diff --git a/_chapters/single-cell-analysis/quiz-03/index.md b/_chapters/single-cell-analysis/quiz-03/index.md index 0b812f0..d7b5227 100644 --- a/_chapters/single-cell-analysis/quiz-03/index.md +++ b/_chapters/single-cell-analysis/quiz-03/index.md @@ -23,7 +23,9 @@ title: 'Quiz' timeout={10}> -Perform cluster exploration on the retinal dataset. Use the data table of known marker genes ([sc-quiz-marker-genes.xlsx](http://file.biolab.si/datasets/sc-quiz-marker-genes.xlsx)) for each cell type (don't forget to pass the marker genes data though the Genes widget to annotate!) and set the aggregation parameter in the Score Cells widget to **Fraction of expressed markers**. +Perform cluster exploration on the retinal dataset. Remember, you have already performed quality control and preprocessing steps on it, as well as annotation. + +a) Use the data table of known marker genes ([sc-quiz-marker-genes.xlsx](http://file.biolab.si/datasets/sc-quiz-marker-genes.xlsx)) for each cell type (don't forget to pass the marker genes data though the Genes widget to annotate!) and set the aggregation parameter in the Score Cells widget to **Fraction of expressed markers**. ![](sc-ex3-q2.jpg) @@ -89,7 +91,7 @@ Liang et al. report that in the peripheral tissue the proportion of rods in comp -Select the top 100 genes that are differentially expressed in cones in comparison to non-cones (T-test). Forward them to the GO widget. Sort the lower list by increasing p-value. +Select the top 100 genes that are differentially expressed in cones in comparison to non-cones (T-test). Forward them to the GO widget. Sort the lower list in the GO widget by increasing p-value. Enlarge the window so you see the enhancement scores on the right side. Scroll down and identify the first three GO terms with a high-p value and an an enrichment score above 40 (you can see the numerical enrichment score by hovering over the bar representation of it on the right side of the window).