Package {CAFT}

Example colon microbiome dataset

Description

This gut microbiome data set is originally included in the R package "curatedMetagenomicData" and the package installation is required through "Bioconductor". It specifically focuses on the adult colorectal cancer studies using the stool samples. More details can be found through the following references.

Usage

data(Colon)

Format

An object of class list of length 3.

References

Pasolli, E., Schiffer, L., Manghi, P., et al. (2017). Accessible, curated metagenomic data through ExperimentHub. Nature Methods, 14(11), 1023–1024.

Examples

data(Colon)

URT upper respiratory tract microbiome data (filtered)

Description

A small example dataset derived from Charlson et al. (2010) as distributed by the LOCOM package and filtered to left oropharyngeal samples. Provided for illustrating CAFT workflows.

Usage

data("URT")

Format

A list with three components:

otu

Integer matrix of counts, samples in rows and taxa in columns.

meta

Data frame of sample metadata (e.g., SmokingStatus, Sex).

tax

Data frame of taxonomy annotations for the taxa.

Details

Zeros in otu correspond to under-detection and are handled as left-censored measurements by CAFT.

Source

Charlson ES et al. (2010) PLoS One 5(12):e15216; dataset accessed via the LOCOM R package.

Examples

data("URT", package = "CAFT")
str(URT, max.level = 1)
# counts:
dim(URT$otu)
# metadata variables:
names(URT$meta)

Rank-Based Compositional Analysis using Log-Linear Models for Microbiome Data with Zero Cells

Description

This is a statistical framework for differential abundance analysis of microbiome data, termed the Compositional Accelerated Failure Time (CAFT) model. The CAFT model addresses zero read counts by treating them as censored observations below the detection limit, similar to censoring mechanisms employed in survival analysis. This approach is inherently resistant to multiplicative bias, eliminates the need for pseudocounts, and addresses compositional bias through the establishment of appropriate score test procedures.

In addition to the default asymptotic CAFT inference, the function optionally provides bootstrap calibration of taxon-level p-values. Bootstrap calibration is mainly intended as a sensitivity analysis or preferred calibration method when the number of taxa is small in the data set.

Usage

caft(
  otu.table,
  x.test = NULL,
  x.adj = NULL,
  x = NULL,
  Gamma = NULL,
  b = NULL,
  filter.thresh = 0.05,
  fdr.nominal = 0.2,
  adjust.method = "BH",
  regularize = TRUE,
  test.method = "rank",
  n.cores = 1L,
  boot.B = 0L,
  boot.parallel = FALSE,
  boot.n.cores = 1L,
  boot.seed = NULL,
  boot.return.dist = FALSE,
  verbose = FALSE,
  return.mr.resid = FALSE
)

Arguments

Details

CAFT fits a rank-based accelerated failure time model to the negative log-relative abundance of each taxon, treating zero counts as censored values below the sample-specific detection limit. The resulting taxon-specific regression coefficients are compared through compositional contrasts.

With the default x.test/x.adj interface, the wrapper constructs the full design matrix internally and tests the columns corresponding to x.test, while adjusting for the columns in x.adj. For more general hypotheses, users should provide a full design matrix x and a hypothesis matrix Gamma.

This wrapper implements the refactored CAFT workflow. It first calls caft_estimate() to compute unrestricted taxon-level estimates, then calls caft_test() to perform the restricted score test. If boot.B >= 2, it further calls caft_bootstrap() for bootstrap calibration. This separation allows the unrestricted estimates to be reused when testing different hypotheses or different choices of b.

When boot.B >= 2, CAFT performs fixed-design bootstrap calibration. The observed model is first fit, and the bootstrap is carried out on the taxa that pass the original filtering step. In each bootstrap replicate, samples are resampled with replacement from the OTU table while the covariates are kept fixed. The model is then refit with filter.thresh = 0 on the same set of taxa. Bootstrap p-values are computed from the centered bootstrap distribution of the tested coefficient contrasts. For a single testing variable, both a two-sided empirical beta-tilde p-value and a studentized chi-square/Wald bootstrap p-value are returned. For multiple testing variables, the bootstrap p-value is based on a multivariate statistic; in this case p.boot and p.boot.chi are identical.

