GEOexplorer: a webserver for gene expression analysis and visualisation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35609980", "kind": "science-record", "revision": 5, "content_hash": "sha256:1d1c8d09ed19ddb18e472f24f187ed96a85a6edaf0bafe44e18ecb9102b7232f", "created_at": "2026-11-01T18:14:31Z", "updated_at": "2026-11-01T22:49:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "GEOexplorer: a webserver for gene expression analysis and visualisation.", "doi": "10.1093/nar/gkac364", "pmid": "35609980", "authors": ["Guy P Hunt", "Luigi Grassi", "Rafael Henkin", "Fabrizio Smeraldi", "Thomas P Spargo", "Renata Kabiljo", "Sulev Koks", "Zina Ibrahim", "Richard J B Dobson", "Ammar Al-Chalabi", "Michael R Barnes", "Alfredo Iacoangeli"], "journal": "Nucleic acids research", "year": 2022, "abstract": "Gene Expression Omnibus (GEO) is a database repository hosting a substantial proportion of publicly available high throughput gene expression data. Gene expression analysis is a powerful tool to gain insight into the mechanisms and processes underlying the biological and phenotypic differences between sample groups. Despite the wide availability of gene expression datasets, their access, analysis, and integration are not trivial and require specific expertise and programming proficiency. We developed the GEOexplorer webserver to allow scientists to access, integrate and analyse gene expression datasets without requiring programming proficiency. Via its user-friendly graphic interface, users can easily apply GEOexplorer to perform interactive and reproducible gene expression analysis of microarray and RNA-seq datasets, while producing a wealth of interactive visualisations to facilitate data exploration and interpretation, and generating a range of publication ready figures. The webserver allows users to search and retrieve datasets from GEO as well as to upload user-generated data and combine and harmonise two datasets to perform joint analyses. GEOexplorer, available at https://geoexplorer.rosalind.kcl.ac.uk, provides a solution for performing interactive and reproducible analyses of microarray and RNA-seq gene expression data, empowering life scientists to perform exploratory data analysis and differential gene expression analysis on-the-fly without informatics proficiency.", "claims": [{"id": "claim-method", "statement": "DGEA is done by fitting a linear model to each probe within the dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35609980", "locator": "sections[9] \"Generating and exploring the results of DGEA\" paragraph 7", "quote": "DGEA is done by fitting a linear model to each probe within the dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GEOexplorer webserver is available at the following URL https://geoexplorer.rosalind.kcl.ac.uk/. The GEOexplorer package can be downloaded from its Bioconductor page http://bioconductor.org/packages/GEOexplorer/ or the GitHub development page https://github.com/KHP-Informatics/GEOexplorer.\nDatasets used in the described example are available from (17-18), GEO Series ID: GSE106382 and GEO Series ID: GSE20589.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35609980", "locator": "sections[12] \"DATA AVAILABILITY\" paragraphs 1-3", "quote": "The GEOexplorer webserver is available at the following URL https://geoexplorer.rosalind.kcl.ac.uk/. The GEOexplorer package can be downloaded from its Bioconductor page http://bioconductor.org/packages/GEOexplorer/ or the GitHub development page https://github.com/KHP-Informatics/GEOexplorer.\nDatasets used in the described example are available from (17-18), GEO Series ID: GSE106382 and GEO Series ID: GSE20589."}], "state": "complete"}, {"id": "claim-target-result", "statement": "From our example, we can see there are 1,590 differentially expressed probes in the model SOD1 spinal motor neurons compared to the post-mortem SOD1 spinal motor neurons (Figure 3F-H).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35609980", "locator": "sections[9] \"Generating and exploring the results of DGEA\" paragraph 13", "quote": "From our example, we can see there are 1,590 differentially expressed probes in the model SOD1 spinal motor neurons compared to the post-mortem SOD1 spinal motor neurons (Figure 3F-H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "From our example, we can see there are 1,590 differentially expressed probes in the model SOD1 spinal motor neurons compared to the post-mortem SOD1 spinal motor neurons (Figure 3F-H).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35609980"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE106382"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE106382"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35609980", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/nar/gkac364", "sha256": "sha256:02160b9ddc707294d9b62ad73834507b9020286d8359ec7465d75e0938f6571c", "bytes": 33265, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/nar/gkac364", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9252785", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE106382", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE106382", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE106382", "version": null, "note": "The GEOexplorer webserver is available at the following URL https://geoexplorer.rosalind.kcl.ac.uk/. The GEOexplorer package can be downloaded from its Bioconductor page http://bioconductor.org/packages/GEOexplorer/ or the GitHub development page https://github.com/KHP-Informatics/GEOexplorer.