Visualize estimated results with forest plots

Source: R/AllGenerics.R, R/plotForest.R

plotForest() creates a feature- or sample-wise forest plot, showing estimated results from a statistical test with their confidence intervals. Additionally, the plot can be enriched with the tree structure and labelled with Confidence Intervals (CIs), p-values and other side information.

plotForest(x, ...)

# S4 method for class 'TreeSummarizedExperiment'
plotForest(
  x,
  by = 1L,
  effect.var = "effect",
  ci.lower.var = "lower",
  ci.upper.var = "upper",
  err.var = NULL,
  pval.var = "pval",
  id.var = "rownames",
  label.by = NULL,
  order.by = NULL,
  facet.by = NULL,
  color.by = colour.by,
  colour.by = NULL,
  conf.level = 0.95,
  tree.name = "phylo",
  show.tree = TRUE,
  ...
)

# S4 method for class 'SummarizedExperiment'
plotForest(
  x,
  by = 1L,
  effect.var = "effect",
  ci.lower.var = "lower",
  ci.upper.var = "upper",
  err.var = NULL,
  pval.var = "pval",
  id.var = "rownames",
  label.by = NULL,
  order.by = NULL,
  facet.by = NULL,
  color.by = colour.by,
  colour.by = NULL,
  conf.level = 0.95
)

# S4 method for class 'data.frame'
plotForest(
  x,
  effect.var = "effect",
  ci.lower.var = "lower",
  ci.upper.var = "upper",
  err.var = NULL,
  pval.var = "pval",
  id.var = "rownames",
  label.by = NULL,
  order.by = NULL,
  facet.by = NULL,
  color.by = colour.by,
  colour.by = NULL,
  conf.level = 0.95
)

Arguments

x

a SummarizedExperiment object, or a data.frame object containing statistical estimates.

...

additional parameters passed to plotRowTree.

by

Character scalar. Determines whether features or samples data is used for the plot. (Default: "rows")

effect.var

Character scalar. Specifies the variable of x which corresponds to the effects or estimated results. (Default: "effect")

ci.lower.var

Character scalar. Specifies the variable of x which corresponds to the lower CI boundaries. (Default: "lower")

ci.upper.var

Character scalar. Specifies the variable of x which corresponds to the upper CI boundaries. (Default: "upper")

err.var

Character scalar. Specifies the variable of x which corresponds to the standard errors associated with effect.var. When defined, it overwrites ci.lower.var and ci.upper.var. (Default: "pval")

pval.var

Character scalar. Specifies the variable of x which corresponds to the p-values associated with effect.var. (Default: "pval")

id.var

Character scalar. Specifies the variable of x which corresponds to the observation identifiers. When "rownames"), the object rownames are used. (Default: "rownames")

label.by

Character vector. Specifies the variables of x or row/colData(x) by which the plot should be labelled. "CI" and "P-Value" are special entries which require either effect.var, ci.lower.var and ci.upper.var or pval.var to be specified, respectively. (Default: NULL)

order.by

Character scalar. Specifies the variable of x by which observations are ordered. If NULL, the observations are ordered by the tree structure if available. (Default: NULL)

facet.by

Character scalar. Specifies the variable of x by which observations are divided into horizontal facets. (Default: NULL)

color.by

Character scalar. Alias to colour.by.

colour.by

Character scalar. Specifies the variable of x by which observations are coloured. (Default: NULL)

conf.level

Numeric scalar. Specifies the confidence level of the interval when inferred from err.var. It is ignored when ci.lower.var and ci.upper.var are defined. (Default: 0.95)

tree.name

Character scalar. Specifies a row/colTree from x. (Default: "phylo")

show.tree

Logical scalar. Should the tree structure of the data be shown next to the forest plot?

Value

a ggplot object.

Examples

library(mia)
library(maaslin3)

# Import dataset
data("Tengeler2020", package = "mia")
tse <- Tengeler2020

# Agglomerate by genus and subset by prevalence
tse <- subsetByPrevalent(tse, rank = "Genus", prevalence = 10/100)
#> Duplicated labels were made unique.

