Update platform_versioned_docs/version-24.1.1/getting-started/quickst…

…art-demo/data-studios.mdx

Co-authored-by: Christopher Hakkaart <[email protected]>
Signed-off-by: Llewellyn vd Berg <[email protected]>

platform_versioned_docs/version-24.1.1/getting-started/quickstart-demo/data-studios.mdx

@@ -66,7 +66,7 @@ In this example, we used RNA sequencing data from the publicly-available ENCODE
 
 What to look for in the PCA plot:
 
-- **Replicate clustering**: Ideally, replicates of the same cell type should cluster closely together. For example, replicates of MCF-7 (breast cancer cell line) group together. This indicates consistent gene expression profiles among replicates.
+- **Replicate clustering**: Ideally, replicates of the same cell type should cluster closely together. For example, replicates of the MCF-7 cells group together. This indicates consistent gene expression profiles among replicates.
 - **Cell type separation**: Different cell types should form distinct clusters. For instance, GM12878, K562, MCF-7, and H1-hESC cells should each form their own separate clusters, reflecting their unique gene expression patterns.
 
 From this PCA plot, you can gain insights into the consistency and quality of your sequencing data, identify any potential issues, and understand the major sources of variation among your samples - all directly in Platform.