This vignette demonstrates a comprehensive meta-analysis workflow for diabetic nephropathy (DN) gene expression datasets. We show how to systematically search, filter, annotate, and visualize relevant GEO datasets to prepare for downstream meta-analysis.
Diabetic nephropathy is a major complication of diabetes and a
leading cause of chronic kidney disease. Conducting a meta-analysis of
gene expression studies can help identify robust molecular signatures.
This workflow demonstrates how to use geokit to:
Different researchers use varying terminology, so we query for both “diabetic nephropathy” and “diabetic kidney disease” and merge the results.
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)
#> → Parsing GEO records
#> → Parsing GEO records
dn_gse <- unique(dplyr::bind_rows(dn_gse_list))
cat(sprintf("Found %d unique GSE datasets\n", nrow(dn_gse)))
#> Found 133 unique GSE datasets
head(dn_gse[, 1:3])
#> Title
#> 1 Reduction of SIRPα connects podocyte metabolism dysfunction to inflammatory activation via promoting PKM2 nuclear translocation in diabetes nephropathy
#> 2 Transcriptional analyses reveal the molecular features that underlie established injury and disease progression in DKD
#> 3 Exercise Alleviates Diabetic Kidney Disease Through PPARδ-CPT1α pathway-dependent Fatty Acid β-oxidation
#> 4 Exercise Alleviates Diabetic Kidney Disease Through PPARδ-CPT1α pathway-dependent Fatty Acid β-oxidation [I]
#> 5 Single-cell RNA sequencing-based identification of novel podocyte injury-associated genes from isolated glomeruli of diabetic db/db mice [RNA-seq]
#> 6 Elevated glucose in kidney organoids induces tissue-intrinsic inflammation driving epithelial detachment
#> Summary
#> 1 A central cause of diabetic nephropathy (DN) is podocyte injury. There are many factors causing podocyte injury in DN, such as hyperglycemia, oxidative stress and inflammation. In this study, a signal regulator protein α (SIRPα) was found to play a critical role in regulating podocyte metabolism, oxidative stress and inflammatory.
#> 2 Diabetes is the leading cause of kidney failure in the developed world. Despite its importance, the molecular events driving the pathogenesis of diabetic kidney disease remain incompletely understood. Here we present a comprehensive exploration of the renal transcriptome in the largest cohort of patients with diabetic kidney disease to be transcriptionally profiled to date. Using RNA-Seq to profile 50 diabetic kidney and 13 healthy control biopsies, we link the renal transcriptome to baseline clinical and histologic parameters, as well as rates of kidney function loss over time. more...
#> 3 This SuperSeries is composed of the SubSeries listed below.
#> 4 Regular physical exercise has been regarded as an effective strategy in alleviating diabetic kidney disease, although the underlying mechanisms are only partially understood. In the present study, we provide evidence that physical exercise restores renal function in diabetic mice through enhancement of PPARδ-CPT1α-dependent fatty acid β-oxidation (FAO), highlight the importance of PPARδ during exercise, and uncover a previously unappreciated link between exercise and PPARδ-CPT1α-dependent FAO in restoring renal function of diabetic kidney disease, which could become new target for therapeutic intervention against diabetic kidney disease.
#> 5 Podocyte injury is a key driver of progression in diabetic kidney disease (DKD). Because podocytes are underrepresented in whole-kidney single-cell RNA sequencing datasets, we performed single-cell RNA sequencing of isolated glomeruli from diabetic db/db mice and control db/m mice to generate a glomerulus-focused transcriptomic dataset. Diabetic glomeruli showed reduced podocyte abundance and extensive transcriptional remodeling. more...
#> 6 Diabetic kidney disease involves hyperglycemia, inflammation, and epithelial cell dysfunction, but the relationship between these factors is not well understood. We demonstrate that human kidney organoids treated with elevated glucose exhibit a phenotype of epithelial detachment driven by tissue-intrinsic upregulation of proinflammatory cytokines, which can be targeted therapeutically. High glucose induces morphological deterioration of organoids featuring podocyte and tubular cell detachment without cytotoxicity. more...
#> Organism
#> 1 Homo sapiens
#> 2 Homo sapiens
#> 3 Mus musculus; Homo sapiens
#> 4 Homo sapiens
#> 5 Homo sapiens
#> 6 Homo sapiensExtract the number of samples from the Contains field to
enable filtering by sample size. This count covers every sample in a
series regardless of group — cases, controls, and technical replicates —
so treat it as an upper bound; group sizes and balance must be verified
from sample annotations before a dataset is included in a
meta-analysis.
Apply filters for sample size and study type to focus on candidate expression profiling studies. Note that this filter admits both microarray and RNA-seq records (“expression profiling by high throughput sequencing”); intensity- and count-based studies cannot be pooled directly, so process them separately or tighten the filter to arrays for a single integrated analysis.
dn_gse_filtered <- dn_gse |>
dplyr::filter(
# At least 6 samples for meaningful analysis
number_of_samples >= 6,
# Focus on expression profiling studies
str_detect(Type, regex("expression profiling", ignore_case = TRUE)),
# Exclude methylation studies (defensive: no current GEO Type string
# passes the filters above, but this guards against future variants)
!str_detect(Type, regex("methylation", ignore_case = TRUE)),
# Exclude compound SuperSeries so each study has a single assay type
!str_detect(Type, ";")
)
cat(sprintf(
"After filtering: %d datasets (%.1f%% of original)\n",
nrow(dn_gse_filtered),
100 * nrow(dn_gse_filtered) / nrow(dn_gse)
))
#> After filtering: 89 datasets (66.9% of original)Fetch detailed metadata for the filtered datasets using
geo_meta(). This creates a local database for offline
analysis and detailed inspection. The download may take several minutes
depending on the number of datasets; if you have an NCBI API key, set it
as the ENTREZ_KEY environment variable to speed up
searching.