# Transform count assay to relative abundances
tse <- transformAssay(tse, assay.type = "counts", method = "relabundance")

# Run maaslin3
maaslin3_out <- maaslin3(
    input_data = tse,
    output = "maaslin_results",
    formula = "~ patient_status",
)
#> 2026-06-15 13:38:55.14 INFO::Writing function arguments to log file
#> 2026-06-15 13:38:55.17 INFO::Verifying options selected are valid
#> 2026-06-15 13:38:55.17 INFO::Determining format of input files
#> 2026-06-15 13:38:55.17 INFO::Input format is data samples as rows and metadata samples as rows
#> 2026-06-15 13:38:55.17 INFO::Running selected normalization method: TSS
#> 2026-06-15 13:38:55.17 INFO::Creating output feature tables folder
#> 2026-06-15 13:38:55.17 INFO::Writing normalized data to file maaslin_results/features/data_norm.tsv
#> 2026-06-15 13:38:55.17 INFO::Filter data based on min abundance, min prevalence, and max prevalence
#> 2026-06-15 13:38:55.17 INFO::Total samples in data: 27
#> 2026-06-15 13:38:55.17 INFO::Min samples required with min abundance for a feature not to be filtered: 0.000000
#> 2026-06-15 13:38:55.17 INFO::Max samples allowed with min abundance for a feature not to be filtered: 27.270000
#> 2026-06-15 13:38:55.18 INFO::Total filtered features: 0
#> 2026-06-15 13:38:55.18 INFO::Filtered feature names from abundance, min prevalence, and max prevalence filtering:
#> 2026-06-15 13:38:55.18 INFO::Total features filtered by non-zero variance filtering: 0
#> 2026-06-15 13:38:55.18 INFO::Filtered feature names from variance filtering:
#> 2026-06-15 13:38:55.18 INFO::Writing filtered data to file maaslin_results/features/filtered_data.tsv
#> 2026-06-15 13:38:55.18 INFO::Running selected transform method: LOG
#> 2026-06-15 13:38:55.18 INFO::Writing normalized, filtered, transformed data to file maaslin_results/features/data_transformed.tsv
#> 2026-06-15 13:38:55.18 INFO::Applying z-score to standardize continuous metadata
#> 2026-06-15 13:38:55.20 INFO::Running the linear model component
#> 2026-06-15 13:38:55.21 INFO::Fitting model to feature number 1, Akkermansia
#> 2026-06-15 13:38:55.22 INFO::Fitting model to feature number 2, Alistipes
#> 2026-06-15 13:38:55.22 INFO::Fitting model to feature number 3, Anaerostipes
#> 2026-06-15 13:38:55.22 INFO::Fitting model to feature number 4, Anaerotruncus
#> 2026-06-15 13:38:55.23 INFO::Fitting model to feature number 5, Bacteroides
#> 2026-06-15 13:38:55.23 INFO::Fitting model to feature number 6, Barnesiella
#> 2026-06-15 13:38:55.23 INFO::Fitting model to feature number 7, Blautia
#> 2026-06-15 13:38:55.23 INFO::Fitting model to feature number 8, Butyricicoccus
#> 2026-06-15 13:38:55.24 WARNING::Fitting problem for feature 8 returning NA
#> 2026-06-15 13:38:55.24 INFO::Fitting model to feature number 9, Catabacter
#> 2026-06-15 13:38:55.24 INFO::Fitting model to feature number 10, Clostridium_sensu_stricto_1
#> 2026-06-15 13:38:55.24 INFO::Fitting model to feature number 11, Coprobacter
#> 2026-06-15 13:38:55.24 INFO::Fitting model to feature number 12, Coprococcus_2
#> 2026-06-15 13:38:55.25 INFO::Fitting model to feature number 13, Dielma
#> 2026-06-15 13:38:55.25 INFO::Fitting model to feature number 14, Eisenbergiella
#> 2026-06-15 13:38:55.25 INFO::Fitting model to feature number 15, Epulopiscium
#> 2026-06-15 13:38:55.25 INFO::Fitting model to feature number 16, Erysipelatoclostridium
#> 2026-06-15 13:38:55.26 INFO::Fitting model to feature number 17, Escherichia-Shigella
