Comprehensive Database for Pathway Enrichment Analysis

Provides access to large-scale genomics data from the South Dakota State University's bioinformatics database, a unified platform for pathway analysis of over 13,000 organisms. It includes various gene mappings, gene characteristics, and pathway mapping data from KEGG, GOBP, GOCC, and many more pathway databases. Also provides various helper functions for processing RNA-Seq data for differential expression analysis and pathway enrichment analysis, occasionally sourced from code from Integrated Differential Expression & Pathway analysis (iDEP), developed by Ge, S.X., Son, E.W. & Yao, R. (2018) .

pathdb

pathdb is an R package designed to facilitate access to the South Dakota State University (SDSU) bioinformatics database (used in iDEP) and perform essential data preparation tasks for gene expression analysis.

It allows users to seamlessly retrieve species-specific gene and pathway information and process RNA-Seq data for downstream analysis using standard Bioconductor workflows.

Installation

You can install the development version of pathdb from GitHub:

# install.packages("remotes")
remotes::install_github("aidanfred24/pathdb")

Core Features

Centralized Database Access: Connect directly to the SDSU bioinformatics database to fetch comprehensive gene and pathway data.
Species Identification: Quickly determine if your organism of interest is supported using various search queries (common names, scientific names).
ID Standardization: Convert gene identifiers into standard Ensembl formats to ensure compatibility with differential expression or pathway enrichment analysis tools.
Data Pre-processing: Clean and transform raw RNA-Seq count matrices (e.g., handling missing values, filtering low counts, etc.) for differential expression analysis.

Quick Overview

Instead of walking through a complete data analysis pipeline (which you can find in our vignettes), here is a brief look at how pathdb can be used to access genomic information for your species of interest:

library(pathdb)

# 1. Check if your species is supported
species_info <- search_species(query = "Human", name_type = "primary")
human_id <- species_info$id[1] # ID is 96

# 2. Standardize gene IDs in your expression data
data(hypoxia_reads)
clean_data <- convert_id(
  genes = rownames(hypoxia_reads),
  data = hypoxia_reads,
  species_id = human_id
)

# 3. Process data for downstream analysis
processed_data <- process_data(
  data = clean_data,
  missing_value = "geneMedian",
  min_cpm = 0.5,
)

# 4. Retrieve pathways for your genes of interest
pathways <- get_pathways(
  species_id = human_id,
  genes = rownames(processed_data),
  category = "GOBP"
)

Vignettes and Documentation

For detailed, step-by-step tutorials on how to fully utilize pathdb, please refer to the package vignettes:

Database Access and Preparation: vignette("data-access", package = "pathdb")
Pathway Enrichment Analysis: vignette("path-enrichment", package = "pathdb")