From e439fdcbd55c360b32bf7f9b1ad80df3da09ae22 Mon Sep 17 00:00:00 2001
From: Ashley Mae Conard <ashleymaeconard@gmail.com>
Date: Mon, 20 Nov 2023 09:09:59 -0500
Subject: [PATCH] Update README.md

---
 README.md | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/README.md b/README.md
index 09d00b6..17f3fc8 100644
--- a/README.md
+++ b/README.md
@@ -88,22 +88,22 @@ Multioviz has three usages: To (1) visualize ranked molecular features as GRNs
   style="display: inline-block; margin: 0 auto; max-width: 1000px">
 
 ### C. Integrate computational method
-- M: Computational method performs feature selection and prioritization
-- I: Required input datasets for computational method
-- Script with runModel() function that has parameters for I and that outputs ML1, ML2, and Map
-    - Contains parameters for I
-    - Runs the user's ranking model
-    - Returns a list of ML1, ML2, and Map
+- Computational method performs feature selection and prioritization
+- The script with your method should call ```runModel()``` function that
+    - has parameters for required inputs (see B. Perturb figure depicting X, y, mask) (# step 1)
+    - runs the user's method (# step 2)
+    - returns ML1, ML2, and Map (see B. Perturb figure depicting molecular level 1 (SNPs) (ML1), molecular level 2 (genes) (ML2), and mapping between molecular levels). (# step 3)
 
+As an example, the script could look something like, where we run BANNs as our example computational method:
 ```
-runModel <- function(X_input, y_input, mask_input) {
+runModel <- function(X_input, y_input, mask_input) { # step 1
 
-  res = BANN(X_input, mask_input, y_input, centered=FALSE, show_progress = TRUE)
-
-  # convert method output to ranking and mapping dataframes 
+  # run your computational method
+  res = BANN(X_input, mask_input, y_input, centered=FALSE, show_progress = TRUE) # 2
 
+  # convert method output to ML1, ML2, and map 
   lst = list()
-  lst$ML1 = ML1_pips
+  lst$ML1 = ML1_pips # step 3
   lst$ML2 = ML2_pips
   lst$map = btw_ML_map
   return(lst)
@@ -111,7 +111,7 @@ runModel <- function(X_input, y_input, mask_input) {
 ```
 
 ## A. Visualize ranked molecular features as a GRN
-To faciliate *in silico* hypothesis generation, Multioviz allows users to visualize ranked lists and maps of enriched molecular features for a given phenotypic state as GRNs.
+To facilitate *in silico* hypothesis generation, Multioviz allows users to visualize ranked lists and maps of enriched molecular features for a given phenotypic state as GRNs.
 
 <img
   src="./app/readme/fig2.png"