#> 2026-06-15 13:38:55.26 INFO::Fitting model to feature number 18, Eubacterium
#> 2026-06-15 13:38:55.26 INFO::Fitting model to feature number 19, Faecalibacterium
#> 2026-06-15 13:38:55.27 INFO::Fitting model to feature number 20, Flavonifractor
#> 2026-06-15 13:38:55.27 INFO::Fitting model to feature number 21, Fusicatenibacter
#> 2026-06-15 13:38:55.27 INFO::Fitting model to feature number 22, Halomonas
#> 2026-06-15 13:38:55.27 INFO::Fitting model to feature number 23, Holdemania
#> 2026-06-15 13:38:55.28 INFO::Fitting model to feature number 24, Hungatella
#> 2026-06-15 13:38:55.28 INFO::Fitting model to feature number 25, Lachnoclostridium
#> 2026-06-15 13:38:55.28 INFO::Fitting model to feature number 26, Lachnospiraceae_ND3007_group
#> 2026-06-15 13:38:55.29 INFO::Fitting model to feature number 27, Odoribacter
#> 2026-06-15 13:38:55.29 INFO::Fitting model to feature number 28, Parabacteroides
#> 2026-06-15 13:38:55.29 INFO::Fitting model to feature number 29, Paraprevotella
#> 2026-06-15 13:38:55.29 INFO::Fitting model to feature number 30, Roseburia
#> 2026-06-15 13:38:55.30 INFO::Fitting model to feature number 31, Ruminiclostridium_5
#> 2026-06-15 13:38:55.30 INFO::Fitting model to feature number 32, Ruminiclostridium_9
#> 2026-06-15 13:38:55.30 INFO::Fitting model to feature number 33, Ruminococcaceae_UCG-004
#> 2026-06-15 13:38:55.30 INFO::Fitting model to feature number 34, Ruminococcaceae_UCG-013
#> 2026-06-15 13:38:55.31 INFO::Fitting model to feature number 35, Ruminococcaceae_UCG-014
#> 2026-06-15 13:38:55.31 WARNING::Fitting problem for feature 35 returning NA
#> 2026-06-15 13:38:55.31 INFO::Fitting model to feature number 36, Ruminococcus_1
#> 2026-06-15 13:38:55.31 INFO::Fitting model to feature number 37, Ruminococcus_2
#> 2026-06-15 13:38:55.32 INFO::Fitting model to feature number 38, Subdoligranulum
#> 2026-06-15 13:38:55.32 INFO::Fitting model to feature number 39, [Clostridium]_innocuum_group
#> 2026-06-15 13:38:55.32 INFO::Fitting model to feature number 40, [Eubacterium]_coprostanoligenes_group
#> 2026-06-15 13:38:55.32 INFO::Fitting model to feature number 41, [Eubacterium]_fissicatena_group
#> 2026-06-15 13:38:55.33 INFO::Fitting model to feature number 42, [Eubacterium]_rectale_group
#> 2026-06-15 13:38:55.33 INFO::Fitting model to feature number 43, [Eubacterium]_xylanophilum_group
#> 2026-06-15 13:38:55.33 INFO::Fitting model to feature number 44, [Ruminococcus]_gauvreauii_group
#> 2026-06-15 13:38:55.34 INFO::Fitting model to feature number 45, [Ruminococcus]_gnavus_group
#> 2026-06-15 13:38:55.34 INFO::Fitting model to feature number 46, uncultured
#> 2026-06-15 13:38:55.34 INFO::Fitting model to feature number 47, uncultured_1
#> 2026-06-15 13:38:55.34 INFO::Fitting model to feature number 48, uncultured_2
#> 2026-06-15 13:38:55.34 INFO::Fitting model to feature number 49, uncultured_bacterium
#> 2026-06-15 13:38:55.35 INFO::Performing tests against medians
#> 2026-06-15 13:38:55.76 INFO::Counting total values for each feature
#> 2026-06-15 13:38:55.76 INFO::Running the logistic model component
#> 2026-06-15 13:38:55.76 INFO::Fitting model to feature number 1, Akkermansia
#> 2026-06-15 13:38:55.77 INFO::Fitting model to feature number 2, Alistipes
#> 2026-06-15 13:38:55.77 INFO::Fitting model to feature number 3, Anaerostipes
#> 2026-06-15 13:38:55.77 INFO::Fitting model to feature number 4, Anaerotruncus
