diff --git a/frontend/doc-site/04__Analyze Public Data/4_1__Hosted Tutorials.mdx b/frontend/doc-site/04__Analyze Public Data/4_1__Hosted Tutorials.mdx
index 9bbd5f7b2560e..2a8d426ab0111 100644
--- a/frontend/doc-site/04__Analyze Public Data/4_1__Hosted Tutorials.mdx
+++ b/frontend/doc-site/04__Analyze Public Data/4_1__Hosted Tutorials.mdx
@@ -14,7 +14,7 @@ CELLxGENE Explorer's user interface organizes single cell data similarly to how
**Key Concepts**: User Interface Explanation
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## Examining Categorical Metadata
@@ -22,7 +22,7 @@ Categorical metadata (such as tissue of origin or cell type) can be used in a nu
**Key Concepts**: Categorical Metadata, Selecting Cells by Category (i.e. cell type), Interaction Between Categorical Metadata Fields
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## Find Cells Where a Gene is Expressed
@@ -30,7 +30,7 @@ Numerical metadata (such gene expression features or QC metrics such as number o
**Key Concepts**: Numerical Metadata, Cell Filtering and Selection, Interaction Between Numerical Metadata, Categorical Metadata Fields
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## Selecting and Subsetting Cells
@@ -38,7 +38,7 @@ Explorer allows for the complex selection of cells via selection directly on the
**Key Concepts**: Categorical Metadata Selection, Numerical Metadata Selection, Complex Selection (combining selection methods)
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## Compare Expression of Multiple Genes
@@ -46,7 +46,7 @@ Explorer allows you to compare the expression of multiple genes via bivariate pl
**Key Concepts**: Gene Expression, Co-expression, Cell Selection, Subsetting
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## Using Gene Sets to Learn About Cell Population Functional Characteristics
@@ -58,7 +58,7 @@ _Listed below is a comma separated gene set list for use with this tutorial._
ACAA1, ACAA2, ACADL, ACADM, ACADS, ACADSB, ACADVL, ACAT1, ACAT2, ACOX1, ACOX3, ACSL1, ACSL3, ACSL4, ACSL5, ACSL6, ADH1A, ADH1B, ADH1C, ADH4, ADH5, ADH6, ADH7, ALDH1B1, ALDH2, ALDH3A2, ALDH7A1, ALDH9A1, CPT1A, CPT1B, CPT1C, CPT2, CYP4A11, CYP4A22, ECHS1, ECI1, ECI2, EHHADH, GCDH, HADH, HADHA, HADHB
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## Find Marker Genes
@@ -66,6 +66,6 @@ Explorer allows you to find marker genes between selected cell populations.
**Key Concepts**: Gene Expression, Differential Expression, Cell Selection, Subsetting
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Note: You can find more information here about how our differential expression is calculated. In brief, we use a Welch's t-test. While we are aware that single cell data does not always meet the assumptions imposed by this test, we utilize it because it performs well at identifying the _most_ differentially expressed genes, and this is what our feature returns.