Seurat Cheatsheet Answer Key

Authors

Noor Sohail

Will Gammerdinger

Published

December 7, 2025

Exercise 1

What are the last 5 cells barcodes and the last 5 genes in the seurat object.

# Show last bin barcodes
Cells(seurat_processed) %>% tail()

[1] "P5NAT_s_008um_00108_00352-1" "P5NAT_s_008um_00243_00304-1"
[3] "P5NAT_s_008um_00365_00197-1" "P5NAT_s_008um_00357_00367-1"
[5] "P5NAT_s_008um_00148_00248-1" "P5NAT_s_008um_00373_00222-1"

# Show last bin barcodes
colnames(seurat_processed) %>% tail()

[1] "P5NAT_s_008um_00108_00352-1" "P5NAT_s_008um_00243_00304-1"
[3] "P5NAT_s_008um_00365_00197-1" "P5NAT_s_008um_00357_00367-1"
[5] "P5NAT_s_008um_00148_00248-1" "P5NAT_s_008um_00373_00222-1"

# Show the last genes names
Features(seurat_processed) %>% tail()

[1] "MT-ND3"  "MT-ND4L" "MT-ND4"  "MT-ND5"  "MT-ND6"  "MT-CYB"

# Show the last genes names
rownames(seurat_processed) %>% tail()

[1] "MT-ND3"  "MT-ND4L" "MT-ND4"  "MT-ND5"  "MT-ND6"  "MT-CYB"

Exercise 2

What are the last 5 identities for the bins in the Seurat object?

# Print final five idents
Idents(seurat_processed) %>% 
  tail()

P5NAT_s_008um_00108_00352-1 P5NAT_s_008um_00243_00304-1 
                          4                           6 
P5NAT_s_008um_00365_00197-1 P5NAT_s_008um_00357_00367-1 
                          5                           7 
P5NAT_s_008um_00148_00248-1 P5NAT_s_008um_00373_00222-1 
                          5                           8 
14 Levels: Tumor B cells Intestinal epithelial cells 4 5 6 7 8 9 10 11 ... 14

Exercise 3

What are the 5 least variable genes in the Seurat object?

# Show five least variable features
VariableFeatures(seurat_processed) %>% 
  tail()

[1] "ADAM23" "PNMT"   "RSPO2"  "HTRA3"  "SLC3A1" "TLR8"

Exercise 4

What are the dimensions for each assay in the Seurat object?

# Obtain the number of bins and genes for the Spatial.008um assay
dim(seurat_processed[["Spatial.008um"]])

[1]  18085 135798

# Obtain the number of bins and genes for the sketch assay
dim(seurat_processed[["sketch"]])

[1] 18085 10000

Exercise 5

Show the code to get the entire sketch log-normalized (data) count matrix.

# Obtain sketch count matrix for normalized count matrix
LayerData(seurat_processed,
          assay = "sketch",
          layer = "data")

Exercise 6

Show how you would use the FetchData() function to generate a dataframe of fullumapsketch_1, fullumapsketch_2 and orig.ident values for each cell.

# Use FetchData to pull selected information from the Seurat object
FetchData(seurat_processed,
          vars = c("fullumapsketch_1", "fullumapsketch_2", "orig.ident")) %>%
  head()

                            fullumapsketch_1 fullumapsketch_2 orig.ident
P5CRC_s_008um_00078_00444-1       -0.6143208        5.1682210      P5CRC
P5CRC_s_008um_00128_00278-1       10.2845659       -1.0381278      P5CRC
P5CRC_s_008um_00052_00559-1        4.1152215       -2.2149165      P5CRC
P5CRC_s_008um_00121_00413-1       -1.9594976       -1.1644329      P5CRC
P5CRC_s_008um_00202_00633-1       -4.8776975       -0.4168439      P5CRC
P5CRC_s_008um_00035_00460-1       -0.5702860        4.3981943      P5CRC

--- title: "Seurat Cheatsheet Answer Key" author: - Noor Sohail - Will Gammerdinger date: "2025-12-07" --- ```{r} #| label: load_data #| echo: false # Load libraries library(Seurat) library(tidyverse) # Load data seurat_processed <- qs2::qs_read("intermediate/08_seurat_processed.qs") ``` # Exercise 1 **What are the last 5 cells barcodes and the last 5 genes in the seurat object.** ```{r} #| label: print_final_genes_cells # Show last bin barcodes Cells(seurat_processed) %>% tail() # Show last bin barcodes colnames(seurat_processed) %>% tail() # Show the last genes names Features(seurat_processed) %>% tail() # Show the last genes names rownames(seurat_processed) %>% tail() ``` # Exercise 2 **What are the last 5 identities for the bins in the Seurat object?** ```{r} #| label: rename_idents #| echo: false # Rename all identities seurat_processed <- RenameIdents(object = seurat_processed, "1" = "Tumor", "2" = "B cells", "3" = "Intestinal epithelial cells") # These new celltype values are only stored in the idents # Good practice is to store these changes in a column seurat_processed$celltype <- Idents(seurat_processed) ``` ```{r} #| label: print_final_idents # Print final five idents Idents(seurat_processed) %>% tail() ``` # Exercise 3 **What are the 5 least variable genes in the Seurat object?** ```{r} #| label: set_var_features #| echo: false # Get list of all variable genes # Remove variable genes that start with MT- var_genes <- VariableFeatures(seurat_processed) var_genes <- var_genes[!startsWith(var_genes, "MT-")] # Now we set our vector of gene names back to VariableFeatures() VariableFeatures(seurat_processed) <- var_genes ``` ```{r} #| label: show_var_features # Show five least variable features VariableFeatures(seurat_processed) %>% tail() ``` # Exercise 4 **What are the dimensions for each assay in the Seurat object?** ```{r} #| label: show_dim_assays # Obtain the number of bins and genes for the Spatial.008um assay dim(seurat_processed[["Spatial.008um"]]) # Obtain the number of bins and genes for the sketch assay dim(seurat_processed[["sketch"]]) ``` # Exercise 5 **Show the code to get the entire sketch log-normalized (`data`) count matrix.** ```{r} #| label: get_counts_mtx #| eval: false # Obtain sketch count matrix for normalized count matrix LayerData(seurat_processed, assay = "sketch", layer = "data") ``` # Exercise 6 **Show how you would use the `FetchData()` function to generate a dataframe of fullumapsketch_1, fullumapsketch_2 and orig.ident values for each cell.** ```{r} #| label: fetchdata_exercise # Use FetchData to pull selected information from the Seurat object FetchData(seurat_processed, vars = c("fullumapsketch_1", "fullumapsketch_2", "orig.ident")) %>% head() ```