo_sens = FALSE, max_iter = 1, and B = 1 is
# only for the sake of speed
# Set pseudo_sens = TRUE, and use default or larger values for max_iter and B
# for better performance
out = ancombc2(data = dietswap, assay_name = "counts", tax_level = "Phylum",
fix_formula = "nationality + timepoint + bmi_group",
rand_formula = "(timepoint | subject)",
p_adj_method = "holm", pseudo = 0, pseudo_sens = FALSE,
prv_cut = 0.10, lib_cut = 1000, s0_perc = 0.05,
group = "bmi_group", struc_zero = TRUE, neg_lb = TRUE,
alpha = 0.05, n_cl = 1, verbose = TRUE,
global = TRUE, pairwise = TRUE, dunnet = TRUE, trend = TRUE,
iter_control = list(tol = 1e-2, max_iter = 1, verbose = TRUE),
em_control = list(tol = 1e-5, max_iter = 1),
lme_control = lme4::lmerControl(),
mdfdr_control = list(fwer_ctrl_method = "holm", B = 1),
trend_control = list(contrast =
list(matrix(c(1, 0, -1, 1),
nrow = 2,
byrow = TRUE)),
node = list(2),
solver = "ECOS",
B = 1))
res_prim = out$res
res_global = out$res_global
res_pair = out$res_pair
res_dunn = out$res_dunn
res_trend = out$res_trend
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