Expression data from pure/mixed brain, liver and lung to test feasability and sensitivity of statistical deconvolution
Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression s...
Provenance — who produced it, who reused it
Linked to 17 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.
- Digital sorting of complex tissues for cell type-specific gene e... 2013 · 241 cites
- Complete deconvolution of cellular mixtures based on linearity o... 2019 · 133 cites
- DeMix: deconvolution for mixed cancer transcriptomes using raw m... 2013 · 119 cites
- TOAST: improving reference-free cell composition estimation by c... 2019 · 105 cites
- Mathematical modelling of transcriptional heterogeneity identifi... 2016 · 95 cites
- Transcriptome Deconvolution of Heterogeneous Tumor Samples with... 2018 · 93 cites
- High Resolution Methylome Map of Rat Indicates Role of Intrageni... 2012 · 88 cites
- MMAD: microarray microdissection with analysis of differences is... 2013 · 87 cites
9 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