Single cell RNA-seq data of human hESCs to evaluate Oscope - a statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-Seq
Oscillatory gene expression is fundamental to mammalian development, but technologies to monitor expression oscillations are limited. We have developed a statistical approach called Oscope to identify and characterize the transcriptional dynamics of oscillating genes in single-cell RNA-seq data from an unsynchronized cell population. Applications to a number of data sets, include a single-cell RNA-seq data set of human embroyonic stem cells (hESCs), demonstrate advantages of the approach and als...
Provenance — who produced it, who reused it
Linked to 9 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.
- Polymer Simulations of Heteromorphic Chromatin Predict the 3D Fo... 2018 · 209 cites
- Universal prediction of cell-cycle position using transfer learn... 2022 · 129 cites
- Normalization Methods on Single-Cell RNA-seq Data: An Empirical... 2020 · 96 cites
5 further papers cite this accession but reuse could not be confirmed.
Deep data QC
95/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The A 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