Merge branch 'devel' into devel

Author: antagomir

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: OMA
 Title: Orchestrating Microbiome Analysis with Bioconductor
-Version: 0.99.5
-Date: 2026-03-26
+Version: 1.1.1
+Date: 2026-05-07
 Authors@R:
     c(
   person(given = "Tuomas", family = "Borman", role = c("aut", "cre"), email = "tuomas.v.borman@utu.fi", comment = c(ORCID = "0000-0002-8563-8884")),

inst/pages/differential_abundance.qmd

@@ -17,7 +17,7 @@ treatment versus control groups.
 The goal of DAA is thus to identify biomarkers of a certain phenotype or
 condition, and gain understanding of a complex system by looking at its isolated
 components. For example, the identification of a bacterial taxon that is more
-abundant in healthy patients compared to diseased patients can lead to important
+abundant in healthy versus diseased individuals can lead to important
 insights into the underlying mechanisms of the disease. In other words,
 differentially abundant taxa can be involved in the dynamics of the disease,
 which in turn helps to understand the system as a whole.
@@ -129,8 +129,9 @@ analysis and the differential analysis of positive (non-zero) abundances.
 ### Preparing the data {#sec-prepare-data}
 
 We use the Tengeler2020 dataset from `r BiocStyle::Biocpkg("mia")` package.
-The dataset includes samples from people with ADHD (N = 13) and without ADHD
-(Control group, N = 14). The people are from three different cohorts.
+The dataset includes stool samples from mice humanized with either ADHD
+individuals' gut microbiome (N = 13) or healthy controls' (N = 14). The samples
+belong to three different cohorts.
 
 ```{r}
 #| label: load_packages_and_data
@@ -177,10 +178,10 @@ tse$cohort <- factor(tse$cohort, levels = c("Cohort_1", "Cohort_2", "Cohort_3"))
 
 ### Simple two-group comparison {#sec-daa-basic}
 
-Here we perform the simplest version of DAA, namely, the comparison of two
+Here, we perform the simplest version of DAA, namely, the comparison of two
 groups without any other covariates included in the analysis.
-In our case this means comparing the relative abundances of genera between the
-people with and without ADHD.
+In our case, this means comparing the relative abundances of genera between the
+microbiome of ADHD individuals versus control.
 
 We employ the `Maaslin2` function from the `r BiocStyle::Biocpkg("Maaslin2")`
 package [@Mallick2020]. The function
@@ -199,11 +200,6 @@ folder for some output files. Lastly, we prevent `Maaslin2()` to create certain
 plots. (Note that `Maaslin2()` prints a lot of some output
 which is suppressed here for convenience.)
 
-```{r}
-#| label: disable_chunks
-knitr::opts_chunk$set(eval = FALSE)
-```
-
 ```{r}
 #| label: run_daa_basic
 #| results: hide
@@ -335,23 +331,15 @@ intervals.
 
 ```{r}
 #| label: plot_daa_est_ci
-
-# First arrange the genera by log2-fold estimate
-top_features <- res_daa_basic[order(res_daa_basic[["coef"]], decreasing = TRUE), "feature"]
-res_daa_basic[["genus_ord"]] <- factor(res_daa_basic[["feature"]], levels = top_features)
-
 # Plot the log2-fold changes and the 95% confidence intervals
-ggplot(res_daa_basic, aes(x = coef, y = genus_ord)) +
-    geom_vline(xintercept = 0, linetype = "dashed") +
-    geom_point() +
-    geom_errorbarh(aes(xmin = ci_lwr, xmax = ci_upr), height = 0.2) +
-    labs(x = "Log2-fold change (95% CI)") +
-    theme_light() +
-    theme(
-        axis.title.y = element_blank(),
-        axis.text.y = element_text(size = 7),
-        plot.title = element_text(hjust = 0.5)
-    )
+plotForest(
+    res_daa_basic,
+    effect.var = "coef",
+    ci.lower.var = "ci_lwr",
+    ci.upper.var = "ci_upr",
+    id.var = "feature",
+    order.by = "coef"
+)
 ```
 
 ### DAA with additional covariates {#sec-daa-cov}
@@ -558,11 +546,6 @@ cohort).
 Note that a similar plot could also be used to illustrate a possible confounding
 effect.
 
