Day 3 - Module 11 Gene Annotations

1 [Breakout Exercise 2] - Finding annotations

15 Minutes

1.0.1 Instructions:

• One group member should share their screen in the breakout room. If nobody volunteers, a helper may randomly select someone.
• The group members should discuss the exercise and work together to find a solution.
• If there is time after a solution is found, allow all members to complete the exercise.

• Add the gene symbols stored in the GeneKey object to the DE results table res_WT and store the new table in an object named res_WT_anno.

Hint:

Look at the documentation for either the merge function or the tidyverse join function for ideas on how to combine the GeneKey and res_WT tables

## check the biomaRt key we created to remember the structure
head(GeneKey)

##      ensembl_gene_id external_gene_name
## 1 ENSMUSG00000000001              Gnai3
## 2 ENSMUSG00000000028              Cdc45
## 3 ENSMUSG00000000031                H19
## 4 ENSMUSG00000000037              Scml2
## 5 ENSMUSG00000000049               Apoh
## 6 ENSMUSG00000000056               Narf

Click for solution for adding annotations

First, create a new table called res_WT_anno that includes a column with the ENSEMBL ids named genes using the mutate function. Then use the left_join function to combine the GeneKey table with the res_WT DE results.

res_WT_anno <- as.data.frame(res_WT) %>%
  mutate(genes = row.names(res_WT)) %>%
  left_join(GeneKey, by =c("genes" = "ensembl_gene_id")) %>%
  relocate(c("genes", "external_gene_name")) # optionally, re-order columns to make output more readable

head(res_WT_anno)

##                genes external_gene_name    baseMean log2FoldChange      lfcSE        stat
## 1 ENSMUSG00000000001              Gnai3 6255.632164   -0.014024321 0.09301588 -0.15077341
## 2 ENSMUSG00000000028              Cdc45 1337.874474    0.522421732 0.13599465  3.84148730
## 3 ENSMUSG00000000031                H19    3.773571   -1.156597290 1.60080258 -0.72251088
## 4 ENSMUSG00000000037              Scml2   27.563275   -0.279611867 0.47655119 -0.58674046
## 5 ENSMUSG00000000049               Apoh    2.256350    4.010718329 1.88912289  2.12305846
## 6 ENSMUSG00000000056               Narf 2194.251314   -0.008544091 0.17722204 -0.04821122
##         pvalue        padj
## 1 0.8801544640 0.960702559
## 2 0.0001222911 0.003948693
## 3 0.4699804359          NA
## 4 0.5573780290 0.804647138
## 5 0.0337489531          NA
## 6 0.9615479086 0.987888734

Alternatively, if you are more familiar with base functions:

res_WT_anno <- res_WT # copy table
res_WT_anno <- cbind(genes=row.names(res_WT_anno), res_WT_anno[ ,c(1:6)])
res_WT_anno <- as.data.frame(res_WT_anno)

# combine the two tables using the merge function (similar to join from `tidyverse`)
res_WT_anno <- merge(GeneKey, res_WT_anno, by.x = "ensembl_gene_id", by.y="genes", all.x = FALSE, all.y = TRUE)

head(res_WT_anno)

##      ensembl_gene_id external_gene_name    baseMean log2FoldChange      lfcSE        stat
## 1 ENSMUSG00000000001              Gnai3 6255.632164   -0.014024321 0.09301588 -0.15077341
## 2 ENSMUSG00000000028              Cdc45 1337.874474    0.522421732 0.13599465  3.84148730
## 3 ENSMUSG00000000031                H19    3.773571   -1.156597290 1.60080258 -0.72251088
## 4 ENSMUSG00000000037              Scml2   27.563275   -0.279611867 0.47655119 -0.58674046
## 5 ENSMUSG00000000049               Apoh    2.256350    4.010718329 1.88912289  2.12305846
## 6 ENSMUSG00000000056               Narf 2194.251314   -0.008544091 0.17722204 -0.04821122
##         pvalue        padj
## 1 0.8801544640 0.960702559
## 2 0.0001222911 0.003948693
## 3 0.4699804359          NA
## 4 0.5573780290 0.804647138
## 5 0.0337489531          NA
## 6 0.9615479086 0.987888734

Notice that not all genes were annotated with an ENSEMBl gene id or gene description. While we are able to annotate our results, this is a helpful reminder that gene symbol is often not a good unique identifier.