If boot.parallel = TRUE, the outer bootstrap loop is parallelized over bootstrap replicates. To avoid nested parallelism, the inner CAFT fitting routine is run with n.cores = 1L inside each bootstrap worker.

Value

Return a list consisting of

beta.est

A matrix containing the unrestricted coefficient estimates from the taxon-level log-linear CAFT model. The number of columns matches the number of covariates in the internally constructed or user-supplied design matrix.

betahat.median

The median-based null reference value used when b = NULL. If b is supplied by the user, this is returned as NA because the median-based null value is not computed.

b.null

The null value actually used in the restricted score test. This equals betahat.median when b = NULL, and equals the user-specified b otherwise.

b.user.specified

Logical indicator of whether b was supplied by the user. If FALSE, b.null was computed as the median-based reference value. If TRUE, b.null equals the user-specified null value.

b.user

The user-specified null value, returned only when b is supplied. Otherwise NULL.

Gamma

The hypothesis matrix used in the restricted score test.

Gamma.source

Character string indicating whether Gamma was user-supplied or constructed internally from x.test/x.adj.

rank.teststat

Test statistics from the proposed score test.

rank.teststat.norm

Numeric matrix with the normalized rank-based test statistics for each taxon (rows) and each tested contrast (columns, in the order of Gamma's rows). Under the null, entries are approximately N(0,1); larger absolute values indicate stronger evidence against the null.

skip.otu

The names of skipped OTU during taxa presence filtering.

skip.rare

Indicator for taxa skipped by the rarity filter.

skip.fail.rank.fit.pen

Indicator for taxa whose unrestricted rank-based fit failed.

skip.fail.rank.test.pen

Indicator for taxa whose restricted estimation or rank-based score test failed.

fit.error.messages

Error messages from failed unrestricted taxon-level fits, if any.

fit.error.messages.test

Error messages from failed restricted taxon-level tests, if any.

p.otu

P-values for individual OTU association tests.

df.test

Degrees of freedom per taxon in the test.

beta.est.r

Matrix of constrained (restricted) estimates from the restricted score test.

p.marginal.otu

Taxa with raw p-values smaller than fdr.nominal based on p.otu.

q.otu

Q-values from adjusting p-values by the method specified in adjust.method, for individual OTU association tests.

q.detected.otu

Detected significantly differential abundant taxa (denoted by the column names of the OTU table) at the nominal FDR based on q.otu.

p.boot

Bootstrap-calibrated empirical beta-tilde p-values. If boot.B < 2, this is not returned.

q.boot

Multiplicity-adjusted values from p.boot.

p.boot.marginal.otu

Taxa detected using p.boot < fdr.nominal. Empty if bootstrap calibration is not run.

q.boot.detected.otu

Taxa detected using q.boot < fdr.nominal. Empty if bootstrap calibration is not run.

p.boot.chi

Studentized chi-square/Wald bootstrap p-values. For multiple testing variables, these are the same as p.boot. If boot.B < 2, this is not returned.

q.boot.chi

Multiplicity-adjusted values from p.boot.chi.

p.boot.chi.marginal.otu

Taxa detected using p.boot.chi < fdr.nominal. Empty if bootstrap calibration is not run.

q.boot.chi.detected.otu

Taxa detected using q.boot.chi < fdr.nominal. Empty if bootstrap calibration is not run.

boot.median

Matrix of bootstrap null values used to center the bootstrap coefficient estimates. When b is user-specified, these values equal the specified null value across bootstrap replicates.

boot.n.success

Number of bootstrap replicates that produced complete bootstrap null values.

boot.n.fail

Number of bootstrap replicates skipped or failed.

boot.beta.tilde

Returned only when boot.return.dist = TRUE. Array of bootstrap-centered tested coefficient contrasts.

boot.taxa

Returned only when boot.return.dist = TRUE. Names of taxa used in the bootstrap calibration.

mr.resid

Returned only when return.mr.resid = TRUE. A list of length equal to the number of taxa. Each element is an n \times p matrix of subject-level compensated Cox-type residual contributions, evaluated at the restricted estimate used in the score test.

