CITE-seq: Large scale simultaneous measuremnt of epitopes and transcriptomes in single cells
Recent high-throughput single-cell sequencing approaches have been transformative for understanding complex cell populations, but are unable to provide additional phenotypic information, such as protein levels of cell-surface markers. Using oligonucleotide-labeled antibodies, we integrate measurements of cellular proteins and transcriptomes into an efficient, sequencing-based readout of single cells. This method is compatible with existing single-cell sequencing approaches and will readily scale...
Provenance — who produced it, who reused it
Linked to 25 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.
- Integrated analysis of multimodal single-cell data 2021 · 15,977 cites
- Functional interpretation of single cell similarity maps 2019 · 285 cites
- Scalable analysis of cell-type composition from single-cell tran... 2019 · 185 cites
- Performance Assessment and Selection of Normalization Procedures... 2019 · 185 cites
- pipeComp, a general framework for the evaluation of computationa... 2020 · 154 cites
- Clustering of single-cell multi-omics data with a multimodal dee... 2022 · 119 cites
- Joint dimension reduction and clustering analysis of single-cell... 2022 · 105 cites
17 further papers cite this accession but reuse could not be confirmed.
Deep data QC
100/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Short-read bulk-RNA-seq from human tissue using high-throughput expression profiling, with 25 bp reads achieving exceptional quality metrics of 99% Q20 and 98.2% Q30 across 2.8 billion bases. The consistent 50.7% GC and absent adapter contamination make this suitable for transcript abundance quantification, though the short read length restricts isoform resolution and may hinder alignment to repetitive regions.
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
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.