maaslin2 --> maaslin3

inst/assets/bibliography.bib

@@ -1136,14 +1136,17 @@ @Article{Das2020
                   microbiomes with bias .pdf}
 }
 
-@Manual{Mallick2020,
-  title =	 {MaAsLin 2: Multivariable Association in
-                  Population-scale Meta-omics Studies.},
-  author =	 {Himel Mallick and Ali Rahnavard and Lauren
-                  J. McIver},
-  year =	 {2020},
+@Manual{Mallick2024,
+  title =	 {MaAsLin 2: Refining and extending generalized 
+            multivariable linear models for meta-omic association 
+            discovery.},
+  author =	 {Nickols, William A. and Kuntz, Thomas and Shen, 
+              Jiaxian and Maharjan, Sagun and Mallick, 
+              Himel and Franzosa, Eric A. and Thompson, 
+              Kelsey N. and Nearing, Jacob T. and Huttenhower, Curtis},
+  year =	 {2024},
   note =	 {R/Bioconductor package},
-  url =		 {http://huttenhower.sph.harvard.edu/maaslin2},
+  url =		 {https://huttenhower.sph.harvard.edu/maaslin3/},
 }
 
 @Article{Paulson2017,

inst/pages/differential_abundance.qmd

@@ -316,50 +316,67 @@ ancombc2_out$res |>
   kable()
 ```
 
-### MaAsLin2
+### MaAsLin3
 
-Let us next illustrate MaAsLin2 [@Mallick2020]. This method is based on
+Let us next illustrate MaAsLin3 [@Mallick2024]. This method is based on
 generalized linear models and flexible for different study designs
 and covariate structures. For details, check their
-[Biobakery tutorial](https://github.com/biobakery/biobakery/wiki/maaslin2).
+[Biobakery tutorial](https://github.com/biobakery/biobakery/wiki/MaAsLin3).
 
-```{r run-maaslin2, warning=FALSE, results="hide"}
+```{r run-maaslin3, warning=FALSE, results="hide"}
 # Load package
-library(Maaslin2)
+library(maaslin3)
 
-# maaslin expects features as columns and samples as rows
-# for both the abundance table as well as metadata
+# MaAsLin3 takes tse as input data
 
 # We can specify different GLMs/normalizations/transforms.
-# specifying a ref is especially important if you have more than 2 levels
-maaslin2_out <- Maaslin2(
-    input_data = as.data.frame(t(assay(tse))),
-    input_metadata = as.data.frame(colData(tse)),
-    output = "DAA example",
-    transform = "AST",
-    fixed_effects = "patient_status",
-    # you can also fit MLM by specifying random effects
-    # random_effects = c(...),
-    reference = "patient_status,Control",
-    normalization = "TSS",
-    standardize = FALSE,
-    # filtering was previously performed
-    min_prevalence = 0)
+maaslin3_out <- maaslin3(input_data = tse,
+     output = "DAA_example",
+     formula = '~ patient_status',
+     normalization = 'TSS',
+     transform = 'LOG',
+     augment = TRUE,
+     standardize = TRUE,
+     max_significance = 0.1,
+     median_comparison_abundance = TRUE,
+     median_comparison_prevalence = FALSE,
+     max_pngs = 10,
+     cores = 1,
+     save_models = TRUE,
+     verbosity = "WARN")
 ```
 
 Which genera are identified as differentially abundant?
 (leave out "head" to see all).
 
-```{r, maaslin2-res}
-maaslin2_out$results |>
-  filter(qval < 0.05) |>
-  kable()
+```{r, maaslin3-res}
+signif_results <- read.csv('DAA_example/significant_results.tsv',
+sep='\t')
+head(signif_results, 20) %>%
+    dplyr::mutate_if(is.numeric, .funs = function(x){(
+        as.character(signif(x, 3)))}) %>%
+    knitr::kable() %>%
+    kableExtra::kable_styling("striped", position = 'center') %>%
+    kableExtra::scroll_box(width = "800px", height = "400px")
 ```
 
-This will create a folder that is called like in the output specified
+This will create a folder that is called in the output specified
 above. It contains also figures to visualize difference between
 significant taxa.
 
+```{r, summary-plot}
+knitr::include_graphics("DAA_example/figures/summary_plot.png")
+```
+
+```{r, echo=FALSE, fig.show='hold', cache = FALSE, include=FALSE, eval=FALSE}
+prefix <- "DAA_example/figures/association_plots/patient_status"
+plot_vec <- c("/linear/patient_status_Coprobacter_linear.png",
+            "/linear/patient_status_Faecalibacterium_linear.png",
+            "/logistic/patient_status_Erysipelatoclostridium_logistic.png",
+            )
+knitr::include_graphics(paste0(prefix, plot_vec))
+```
+
 ### PhILR
 
 PhILR is a tree-based method that tests group-wise associations based on

oma_packages/oma_packages.csv

@@ -34,7 +34,7 @@ gsEasy
 igraph
 IntegratedLearner
 knitr
-Maaslin2
+maaslin3
 mia
 miaTime
 miaViz