-```{r}
-#| label: enable_chunks
-knitr::opts_chunk$set(eval = TRUE)
-```
-
 ### Differential prevalence analysis (DPA) {#sec-DPA}
 
 We show here how to perform DPA with logistic regression. We show the case
@@ -766,7 +749,9 @@ comparison, but it can also incorporate covariates (and interaction terms)
 
 As there does not currently exist a good implementation of ORM for
 DAA one must use a custom function. Such custom function with application
-examples is given in [@sec-extra_daa]. We also note that, in contrast to
+examples is given in
+[extra materials](https://microbiome.github.io/OMA-extras/docs/devel/pages/extra_daa.html#sec-orm).
+We also note that, in contrast to
 `Maaslin2()` function, the custom function provides confidence intervals and
 q-values (calculated for the variable of interest) automatically in its output.
 
@@ -780,7 +765,7 @@ statistical power, we recommend using a version of logistic regression
 introduced by @Firth1993. As there currently exists no convenient
 implementation of Firth logistic regression for DPA, one needs to use a custom
 function. Such custom function with application examples is given in the
-[@sec-extra_daa].
+[extra materials](https://microbiome.github.io/OMA-extras/docs/devel/pages/extra_daa.html#sec-firth).
 
 ::: {.callout-tip icon=false}
 
@@ -818,7 +803,8 @@ with interactions.
 of positive abundances with your data.
 
 10. Bonus 2: Try to run DAA and DPA with ordinal regression model and Firth 
-logistic regression with the custom functions provided in the extra materials.
+logistic regression with the custom functions provided in the
+[extra materials](https://microbiome.github.io/OMA-extras/docs/devel/pages/extra_daa.html#sec-orm).
 See how the results differ from the results obtained with `Maaslin2()` and
 `maaslin3()`.
 

inst/pages/mediation.qmd

@@ -147,7 +147,7 @@ med_df <- getMediation(
 )
 
 # Plot results as a forest plot
-plotMediation(med_df, layout = "forest")
+plotMediation(med_df, layout = "forest") + theme_minimal()
 ```
 
 The forest plot above shows significance for both ACME and ADE, which suggests
@@ -195,11 +195,13 @@ Significant findings can be marked with p-values or stars.
 #| label: mediation5
 
 # Plot results as a heatmap
-plotMediation(
+p <- plotMediation(
     tse, "assay_mediation",
     layout = "heatmap",
     add.significance = "symbol"
 )
+
+p + theme_minimal()
 ```
 
 Results suggest that only four out of eight features (Bacteroidetes, Firmicutes,
@@ -274,7 +276,7 @@ p2 <- plotLoadings(
 )
 
 # Combine plots
-p1 / p2
+(p1 / p2) & theme_minimal()
 ```
 
 The plot above suggests that only PC1 partially mediates the effect of living

inst/pages/phyloseq_cheatsheet.qmd

@@ -1,12 +1,5 @@
 # phyloseq vs TreeSE cheatsheet {#sec-phyloseq_cheatsheet}
 
-```{r}
-#| label: setup
-#| echo: false
-
-knitr::opts_chunk$set(eval = TRUE, warning = FALSE, message = FALSE)
-```
-
 This section has a cheatsheet for translating common functions in
 `r BiocStyle::Biocpkg("phyloseq")` to
 `r BiocStyle::Biocpkg("TreeSummarizedExperiment")` (`TreeSE`) /

oma_packages/oma_packages.csv

@@ -1,87 +1,44 @@
-ALDEx2
-ANCOMBC
 ape
-biclust
 BiocBook
 BiocManager
-BiocParallel
 BiocStyle
-Biostrings
 bluster
-caret
-circlize
-cluster
-cobiclust
 ComplexHeatmap
-corpcor
-cowplot
 curatedMetagenomicData
-dada2
 dendextend
-devtools
-DirichletMultinomial
 dplyr
 DT
 factoextra
-fido
-forcats
 ggdag
 ggExtra
 ggplot2
 ggpubr
 ggtree
-glmnet
 glue
-grid
-gsEasy
 gt
-gtools
-igraph
-IntegratedLearner
 knitr
-limma
-logistf
-Maaslin2
 maaslin3
 mediation
 mia
 miaTime
 miaViz
-microbiome
 microbiomeDataSets
-MicrobiomeStat
 mikropml
-MMUPHin
 MOFA2
-multiview
 NbClust
-NetCoMi
 NMF
 patchwork
 philr
 phyloseq
-plotly
-purrr
 qgraph
-RColorBrewer
 rebook
-reshape2
-reticulate
-rgl
-rms
 scales
 scater
 scuttle
 sechm
 sessioninfo
 shadowtext
-SpiecEasi
-SPRING
 stats
-stringr
-SuperLearner
 tidySingleCellExperiment
 tidyverse
 vegan
-WGCNA
-xgboost (== 1.7.7.1)