diff --git a/README.md b/README.md
index 41e6b49a..d3aeb6da 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,41 @@
 [![Docker Build](https://github.com/RealPaiLab/netDx/actions/workflows/push-docker.yml/badge.svg)](https://github.com/RealPaiLab/netDx/actions/workflows/push-docker.yml)
 [![R CMD check bioc](https://github.com/RealPaiLab/netDx/actions/workflows/check-bioc.yml/badge.svg)](https://github.com/RealPaiLab/netDx/actions/workflows/check-bioc.yml)
 
+## Project Statement
+
+netDx is for biomedical researchers who want to integrate multi-modal patient data to predict outcome or patient subtype. netDx builds interpretable machine-learning patient classifiers. Unlike standard machine-learning tools, netDx allows modeling of user-defined biological groups as input features; examples include pathways and co-regulated elements. In addition to patient classification, top-scoring features provide mechanistic insight, helping drive hypothesis generation for downstream experiments. netDx currently provides native support for pathway-level features but can be generalized to any user-defined data type and grouping.
+
+## Install
+
+These steps were tested on a 2020 Mac Book Pro with 32 Gb RAM.
+
+1) Install Docker, and under Resources for Docker Desktop's settings set Memory to 5 GB.
+2) In terminal, pull the netDx image from Docker:
+	`docker pull realpailab/netdx`
+3) To run the image as a container,  save the following code as a bash script named `startDocker.sh`:
+```
+#!/bin/bash
+	
+docker run -d --rm \
+    -p 8787:8787 \
+    -e PASSWORD=netdx \
+    -v /path/to/software/dir:/software \
+    --name [containerName] \
+    realpailab/netdx
+```
+4) Modify the bash script so that it's executable and run it:
+```
+chmod u+x startDocker.sh
+./startDocker.sh
+```
+5) Access RStudio by going to your web browser and typing in `localhost:8787`, and sign in with the username `rstudio` and password `netdx`.
+6) You can then run the vignettes in R:
+```
+setwd("/home/rstudio/vignettes")
+rmarkdown::render("ThreeWayClassifier.Rmd")
+```
+
+---
 
 ### Main repo for netDx dev work as of Sep 2021.
 
@@ -12,6 +47,7 @@ Visit http://bioconductor.org/packages/release/bioc/html/netDx.html to install t
 
 Contact Shraddha Pai at shraddha.pai@utoronto.ca in case of questions.
 
+
 References: 
 
 1. Pai S, Hui S, Isserlin R, Shah MA, Kaka H and GD Bader (2019). netDx: Interpretable patient classification using patient similarity networks. *Mol Sys Biol*. 15: e8497. [Read the paper here](https://www.embopress.org/doi/full/10.15252/msb.20188497).
diff --git a/vignettes/ThreeWayClassifier.Rmd b/vignettes/ThreeWayClassifier.Rmd
index e817e7b8..bce52012 100755
--- a/vignettes/ThreeWayClassifier.Rmd
+++ b/vignettes/ThreeWayClassifier.Rmd
@@ -64,7 +64,8 @@ pID <- colData(brca)$patientID
 colData(brca)$ID <- pID
 ```
 
-ADD HOLDOUT TEXT HERE
+The function `subSampleValidationData` partitions the TCGA data into two smaller datasets: a training and holdout validation dataset. This is to facilitate model validation after an initial model is built by the netDx algorithm - netDx will train a model on the training dataset through the `buildPredictor()` call, and this model can validate the held-out validation dataset through the `predict()` function call.
+
 ```{r}
 set.seed(123)
 dsets <- subsampleValidationData(brca,pctValidation=0.1)
@@ -105,9 +106,15 @@ groupList[[names(brca)[3]]] <- tmp
 
 ## Define patient similarity for each network
 
-ADD SOME INTRO TEXT HERE
+`sims` is a list that specifies the choice of similarity metric to use for each grouping we're passing to the netDx algorithm. You can choose between several built-in similarity functions provided in the `netDx` package:
+
+* `normDiff` (normalized difference)
+* `avgNormDiff` (average normalized difference)
+* `sim.pearscale` (Pearson correlation followed by exponential scaling)
+* `sim.eucscale` (Euclidean distance followed by exponential scaling) or
+* `pearsonCorr` (Pearson correlation)
 
-`normDiff` is a function provided in the `netDx` package, but the user may define custom similarity functions in this block of code and pass those to `makePSN_NamedMatrix()`, using the `customFunc` parameter.
+You may also define custom similarity functions in this block of code and pass those to `makePSN_NamedMatrix()`, using the `customFunc` parameter.
 
 ```{r,eval=TRUE}
 sims <- list(a="pearsonCorr",b="normDiff",c="pearsonCorr",d="pearsonCorr")