\nDatasets used in the described example are available from (17-18), GEO Series ID: GSE106382 and GEO Series ID: GSE20589.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The GEOexplorer webserver is available at the following URL https://geoexplorer.rosalind.kcl.ac.uk/. The GEOexplorer package can be downloaded from its Bioconductor page http://bioconductor.org/packages/GEOexplorer/ or the GitHub development page https://github.com/KHP-Informatics/GEOexplorer.\nDatasets used in the described example are available from (17-18), GEO Series ID: GSE106382 and GEO Series ID: GSE20589.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2836934", "GSM2836935", "GSM2836936", "GSM2836937", "GSM2836938", "GSM2836939", "GSM2836940", "GSM2836941", "GSM2836942", "GSM2836943", "GSM2836944", "GSM2836945", "GSM2836946", "GSM2836947", "GSM2836948", "GSM2836949", "GSM2836950", "GSM2836951", "GSM2836952", "GSM2836953", "GSM2836954", "GSM2836955", "GSM2836956", "GSM2836957"], "sample_count": 24, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Expression data of iPSC-derived motor neurons from healthy donor, FALS, SALS, and drug-treated them", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE20589", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20589", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE20589", "version": null, "note": "The GEOexplorer webserver is available at the following URL https://geoexplorer.rosalind.kcl.ac.uk/. The GEOexplorer package can be downloaded from its Bioconductor page http://bioconductor.org/packages/GEOexplorer/ or the GitHub development page https://github.com/KHP-Informatics/GEOexplorer.\nDatasets used in the described example are available from (17-18), GEO Series ID: GSE106382 and GEO Series ID: GSE20589.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The GEOexplorer webserver is available at the following URL https://geoexplorer.rosalind.kcl.ac.uk/. The GEOexplorer package can be downloaded from its Bioconductor page http://bioconductor.org/packages/GEOexplorer/ or the GitHub development page https://github.com/KHP-Informatics/GEOexplorer.\nDatasets used in the described example are available from (17-18), GEO Series ID: GSE106382 and GEO Series ID: GSE20589.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM480304", "GSM480305", "GSM480306", "GSM480307", "GSM480308", "GSM480309", "GSM480310", "GSM517143", "GSM517144", "GSM517145"], "sample_count": 10, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Microarray analysis identifies the gene signature of surviving motor neurons in human SOD1-related motor neuron disease", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE106382 not downloaded", "asset:data:geo:GSE106382 licence not read", "asset:data:geo:GSE20589 not downloaded", "asset:data:geo:GSE20589 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "DGEA is done by fitting a linear model to each probe within the dataset."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "principal component analysis"}}, {"id": "method-3", "family": "differential_expression", "specific": "edger", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "edgeR"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE106382", "GSE20589"], "resolved_accessions": ["GSE106382", "GSE20589"], "statements": ["The GEOexplorer webserver is available at the following URL https://geoexplorer.rosalind.kcl.ac.uk/. The GEOexplorer package can be downloaded from its Bioconductor page http://bioconductor.org/packages/GEOexplorer/ or the GitHub development page https://github.com/KHP-Informatics/GEOexplorer.\nDatasets used in the described example are available from (17-18), GEO Series ID: GSE106382 and GEO Series ID: GSE20589."], "basis": "The paper names GSE106382, GSE20589 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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KNN imputation can be performed on microarray datasets to fill in missing values using the", "d. GEOexplorer will automatically source GEO microarray datasets and several formats of GEO RNA-seq"], "order_basis": "paper_text_order:825", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["e gene expression analysis of microarray and RNA-seq datasets, while producing a wealth of intera", "and reproducible analyses of microarray and RNA-seq gene expression data, empowering life scient", "/packages/release/bioc/html/impute.html, and RNA-seq datasets can be converted to counts per mill", "croarray datasets and several formats of GEO RNA-seq datasets. Users can also upload their own ge"], "order_basis": "paper_text_order:840", "id": "assay:2"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["DGEA is done by fitting a linear model to each probe within the dataset."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}