Examples

data(Colon)

count.tab <- Colon$otu
sample.tab <- Colon$meta

keep.sample <- !is.na(sample.tab$age)
count.tab <- count.tab[keep.sample, , drop = FALSE]
sample.tab <- sample.tab[keep.sample, , drop = FALSE]

## Use a small subset of taxa for a fast example.
prev <- colSums(count.tab > 0)
keep.top <- names(sort(prev, decreasing = TRUE))[
  seq_len(min(20L, length(prev)))
]
count.tab <- count.tab[, keep.top, drop = FALSE]

## Remove samples with zero library size after subsetting taxa.
keep.lib <- rowSums(count.tab) > 0
count.tab <- count.tab[keep.lib, , drop = FALSE]
sample.tab <- sample.tab[keep.lib, , drop = FALSE]

Disease1 <- Disease2 <- rep(0, nrow(sample.tab))
Disease1[sample.tab$disease == "CRC"] <- 1
Disease2[sample.tab$disease == "adenoma"] <- 1

Age <- as.numeric(sample.tab$age)
Gender <- as.numeric(factor(sample.tab$gender)) - 1

x.test <- cbind(CRC = Disease1, adenoma = Disease2)
x.adj <- cbind(Age = Age, Gender = Gender)

## Standard CAFT wrapper with a user-specified null value.
res <- caft(
  otu.table = count.tab,
  x.test = x.test,
  x.adj = x.adj,
  b = c(0, 0),
  boot.B = 0L
)

head(res$p.otu)
res$q.detected.otu
res$Gamma
res$Gamma.source

## Separated estimation and testing workflow.
x <- cbind(x.test, x.adj)

Gamma <- cbind(
  diag(ncol(x.test)),
  matrix(0, nrow = ncol(x.test), ncol = ncol(x.adj))
)

est <- caft_estimate(
  otu.table = count.tab,
  x = x
)

res.test <- caft_test(est, Gamma = Gamma, b = c(0, 0))
head(res.test$p.otu)
res.test$b.null

## Reuse the same unrestricted estimates with a different user-specified
## null value.
res.b <- caft_test(est, Gamma = Gamma, b = c(0.1, 0.1))
res.b$b.null


## Bootstrap calibration with a small number of replicates for illustration.
res.boot <- caft(
  otu.table = count.tab,
  x.test = x.test,
  x.adj = x.adj,
  b = c(0, 0),
  boot.B = 2L,
  boot.seed = 1L
)

res.boot$boot.n.success
head(res.boot$p.boot)

Bootstrap Calibration for CAFT Tests

Description

Provides bootstrap calibration of taxon-level p-values for an existing CAFT restricted score test object returned by caft_test(). Bootstrap calibration is mainly intended as a sensitivity analysis or preferred calibration method when the number of taxa is small in the data set.

Usage

caft_bootstrap(
  res,
  boot.B = 1000L,
  boot.parallel = FALSE,
  boot.n.cores = 1L,
  boot.seed = NULL,
  boot.return.dist = FALSE,
  verbose = FALSE
)

Arguments

Details

This function is intended to be used after caft_estimate() and caft_test(). The observed restricted score test is not recomputed. Instead, caft_bootstrap() uses the hypothesis matrix Gamma, the null value b.null, the nominal FDR threshold, and the adjustment method stored in the caft_test object.

CAFT bootstrap calibration uses a fixed-design bootstrap procedure. Samples are resampled with replacement from the observed OTU table while the covariates are kept fixed. The bootstrap is carried out on the taxa that pass the original filtering step. In each bootstrap replicate, the unrestricted CAFT model is refit using caft_estimate() with filter.thresh = 0 on the same set of taxa. The inner CAFT fitting routine is run with n.cores = 1L inside each bootstrap replicate to avoid nested parallelism.

Bootstrap p-values are computed from the centered bootstrap distribution of the tested coefficient contrasts. If the original test used the default median-based null value, the bootstrap null value is recomputed within each bootstrap replicate from the bootstrap taxon-level contrasts. If the original test used a user-specified b, the same fixed null value is used in every bootstrap replicate.

For a single testing variable, both a two-sided empirical beta-tilde bootstrap p-value and a studentized chi-square/Wald bootstrap p-value are returned. For multiple testing variables, the bootstrap p-value is based on a multivariate statistic; in this case p.boot and p.boot.chi are identical.

