Loading Spatial Data - Answer Key

Exercise 1

1. Given the information that we know from the metadata, what might be some questions that we want to answer using our data?

• Identify and characterize tumor cells in CRC
• Construct cell-communication networks, using spatial locations to constrain our results
• What are unique subtypes/cell states that are found in CRC vs. NAT
• Associate cell states with spatial locations
• Establish domains and niches of cell populations in quadrants of the slides
• etc.

2. What are some of the limitations of this dataset that we should keep in mind as we analyze it?

We do not have any replicates, so we cannot make any conclusions about the generalizability of our findings with statistical confidence.

It can be challenging to evaluate differences in cell type composition between the tumor and normal samples as the bin-based method potentially includes a mixture of cells in each bin. Furthermore we are looking at a single section of the tissue, so we may not be capturing the full heterogeneity of the tumor microenvironment.

Exercise 2

3. What differences do you see between the 8um and 16um bins?

There are more cells in the 8um compared to the 16um. The number of features/genes remain the same.

This is why the bin value matters, it determines how many “cells” are ultimately generated. A smaller bin size results in more spots. This is because we are taking smaller sections of the same data, meaning we use more bins to cover the same area as a large bin size.

Exercise 3

3. What function can we use to double check that we have a singular counts layer?

Layers(seurat_merged)