xCell: digitally portraying the tissue cellular heterogeneity landscape — TEI-tagged view

Dvir Aran, Zicheng Hu, Atul J. Butte · 2017 · biology · doi:10.1186/s13059-017-1349-1 · TEI xml · tagged edition · index · concepts · authors
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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, whether or not the words occur in the text below — a number is how many papers in the corpus carry that tag; a tag with no number is unique to this paper
enrichment scores proportions2 spillover effect deconvolution5 gene signatures2 simulated mixtures real mixtures tumor microenvironment6 CD8A ssGSEA system-or-tool-building173
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

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
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— “Finally, many of the cell types we estimate are not expected to be in a given mixture; however, the pipeline we described will often produce non-zero scores. In the 18 test simulated mixtures, 56.4% of the scores for cell types that are not part of the mixture were non-negligible (> 0.001).”
claim-typemethod— “The procedure was performed for each data source where the corresponding cell type was available, except the data source from which the signature was learned. Thus, a signature was only chosen if it is reliable in a data source it was not trained upon.”
method — evidence— “We then fit a power function to the scores corresponding to abundances of 0.8 to 25.6%. We used this range because we are mostly interested in identifying cell types with low abundance, and above that the function exponential increase may interfere with precise fitting.”
object of study — evidence— “At least seven major issues raise concerns that the in silico methods could be prone to errors and cannot reliably portray the cellular heterogeneity of the tumor microenvironment. First, current techniques depend on the expression profiles of purified cell types to identify reference genes and therefore rely heavily on the data source from…
research question — evidence— “However, because of this independence, it is many times hard to differentiate between closely related cell types. In addition, gene signature-based methods only provide enrichment scores and thus do not allow comparison across cell types and cannot provide insights into the abundance of cell types in the mixture.”
contribution — evidence— “The simulations also revealed another limitation of the raw scores: closely related cell types tend to have correlating scores (Additional file 2: Figure S5). That is, scores may show enrichment for a cell type due to a “spillover effect” between closely related cell types. This problem mimics the spillover problem in flow cytometry, in which…
transferfrom flow cytometry: spillover compensation, carried from flow cytometry - where fluorophore emission spectra overlap - into gene signature scoring, where marker genes overlap— “The simulations also revealed another limitation of the raw scores: closely related cell types tend to have correlating scores (Additional file 2: Figure S5). That is, scores may show enrichment for a cell type due to a “spillover effect” between closely related cell types. This problem mimics the spillover problem in flow cytometry, in which…transfers[0]
transferfrom statistics: null-distribution fitting for a presence test, carried from statistical hypothesis testing to ask whether a cell type is in a mixture at all— “In five of the cell types the scores from the random mixtures consistently produced 0; thus, we define those distributions as constant 0.001 (Additional file 2: Figure S7).”transfers[1]
mechanismspectral overlap as a statistical analogy closely 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— “The simulations also revealed another limitation of the raw scores: closely related cell types tend to have correlating scores (Additional file 2: Figure S5). That is, scores may show enrichment for a cell type due to a “spillover effect” between closely related cell types. This problem mimics the spillover problem in flow cytometry, in which…mechanisms[0]
mechanismcompensation trading independence for separability before 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— “It is important to note that, until this step, the cell type scores are independent of each other, and a false inference of one cell type will not harm all other cell types. However, the spillover correction adjustment removes this strict independence between cell type inferences, as in deconvolution methods. Yet, the compensation is very…mechanisms[1]
mechanismunbounded scores for absent cell types a 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— “Finally, many of the cell types we estimate are not expected to be in a given mixture; however, the pipeline we described will often produce non-zero scores. In the 18 test simulated mixtures, 56.4% of the scores for cell types that are not part of the mixture were non-negligible (> 0.001).”mechanisms[2]
mechanismpurified profiles differing from the same cells in a mixture signatures 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— “However, other explanations for the lower success when inferring abundances in real samples are possible—it may well be possible that the expression patterns of marker genes in mixtures are different to those in purified cells. Recent technologies such as single-cell RNA-sequencing may be able to clarify how much this may perturb the…mechanisms[3]
critiques 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— “At least seven major issues raise concerns that the in silico methods could be prone to errors and cannot reliably portray the cellular heterogeneity of the tumor microenvironment. First, current techniques depend on the expression profiles of purified cell types to identify reference genes and therefore rely heavily on the data source from…claim_relations[0]
qualifies CIBERSORT and other deconvolution approaches, whose strict inter-cell-type dependencies make them fragile on tumour expression profiles and tied to their reference matrix— “A fifth challenge is that deconvolution approaches are prone to many different biases because of the strict dependencies among all cell types that are inferred. This could highly affect reliability when analyzing tumor samples, which are prone to form non-conventional expression profiles. A sixth problem comes with inferring an increasing…claim_relations[1]
extends gene set enrichment analysis, by calibrating its scores onto a common scale and compensating the correlation between related signatures— “However, because of this independence, it is many times hard to differentiate between closely related cell types. In addition, gene signature-based methods only provide enrichment scores and thus do not allow comparison across cell types and cannot provide insights into the abundance of cell types in…claim_relations[2]
qualifies its own results and every comparable method's - digital dissection findings must be validated by other technologies before they are believedclaim_relations[3]
sensexCell'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]
sensexCell'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]
sensean xCell score - AS COMPUTED: a single-sample gene-set enrichment score, interpretable only against other samples
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senses[2]
sensean 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]
controlsignatures selected only if they hold up in a data source they were NOT trained on — held
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— rules out 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