If boot.parallel = TRUE, the outer bootstrap loop is parallelized over bootstrap replicates using foreach/doParallel. Reproducible parallel bootstrap sampling is handled through doRNG.

Value

The input caft_test object with additional bootstrap calibration results:

p.boot

Bootstrap-calibrated empirical beta-tilde p-values. If boot.B < 2, this is a named vector of NA values.

q.boot

Multiplicity-adjusted values from p.boot, computed using the adjustment method stored in res$adjust.method.

p.boot.marginal.otu

Taxa detected using p.boot < fdr.nominal. Empty if bootstrap calibration is not run.

q.boot.detected.otu

Taxa detected using q.boot < fdr.nominal. Empty if bootstrap calibration is not run.

p.boot.chi

Studentized chi-square/Wald bootstrap p-values. For multiple testing variables, these are the same as p.boot. If boot.B < 2, this is a named vector of NA values.

q.boot.chi

Multiplicity-adjusted values from p.boot.chi.

p.boot.chi.marginal.otu

Taxa detected using p.boot.chi < fdr.nominal. Empty if bootstrap calibration is not run.

q.boot.chi.detected.otu

Taxa detected using q.boot.chi < fdr.nominal. Empty if bootstrap calibration is not run.

boot.median

Matrix of bootstrap null values used to center the bootstrap coefficient estimates. When b is user-specified, these values equal the specified null value across bootstrap replicates.

boot.n.success

Number of bootstrap replicates that produced complete bootstrap null values.

boot.n.fail

Number of bootstrap replicates skipped or failed.

boot.beta.tilde

Returned only when boot.return.dist = TRUE. Array of bootstrap-centered tested coefficient contrasts.

boot.taxa

Returned only when boot.return.dist = TRUE. Names of taxa used in the bootstrap calibration.

Examples

data(Colon)

count.tab <- Colon$otu
sample.tab <- Colon$meta

keep.sample <- !is.na(sample.tab$age)
count.tab <- count.tab[keep.sample, , drop = FALSE]
sample.tab <- sample.tab[keep.sample, , drop = FALSE]

## Use a small subset of taxa for a fast example.
prev <- colSums(count.tab > 0)
keep.top <- names(sort(prev, decreasing = TRUE))[seq_len(min(5L, length(prev)))]
count.tab <- count.tab[, keep.top, drop = FALSE]

## Remove samples with zero library size after subsetting taxa.
keep.lib <- rowSums(count.tab) > 0
count.tab <- count.tab[keep.lib, , drop = FALSE]
sample.tab <- sample.tab[keep.lib, , drop = FALSE]

Disease1 <- rep(0, nrow(sample.tab))
Disease1[sample.tab$disease == "CRC"] <- 1

Age <- as.numeric(sample.tab$age)
Gender <- as.numeric(factor(sample.tab$gender)) - 1

x.test <- cbind(CRC = Disease1)
x.adj <- cbind(Age = Age, Gender = Gender)

x <- cbind(x.test, x.adj)

Gamma <- cbind(
  diag(ncol(x.test)),
  matrix(0, nrow = ncol(x.test), ncol = ncol(x.adj))
)

est <- caft_estimate(
  otu.table = count.tab,
  x = x,
  regularize = TRUE,
  n.cores = 1L
)

res <- caft_test(est, Gamma = Gamma, b = 0)


## Use a very small number of replicates only for illustration.
res.boot <- caft_bootstrap(
  res,
  boot.B = 2L,
  boot.seed = 1L
)

res.boot$boot.n.success
head(res.boot$p.boot)

Unrestricted CAFT Estimation

Description

Fits the unrestricted rank-based compositional accelerated failure time model for each taxon in microbiome count data with zero cells. Zero counts are treated as censored observations below sample-specific detection limits. This function performs only taxon-level unrestricted estimation and does not conduct hypothesis testing, multiple-testing adjustment, or bootstrap calibration.

Usage

caft_estimate(
  otu.table,
  x,
  filter.thresh = 0.05,
  regularize = TRUE,
  n.cores = 1L
)

Arguments

Details

CAFT fits a rank-based accelerated failure time model to the negative log-relative abundance of each taxon. Zero counts are treated as censored observations below the sample-specific detection limit, avoiding the use of pseudocounts.

This function performs only the unrestricted taxon-level estimation step. It returns the unrestricted coefficient estimates and the transformed survival-type data needed by downstream CAFT testing functions. It does not compute the median-based null value, restricted estimates, score test statistics, p-values, q-values, or bootstrap-calibrated p-values.

Value

An object of class "caft_est" containing unrestricted taxon-level coefficient estimates, censoring variables, the centered design matrix, and filtering/fitting status indicators.

call

The matched function call.

otu.table

The OTU count table used in the unrestricted CAFT estimation after input checking.

lib.size

The sequencing library size for each sample.

x.raw

The original, uncentered covariate matrix used in the model.

x

The centered covariate matrix used in the unrestricted rank-based AFT estimation.

x.name

Names of the covariates in the design matrix.

filter.thresh

The prevalence filtering threshold used in the analysis.

regularize

Logical value indicating whether regularized rank-based AFT estimation was used.

n.data

Number of samples used in the unrestricted estimation.

n.taxa

Number of taxa in the OTU table.

n.param

Number of covariates in the design matrix.

taxa.name

Names of the taxa in the OTU table.

taxa.used

Names of taxa that passed the prevalence filter and had successful unrestricted estimation.

t.star.all

Transformed censored abundance outcomes used in the rank-based AFT estimation.

delta.all

Censoring indicators for t.star.all, where one indicates an observed nonzero count and zero indicates a censored zero count.

beta.est

A matrix that includes the unrestricted estimated coefficients obtained by fitting each OTU count to the CAFT log-linear model. The number of columns matches the number of covariates from x.

skip.otu

Indicator for OTUs skipped during taxa presence filtering.

skip.rare

Indicator for OTUs skipped during taxa presence filtering.

skip.fail.rank.fit.pen

Indicator for OTUs whose unrestricted regularized rank-based AFT estimation failed.

fit.error.messages

Error messages from failed unrestricted taxon-level fits, if any.

Examples

data(Colon)

count.tab <- Colon$otu
sample.tab <- Colon$meta

keep.sample <- !is.na(sample.tab$age)
count.tab <- count.tab[keep.sample, , drop = FALSE]
sample.tab <- sample.tab[keep.sample, , drop = FALSE]

keep.otu <- colSums(count.tab > 0) > 1
count.tab <- count.tab[, keep.otu, drop = FALSE]

## Use a small subset of taxa for a fast example.
prev <- colSums(count.tab > 0)
keep.top <- names(sort(prev, decreasing = TRUE))[seq_len(min(10L, length(prev)))]
count.tab <- count.tab[, keep.top, drop = FALSE]

## Remove samples with zero library size after subsetting taxa.
keep.lib <- rowSums(count.tab) > 0
count.tab <- count.tab[keep.lib, , drop = FALSE]
sample.tab <- sample.tab[keep.lib, , drop = FALSE]

Disease1 <- Disease2 <- rep(0, nrow(sample.tab))
Disease1[sample.tab$disease == "CRC"] <- 1
Disease2[sample.tab$disease == "adenoma"] <- 1

Age <- as.numeric(sample.tab$age)
Gender <- as.numeric(factor(sample.tab$gender)) - 1

x.test <- cbind(CRC = Disease1, adenoma = Disease2)
x.adj <- cbind(Age = Age, Gender = Gender)
x <- cbind(x.test, x.adj)

est <- caft_estimate(
  otu.table = count.tab,
  x = x,
  regularize = TRUE,
  n.cores = 1L
)

print(est)

CAFT Restricted Score Test

Description

Performs the CAFT restricted score test using an existing unrestricted estimation object returned by caft_estimate(). This function separates testing from unrestricted estimation: the unrestricted taxon-level coefficient estimates are reused from caft_estimate(), and only the restricted estimation and score testing steps are carried out for the specified hypothesis.

Usage

caft_test(
  est,
  Gamma = NULL,
  x.test = NULL,
  x.adj = NULL,
  b = NULL,
  fdr.nominal = 0.2,
  adjust.method = "BH",
  regularize = est$regularize,
  test.method = "rank",
  n.cores = 1L,
  return.mr.resid = FALSE
)

Arguments

Details

Let \beta_j denote the regression coefficient vector for taxon j. CAFT tests taxon-level departures from a compositional reference value through

H_{0j}: \Gamma \beta_j = b,

where Gamma specifies the tested linear combinations of regression coefficients and b is the corresponding null reference value.

This function is intended for use after caft_estimate(). The unrestricted estimates \hat\beta_j stored in est$beta.est are not recomputed. This allows users to test multiple hypotheses, or multiple choices of b, using the same unrestricted CAFT fit.

The hypothesis can be specified either by supplying Gamma directly or by supplying x.test and optionally x.adj, in which case caft_test() constructs the corresponding hypothesis matrix internally.

This keeps unrestricted estimation separate from hypothesis specification: caft_estimate() computes and stores unrestricted estimates, while caft_test() computes the hypothesis-specific restricted estimates and score tests for the supplied Gamma and b.

If b = NULL, the null reference value is estimated from the unrestricted taxon-level contrasts \Gamma \hat\beta_j. For a one-dimensional contrast, the ordinary median across taxa is used. For multiple contrasts, a robust multivariate location estimate from ICSNP::HR.Mest() is used. If b is supplied by the user, the supplied value is used directly as the null reference value.

The output records whether the null value was user-specified through b.user.specified. This is used by caft_bootstrap() to determine whether the same fixed null value should be used in every bootstrap replicate, or whether the median-based null value should be recomputed within each bootstrap replicate.

For each taxon, CAFT obtains the restricted estimate under H_{0j}: \Gamma \beta_j = b and then computes the rank-based score test statistic. Raw taxon-level p-values are returned as p.otu, and multiplicity-adjusted values are returned as q.otu. Bootstrap calibration is not performed by this function; it is handled separately by caft_bootstrap().

Value

An object of class "caft_test", which also inherits from "caft_est". The object contains the original unrestricted estimation object together with the restricted testing results.

beta.est

Unrestricted taxon-level coefficient estimates inherited from caft_estimate().

Gamma

The hypothesis matrix used in the restricted score test.

Gamma.source

Character string indicating whether Gamma was user-supplied or constructed internally from x.test/x.adj.

Lambda

The nuisance-parameter contrast matrix used in the restricted estimation step.

Gamma.ginv

Generalized inverse of Gamma.

Lambda.ginv

Generalized inverse of Lambda, when applicable.

n.test

Number of tested contrasts, equal to the number of rows of Gamma.

betahat.median

The median-based null reference value estimated from the unrestricted taxon-level contrasts \Gamma \hat\beta_j when b = NULL. If b is supplied by the user, this is returned as NA because the median-based null value is not computed.

b.null

The null value actually used in the restricted score test. This equals betahat.median when b = NULL, and equals the user-specified b otherwise.

b.user.specified

Logical indicator of whether b was supplied by the user. If FALSE, b.null was computed as the median-based reference value. If TRUE, b.null equals the user-specified null value.

b.user

The user-specified null value, returned only when b is supplied. Otherwise NULL.

rank.teststat

Rank-based score test statistics for individual taxa.

rank.teststat.norm

Numeric matrix with normalized score statistics for each taxon and each tested contrast. Rows correspond to taxa and columns correspond to the rows of Gamma.

p.otu

Raw p-values for individual taxon-level association tests.

df.test

Degrees of freedom used in the taxon-level score tests.

beta.est.r

Restricted coefficient estimates obtained under H_{0j}: \Gamma \beta_j = b.

p.marginal.otu

Taxa with raw p-values smaller than fdr.nominal based on p.otu.

q.otu

Multiplicity-adjusted p-values computed from p.otu using the method specified by adjust.method.

q.detected.otu

Taxa with adjusted p-values smaller than fdr.nominal.

skip.fail.rank.test.pen

Indicator for taxa whose restricted estimation or rank-based score test failed.

fit.error.messages.test

Error messages from failed restricted taxon-level tests, if any.

fdr.nominal

The nominal threshold used to report detected taxa.

adjust.method

The p-value adjustment method used to compute q.otu.

test.method

The variance estimator used in the score test.

mr.resid

Returned only when return.mr.resid = TRUE. A list of subject-level residual contribution matrices evaluated at the restricted estimates.