dn_metadb_dir <- "dn_metadb"
dir.create(dn_metadb_dir, showWarnings = FALSE, recursive = TRUE)
dn_metadb <- geo_meta(
dn_gse_filtered[["Series Accession"]],
odir = dn_metadb_dir
)
# Save for future use
saveRDS(dn_metadb, file.path(dn_metadb_dir, "dn_metadb.rds"))In a later session, reload the saved database and calculate the actual sample counts from the sample identifier list:
Categorize datasets by criteria relevant to diabetic nephropathy research, searching the title, summary, and overall design fields of each series. These keyword assignments are screening heuristics only — inspect each candidate’s design and sample annotations (tissue, cell type, group definitions) before final inclusion, as keyword matches can also pick up cell-line or animal studies.
dn_metadb <- dn_metadb |>
dplyr::mutate(
search_text = paste(Series_title, Series_summary, Series_overall_design),
has_nephropathy = str_detect(
search_text, regex("nephropath", ignore_case = TRUE)
),
has_kidney = str_detect(
search_text, regex("kidney|renal", ignore_case = TRUE)
),
tissue_type = dplyr::case_when(
str_detect(search_text, regex("glomerul", ignore_case = TRUE)) ~
"glomerular",
str_detect(search_text, regex("tubul", ignore_case = TRUE)) ~ "tubular",
str_detect(search_text, regex("kidney|renal", ignore_case = TRUE)) ~
"kidney",
TRUE ~ "other"
)
)
table(dn_metadb$tissue_type)GSE accession numbers are assigned chronologically, so they can serve
as a time proxy for when studies were submitted. Depending on the
records, the SOFT metadata may also provide explicit dates (e.g.,
Series_submission_date) that you can parse instead.
dn_metadb <- dn_metadb |>
dplyr::mutate(
gse_number = as.integer(str_extract(Series_geo_accession, "\\d+"))
)
ggplot(dn_metadb, aes(x = gse_number)) +
geom_histogram(bins = 30, fill = "steelblue", color = "white") +
labs(
title = "Distribution of DN Datasets by GEO Accession Number",
subtitle = "GSE numbers are assigned chronologically (lower = older)",
x = "GSE Number",
y = "Number of Datasets"
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5, size = 9),
panel.grid.minor = element_blank()
)ggplot(dn_metadb, aes(x = number_of_samples)) +
geom_histogram(binwidth = 5, fill = "coral", color = "white") +
geom_vline(
xintercept = median(dn_metadb$number_of_samples, na.rm = TRUE),
linetype = "dashed", color = "red", linewidth = 1
) +
labs(
title = "Distribution of Sample Sizes in DN Datasets",
subtitle = sprintf(
"Median: %s samples",
format(median(dn_metadb$number_of_samples, na.rm = TRUE))
),
x = "Number of Samples",
y = "Number of Datasets"
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5),
panel.grid.minor = element_blank()
)platform_summary <- dn_metadb |>
dplyr::count(Series_platform_id, sort = TRUE) |>
dplyr::slice_max(n, n = 10)
ggplot(platform_summary, aes(x = reorder(Series_platform_id, n), y = n)) +
geom_col(fill = "darkgreen", alpha = 0.7) +
geom_text(aes(label = n), hjust = -0.2, size = 3.5) +
coord_flip() +
labs(
title = "Top 10 Platforms Used in DN Studies",
x = "Platform ID",
y = "Number of Studies"
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, face = "bold"),
panel.grid.major.y = element_blank()
)Create a final curated list of datasets suitable for meta-analysis.
dn_curated <- dn_metadb |>
dplyr::filter(
number_of_samples >= 10,
has_nephropathy | has_kidney
) |>
dplyr::arrange(desc(number_of_samples)) |>
dplyr::select(
Series_geo_accession,
Series_title,
number_of_samples,
Series_platform_id,
tissue_type
)
head(dn_curated, 10)
write.csv(
dn_curated,
file.path(dn_metadb_dir, "dn_curated_datasets.csv"),
row.names = FALSE
)With this curated list, you can proceed to:
geo_matrix() to download series matrix filestop_gse <- dn_curated$Series_geo_accession[1]
eset <- geo_matrix(top_gse, odir = tempdir())
print(eset)
dim(Biobase::exprs(eset))Instead of manually inspecting the curated list, you can also hand it
to an LLM for interactive exploration with geo_chat(); see
vignette("geo-chat") for details.
This workflow demonstrated how to:
geo_meta()The systematic approach supports reproducibility and helps identify the most suitable datasets for gene expression meta-analysis in diabetic nephropathy research.
sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
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#> time zone: Etc/UTC
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#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ggplot2_4.0.3 stringr_1.6.0 dplyr_1.2.1 geokit_0.0.2 rmarkdown_2.32
#>
#> loaded via a namespace (and not attached):
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#> [37] farver_2.1.2 codetools_0.2-20 buildtools_1.0.0 httr_1.4.9
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