Excel · study · 2026
A Versatile Distance-Based Approach for Gene Expression Selection Across Diverse Biological Systems
Listed in NCBI GEO
Differential gene expression analysis is essential for characterizing immune cell phenotypes, yet conventional approaches—typically based on log₂ fold‑change (log₂FC) and False Discovery Rate (FDR) thresholds—often struggle to capture the complexity and continuum of transcriptional states.
Description
To address this limitation, we developed a new computational method for gene selection from mRNA‑seq data: the Cartesian Distance‑Based Gene Expression (CDBGE) selector.
This algorithm identifies differentially expressed genes by leveraging multidimensional expression distances rather than relying on traditional univariate statistical cutoffs, enabling a more refined and biologically coherent gene‑marker selection. We applied the CDBGE selector to construct a gene‑based framework for distinguishing macrophage polarization states. The model was trained using publicly available macrophage transcriptomic datasets and subsequently validated with in vitro human macrophages stimulated with IFN‑γ/LPS, conditioned medium from HepG2 liver cancer cells, or IL10.
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To evaluate its generalizability beyond macrophage biology, we further tested the method on human embryonic stem cell differentiation datasets. Compared with standard differential expression pipelines, the CDBGE selector more effectively identified subtype‑specific markers and revealed dynamic transcriptional transitions over time. These findings demonstrate that distance‑based gene classification provides an improved strategy for analyzing complex mRNA‑seq datasets.
Overall, the CDBGE selector offers a robust, scalable, and broadly applicable tool for differential gene expression analysis and phenotype characterization. Overall design Transcriptomic RNA-seq from Human macrophages stimulated with IFN‑gamma/LPS, conditioned medium from HepG2 liver cancer cells, or IL10 at different times.
Links
Get the data
- GEO FTP directory ftp.ncbi.nlm.nih.gov/geo/series/GSE331nnn/GSE331143 ↗
download · from NCBI GEO
Where it is published
- GEO accession page ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE331143 ↗
landing page · from NCBI GEO
Documentation and papers
- PRJNA1466963 ncbi.nlm.nih.gov/bioproject/PRJNA1466963 ↗
project · from NCBI GEO
- PubMed 42516394 pubmed.ncbi.nlm.nih.gov/42516394 ↗
publication · from NCBI GEO
Topics
- Stated by source
- Expression profiling by high throughput sequencing · Homo sapiens
- From keywords
- Life Sciences
- Inferred from text
- Cancer 75% · RNA sequencing 65%
Provenance · 1 source records, 9 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| NCBI GEO | GSE331143 | 14 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · NCBI GEO | connector:ncbi_geo@1.0.0 | |
| concepts[disease].local:disease:cancer | enrichment · NCBI GEO | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].local:field:life-sciences | mapping · NCBI GEO | connector:ncbi_geo@1.0.0 | |
| concepts[method].geo_series_type:expression-profiling-by-high-throughput-sequencing | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /gdstype |
| concepts[modality].local:modality:rna-seq | enrichment · NCBI GEO | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[organism].NCBITaxon:9606 | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /taxon |
| description | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /summary |
| publication_date | source · NCBI GEO | connector:ncbi_geo@1.0.0 | |
| title | source · NCBI GEO | connector:ncbi_geo@1.0.0 | /title |