xCell: digitally portraying the tissue cellular heterogeneity landscape

Dvir Aran, Zicheng Hu, Atul J. Butte · Genome biology · 2017 · biology · 10.1186/s13059-017-1349-1 · record aran2017-xcell-digitally-portraying-the-tis
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Other fields define these differently the same word, defined another way in another discipline
tumor microenvironment 4 fields · deconvolution 2 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 10 concept tags on this paper, all 10 are grounded in a quoted sentence. All carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
enrichment scores proportions spillover effect deconvolution gene signatures simulated mixtures real mixtures tumor microenvironment CD8A ssGSEA spectral overlap as a statistical analogy compensation trading independence for separability unbounded scores for absent cell types purified profiles differing from the same cells in a mixture p value system-or-tool-building
Method, as described: signatures generated per cell type per data source and selected on a source they were not trained on, scored by ssGSEA, calibrated by a fitted power function, then compensated against a spillover matrix built from simulated mixtures

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relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
contributioncontributionwhat the paper claims to add
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
object of studyobject_of_studywhat the paper is about — the thing it investigates
phenomenonphenomenonthe effect or regularity under study
research questionresearch_questionthe question the paper sets itself
sensesensesa distinct use of a term, quoted where the paper fixes it
transfertransfersa method or concept carried in from another field

The paper

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Every tag on this page

claim_relations 4 · claim_type 1 · concept_edges 2 · concepts 10 · content_tags 13 · contribution 1 · controls 6 · limitations 6 · mechanisms 4 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 4 · transfers 2

