From 7982a40b1d86602d302b01d25ae1c53fa76e4ed7 Mon Sep 17 00:00:00 2001
From: Yi Wang <37149810+yiwang12@users.noreply.github.com>
Date: Sun, 17 Nov 2024 14:30:44 +0100
Subject: [PATCH] Update mnsf-tutorial-mouse.md

---
 tutorial/mnsf-tutorial-mouse.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorial/mnsf-tutorial-mouse.md b/tutorial/mnsf-tutorial-mouse.md
index dbc8b5b..26f3f2a 100644
--- a/tutorial/mnsf-tutorial-mouse.md
+++ b/tutorial/mnsf-tutorial-mouse.md
@@ -335,9 +335,9 @@ for ksample in range(nsample):
 Setting up induced points:
    - Induced points are a subset of spatial locations used to reduce computational complexity while maintaining model accuracy.
    - For each sample:
-     - `ninduced = round(list_D[ksample]['X'].shape[0] * 0.15)`: Calculates the number of induced points as 15% of total spots.
+     - `ninduced = round(list_Dlist_D_chunked['X'].shape[0] * 0.15)`: Calculates the number of induced points as 15% of total spots.
      - `rd_ = random.sample(...)`: Randomly selects the induced points.
-     - `list_D[ksample]["Z"] = list_D[ksample]['X'][rd_, :]`: Stores the selected points in the data structure.
+     - `list_D_chunked[ksample]["Z"] = list_D_chunked[ksample]['X'][rd_, :]`: Stores the selected points in the data structure.
 
 The number of induced points (15% here) is a trade-off between computational efficiency and accuracy. You might need to adjust this percentage based on your dataset size and available computational resources.