quanTIseq: quantifying immune contexture of human tumors
We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.
Provenance — who produced it, who reused it
Linked to 5 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.
4 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
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently
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.