Reprogramming of Tumor-infiltrating Immune Cells in Early Stage of NSCLC
Comparing the relative proportions of immune cells in tumor and adjacent normal tissue from NSCLC patients demonstrates the early changes of tumor immunity and provides insights to guide immunotherapy design. We mapped the immune ecosystem using computational deconvolution of bulk transcriptome data from the Cancer Genome Atlas (TCGA) and single cell RNA sequencing (scRNA-seq) data of dissociated tumors from early-stage non-small cell lung cancer (NSCLC) to investigate early immune landscape cha...
Provenance — who produced it, who reused it
Linked to 21 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.
20 further papers cite this accession but reuse could not be confirmed.
Deep data QC
70/100 · CStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Human bulk-RNA-seq with 98 bp reads showed 89.3% Q20 and 84.8% Q30, indicating borderline sequencing quality with decreased basecall confidence across run positions. The moderate mean quality (32.6) and 49.8% GC suggest acceptable data for transcript counting; however, more stringent mapping filters and alignment-quality thresholds should be applied to mitigate systematic errors.
The C 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.