Update README.md

README.md

@@ -122,10 +122,31 @@ In Step 3, low quality cells are filtered based on the user-defined thresholds f
 **Step 4: Step 4: Doublet removal (standard track)** <br />
 In Step 4 of the standard analysis track, doublets (barcodes produced by sequencing two or more cells) are identified and optionally removed from downstream analysis using the DoubletFinder tool ([McGinnis et al. 2019](https://www.cell.com/cell-systems/pdfExtended/S2405-4712(19)30073-0)).<br />
 
+**Step 4: Demultiplexing and doublet detection (HTO track)** <br />
+In Step 4 of the HTO track, Seurat’s implementation (MULTIseqDemux) of the tag assignment algorithm outlined in Multi-seq is used to demultiplex pooled samples and identify doublets according to the expression matrices of the sample-specific barcodes ([McGinnis et al. 2019](https://pubmed.ncbi.nlm.nih.gov/31209384/)).<br />
 
-- **Step 5: Integration and linear dimensional reduction** - Individual Seurat objects are integrated to enable the joint analysis across sequencing runs using Seurat's integration algorithm (Stuart et al. 2019); if experiments are limited to a single sequencing run, the integration Step can be bypassed. Linear dimensional reduction is then performed on the resulting Seurat object to inform the optimal parameters for clustering in Step 6.<br />
-- **Step 6: Clustering** - Clustering is performed to define groups of cells with similar expression profiles using the graph-based clustering approach implemented in the Seurat framework (Macosko et al. 2015).<br />
-- **Step 7: Cluster annotation** - Cell populations, or clusters, with similar expression profiles are annotated to define cell types by three distinct methods:<br />
+**Step 5: Integration**<br />
+In Step 5, individual Seurat objects from each sample are combined to enable the joint analysis across samples. Users can either merge or integrate their Seurat objects ([Stuart et al. 2019](https://pubmed.ncbi.nlm.nih.gov/31178118/)). Scaling, and linear dimensional reduction is then performed to inform the optimal parameters for clustering in Step 6. <br />
+
+**Step 6: Clustering**<br />
+In Step 6, clustering is performed to define groups of cells with similar expression profiles using the Seurat implementation of the Louvain network detection with PCA dimensionality reduction as input ([Macosko et al. 2015](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4481139/)).<br />
+
+**Step 7: Cluster annotation** 
+In Step 7, cluster annotation is performed to define the cell types comprising the clusters identified in Step 6. ScRNAbox provides three tools to identify cell types comprising the clusters:
+
+_Tool 1: Cluster marker gene identification and gene set enrichment analysis (GSEA)_
+Seurat's FindAllMarkers function is used to identify differentially expressed marker genes (DEG) by the Wilcoxon rank-sum test (Macosko et al. 2015). DEGs in the positive direction (Log2 fold-change > 0.00) are then tested for enrichment across user-defined gene set libraries that define cell types using the EnrichR tool (Chen et al. 2013). 
+
+_Tool 2: Expression profiling of cell type markers and module scores_ 
+Users can visualize the expression of individual genes and the aggregated expression of multiple genes. For each gene in a user-defined list, plots are produced to visualize its expression at the cluster or cell level. The aggregated expression of genes in a user-defined list are calculated using the Seurat AddModuleScore function (Tirosh et al. 2016. 
+
+_Tool 3: Cell type predictions based on reference data_
+Seurat's FindTransferAnchors and TransferData functions are used to leverage cell-type annotations from a reference Seurat object and generate annotation predictions for the query dataset (Macosko et al. 2015). 
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+Cell populations, or clusters, with similar expression profiles are annotated to define cell types by three distinct methods:<br />
     1. _Cluster marker gene set enrichment analysis (GSEA)_: Seurat's _FindAllMarkers_ function is used to identify differentially expressed marker genes (DEG) by the Wilcoxon rank-sum test (Macosko et al. 2015). DEGs in the positive direction     (Log2 fold-change > 0.00) are then tested for enrichment across user-defined gene set libraries that define cell types using the EnrichR tool (Chen et al. 2013).<br />
     2. _Reference-based annotation_: Seurat's _FindTransferAnchors_ and _TransferData_ functions are used to leverage cell-type annotations from a reference Seurat object and generate annotation predictions for the query dataset (Macosko et        al. 2015). User's must define the location of their referene Seurat object in the parameters file of Step 7.<br />
     3. _Module score_: Seurat’s implementation (_AddModuleScore_) of Tirosh et al.’s algorithm is used to comparatively quantify the expression of gene sets across clusters at the single-cell level (Tirosh et al. 2016). Users must          define their desired gene sets in the parameters file of Step 7.<br />