Update topics/microbiome/tutorials/multivariable-association/tutorial.md

Co-authored-by: paulzierep <[email protected]>

topics/microbiome/tutorials/multivariable-association/tutorial.md

@@ -51,7 +51,7 @@ handling of sparsity, and treatment of compositional data. Some of them are ment
 | **ALDEx2**          | Robust to sparsity, small sample sizes        | Limited handling of complex metadata     | ALDEx2 is suitable for small datasets, but MaAsLin2 is superior in handling multivariable data and covariates. |
 | **MetagenomeSeq**   | Handles zero-inflation, sparse data           | Computationally heavy for large datasets | MetagenomeSeq is great for zero-inflated data, but lacks MaAsLin2's multivariable modeling capacity. |
 | **Corncob**         | Models both abundance and variability        | Complex to use, requires R expertise     | Corncob excels at overdispersion analysis, but MaAsLin2 is easier for broader multivariable models. |
-| **Phyloseq + DESeq2**| Strong for RNA-seq and transcriptomics; integrates with Phyloseq | Lacks compositionality awareness         | While DESeq2 works for microbiome data, MaAsLin2 offers more suitable options for compositional data and covariate handling. |
+| **DESeq2**| Originally developed for RNA-seq and transcriptomics | Lacks compositionality awareness         | While DESeq2 works for microbiome data, MaAsLin2 offers more suitable options for compositional data and covariate handling. |
 | **Limma-Voom**      | Effective for RNA-seq and microarray data, handles low counts | Not tailored for compositional microbiome data | Limma-Voom is well-suited for gene expression, but MaAsLin2 better accounts for the unique characteristics of microbiome data. |
 
 - ANCOM-BC and MaAsLin2, outperform general-purpose tools like DESeq2 and limma-voom when it comes to microbiome data. This is due to their handling of the compositional nature of microbiome data and the sparsity typical of microbial datasets. ({% cite Yang2023 %})