The NCBI uses a search term syntax which can be associated with a
specific search field enclosed by a pair of square brackets. So, for
instance "Homo sapiens[ORGN]" denotes a search for
Homo sapiens in the "Organism" field. Details
see https://www.ncbi.nlm.nih.gov/geo/info/qqtutorial.html.
We can use the same term to query our desirable results in
geo_search(). geo_search() will parse the
searching results and return a data.frame object containing
all the records based on the search term. The internal of
geo_search() is based on rentrez
package, which provides functions working with the NCBI Eutils API,
so we can utilize NCBI API key to increase the searching
speed, details see https://docs.ropensci.org/rentrez/articles/rentrez_tutorial.html#rate-limiting-and-api-keys.
Providing we want GSE GEO records related
to human diabetes, we can get these records by
following code, the returned object is a data.frame:
diabetes_gse_records <- geo_search(
"diabetes[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]"
)
#> ■■■■■■■■■ 500/1911 [366/s] | ETA: 4s
#> ■■■■■■■■■■■■■■■■■■■■■■■■■ 1500/1911 [413/s] | ETA: 1s
#> Get records from NCBI for 1911 queries in 4.8s
#>
#> → Parsing GEO records
head(diabetes_gse_records[1:5])
#> Title
#> 1 Transcriptional analyses reveal the molecular features that underlie established injury and disease progression in DKD
#> 2 Epigenome-Wide Association Study and functional analyses reveal regulatory loci linked to lung function and diabetes in cystic fibrosis
#> 3 A Transcriptional Atlas Defining Metabolically Healthy and Unhealthy Obesity in Human Adipose Tissue and Liver [bulk RNA-seq]
#> 4 Exercise Alleviates Diabetic Kidney Disease Through PPARδ-CPT1α pathway-dependent Fatty Acid β-oxidation
#> 5 Exercise Alleviates Diabetic Kidney Disease Through PPARδ-CPT1α pathway-dependent Fatty Acid β-oxidation [I]
#> 6 Donor-derived CD8+CD122+ Tregs generated in mixed donor chimeric NOD mice delete autoreactive T cells
#> Summary
#> 1 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...
#> 2 We carried out an epigenome-wide association study (EWAS) in whole blood samples from patients with Cystic Fibrosis. Whole blood samples were collected from 45 patients with cystic fibrosis and 19 healthy controls and DNA methylation was profiled using human methylation BeadChips (EPIC). We identified differentially methylated CpG sites that correlated with the disease, lung function (FEV1pp), BMI and CFTR-related diabetes. more...
#> 3 A sub-population of individuals with obesity do not exhibit metabolic disease independent of absolute adiposity, termed metabolically ‘healthy’ obesity. However, the biological factors conferring this phenotype are incompletely understood. We recruited 58 individuals with obesity with a clinical diagnosis of type-II diabetes mellitus (T2D) and glycated haemoglobin (HbA1c) ≥5.7% (Ob+T2D; n=21) or without T2D and HbA1c <5.7% (Ob; n=37). more...
#> 4 This SuperSeries is composed of the SubSeries listed below.
#> 5 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.
#> 6 The establishment of mixed hematopoietic chimerism is a promising way to induce immune tolerance for islet replacement therapy and to treat the underlying autoimmunity in Type 1 diabetes (T1D). Mixed chimerism not only promotes effective thymic negative selection of autoreactive cells but also restores regulatory T cell (Treg) function and peripheral tolerance. In the current study, we determined that a novel class of donor-derived CD8+CD44+CD122+ Tregs (d-CD8+CD122+ Tregs) plays a crucial role in controlling autoimmunity in non-obese diabetic (NOD) mice with induced mixed chimerism. more...
#> Organism
#> 1 Homo sapiens
#> 2 Homo sapiens
#> 3 Homo sapiens
#> 4 Homo sapiens; Mus musculus
#> 5 Homo sapiens
#> 6 Homo sapiens; Mus musculus
#> Type
#> 1 Expression profiling by high throughput sequencing
#> 2 Methylation profiling by array
#> 3 Expression profiling by high throughput sequencing
#> 4 Expression profiling by high throughput sequencing
#> 5 Expression profiling by high throughput sequencing
#> 6 Other; Expression profiling by high throughput sequencing
#> FTP download
#> 1 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE273nnn/GSE273763/
#> 2 GEO (IDAT, RDATA) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE326nnn/GSE326520/
#> 3 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE278nnn/GSE278525/
#> 4 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE245nnn/GSE245570/
#> 5 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE245nnn/GSE245393/
#> 6 GEO (TSV, TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE338nnn/GSE338315/Once you have the search results, you can filter them based on specific criteria. For instance, to filter for GSE datasets that contain at least 6 diabetic nephropathy samples with expression profiling, use the following code:
diabetes_nephropathy_gse_records <- diabetes_gse_records |>
dplyr::mutate(
number_of_samples = str_match(Contains, "(\\d+) Samples?")[
, 2L,
drop = TRUE
],
number_of_samples = as.integer(number_of_samples)
) |>
dplyr::filter(
dplyr::if_any(
c(Title, Summary),
~ str_detect(.x, regex("diabetes|diabetic", ignore_case = TRUE))
),
dplyr::if_any(
c(Title, Summary),
~ str_detect(.x, regex("nephropathy", ignore_case = TRUE))
),
str_detect(Type, regex("expression profiling", ignore_case = TRUE)),
number_of_samples >= 6L
)
head(diabetes_nephropathy_gse_records[1:5, 1:5])
#> Title
#> 1 Single-nucleus transcriptomic landscape of human glomerular diseases [10x Genomics]
#> 2 DJ-1 alleviates high glucose-induced podocyte injury via activating ERK1/2 signaling
#> 3 RBBP6 orchestrates diabetic endothelial dysfunction viadisrupting JUNB-centric chromatin topology
#> 4 Integrative RNA-seq and CLIP-seq analysis reveals hnRNP-F regulation of the TNFα/NFκB signaling in high glucose conditions
#> 5 Effect of FGF9 on human renal tubular epithelial cells in high glucose culture
#> Summary
#> 1 Disease-specific similarities and differences in single-cell-level transcriptomic alterations in various glomerular diseases are necessary to understand the pathophysiology of kidney glomerular diseases. We analyzed single-nucleus RNA sequencing (snRNA-seq) data from 262,984 nuclei from kidney biopsy cores from 28 subjects with glomerular disease, including minimal change disease, membranous nephropathy, IgA nephropathy, and diabetic kidney disease and control individuals. more...