Examples

data(Colon)

count.tab <- Colon$otu
sample.tab <- Colon$meta

keep.sample <- !is.na(sample.tab$age)
count.tab <- count.tab[keep.sample, , drop = FALSE]
sample.tab <- sample.tab[keep.sample, , drop = FALSE]

## Use a small subset of taxa for a fast example.
prev <- colSums(count.tab > 0)
keep.top <- names(sort(prev, decreasing = TRUE))[seq_len(min(10L, length(prev)))]
count.tab <- count.tab[, keep.top, drop = FALSE]

## Remove samples with zero library size after subsetting taxa.
keep.lib <- rowSums(count.tab) > 0
count.tab <- count.tab[keep.lib, , drop = FALSE]
sample.tab <- sample.tab[keep.lib, , drop = FALSE]

Disease1 <- Disease2 <- rep(0, nrow(sample.tab))
Disease1[sample.tab$disease == "CRC"] <- 1
Disease2[sample.tab$disease == "adenoma"] <- 1

Age <- as.numeric(sample.tab$age)
Gender <- as.numeric(factor(sample.tab$gender)) - 1

x.test <- cbind(CRC = Disease1, adenoma = Disease2)
x.adj <- cbind(Age = Age, Gender = Gender)

x <- cbind(x.test, x.adj)
Gamma <- cbind(
  diag(ncol(x.test)),
  matrix(0, nrow = ncol(x.test), ncol = ncol(x.adj))
)

est <- caft_estimate(
  otu.table = count.tab,
  x = x,
  regularize = TRUE,
  n.cores = 1L
)

res <- caft_test(est, Gamma = Gamma, b = c(0, 0))
print(res)

## Equivalent interface: construct Gamma internally from x.test/x.adj.
res.default <- caft_test(est, x.test = x.test, x.adj = x.adj, b = c(0, 0))
res.default$Gamma

## Reuse the same unrestricted estimates with a different user-specified
## null value.
res.b <- caft_test(est, Gamma = Gamma, b = c(0.1, 0.1))

## Example with a full design matrix and an explicit Gamma.
x.all <- cbind(CRC = Disease1, adenoma = Disease2, Age = Age, Gender = Gender)
est2 <- caft_estimate(otu.table = count.tab, x = x.all)
Gamma2 <- rbind(c(1, 0, 0, 0), c(0, 1, 0, 0))
res2 <- caft_test(est2, Gamma = Gamma2, b = c(0, 0))

Generates a ggplot2-based boxplot showing the relative abundance of a specified OTU (Operational Taxonomic Unit) across different sample groups.

Description

Generates a ggplot2-based boxplot showing the relative abundance of a specified OTU (Operational Taxonomic Unit) across different sample groups.

Usage

otuboxplot(plot.otu, count.data, groups, plot.title = NULL, type = "original")

Arguments

Details

The function converts raw counts to relative abundances, selects the OTU specified in plot.otu, and creates a grouped boxplot. If groups is not a factor, the function will return an error. Group means are shown as red dots.

Value

A ggplot object showing the OTU's relative abundance across sample groups.

Examples

# Example 1: Basic usage
set.seed(123)
count.data <- matrix(rpois(300, lambda = 10), nrow = 30, ncol = 10)
colnames(count.data) <- paste0("OTU", 1:10)
groups <- factor(rep(c("Control", "Treatment"), each = 15))
otuboxplot("OTU3", count.data, groups)

# Example 2: Combining two binary indicators into one group variable
set.seed(456)
n <- 40
count.data <- matrix(rpois(n * 8, lambda = 12), nrow = n, ncol = 8)
colnames(count.data) <- paste0("OTU", 1:8)
group1 <- rbinom(n, 1, 0.5)
group2 <- rbinom(n, 1, 0.5)
group <- factor(paste0("Y", group1, "_C", group2))
otuboxplot("OTU5", count.data, group, type = "transformed")

Print Method for Unrestricted CAFT Estimation

Description

Print Method for Unrestricted CAFT Estimation

Usage

## S3 method for class 'caft_est'
print(x, ...)

Arguments

Value

Invisibly returns x.

Print Method for CAFT Restricted Score Test

Description

Print Method for CAFT Restricted Score Test

Usage

## S3 method for class 'caft_test'
print(x, ...)

Arguments

Value

Invisibly returns x.