https://PollEv.com/thomasmanke101

maxplanck-ie · Jun 21, 2024 · 6f88398 · 6f88398
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diff --git a/rmd/30_BatchEffects.Rmd b/rmd/30_BatchEffects.Rmd
@@ -77,7 +77,7 @@ panc_sub <- UpdateSeuratObject(readRDS(fdest)) ## Seurat v5 would require this
 
 > 🧭✨ Poll:
 >
-> [Which sequencing technologies are retained in the subset dataset?](https://PollEv.com/multiple_choice_polls/j2zBwetamS5edfxq0JDs8/respond)
+> Which sequencing technologies are retained in the subset dataset?
 >
 > How many cells were sequenced in each experiment?
 
@@ -99,7 +99,7 @@ p1 + p2
 > In the previous course units, you have learned to call differentially expressed genes with Seurat.
 > In this task, we ask you to: - call genes differentially expressed between cells sequenced with the smartseq2 technology and those with the celseq technology - plot a violin plot for the top gene - plot a feature plot for the top gene
 
-> 🧭✨ Poll: [What is the name of the top DE gene ?](https://PollEv.com/multiple_choice_polls/D5BfavArbiXgs8QeHvDNn/respond)
+> 🧭✨ Poll: What is the name of the top DE gene ?
 
 Here's our proposed solution:
 
@@ -214,7 +214,7 @@ gc()
 
 Let's have a brief look at the panc.combined dataset - a new Assay has been created by the integration procedure.
 
-> 🧭✨ Poll: [What is the new assay called?](<https://PollEv.com/multiple_choice_polls/4ArWqQjwtyVafBYCiaqbr/respond>) Hint: you can access assays of a Seurat object with `Assays()`.
+> 🧭✨ Poll: What is the new assay called? Hint: you can access assays of a Seurat object with `Assays()`.
 
 ## Process the newly integrated dataset
 

diff --git a/rmd/31_SCTransform.Rmd b/rmd/31_SCTransform.Rmd
@@ -77,7 +77,7 @@ p2 <- FeaturePlot(ctrl, features = "nCount_RNA") + theme(legend.position = "righ
 p1 + p2
 ```
 
-> 🧭✨ Poll: [Which cell population has the highest total counts?](https://PollEv.com/multiple_choice_polls/jHulGlJWYI49AaEVkAbxm/respond)
+> 🧭✨ Poll: Which cell population has the highest total counts?
 > Hint: use the `FetchData` to retrieve variables `nCounts` and `seurat_annotations` to a data frame. Process it further with Dplyr functions: `group_by`, `summarize(total_counts = sum(nCount_RNA))`, and `arrange`.
 
 ## Seurat SCTransform
@@ -131,7 +131,7 @@ ctrl_ln + ctrl_sct
 
 What has changed and what hasn't?
 
-> 🧭✨ Poll: [What amount of standard deviation does PC1 explain after sc-transform?](https://PollEv.com/multiple_choice_polls/AQwroH3oNpZ2uQRcly2eO/respond)
+> 🧭✨ Poll: What amount of standard deviation does PC1 explain after sc-transform?
 > Hint: Function `Stdev(object)` returns the absolute values of standard deviations for principal components. It's the analogous to `object@reductions$pca@stdev`.
 
 
@@ -154,7 +154,7 @@ What has changed and what hasn't ?
 
 > ⌨🔥 Exercise: Apply SCTransform to the IfnB-stimulated dataset as well.
 
-> 🧭✨ Poll: [What amount of standard deviation does the first PC explain after applying sc-transform to the stimulated dataset?](https://PollEv.com/multiple_choice_polls/coHM0QIGIgmNdcxkYIb0i/respond)
+> 🧭✨ Poll: What amount of standard deviation does the first PC explain after applying sc-transform to the stimulated dataset?
 
 ## Prepare both datasets for integration