#> 2 Diabetic nephropathy (DN) is one of the most common complications of diabetes. DJ-1 has been reported to participate in the response to renal ischemia/reperfusion (I/R) injury. However, the underlying mechanisms of DJ-1 in the regulation of high glucose-induced renal injury remain obscure. In this study, we performed RNA-seq to explore the function of high glucose on human podocyte cells (HPC), and found that high glucose widely regulated a variety of signaling pathways, including cell growth and death, signal transduction, etc. more...
#> 3 Diabetes mellitus, a chronic metabolic disease affecting over 536.6 million people globally, is closely associated with vascular endothelial dysfunction, an early hallmark of diabetic cardiovascular complications. This dysfunction is characterized by impaired endothelial nitric oxide synthase (eNOS) activity, reduced nitric oxide (NO) production, and diminished angiogenic capacity, ultimately contributing to tissue ischemia and complications such as diabetic nephropathy, coronary artery disease, and peripheral arterial disease. more...
#> 4 Using RNA-seq and ChIP-seq we found that we found that hnRNP-F may bind to lncRNA SNHG1 to negatively regulate the transcription of genes involved in the TNFα/NFκB signaling pathway in diabetic nephropathy. Our study suggests that hnRNP-F may play a role in diabetic nephropathy by regulating the differential expression and variable splicing of diabetic nephropathy-associated genes, especially those related to inflammatory response.
#> 5 Diabetic nephropathy is characterised by the accumulation of extracellular matrix in the glomerular tunica and tubular interstitium, which ultimately leads to excessive renal scarring and decreased excretory function. The main pathological changes of renal fibrosis are: glomerulosclerosis, tubulointerstitial fibrosis, infiltration of inflammatory mediators and activation of α-SMA-positive myofibroblasts. more...
#> Organism
#> 1 Homo sapiens
#> 2 Homo sapiens
#> 3 Mus musculus; Homo sapiens
#> 4 Homo sapiens
#> 5 Homo sapiens
#> Type
#> 1 Expression profiling by high throughput sequencing
#> 2 Expression profiling by high throughput sequencing
#> 3 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other
#> 4 Expression profiling by high throughput sequencing
#> 5 Expression profiling by high throughput sequencing
#> FTP download
#> 1 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE271nnn/GSE271188/
#> 2 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE307nnn/GSE307956/
#> 3 GEO (BIGWIG, BW, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE291nnn/GSE291636/
#> 4 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE273nnn/GSE273001/
#> 5 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE265nnn/GSE265918/After applying the filter, we obtain 41 candidate datasets. This filtering step significantly reduces the time spent manually reviewing summary records.
You can also use geo_meta() to dynamically create a
self-knowledge-concerned database in real-time. See
vignette("geometadb") for details.
sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] stringr_1.6.0 geokit_0.0.2 rmarkdown_2.32
#>
#> loaded via a namespace (and not attached):
#> [1] jsonlite_2.0.0 dplyr_1.2.1 compiler_4.6.1
#> [4] tidyselect_1.2.1 Biobase_2.73.2 xml2_1.6.0
#> [7] rentrez_1.2.4 jquerylib_0.1.4 yaml_2.3.12
#> [10] fastmap_1.2.0 R6_2.6.1 generics_0.1.4
#> [13] curl_8.0.0 knitr_1.52 BiocGenerics_0.59.12
#> [16] XML_3.99-0.25 tibble_3.3.1 maketools_1.3.2
#> [19] bslib_0.12.0 pillar_1.11.1 rlang_1.3.0
#> [22] cachem_1.1.0 stringi_1.8.9 xfun_0.61
#> [25] sass_0.4.10 sys_3.4.3 otel_0.2.0
#> [28] cli_3.6.6 withr_3.0.3 magrittr_2.0.5
#> [31] digest_0.6.39 lifecycle_1.0.5 vctrs_0.7.3
#> [34] evaluate_1.0.5 glue_1.8.1 codetools_0.2-20
#> [37] buildtools_1.0.0 httr_1.4.9 tools_4.6.1
#> [40] pkgconfig_2.0.3 htmltools_0.5.9