README.md

In clemenshug/enrichr.db: Enrichr database of gene lists

enrichr.db

The goal of enrichr.db is to provide the data from Enrichr in a format that makes it easy to access the gene sets programmatically from R.

The current Enrichr release included is from 2019-01-23.

Installation

You can install the released version of enrichr.db from Github with:

if (!requireNamespace("remotes", quietly = TRUE))
  install.packages("remotes")
remotes::install_url("https://github.com/labsyspharm/enrichr.db/releases/download/v0.1/enrichr.db_0.1.tar.gz")

Example

This is a basic example which shows how to query the gene set database and use the gene sets for enrichment analysis using fgsea.

Querying the database

library(tidyverse)
#> ── Attaching packages ───────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 3.2.0     ✔ purrr   0.3.2
#> ✔ tibble  2.1.3     ✔ dplyr   0.8.2
#> ✔ tidyr   0.8.3     ✔ stringr 1.4.0
#> ✔ readr   1.3.1     ✔ forcats 0.4.0
#> ── Conflicts ──────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
library(enrichr.db)

# Finding all libraries that have something to do with drug treatments
drug_gene_sets <- enrichr_terms %>%
  filter(grepl("drug", library, ignore.case = TRUE))

This gives a data frame of gene set libraries. The gene sets are in the data column of the data frame and are implemented as lists. Each list element is a gene set and contains a vector of HGCN gene symbols.

| library | year | n_terms | gene_coverage | n_genes_per_term | category | description | data | | :------------------------------------------------------- | :--- | -------: | -------------: | ------------------: | :------------- | :------------------------------------------------------------------- | :----------------- | | Drug_Perturbations_from_GEO_2014 | 2014 | 701 | 47107 | 509 | Legacy | {1} is differentially expressed when the cell is perturbed with {0}. | \<701 gene sets> | | Drug_Perturbations_from_GEO_down | NA | 906 | 23877 | 302 | Crowd | {0} is downregulated by {1}. | \<906 gene sets> | | Drug_Perturbations_from_GEO_up | NA | 906 | 24350 | 299 | Crowd | {0} is upregulated by {1}. | \<906 gene sets> | | DrugMatrix | NA | 7876 | 5209 | 300 | Diseases/Drugs | {0} is differentially expressed in sample {1} | \<7876 gene sets> | | RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO | NA | 1302 | 22440 | 505 | Crowd | {0} is differentially expressed in {1}. | \<1302 gene sets> |

We can have a look a the first five gene sets in the Drug_Perturbations_from_GEO_2014 library. We can print the first 10 genes of each gene set.

print(
  # Drug_Perturbations_from_GEO_2014 is the first library
  drug_gene_sets$data[[1]] %>%
    head(n = 5) %>%
    map(head, n = 10)
)
#> $`estradiol_mus musculus_gpl339_gse16854_chdir_down`
#>  [1] "FZD2"   "ANXA2"  "ELOVL5" "TPI1"   "RPS6"   "MIF"    "HSPA4" 
#>  [8] "CYP7B1" "RPSA"   "TGFBR2"
#> 
#> $`letrozole_homo sapiens_gpl3921_gse33366_chdir_down`
#>  [1] "CKMT1A"  "MX2"     "PSMB9"   "SCAMP4"  "SCD"     "PBEF1"   "OPTN"   
#>  [8] "CYFIP2"  "GPATCH2" "ASAH1"  
#> 
#> $`mifepristone_homo sapiens_gpl6947_gse39654_chdir_down`
#>  [1] "ACSL3"     "RPL21"     "ACSL1"     "RPL23"     "CKMT1A"   
#>  [6] "CNBP"      "DEK"       "HSPE1"     "RPL13AP20" "SCD"      
#> 
#> $`tamoxifen_homo sapiens_gpl96_gds2367_chdir_down`
#>  [1] "UBE2C"    "HUWE1"    "HN1"      "HNRNPAB"  "RPSA"     "C20ORF24"
#>  [7] "CDC20"    "RAB31"    "PPIA"     "SCD"     
#> 
#> $`fluorouracil_homo sapiens_gpl550_zr-75-1_gds1627_chdir_up`
#>  [1] "DUSP4"   "NME3"    "AKR1A1"  "TUBB2A"  "AKR7A2"  "PPP1CB"  "MRPL23" 
#>  [8] "TMEM258" "TMCO6"   "KRT19"

Enrichment analysis

We can use the gene sets in the data column directly for gene set enrichment analysis using fgsea. Again, we’re using the first library that we found (Drug_Perturbations_from_GEO_2014).

library(fgsea)
#> Loading required package: Rcpp

# Making random fake expression results
genes <- Reduce(union, drug_gene_sets$data[[1]])
expression_res <- setNames(
  rnorm(length(genes), 0, 0.5),
  genes
)

enrichment_res <- fgseaMultilevel(
  pathways = drug_gene_sets$data[[1]],
  stats = expression_res,
  sampleSize = 101,
  minSize = 15
)

| pathway | pval | padj | log2err | ES | NES | size | | :------------------------------------------------------------------------------------- | --------: | --------: | --------: | ----------: | ---------: | ---: | | temozolomide_homo sapiens_gpl10558_gse43452_chdir_down | 0.0001062 | 0.0721128 | 0.5384341 | -0.3282901 | -1.641434 | 203 | | probucol_mus musculus_gpl9525_gds3618_chdir_down | 0.0003863 | 0.1311507 | 0.4984931 | 0.3805265 | 1.790300 | 111 | | calcitriol_homo sapiens_gpl570_gse35925_chdir_down | 0.0011894 | 0.2098985 | 0.4550599 | -0.3263342 | -1.562751 | 144 | | troglitazone_rattus norvegicus_gpl341_skeletal muscle_gds3850_chdir_up | 0.0012365 | 0.2098985 | 0.4550599 | -0.3995772 | -1.738634 | 79 | | alitretinoin_rattus norvegicus_gpl85_mammary gland_gds2385_chdir_down | 0.0257551 | 0.5797157 | 0.3524879 | -0.3982703 | -1.524028 | 42 | | amoxicillin_rattus norvegicus_gpl341_proximal small intestine_gds1273_chdir_down | 0.0149071 | 0.5797157 | 0.3807304 | 0.3562649 | 1.534098 | 69 |

Funding

We gratefully acknowledge support by NIH Grant 1U54CA225088-01: Systems Pharmacology of Therapeutic and Adverse Responses to Immune Checkpoint and Small Molecule Drugs.

clemenshug/enrichr.db documentation built on July 23, 2019, 8:08 a.m.