#> 2026-06-15 13:38:55.78 INFO::Fitting model to feature number 5, Bacteroides
#> 2026-06-15 13:38:55.78 INFO::Fitting model to feature number 6, Barnesiella
#> 2026-06-15 13:38:55.78 INFO::Fitting model to feature number 7, Blautia
#> 2026-06-15 13:38:55.79 INFO::Fitting model to feature number 8, Butyricicoccus
#> 2026-06-15 13:38:55.79 INFO::Fitting model to feature number 9, Catabacter
#> 2026-06-15 13:38:55.80 INFO::Fitting model to feature number 10, Clostridium_sensu_stricto_1
#> 2026-06-15 13:38:55.80 INFO::Fitting model to feature number 11, Coprobacter
#> 2026-06-15 13:38:55.80 INFO::Fitting model to feature number 12, Coprococcus_2
#> 2026-06-15 13:38:55.81 INFO::Fitting model to feature number 13, Dielma
#> 2026-06-15 13:38:55.81 INFO::Fitting model to feature number 14, Eisenbergiella
#> 2026-06-15 13:38:55.82 INFO::Fitting model to feature number 15, Epulopiscium
#> 2026-06-15 13:38:55.82 INFO::Fitting model to feature number 16, Erysipelatoclostridium
#> 2026-06-15 13:38:55.82 INFO::Fitting model to feature number 17, Escherichia-Shigella
#> 2026-06-15 13:38:55.83 INFO::Fitting model to feature number 18, Eubacterium
#> 2026-06-15 13:38:55.83 INFO::Fitting model to feature number 19, Faecalibacterium
#> 2026-06-15 13:38:55.84 INFO::Fitting model to feature number 20, Flavonifractor
#> 2026-06-15 13:38:55.84 INFO::Fitting model to feature number 21, Fusicatenibacter
#> 2026-06-15 13:38:55.84 INFO::Fitting model to feature number 22, Halomonas
#> 2026-06-15 13:38:55.85 INFO::Fitting model to feature number 23, Holdemania
#> 2026-06-15 13:38:55.85 INFO::Fitting model to feature number 24, Hungatella
#> 2026-06-15 13:38:55.85 INFO::Fitting model to feature number 25, Lachnoclostridium
#> 2026-06-15 13:38:55.86 INFO::Fitting model to feature number 26, Lachnospiraceae_ND3007_group
#> 2026-06-15 13:38:55.86 INFO::Fitting model to feature number 27, Odoribacter
#> 2026-06-15 13:38:55.86 INFO::Fitting model to feature number 28, Parabacteroides
#> 2026-06-15 13:38:55.87 INFO::Fitting model to feature number 29, Paraprevotella
#> 2026-06-15 13:38:55.87 INFO::Fitting model to feature number 30, Roseburia
#> 2026-06-15 13:38:55.88 INFO::Fitting model to feature number 31, Ruminiclostridium_5
#> 2026-06-15 13:38:55.88 INFO::Fitting model to feature number 32, Ruminiclostridium_9
#> 2026-06-15 13:38:55.88 INFO::Fitting model to feature number 33, Ruminococcaceae_UCG-004
#> 2026-06-15 13:38:55.89 INFO::Fitting model to feature number 34, Ruminococcaceae_UCG-013
#> 2026-06-15 13:38:55.89 INFO::Fitting model to feature number 35, Ruminococcaceae_UCG-014
#> 2026-06-15 13:38:55.89 INFO::Fitting model to feature number 36, Ruminococcus_1
#> 2026-06-15 13:38:55.90 INFO::Fitting model to feature number 37, Ruminococcus_2
#> 2026-06-15 13:38:55.90 INFO::Fitting model to feature number 38, Subdoligranulum
#> 2026-06-15 13:38:55.91 INFO::Fitting model to feature number 39, [Clostridium]_innocuum_group
#> 2026-06-15 13:38:55.91 INFO::Fitting model to feature number 40, [Eubacterium]_coprostanoligenes_group
#> 2026-06-15 13:38:55.91 INFO::Fitting model to feature number 41, [Eubacterium]_fissicatena_group
#> 2026-06-15 13:38:55.92 INFO::Fitting model to feature number 42, [Eubacterium]_rectale_group
#> 2026-06-15 13:38:55.92 INFO::Fitting model to feature number 43, [Eubacterium]_xylanophilum_group
#> 2026-06-15 13:38:55.93 INFO::Fitting model to feature number 44, [Ruminococcus]_gauvreauii_group
