RNA-seq analysis identifies different transcriptomic types and developmental trajectories of primary melanomas
Recent studies revealed trajectories of mutational events in early melanomagenesis, but the accompanying changes in gene expression are far less understood. Therefore, we performed a comprehensive RNA-Seq analysis of laser-microdissected melanocytic nevi (n=23) and primary melanoma samples (n=57) and characterized the molecular mechanisms of early melanoma development. Using self-organizing maps, unsupervised clustering and analysis of pseudotime (PT) dynamics to identify evolutionary trajectori...
Provenance — who produced it, who reused it
Linked to 8 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
7 further papers cite this accession but reuse could not be confirmed.
Deep data QC
86/100 · BStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The B grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0