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 2 of 2 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studyinferring which cell types are present in a tissue from its bulk gene expression - and how far such an inference can be trusted
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object_of_studythe paper's words
research_questionwhether enrichment scores can be made comparable across cell types and freed of the correlation between closely related ones, which is what signature methods could not previously do
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research_questionthe paper's words
contributionxCell - 489 signatures for 64 cell types chosen on held-out data, a power-law calibration onto a common scale, a spillover compensation borrowed from flow cytometry, and a null distribution for absence
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contributionthe paper's words
claim_typemethod
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claim_typethe paper's words
phenomenona measurement that returns a non-zero score for cell types that are not in the sample, and scores that look like proportions without being them
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phenomenonthe paper's words
methodsystem-or-tool-building
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methodthe paper's words
mechanismsclosely related cell types share marker genes, so a signature for one is partly satisfied by the other - formally the same problem as fluorophores whose emission spectra overlap, and correctable by the same device, a compensation matrix estimated from mixtures where the true answer is known
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mechanisms[0]0.95the tagger's words
mechanismsbefore compensation each cell type's score is computed alone, so one wrong inference cannot contaminate the rest; the correction couples them, which is exactly the property that makes deconvolution fragile - the paper accepts a limited amount of it and says how limited
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mechanisms[1]0.95the tagger's words
mechanismsa signature returns a score for any input, so a cell type absent from the mixture still receives one; only a null distribution built from mixtures that exclude it can say whether the score means presence
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mechanisms[2]0.95the tagger's words
mechanismssignatures are learned from cells isolated and profiled alone, and applied to cells sitting in tissue; if marker expression differs between those two states the method is calibrated on a population it never meets
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mechanisms[3]0.95the tagger's words
limitationsthe outputs are enrichment scores and cannot be interpreted as proportions, although they are calibrated to resemble them
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limitations[0]0.95the tagger's words
limitationsevery method, this one included, performs significantly better on simulated mixtures than on real ones, and the real-data correlations are called far from perfect
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limitations[1]0.95the tagger's words
limitations56.4% of scores for cell types absent from a test mixture were non-negligible; the significance test brings that to 28.8% and costs 13.5 points of the true positives
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limitations[2]0.95the tagger's words
limitationsthe spillover correction removes the independence between cell type inferences that was the signature approach's main protection - limited, and the authors say so
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limitations[3]0.95the tagger's words
limitationson CD8+ T cells the method is beaten by using CD8A expression alone, with the caveat that the single gene may not be reliable in cancer
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limitations[4]0.95the tagger's words
limitationsfor five cell types the null distribution degenerated to zero and was replaced by a hand-set constant of 0.001
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limitations[5]0.85the tagger's words
transfersspillover compensation, carried from flow cytometry - where fluorophore emission spectra overlap - into gene signature scoring, where marker genes overlap
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transfers[0]0.95the tagger's words
transfersnull-distribution fitting for a presence test, carried from statistical hypothesis testing to ask whether a cell type is in a mixture at all
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transfers[1]0.95the tagger's words
controlssignatures selected only if they hold up in a data source they were NOT trained on
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eliminates: overfitting to the source a signature was learned from as the explanation of its performance - the reliance on reference data that the paper names as the first of the field's seven problems · outcome: held
controls[0]0.95the tagger's words
controlssimulated mixtures built from an independent data source never used in development (GSE60424), scored before and after spillover compensation
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eliminates: the development data as the source of the improvement - a correction tuned on the training sources could otherwise be measuring itself · outcome: held
controls[1]0.95the tagger's words
controlscomparison against 53 previously published signatures and against CIBERSORT, a deconvolution method, on the same mixtures
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eliminates: the possibility that any signature set would score as well - the comparison is to named alternatives on identical inputs rather than to nothing · outcome: held
controls[2]0.95the tagger's words
controlsvalidation against cytometry immunophenotyping in 165 individuals across two independent studies, rather than against simulations alone
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eliminates: in-silico mixing as the reason the method works - arithmetic admixtures cannot test whether marker expression survives being in real tissue · outcome: partial
controls[3]0.95the tagger's words
controlsa null distribution per cell type, learned from random mixtures that EXCLUDE that cell type and its relatives, to test whether a score means presence at all
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eliminates: a non-zero score as evidence of presence - without this the method reports something for every cell type in every sample · outcome: partial
controls[4]0.95the tagger's words
controlsthe significance threshold set at 20% and the trade-off stated, rather than chosen silently
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eliminates: a threshold tuned to flatter the result - the cost in true-positive power is reported alongside the gain in false-positive suppression · outcome: held
controls[5]0.95the tagger's words
conceptsenrichment scores
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concepts[0]0.95the paper's words
conceptsproportions
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also in: proportions
concepts[1]0.95the paper's words
conceptsspillover effect
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concepts[2]0.95the paper's words
conceptsdeconvolution
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also in: deconvolution
concepts[3]0.95the paper's words
conceptsgene signatures
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concepts[4]0.95the paper's words
conceptssimulated mixtures
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concepts[5]0.95the paper's words
conceptsreal mixtures
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also in: real mixtures
concepts[6]0.95the paper's words
conceptstumor microenvironment
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concepts[7]0.95the paper's words
conceptsCD8A
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also in: CD8A
concepts[8]0.95the paper's words
conceptsssGSEA
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also in: ssGSEA
concepts[9]0.95the paper's words
sensesxCell's performance - IN SIMULATED MIXTURES: outperforms every compared method in the large majority of cell types, across data sources and platforms
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senses[0]0.95the tagger's words
sensesxCell's performance - IN REAL MIXTURES: significantly better than the alternatives and still, in the authors' words, far from perfect - and beaten outright on CD8+ T cells by using one gene
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senses[1]0.95the tagger's words
sensesan xCell score - AS COMPUTED: a single-sample gene-set enrichment score, interpretable only against other samples
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senses[2]0.95the tagger's words
sensesan xCell score - AS CALIBRATED: deliberately transformed to resemble a proportion so cell types can be compared, and explicitly not usable as a proportion
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senses[3]0.95the tagger's words
claim_relationscritiques → in-silico cell-type dissection methods generally, on seven named grounds including reliance on the reference source, narrow cell-type coverage, and validation on few cell types in few samples
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claim_relations[0]0.95the tagger's words
claim_relationsqualifies → CIBERSORT and other deconvolution approaches, whose strict inter-cell-type dependencies make them fragile on tumour expression profiles and tied to their reference matrix
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claim_relations[1]0.95the tagger's words
claim_relationsextends → gene set enrichment analysis, by calibrating its scores onto a common scale and compensating the correlation between related signatures
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claim_relations[2]0.95the tagger's words
claim_relationsqualifies → its own results and every comparable method's - digital dissection findings must be validated by other technologies before they are believed
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claim_relations[3]0.95the tagger's words
concept_edgesenrichment scores —distinct-from→ proportions
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differs in: whether the value can be read as a share of the mixture · the relation itself is separately evidenced · strength of the relation: 0.95 · differs in (stated by the tagger, no quotation): whether the value can be read as a share of the mixture · the relation stated: the score is calibrated to resemble a fraction and cannot be used as one
concept_edges[0]0.95the tagger's words
concept_edgessimulated mixtures —contrasted-with→ real mixtures
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differs in: whether the cells were mixed by arithmetic or by biology · the relation itself is separately evidenced · strength of the relation: 0.95 · differs in (stated by the tagger, no quotation): whether the cells were mixed by arithmetic or by biology · the relation stated: every method scores better on in-silico admixtures than on tissue
concept_edges[1]0.95the tagger's words
content_tagsevery method, including this one, performs significantly better on simulated mixtures than on real ones
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content_tags.findings[0]0.95the tagger's words
content_tagsthe scores are calibrated to resemble proportions and cannot be used as proportions
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content_tags.findings[1]0.95the tagger's words
content_tags56.4% of scores for cell types absent from a mixture were non-negligible before the significance test, and 28.8% after
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content_tags.findings[2]0.95the tagger's words
content_tagsSpillover correction removes the strict independence between cell-type inferences, though compensation is limited and most inferences stay independent.
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content_tags.findings[3]0.95the tagger's words
content_tagson CD8+ T cells a single gene outperforms the whole method, with the caveat that the gene may be unreliable in cancer
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content_tags.findings[4]0.95the tagger's words
content_tagsa cell type's score showing enrichment because a closely related cell type is present - the same shape as spectral overlap between fluorophores in flow cytometry
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content_tags.definitions[0]0.95the tagger's words
content_tagsthe inferences this method produces - strictly scores, calibrated to resemble proportions and explicitly not interpretable as them
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content_tags.definitions[1]0.95the tagger's words
content_tagsscores for cell types absent from the mixture that were nonetheless non-negligible
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content_tags.quantities[0]0.95the tagger's words
content_tagsthe share of genes xCell requires that were actually present in one validation study
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content_tags.quantities[1]0.95the tagger's words
content_tagsthe significance threshold, chosen as a stated trade-off
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content_tags.quantities[2]0.95the tagger's words
content_tagsssGSEA
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also in: ssGSEA
content_tags.statistics[0]0.95the tagger's words
content_tagsp value
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also in: p value
content_tags.statistics[1]0.95the tagger's words
content_tagsenrichment scores
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content_tags.variables[0]0.95the tagger's words

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