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 Multiplex Analysis Web Apps (MAWA) is a suite of web-based tools for performing spatial analysis on multiplex data. The target audience is biologists looking to uncover relationships between cell populations in their multiplex data. Users access MAWA using the popular Python front-end [Streamlit](https://streamlit.io), which offers a user-friendly, point-and-click interface.
 
-MAWA consists of four main components, separated by groups of tabs:
+MAWA consists of four main components, separated by groups of tabs in MAWA:
 
 1. **Data loader.** This tool 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 tool 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.
@@ -19,4 +19,13 @@ MAWA consists of four main components, separated by groups of tabs:
 
 ## Access
 
-NIH Integrated Data Analysis Platform, local (advanced)
+NIH users can access a sample deployment of MAWA on the NIH Integrated Data Analysis Platform (NIDAP) [here](dummy_url). This instance utilizes a test dataset.
+
+NIH users can request a deployment of MAWA to analyze their own data on NIDAP [here](dummy_url).
+
+Alternatively, advanced, non-NIH users can deploy MAWA locally by cloning this repository using `git clone git@github.com:ncats/multiplex-analysis-web-apps.git` and running `./run.sh` in a Python environment that contains Streamlit.
+
+## Points of contact
+
+Andrew Weisman ([andrew.weisman@nih.gov](mailto:andrew.weisman@nih.gov))  
+Dante Smith ([dante.smith@nih.gov](mailto:dante.smith@nih.gov))