Update MAWA docs

index.md

@@ -13,7 +13,7 @@ Multiplex Analysis Web Apps (MAWA) is a suite of web-based tools for performing
 MAWA consists of four main components, separated by groups of tabs in MAWA:
 
 1. **Data loader.** This app is responsible for efficiently transferring data to and from the computational environment. This is particularly useful for saving the results generated by other components of the suite so that the component analyses or visualizations can be quickly re-initialized later.
-1. **Phenotyper.** This app reads in a text file with cells in rows and spatial coordinates and marker positivities/intensities in columns and assigns a phenotype to each cell in the datafile. Current phenotyping methods include "species" (each unique combination of positive markers represents a new phenotype), "marker" (each positive marker is treated as an independent cell), and "custom" (the user utilizes the app to efficiently assign a phenotype to each unique "species" present in the dataset, potentially assigning more than one species to a single phenotype). A fourth phenotyping method in development is multiaxial gating, in which the user defines phenotypes based on the combination of any number of marker intensity ranges. The app includes dynamic scatterplot labeling so the user can clearly visualize the chosen phenotype assignments. The phenotyper further serves as input to the following two tools that are focused on studying potential interactions between the selected phenotypes.
+1. **Phenotyper.** This app reads in a text file with cells in rows and spatial coordinates and marker positivities/intensities in columns and assigns a phenotype to each cell in the datafile. Current phenotyping methods include "species" (each unique combination of positive markers represents a new phenotype), "marker" (each positive marker is treated as an independent cell), and "custom" (the user utilizes the app to efficiently assign a phenotype to each unique "species" present in the dataset, potentially assigning more than one species to a single phenotype). A fourth phenotyping method in development is multiaxial gating, in which the user defines phenotypes based on the combination of any number of marker intensity ranges. The app includes dynamic scatterplot labeling so the user can clearly visualize the chosen phenotype assignments. The phenotyper further serves as input to the following two tools that are focused on studying potential interactions between the assigned phenotypes.
 1. **Spatial interaction tool.** This app aims to answer the question: "In specific parts of a slide (e.g., tumor region, necrotic region, entire slide, etc.), do cells of phenotype A tend to be interacting with cells of phenotype B, and if so, to what extent?" Density heatmaps are employed to visualize results, and metrics can be calculated using a Poisson method, a permutation method defining neighbors by radius, or a permutation method defining neighbors by the k-nearest. A user can input the data for an entire slide, and the app will automatically partition the slide into regions of interest (ROIs), perform density calculations for every ROI, and average the resulting heatmaps over the ROIs, weighted by annotation region type. Useful visualizations are implemented in the app that allow the user to observe the results on a ROI-by-ROI or slide-by-slide basis.
 1. **Neighborhood profile analyzer.** This app aims to answer the question: "What unique sets of neighbor cells exist in the dataset and what can we learn from them?" Counts of cells of different phenotypes are calculated around every cell in the dataset, within concentric annuli; the results are reduced to two dimensions using UMAP decomposition; and the resulting 2D datapoints are clustered together to identify groupings of cellular neighborhoods. The app further provides exploratory visualizations for studying cluster compositions and comparisons and for investigating potential correlations with other features in the dataset.