fix the name for the Holland reference (#289)

jupyter-book/conditions/gsea_pathway.ipynb

@@ -109,7 +109,7 @@
     "\n",
     "*DoRothEA* {cite}`garcia2019benchmark` and *PROGENy* {cite}`schubert2018perturbation` are among functional analysis tools developed to infer transcription factor (TF) - target activities originally in Bulk RNA data. Holland et al. {cite}`holland2020robustness` found that Bulk RNA-seq methods *DoRothEA* and *PROGENy* have optimal performance in simulated scRNA-seq data, and even partially outperform tools specifically designed for scRNA-seq analysis despite the drop-out events and low library sizes in single cell data. Holland et al. also concluded that pathway and TF activity inference is more sensitive to the choice of gene sets rather than the statistical methods. This observation though can be specific to functional enrichment analyses and be explained by the fact that TF-target relations are context-specific; that is TF-target associations in one cell type may actually differ from another cell type or tissue.  \n",
     "\n",
-    "In contrast to Holand et al., Zhang et al. {cite}`zhang2020benchmarking` found that single-cell-based tools, specifically Pagoda2, outperform bulk-base methods from three different aspects of accuracy, stability and scalability. It should be noted that pathway and gene set activity inference tools inherently do not account for batch effects or biological variations other than the biological variation of interest. Therefore, it is up to the data analyst to ensure that the differential gene expression analysis step has worked properly.\n",
+    "In contrast to Holland et al., Zhang et al. {cite}`zhang2020benchmarking` found that single-cell-based tools, specifically Pagoda2, outperform bulk-base methods from three different aspects of accuracy, stability and scalability. It should be noted that pathway and gene set activity inference tools inherently do not account for batch effects or biological variations other than the biological variation of interest. Therefore, it is up to the data analyst to ensure that the differential gene expression analysis step has worked properly.\n",
     "\n",
     "Furthermore, while the tools mentioned here score every gene set in individual cells, they are not able to select for the most biologically relevant gene sets among all scored gene sets. scDECAF (https://github.com/DavisLaboratory/scDECAF) is a gene set activity inference tool that allows data-driven selection of the most informative gene sets, thereby aids in dissecting meaningful cellular heterogeneity."
    ]