microbiome · TuomasBorman · May 24, 2024 · Apr 26, 2024 · Apr 26, 2024 · Apr 28, 2024
diff --git a/inst/assets/bibliography.bib b/inst/assets/bibliography.bib
@@ -2150,4 +2150,26 @@ @article{Legendre2001
   pages = {271--280},
   doi = {10.1007/s004420100716},
   year = {2001}
-}
+}
+
+@article{Schloss2024rarefaction1,
+    author = {Patrick D. Schloss},
+    title = {Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses},
+    journal = {{mSphere}},
+    volume = {9},
+    number = {2},
+    pages = {e00354-23},
+    doi = {10.1128/msphere.00354-23},
+    year = {2024}
+}
+
+@article{Schloss2024rarefaction2,
+    author = {Patrick D. Schloss},
+    title = {Waste not, want not: revisiting the analysis that called into question the practice of rarefaction},
+    journal = {{mSphere}},
+    volume = {9},
+    number = {1},
+    pa
10000
ges = {e00355-23},
+    doi = {10.1128/msphere.00355-23},
+    year = {2024}
+}
diff --git a/inst/pages/12_quality_control.qmd b/inst/pages/12_quality_control.qmd
@@ -325,9 +325,10 @@ plotColData(tse,"sum","SampleType", colour_by = "SampleType") +
 ```
 
 In addition, data can be rarefied with
-[subsampleCounts](https://microbiome.github.io/mia/reference/subsampleCounts.html),
-which normalises the samples to an equal number of reads. However,
-this practice has been discouraged for the analysis of differentially
+[rarefyAssay](https://microbiome.github.io/mia/reference/rarefyAssay.html),
+which normalises the samples to an equal number of reads. 
+This remains controversial, however, and strategies to mitigate the information loss in rarefaction have been proposed [@Schloss2024rarefaction1] [@Schloss2024rarefaction2].
+Moreover, this practice has been discouraged for the analysis of differentially
 abundant microorganisms (see [@mcmurdie2014waste]).
 
 ### Contaminant sequences

diff --git a/inst/pages/98_exercises.qmd b/inst/pages/98_exercises.qmd
@@ -576,7 +576,7 @@ colData(tse)$Barcode_full_length
 ### Library sizes
 
 1. Calculate library sizes  
-2. Subsample / rarefy the counts (see: subsampleCounts)  
+2. Subsample / rarify the counts (see: rarefyAssay)
 
 Useful functions: nrow, ncol, dim, summary, table, quantile, unique, scater::addPerCellQC, mia::mergeFeaturesByRank
 
@@ -1296,4 +1296,3 @@ experiments in the MAE.
 Great job! You performed a cross-correlation analysis between two experiments of
 a MAE and visualized the results with a heatmap. You are also able to customise
 the correlation method and significance testing used for the analysis.
-