#241 and #522 (#590)

inst/_quarto.yml

@@ -1,7 +1,7 @@
 project:
   type: book
   output-dir: docs
-
+mathjax: null
 metadata-files:
   - assets/_book.yml
   - assets/_website.yml

inst/assets/bibliography.bib

@@ -1,4 +1,27 @@
 
+@Article{Xu2023,
+  author =	 {Shuangbin Xu and Li Zhan and Wenli Tang and Qianwen Wang and Zehan Dai and Lang Zhou and Tingze Feng and Meijun Chen and Tianzhi Wu and Erqiang Hu and Guangchuang Yu},
+  title =	 {{MicrobiotaProcess}: {A} comprehensive {R} package for deep mining microbiome},
+  journal =	 {{The Innovation}},
+  year =	 {2023},
+  issn = {2666-6758},
+  volume =	 4,
+  number = {2},
+  pages = {100388},
+  doi =		 {10.1016/j.xinn.2023.100388}
+}
+
+@Article{Deng2024,
+  author =	 {Yongcui Deng and Alexander K Umbach and Josh D Neufeld},
+  title =	 {Nonparametric richness estimators {Chao1} and {ACE} must not be used with amplicon sequence variant data},
+  journal =	 {{The ISME Journal}},
+  year =	 {2024},
+  volume =	 18,
+  number = {1},
+  pages = {wrae106}
+  doi =		 {10.1093/ismejo/wrae106}
+}
+
 @Article{Willis2019,
   author =	 {Amy D. Willis},
   title =	 {Rarefaction, Alpha Diversity, and Statistics},

inst/pages/acknowledgments.qmd

@@ -98,21 +98,45 @@ in writing the chapters on network learning and comparison.
 
 - *Ely Seraidarian*
 
--*Théotime Pralas*
+- *Théotime Pralas*
 
--*Muluh Muluh*
+- *Geraldson Muluh*
 
--*Jiya Chaudhary*
+- *Jiya Chaudhary*
 
--*Elina Chiesa*
+- *Elina Chiesa*
 
--*Pande Erawijantari*
+- *Pande Erawijantari*
 
--*Shadman Ishraq*
+- *Shadman Ishraq*
 
--*Sam Hillman*
+- *Sam Hillman*
 
--*Matteo Calgaro*
+- *Matteo Calgaro*
+
+- *Basil Courbayre Dussau*
+
+- *Yang Cao*
+
+- *Eineje Ameh*
+
+- *Domenick J. Braccia*
+
+- *Renuka Potbhare*
+
+- *Hervé Pagès*
+
+- *Moritz E. Beber*
+
+- *Nitesh Turaga*
+
+- *Vivian Ikeh*
+
+- *Yu Gao*
+
+- *Daniel Garza*
+
+- *Karoline Faust*
 
 - *Jacques Serizay* converted the _OMA_ book to the `BiocBook` format. This
 allows the _OMA_ book to be built and distributed by Bioconductor.

inst/pages/alpha_diversity.qmd

@@ -39,6 +39,20 @@ is often used to collectively refer to all these variants.
   including for instance ACE [@Chao1992] and Chao1 [@Chao1984]
   indices. Richness estimates do not aim to characterize variations in
   species abundances.
+
+Nonparametric richness estimators such as Chao1 and ACE, however, must not be 
+used with amplicon sequence variant (ASV) data. Algorithms that generate ASVs, 
+like DADA2 and Deblur, typically remove singletons, which are essential for 
+these richness calculations. This removal leads to meaningless results. 
+Although ASVs offer higher resolution than operational taxonomic units (OTUs) 
+and are increasingly used, the removal of singletons invalidates the application 
+of Chao1 and ACE. Therefore, alternative alpha diversity metrics that do not 
+depend on singletons or doubletons should be considered, or OTUs could be 
+used specifically for alpha diversity analysis to retain low-abundance taxa.
+Additionally, the inability of denoising algorithms to distinguish true 
+singleton sequences from artifacts further complicates the issue, making 
+traditional richness estimators unsuitable for ASV datasets, which are often 
+standardized for sequencing depth.[@Deng2024]
 
 **Evenness** focuses on the distribution of species abundances, and it
   can thus complement the number of species. Pielou's evenness is a

inst/pages/miaverse.qmd

@@ -116,18 +116,20 @@ analysis and visualization.
 
 The following DA methods support `(Tree)SummarizedExperiment`.
 
-- [ANCOMBC](https://bioconductor.org/packages/devel/bioc/html/ANCOMBC.html) for differential abundance analysis
-- [benchdamic](https://bioconductor.org/packages/release/bioc/vignettes/benchdamic/inst/doc/intro.html) for benchmarking differential abundance methods
-- [ALDEx2](https://www.bioconductor.org/packages/release/bioc/html/ALDEx2.html) for differential abundance analysis
+- [ANCOMBC](https://bioconductor.org/packages/devel/bioc/html/ANCOMBC.html) [@ancombc2020] for differential abundance analysis
+- [benchdamic](https://bioconductor.org/packages/release/bioc/vignettes/benchdamic/inst/doc/intro.html) [@Calgaro2022] for benchmarking differential abundance methods
+- [ALDEx2](https://www.bioconductor.org/packages/release/bioc/html/ALDEx2.html) [@Gloor2016] for differential abundance analysis
 
 ### Other relevant packages
+
 - [MGnifyR](https://github.com/EBI-Metagenomics/MGnifyR) for accessing and processing MGnify data in R
     - [MGnify Notebooks](https://docs.mgnify.org/src/notebooks_list.html)
     - [EMBL-EBI MGnify user guides and resources](https://github.com/EBI-Metagenomics/notebooks)
-- [LinDA](https://github.com/zhouhj1994/LinDA) for differential abundance analysis
-- [vegan](https://vegandevs.github.io/vegan/) for community ecologists
+- [LinDA](https://github.com/zhouhj1994/LinDA)[@Zhou2022] for differential abundance analysis
+- [vegan](https://vegandevs.github.io/vegan/) [@R_vegan] for community ecologists
+
 - [philr](http://bioconductor.org/packages/devel/bioc/html/philr.html) (@Silverman2017) phylogeny-aware phILR transformation
-- [MicrobiotaProcess](https://bioconductor.org/packages/release/bioc/html/MicrobiotaProcess.html) for the "tidy" analysis of microbiome and other ecological data
+- [MicrobiotaProcess](https://bioconductor.org/packages/release/bioc/html/MicrobiotaProcess.html) [@Xu2023] for the "tidy" analysis of microbiome and other ecological data
 - [Tools for Microbiome Analysis](https://microsud.github.io/Tools-Microbiome-Analysis/)
 site listed over 130 R packages for microbiome data science in 2023.
 Many of these are not in Bioconductor, or do not directly support the