#> 2026-06-15 13:38:55.93 INFO::Fitting model to feature number 45, [Ruminococcus]_gnavus_group
#> 2026-06-15 13:38:55.93 INFO::Fitting model to feature number 46, uncultured
#> 2026-06-15 13:38:55.94 INFO::Fitting model to feature number 47, uncultured_1
#> 2026-06-15 13:38:55.94 INFO::Fitting model to feature number 48, uncultured_2
#> 2026-06-15 13:38:55.95 INFO::Fitting model to feature number 49, uncultured_bacterium
#> 2026-06-15 13:38:55.95 INFO::Counting total values for each feature
#> 2026-06-15 13:38:55.95 INFO::Re-running abundances for warn_prevalence
#> 2026-06-15 13:38:55.95 INFO::Running selected normalization method: TSS
#> 2026-06-15 13:38:55.96 INFO::Running selected transform method: LOG
#> 2026-06-15 13:38:55.96 INFO::Fitting model to feature number 1, Akkermansia
#> 2026-06-15 13:38:55.97 INFO::Fitting model to feature number 2, Alistipes
#> 2026-06-15 13:38:55.97 INFO::Fitting model to feature number 3, Anaerostipes
#> 2026-06-15 13:38:55.97 INFO::Fitting model to feature number 4, Anaerotruncus
#> 2026-06-15 13:38:55.98 INFO::Fitting model to feature number 5, Bacteroides
#> 2026-06-15 13:38:55.98 INFO::Fitting model to feature number 6, Barnesiella
#> 2026-06-15 13:38:55.98 INFO::Fitting model to feature number 7, Blautia
#> 2026-06-15 13:38:55.98 INFO::Fitting model to feature number 8, Butyricicoccus
#> 2026-06-15 13:38:55.99 WARNING::Fitting problem for feature 8 returning NA
#> 2026-06-15 13:38:55.99 INFO::Fitting model to feature number 9, Catabacter
#> 2026-06-15 13:38:55.99 INFO::Fitting model to feature number 10, Clostridium_sensu_stricto_1
#> 2026-06-15 13:38:55.99 INFO::Fitting model to feature number 11, Coprobacter
#> 2026-06-15 13:38:55.99 INFO::Fitting model to feature number 12, Coprococcus_2
#> 2026-06-15 13:38:56.00 INFO::Fitting model to feature number 13, Dielma
#> 2026-06-15 13:38:56.00 INFO::Fitting model to feature number 14, Eisenbergiella
#> 2026-06-15 13:38:56.00 INFO::Fitting model to feature number 15, Epulopiscium
#> 2026-06-15 13:38:56.00 INFO::Fitting model to feature number 16, Erysipelatoclostridium
#> 2026-06-15 13:38:56.01 INFO::Fitting model to feature number 17, Escherichia-Shigella
#> 2026-06-15 13:38:56.01 INFO::Fitting model to feature number 18, Eubacterium
#> 2026-06-15 13:38:56.01 INFO::Fitting model to feature number 19, Faecalibacterium
#> 2026-06-15 13:38:56.02 INFO::Fitting model to feature number 20, Flavonifractor
#> 2026-06-15 13:38:56.02 INFO::Fitting model to feature number 21, Fusicatenibacter
#> 2026-06-15 13:38:56.02 INFO::Fitting model to feature number 22, Halomonas
#> 2026-06-15 13:38:56.02 INFO::Fitting model to feature number 23, Holdemania
#> 2026-06-15 13:38:56.03 INFO::Fitting model to feature number 24, Hungatella
#> 2026-06-15 13:38:56.03 INFO::Fitting model to feature number 25, Lachnoclostridium
#> 2026-06-15 13:38:56.03 INFO::Fitting model to feature number 26, Lachnospiraceae_ND3007_group
#> 2026-06-15 13:38:56.04 INFO::Fitting model to feature number 27, Odoribacter
#> 2026-06-15 13:38:56.04 INFO::Fitting model to feature number 28, Parabacteroides
#> 2026-06-15 13:38:56.04 INFO::Fitting model to feature number 29, Paraprevotella
#> 2026-06-15 13:38:56.05 INFO::Fitting model to feature number 30, Roseburia
#> 2026-06-15 13:38:56.05 INFO::Fitting model to feature number 31, Ruminiclostridium_5
