• AnnotationHub is web resource of reference materials maintained by the Bioconductor community
• It provides a central location from where genomic reference data (e.g., FASTA, GTF, bed) can be downloaded
• It included references from standard locations (e.g., UCSC, Ensembl)
• The resource includes metadata about each resource e.g., a textual description, tags, and date of modification
• The AnnotationHub package creates and manages a local cache of files, helping with quick and reproducible access.

• Create an annotationhub instance

library(AnnotationHub)

ah <- AnnotationHub()
ah

## AnnotationHub with 67229 records
## # snapshotDate(): 2022-04-25
## # $dataprovider: Ensembl, BroadInstitute, UCSC, ftp://ftp.ncbi.nlm.nih.gov/g...
## # $species: Homo sapiens, Mus musculus, Drosophila melanogaster, Bos taurus,...
## # $rdataclass: GRanges, TwoBitFile, BigWigFile, EnsDb, Rle, OrgDb, ChainFile...
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## #   rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["AH5012"]]' 
## 
##              title                                                         
##   AH5012   | Chromosome Band                                               
##   AH5013   | STS Markers                                                   
##   AH5014   | FISH Clones                                                   
##   AH5015   | Recomb Rate                                                   
##   AH5016   | ENCODE Pilot                                                  

• Query AnnotationHub to find the resource we need

ahQueryResult <- query(ah, c("EnsDb", "Mus musculus", "102"))
ahQueryResult

## AnnotationHub with 1 record
## # snapshotDate(): 2023-04-25
## # names(): AH89211
## # $dataprovider: Ensembl
## # $species: Mus musculus
## # $rdataclass: EnsDb
## # $rdatadateadded: 2020-10-27
## # $title: Ensembl 102 EnsDb for Mus musculus
## # $description: Gene and protein annotations for Mus musculus based on Ensem...
## # $taxonomyid: 10090
## # $genome: GRCm38
## # $sourcetype: ensembl
## # $sourceurl: http://www.ensembl.org
## # $sourcesize: NA
## # $tags: c("102", "AHEnsDbs", "Annotation", "EnsDb", "Ensembl", "Gene",
## #   "Protein", "Transcript") 
## # retrieve record with 'object[["AH89211"]]'

• Retrieve the data base and extract gene annotations

MouseEnsDb <- ahQueryResult[[1]]
annotations <- genes(MouseEnsDb, return.type = "data.frame")

colnames(annotations)

##  [1] "gene_id"              "gene_name"            "gene_biotype"        
##  [4] "gene_seq_start"       "gene_seq_end"         "seq_name"            
##  [7] "seq_strand"           "seq_coord_system"     "description"         
## [10] "gene_id_version"      "canonical_transcript" "symbol"              
## [13] "entrezid"

• Select relevant columns and join to DESeq2 results table

annot <- annotations %>%
  select(GeneID = gene_id, Symbol = symbol, Description = description)

annot.interaction.11 <- results.interaction.11 %>%
  as.data.frame() %>% 
  rownames_to_column("GeneID") %>% 
  left_join(annot, by="GeneID")
head(annot.interaction.11)

## # A tibble: 20,091 × 9
##    GeneID           baseMean log2FoldChange  lfcSE   stat  pvalue    padj Symbol
##    <chr>               <dbl>          <dbl>  <dbl>  <dbl>   <dbl>   <dbl> <chr> 
##  1 ENSMUSG00000000…  1103.           -0.164 0.141  -1.16  2.45e-1 0.688   Gnai3 
##  2 ENSMUSG00000000…    58.6           0.427 0.393   1.09  2.77e-1 0.719   Cdc45 
##  3 ENSMUSG00000000…    49.2          -0.238 0.627  -0.380 7.04e-1 0.929   Scml2 
##  4 ENSMUSG00000000…     7.99          0.819 0.974   0.841 4.00e-1 0.803   Apoh  
##  5 ENSMUSG00000000…  1981.            0.128 0.0998  1.29  1.99e-1 0.640   Narf  
## # ℹ 20,086 more rows
## # ℹ 1 more variable: Description <chr>

Examples of expected overall distribution

1. anti-conservative p-values (“Hooray!”)
2. very low counts genes usually have large p-values
3. do not expect positive tests after correction

Examples of unexpected overall distribution

1. indicates a batch effect (confounding hidden variables)
2. the test statistics may be inappropriate (due to strong correlation structure for instance)
3. discrete distribution of p-values !!!????!?!?!

• Venn diagram showing overlap between differentially expressed genes for different contrasts