Help & Documentation

Profiling ceRNA expression and correlation landscapes across individuals. Pick one lncRNA–mRNA pair in a dataset: the top row paints lncRNA expression, mRNA expression and the ceRNA significance (−log₁₀ p) on the dataset's UMAP / t-SNE embedding; the bottom row gives three Pearson regression scatters so you can see how tightly the two transcripts track each other.

Profiling ceRNA expression changes across stratified clinical individuals. Overlay a clinical variable — stage, age, subtype and the like — on a pair's three embedding maps, then read how lncRNA / mRNA expression and ceRNA significance split across the clinical groups as a boxplot or scatter.

Bridging ceRNA expression and functional omics across individuals. For one pair and one pathway it draws the three expression maps (lnc / mRNA expr + ceRNA significance) alongside a fourth map of the pathway-enrichment score on the same embedding, then correlates the pathway score against lncRNA expression, mRNA expression or the ceRNA significance — switchable, Pearson or Spearman.

Comparing functional signatures across multi-context biological profiles. Project per-sample / per-cell enrichment scores onto the embedding and set two signatures against each other. Scores come from six categories — GO BP / CC / MF, MSigDB Hallmark, KEGG pathway and an extra enrichment set — precomputed for every dataset.

Evaluating ceRNA interaction specificity across tissues, cell lines, organoids and single-cell transcriptomes. Pick a pair and see exactly where it is detected: bulk datasets draw a per-sample lollipop, single-cell datasets draw a grouped bar across cell types — so you can tell a broadly active interaction from a context-specific one.

Constructing multi-scale ceRNA regulatory networks from bulk to single-cell resolution. A focal lncRNA or mRNA sits at the centre, ringed by its top-N partners in the chosen dataset; node size and edge width scale with the number of shared miRNAs. Switch between force and circular layouts, set Top-N, and click any node to open that partner's profile.

Constructing sample-level ceRNA networks for tumor heterogeneity analysis. Rebuild the full ceRNA network active in one chosen sample (a bulk GSM) or one cell type (single cell) from the top significant pairs in that context — a way to see which interactions are switched on here, not just across the whole cohort.

Linking ceRNA axes to clinical outcomes via large-scale survival analysis. Split patients into high- and low-expression groups for a chosen lncRNA in a chosen disease and draw the Kaplan–Meier curve with its log-rank significance — a quick read on a lncRNA's prognostic value. Curve colours are yours to set.