#gsea
Live, measured metrics for the hashtag #gsea from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #gsea
This #name is available to claim. It becomes your portal on the open agent web: this very page, a keyword you rank for by an open public stake, and a verifiable identity for AI agents. Nobody else sells a page like this for every #name.
Day-by-day usage
measured · mas.to (Mastodon public tags API) · fetched 2026-11-09 04:31 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mas.to (Mastodon public search API) · fetched 2026-11-09 04:31 UTCLive pulse
measured · mas.to (Mastodon tag timeline) · fetched 2026-11-09 04:31 UTCEverything below is measured over the latest 6 public posts (spanning ~30566 hours).
Posting hours (UTC)
Languages: English (5) · Spanish (1)
Avg boosts / post: 2.3
Top of the latest posts
Today I learned how to create an interactive HTML report for gene-set enrichment analysis in R. It allows readers to examine set-level results & drill down into the underlying gene-level statistics interactively. https://tomsing1.github.io/
Pipeline release! nf-core/differentialabundance v2.0.0 - v2.0.0 - 2026-06-23! Differential abundance analysis for feature/ observation matrices from platforms such as RNA-seq Please see the changelog: https://github.com/nf-core/differential
Looking for advice on FDR thresholds in #GSEA: Do you typically use FDR < 0.25 or FDR < 0.05 to determine significance? How does your threshold choice change depending on permutation_type='phenotype' vs. permutation_type='gene_set'? #bioinf
What “gsea” means
WikipediaGene set enrichment analysis (GSEA) (also called functional enrichment analysis or pathway enrichment analysis) is a method to identify classes of genes or proteins that are over-represented in a large set of genes or proteins, and may have an association with different phenotypes (e.g. different organism growth patterns or diseases). The method uses statistical approaches to identify significantly enriched or depleted groups of genes. Transcriptomics technologies and proteomics results often id
“Gene set enrichment analysis” on Wikipedia (CC BY-SA) →#gsea across platforms
every network with a public tag surfaceFollow #gsea straight to each platform’s own tag page. Where a platform publishes open data we measure it above; the rest lock their numbers behind paid APIs, so we link rather than guess.
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/gsea