#> 2026-06-15 13:38:56.05 INFO::Fitting model to feature number 32, Ruminiclostridium_9
#> 2026-06-15 13:38:56.05 INFO::Fitting model to feature number 33, Ruminococcaceae_UCG-004
#> 2026-06-15 13:38:56.06 INFO::Fitting model to feature number 34, Ruminococcaceae_UCG-013
#> 2026-06-15 13:38:56.06 INFO::Fitting model to feature number 35, Ruminococcaceae_UCG-014
#> 2026-06-15 13:38:56.06 WARNING::Fitting problem for feature 35 returning NA
#> 2026-06-15 13:38:56.06 INFO::Fitting model to feature number 36, Ruminococcus_1
#> 2026-06-15 13:38:56.07 INFO::Fitting model to feature number 37, Ruminococcus_2
#> 2026-06-15 13:38:56.07 INFO::Fitting model to feature number 38, Subdoligranulum
#> 2026-06-15 13:38:56.07 INFO::Fitting model to feature number 39, [Clostridium]_innocuum_group
#> 2026-06-15 13:38:56.07 INFO::Fitting model to feature number 40, [Eubacterium]_coprostanoligenes_group
#> 2026-06-15 13:38:56.08 INFO::Fitting model to feature number 41, [Eubacterium]_fissicatena_group
#> 2026-06-15 13:38:56.08 INFO::Fitting model to feature number 42, [Eubacterium]_rectale_group
#> 2026-06-15 13:38:56.08 INFO::Fitting model to feature number 43, [Eubacterium]_xylanophilum_group
#> 2026-06-15 13:38:56.08 INFO::Fitting model to feature number 44, [Ruminococcus]_gauvreauii_group
#> 2026-06-15 13:38:56.09 INFO::Fitting model to feature number 45, [Ruminococcus]_gnavus_group
#> 2026-06-15 13:38:56.09 INFO::Fitting model to feature number 46, uncultured
#> 2026-06-15 13:38:56.09 INFO::Fitting model to feature number 47, uncultured_1
#> 2026-06-15 13:38:56.10 INFO::Fitting model to feature number 48, uncultured_2
#> 2026-06-15 13:38:56.10 INFO::Fitting model to feature number 49, uncultured_bacterium
#> 2026-06-15 13:38:56.11 INFO::Creating fits folder
#> 2026-06-15 13:38:56.11 INFO::Writing residuals to file maaslin_results/fits/residuals_linear.rds
#> 2026-06-15 13:38:56.11 INFO::Writing fitted values to file maaslin_results/fits/fitted_linear.rds
#> 2026-06-15 13:38:56.11 INFO::Writing residuals to file maaslin_results/fits/residuals_logistic.rds
#> 2026-06-15 13:38:56.11 INFO::Writing fitted values to file maaslin_results/fits/fitted_logistic.rds
#> 2026-06-15 13:38:56.11 INFO::Writing all the results to file (ordered 
#>             by increasing individual q-values): maaslin_results/all_results.tsv
#> 2026-06-15 13:38:56.12 INFO::Writing the significant results without errors (those which have joint q-values less than or equal to the threshold of 0.100000 ) to file (ordered by increasing individual q-values): maaslin_results/significant_results.tsv
#> 2026-06-15 13:38:56.12 INFO::Creating output figures folder
#> 2026-06-15 13:38:56.12 INFO::Writing summary plot of significant
#>                         results to file: maaslin_results/figures/summary_plot.pdf
#> 2026-06-15 13:38:57.20 INFO::Writing association plots (one for each significant association) to output folder: maaslin_results/figures
#> 2026-06-15 13:38:57.20 INFO::Plotting associations from most to least significant, grouped by metadata
#> 2026-06-15 13:38:57.20 INFO::Creating box plot for categorical data (linear), 
#>                         patient_status vs Coprobacter
#> 2026-06-15 13:38:57.49 INFO::Creating box plot for categorical data (linear), 
#>                         patient_status vs Faecalibacterium
#> 2026-06-15 13:38:57.80 INFO::Creating tile plot for categorical data (logistic), patient_status vs Erysipelatoclostridium

