Add diabetic nephropathy and urothelial cancer case study vignettes

[Copilot]

Adds two comprehensive vignettes demonstrating real-world meta-analysis workflows for gene expression studies in diabetic nephropathy and urothelial cancer.

Vignettes Added

vignettes/case-diabetic-nephropathy.Rmd - Meta-analysis workflow covering:

• Multi-term search strategies via geo_search() (DN + DKD terminology)
• Metadata database construction with geo_meta()
• Quality filtering (sample size, platform type, study relevance)
• Visualization of temporal distribution, sample sizes, and platform usage
• Curated dataset preparation for downstream analysis

vignettes/case-urothelial-cancer.Rmd - Expression analysis workflow covering:

• Combined bladder/urothelial cancer dataset discovery
• Stringent filtering (≥20 samples, microarray only)
• Expression matrix retrieval via geo_matrix()
• Log2 transformation with log_trans()
• Exploratory analysis (PCA, expression distributions, correlation heatmaps)
• Cross-dataset integration considerations

Key Patterns Demonstrated

# Multi-strategy search with deduplication
dn_search_terms <- c(
  "diabetic nephropathy[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]",
  "diabetic kidney disease[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]"
)
dn_gse <- unique(dplyr::bind_rows(lapply(dn_search_terms, geo_search)))

# Metadata database construction
dn_metadb <- geo_meta(dn_gse_filtered[["Series Accession"]], odir = "dn_metadb")

# Expression data download and transformation
expression_sets <- lapply(gse_ids, function(id) geo_matrix(id, odir = tempdir()))
expression_sets_log <- lapply(expression_sets, log_trans)

Implementation Notes

• All download-dependent chunks use eval = FALSE to avoid build-time network access
• Search chunks use cache = TRUE for efficiency
• Uses only existing Suggests dependencies (added ggplot2, tidyr to DESCRIPTION)
• Backward compatible with older dplyr/ggplot2 versions (avoids if_any(), uses size not linewidth)
• GSE numbers used as chronological proxy where actual submission dates unavailable

Original prompt

Summary

Add two comprehensive case study vignettes demonstrating real-world applications of the geokit package for gene expression meta-analysis.

Vignettes to Add

1. Diabetic Nephropathy Meta-Analysis (vignettes/case-diabetic-nephropathy.Rmd)

This vignette demonstrates a meta-analysis workflow for diabetic nephropathy gene expression datasets:

• Search functionality: Use geo_search() with multiple query terms to identify DN datasets exclusively through NCBI E-Utils interface
• Build metadb: Fetch detailed metadata using geo_meta() to create a customized metadata database
• Filter and categorize: Apply criteria for platform type, sample size, and study relevance
• Visualization: Timeline of dataset submissions, sample size distributions, platform trends using ggplot2
• Prepare for meta-analysis: Curate list of datasets suitable for downstream integration

Key code patterns to include:

# Multiple search strategies
dn_search_terms <- c(
  "diabetic nephropathy[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]",
  "diabetic kidney disease[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]"
)
dn_gse_list <- lapply(dn_search_terms, geo_search)
dn_gse <- unique(dplyr::bind_rows(dn_gse_list))

# Build metadb
dn_metadb <- geo_meta(dn_gse_filtered[["Series Accession"]], odir = "dn_metadb")

2. Urothelial Cancer Example (vignettes/case-urothelial-cancer.Rmd)

This vignette demonstrates extracting, filtering, downloading, and analyzing urothelial cancer expression data:

• Dataset discovery: Search for bladder/urothelial cancer datasets
• Filtering: Select datasets based on sample size (≥20), platform type (microarray), and relevance
• Download: Retrieve expression matrices using geo_matrix()
• Data integration: Log2 transformation, handling multiple datasets
• Exploratory analysis:
  • Expression distribution boxplots
  • Principal Component Analysis (PCA)
  • Sample correlation heatmaps
  • Summary statistics
• Visualization: All plots using ggplot2 with publication-quality formatting

Key code patterns to include:

# Search and combine
uc_searches <- list(
  bladder = geo_search("bladder cancer[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]"),
  urothelial = geo_search("urothelial cancer[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]")
)
uc_gse <- unique(dplyr::bind_rows(uc_searches))

# Download and analyze
expression_sets <- lapply(demo_gse, function(gse_id) {
  geo_matrix(gse_id, odir = tempdir())
})

# Log transformation
expression_sets_log <- lapply(expression_sets, log_trans)

Technical Requirements

1. Both vignettes should follow the existing vignette style in the repository (see vignettes/geometadb.Rmd for reference)
2. Use standard R Markdown vignette headers with proper VignetteIndexEntry
3. Include knitr::opts_chunk$set() for consistent formatting
4. Use cache = TRUE for long-running chunks
5. Use eval = FALSE for code that requires actual downloads in non-demo sections
6. Include sessionInfo() at the end
7. Dependencies: geokit, dplyr, tidyr, stringr, ggplot2, patchwork (for combining plots)

File Locations

• vignettes/case-diabetic-nephropathy.Rmd
• vignettes/case-urothelial-cancer.Rmd

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