Merge pull request #24 from 3mmaRand/babs4/week4

Babs4/week4

r4babs4/week-4/overview.qmd

@@ -6,37 +6,36 @@ toc-location: right
 
 ---
 
-Last week you . You will now extend t
+This is the second of the three workshops which are specific to the Immunobiology strand. The aim of these workshops is to teach you how to analyse the flow cytometry data you will collect in the practicals. In this workshop, apply the workflow your learned in [Week 2](../week2/overview.qmd) to your own data.
 
 ### Learning objectives
 
 The successful student will be able to:
 
--   
+-   Explain the principles of each step in workflow provided in week 2
 
--   
+-   Edit the workflow provided in week 2 and apply it to their own data
 
--   
+-   Determine values from their data that contribute to the 
+    [class data set](https://3mmarand.github.io/R4BABS/r4babs4/week-4/workshop.html#data-for-the-class-dataset).
+
+-   change the name of an RStudio project
 
--   
+-   Apply the principles covered in [Data Analysis 1: Core](https://3mmarand.github.io/R4BABS/r4babs4/week-1/overview.html) to their analysis
 
--   
 
 
 ### Instructions
 
 1.  [Prepare](study_before_workshop.qmd)
 
-    i.  📖 Read 
 
 
 2.  [Workshop](workshop.qmd)
 
-    i. 💻 One-way ANOVA
 
 
 3.  [Consolidate](study_after_workshop.qmd)
 
-    i.  💻 
 
 

r4babs4/week-4/workshop.qmd

@@ -33,25 +33,25 @@ In this workshop you will use the tools you used in the previous workshop (and b
 
 You need to provide the following information for the class dataset:
 
--   group_name:	A name for your group. Take care not to use a name used by others. Take care to use the exactly the same name for each of your rows
+-   `group_name`:	A name for your group. Take care not to use a name used by others. Take care to use the exactly the same name for each of your rows
 
--   cell_treatment:	One of MEDIA, LPS or ECOLIGreen
+-   `cell_treatment`:	One of MEDIA, LPS or ECOLIGreen
 
--   antibody:	One of ISOTYPE or TNFAPC
+-   `antibody`:	One of ISOTYPE or TNFAPC
 
--   n_cells:	number of cells in sample after flowAI cleaning
+-   `n_cells`:	number of cells in sample after flowAI cleaning
 
--   n_cells_live:	number of cells in sample after flowAI cleaning and removing debris
+-   `n_cells_live`:	number of cells in sample after flowAI cleaning and removing debris
 
--   perc_nondebris:	% "cleaned" cells that were not debris
+-   `perc_nondebris`:	% "cleaned" cells that were not debris
 
--   apc_cut:	threshold for logicle transformed TNFa_APC_Lin. This will be the same for all your samples
+-   `apc_cut`:	threshold for logicle transformed TNFa_APC_Lin. This will be the same for all your samples
 
--   apc_mfi:	Mean fluorescence intensity of the logicle transformed TNFa_APC_Lin in the TNF-α positive cells
+-   `apc_mfi`:	Mean fluorescence intensity of the logicle transformed TNFa_APC_Lin in the TNF-α positive cells
 
--   n_pos_tnfa:	Number of TNF-α positive cells
+-   `n_pos_tnfa`:	Number of TNF-α positive cells
 
--   perc_tfna_pos:	% non debris cells that are TNF-α positive cells    
+-   `perc_tfna_pos`:	% non debris cells that are TNF-α positive cells    
 
 Enter these in the [BIO00066I Biomedical Sciences class data](https://docs.google.com/spreadsheets/d/104EXdgsiIq-FuRF9Ly9zewEVdpkVWbyOwxSAmiqJepg/edit#gid=0)