From a0dbec9d7c7d098efb9e8e09a2a2ce88da2d58a4 Mon Sep 17 00:00:00 2001
From: Evelyn Schmidt <57552529+evelyn-schmidt@users.noreply.github.com>
Date: Tue, 30 Apr 2024 10:01:22 -0400
Subject: [PATCH] Update 0008-02-01-QA_clustering.md

---
 _posts/0008-02-01-QA_clustering.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/_posts/0008-02-01-QA_clustering.md b/_posts/0008-02-01-QA_clustering.md
index c423e88..6b75a91 100644
--- a/_posts/0008-02-01-QA_clustering.md
+++ b/_posts/0008-02-01-QA_clustering.md
@@ -448,7 +448,7 @@ table(Idents(merged)) # get summary table
 
 ### Normalize Data 
 
-Standard practice for scRNA data is to normalize your counts. Many functions will only use the normalized counts and not look at the raw counts. The `NormalizeData` function takes our merged object and will log normalize our RNA assay. Log normalized in this context means that the  feature counts for each cell are divided by the total counts for that cell and multiplied by the `scale.factor`, then natural-log transformed using `log1p`. 
+Standard practice for scRNA data is to [normalize your counts](https://bioconductor.org/books/3.17/OSCA.basic/normalization.html). Many functions will only use the normalized counts and not look at the raw counts. The `NormalizeData` function takes our merged object and will log normalize our RNA assay. Log normalized in this context means that the  feature counts for each cell are divided by the total counts for that cell and multiplied by the `scale.factor`, then natural-log transformed using `log1p`. 
 
 ```R
 merged <- NormalizeData(merged, assay = "RNA", normalization.method = "LogNormalize", scale.factor = 10000)