# Retrieve abundance results
maaslin3_abund <- maaslin3_out$fit_data_abundance$results
maaslin3_abund <- maaslin3_abund[!is.na(maaslin3_abund$coef), ]

# Visualize abundance results
plotForest(
    maaslin3_abund,
    effect.var = "coef",
    err.var = "stderr",
    pval.var = "qval_joint",
    id.var = "feature",
    label.by = c("CI", "P-Value"),
    order.by = "coef"
)


# Add abundance results to TreeSE rowData
rownames(maaslin3_abund) <- maaslin3_abund$feature
tax_order <- match(rownames(tse), rownames(maaslin3_abund))
rowData(tse) <- cbind(rowData(tse), maaslin3_abund[tax_order, ])

# Visualise abundance results with tree structure
plotForest(
    tse,
    effect.var = "coef",
    err.var = "stderr",
    pval.var = "qval_joint",
    label.by = c("CI", "P-Value")
)
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> ℹ The deprecated feature was likely used in the miaViz package.
#>   Please report the issue at <https://github.com/microbiome/miaViz/issues>.
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_point()`).


# Retrieve prevalence results
maaslin3_prev <- maaslin3_out$fit_data_prevalence$results
maaslin3_prev <- maaslin3_prev[!is.na(maaslin3_prev$coef), ]

# Combine abundance and prevalence results
maaslin3_res <- rbind(maaslin3_abund, maaslin3_prev)

maaslin3_res$association <- c(
    rep("Abundance", nrow(maaslin3_abund)),
    rep("Prevalence", nrow(maaslin3_prev))
)

# Visualize combined results
plotForest(
    maaslin3_res,
    effect.var = "coef",
    err.var = "stderr",
    pval.var = "qval_joint",
    id.var = "feature",
    label.by = c("CI", "P-Value"),
    order.by = "coef",
    facet.by = "association",
    colour.by = "association"
)