Update topics/single-cell/tutorials/pseudobulk-analysis/tutorial.md

Co-authored-by: Pavankumar Videm <[email protected]>

topics/single-cell/tutorials/pseudobulk-analysis/tutorial.md

@@ -32,7 +32,7 @@ Pseudobulk analysis is a powerful technique that bridges the gap between single-
 
 A key advantage of this approach in differential expression (DE) analysis is that it avoids treating individual cells as independent samples, which can underestimate variance and lead to inflated significance or overly optimistic p-values ({% cite Squair2021 %}). This occurs because cells from the same biological replicate are inherently more similar to each other than cells from different samples. By grouping data into pseudobulk samples, the analysis aligns with the experimental design, as in bulk RNA-seq, leading to more reliable and robust statistical results ({% cite Murphy2022 %}).
 
-Beyond enhancing statistical validity, pseudobulk analysis enables the identification of cell-type-specific gene expression and functional changes across biological conditions. It balances the detailed resolution of single-cell data with the statistical power of bulk RNA-seq, providing insights into the functional transcriptomic landscape relevant to biological questions. Overall, for differential expression analysis in multi-sample single-cell experiments, pseudobulk approaches demonstrate superior performance compared to single-cell-specific DE methods ({% cite Squair2021 %}). 
+Beyond enhancing statistical validity, pseudobulk analysis enables the identification of cell-type-specific gene expression and functional changes across biological conditions. It balances the detailed resolution of single-cell data with the statistical power of bulk RNA-seq, providing insights into the functional transcriptomic landscape relevant to biological questions. Overall, for DE analysis in multi-sample single-cell experiments, pseudobulk approaches demonstrate superior performance compared to single-cell-specific DE methods ({% cite Squair2021 %}). 
 
 In this tutorial, we will guide you through a pseudobulk analysis workflow using the **Decoupler** and **edgeR** tools available in Galaxy ({% cite Badia-iMompel2022 %}) ({% cite Liu2015 %}). These tools facilitate functional and differential expression analysis, and their output can be integrated with other Galaxy tools to visualize results, such as creating Volcano Plots, which we will also cover in this tutorial.