T cell landscape of non-small cell lung cancer revealed by deep single-cell RNA sequencing
Cancer immunotherapies have shown sustained clinical responses in treating non-small cell lung cancer (NSCLC), but the clinical outcome is not uniform among patients, with complex tumour-immune interactions playing key roles. To depict and dissect the baseline landscape of the composition, lineage and functional states of tumor-infiltrating lymphocytes (TILs) in lung cancer, here we generated deep single-cell RNA sequencing data for 12346 T cells from the tumour, adjacent normal tissues and peri...
Provenance — who produced it, who reused it
Linked to 24 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.
- ImmuCellAI: A Unique Method for Comprehensive T‐Cell Subsets Abu... 2020 · 1,026 cites
- Single-cell profiling of tumor heterogeneity and the microenviro... 2021 · 799 cites
- Joint analysis of heterogeneous single-cell RNA-seq dataset coll... 2019 · 343 cites
- Spatial Positioning and Matrix Programs of Cancer-Associated Fib... 2022 · 312 cites
- Tumour heterogeneity and intercellular networks of nasopharyngea... 2021 · 290 cites
- Assessing single-cell transcriptomic variability through density... 2021 · 172 cites
- Reconstruction of cell spatial organization from single-cell RNA... 2020 · 158 cites
17 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently