babs4/week-6 overview

3mmaRand · Mar 15, 2024 · 532b8c5 · 532b8c5
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diff --git a/r4babs4/week-6/overview.qmd b/r4babs4/week-6/overview.qmd
@@ -5,19 +5,23 @@ toc: true
 toc-location: right
 ---
 
-This week you will 
+
 
 ### Learning objectives
 
 The successful student will be able to:
 
--   
+-   evaluate their progress and identify any steps they have missed
+
+-   create density plots to visualise the distribution, and gating of,
+    the APC TNF-α and FTIC signals in live cells
 
--  
+-   calculate the the percentage of cells in each quadrant of a quadrant
+    gated gated plot of TNFa_APC_Lin signal against the E_coli_FITC_Lin 
 
--   
+-   annotate ggplots as desired
 
--   
+-   import data from a googlesheet
 
 ### Instructions
 
@@ -26,7 +30,15 @@ The successful student will be able to:
 
 2.  [Workshop](workshop.qmd)
 
-    i.💻 
+    i. 💻  Create density plots to visualise the distribution, and gating of,
+    the APC TNF-α and FTIC signals in live cells
+
+    ii. 💻  Calculate the the percentage of cells in each quadrant of a quadrant
+    gated gated plot of TNFa_APC_Lin signal against the E_coli_FITC_Lin
+
+    iii. 💻  Annotate ggplots
+
+    iv. 💻  Import data from a googlesheet
 
 3.  [Consolidate](study_after_workshop.qmd)
 

diff --git a/r4babs4/week-6/study_before_workshop.qmd b/r4babs4/week-6/study_before_workshop.qmd
@@ -56,7 +56,7 @@ from wherever you got to.
     vi. You should be able add a gate for the FITC positive 
         cells and label the rows (cells) in the dataframe as 
         FITC positive or FITC negative. You should be able to 
-        calculate the number and percentage of TNF-α positive cells 
+        calculate the number and percentage of FITC positive cells 
         in each sample using the same logic as in v.
 
 3.  💻  Prepare to analyse your own data / the model data