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controls[0]
controlsimulated mixtures built from an independent data source never used in development (GSE60424), scored before and after spillover compensation — held
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— rules out the development data as the source of the improvement - a correction tuned on the training sources could otherwise be measuring itself
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controls[1]
controlcomparison against 53 previously published signatures and against CIBERSORT, a deconvolution method, on the same mixtures — held
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— rules out the possibility that any signature set would score as well - the comparison is to named alternatives on identical inputs rather than to nothing
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controls[2]
controlvalidation against cytometry immunophenotyping in 165 individuals across two independent studies, rather than against simulations alone — partial
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— rules out in-silico mixing as the reason the method works - arithmetic admixtures cannot test whether marker expression survives being in real tissue
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controls[3]
controla 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 — partial
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— rules out a non-zero score as evidence of presence - without this the method reports something for every cell type in every sample
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controls[4]
controlthe significance threshold set at 20% and the trade-off stated, rather than chosen silently — held
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— rules out a threshold tuned to flatter the result - the cost in true-positive power is reported alongside the gain in false-positive suppression
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controls[5]
weightin-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 — 0.95
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claim_relations[0]
weightCIBERSORT and other deconvolution approaches, whose strict inter-cell-type dependencies make them fragile on tumour expression profiles and tied to their reference matrix — 0.9
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claim_relations[1]
weightgene set enrichment analysis, by calibrating its scores onto a common scale and compensating the correlation between related signatures — 0.9
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claim_relations[2]
weightits own results and every comparable method's - digital dissection findings must be validated by other technologies before they are believed — 0.9
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claim_relations[3]
differs in (stated by the tagger, no quotation)enrichment scores —distinct-from→ proportions — whether the value can be read as a share of the mixtureconcept_edges[0]
relationenrichment scores —distinct-from→ proportions — the score is calibrated to resemble a fraction and cannot be used as one
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concept_edges[0]
weightenrichment scores —distinct-from→ proportions — 0.95
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concept_edges[0]
differs in (stated by the tagger, no quotation)simulated mixtures —contrasted-with→ real mixtures — whether the cells were mixed by arithmetic or by biologyconcept_edges[1]
relationsimulated mixtures —contrasted-with→ real mixtures — every method scores better on in-silico admixtures than on tissue
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concept_edges[1]
weightsimulated mixtures —contrasted-with→ real mixtures — 0.95
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concept_edges[1]
outcomecontrol — held
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controls[0]
outcomecontrol — held
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controls[1]
outcomecontrol — held
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controls[2]
outcomecontrol — partial
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controls[3]
outcomecontrol — partial
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controls[4]
outcomecontrol — held
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controls[5]
limitationthe outputs are enrichment scores and cannot be interpreted as proportions, although they are calibrated to resemble them
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limitations[0]
limitationevery 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]
limitation56.4% 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]
limitationthe 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]
limitationon 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]
limitationfor 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]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
finding
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content_tags.findings[0]
finding
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content_tags.findings[1]
finding
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content_tags.findings[2]
finding
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content_tags.findings[3]
finding
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content_tags.findings[4]
quantityscores for cell types absent from the mixture that were nonetheless non-negligible
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content_tags.quantities[0]
quantitythe share of genes xCell requires that were actually present in one validation study
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content_tags.quantities[1]
quantitythe significance threshold, chosen as a stated trade-off
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content_tags.quantities[2]
variableenrichment scores
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content_tags.variables[0]
definitionspillover effect
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content_tags.definitions[0]
definitionenrichment scores
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content_tags.definitions[1]
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptenrichment scores — not defined in this paper's own words; what a signature method produces - a per-sample, per-cell-type score that is not a fraction of the mixture and is calibrated here to look like one
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concepts[0]
conceptproportions — not defined in this paper's own words; the quantity a reader wants and the paper says it cannot deliver, because ssGSEA's extremes do not translate into fractions
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concepts[1]
conceptspillover effect — not defined in this paper's own words; a cell type scoring high because a closely related one is present - named after, and corrected by an analogue of, the spectral overlap problem in flow cytometry
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concepts[2]
conceptdeconvolution — not defined in this paper's own words; inferring the whole composition at once, so every cell type's estimate depends on every other's - powerful and, the paper argues, fragile on tumour samples
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concepts[3]
conceptgene signatures — not defined in this paper's own words; independent per-cell-type scores, rank-based and cross-platform, whose independence is both their protection and their inability to separate related cell types
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concepts[4]
conceptsimulated mixtures — not defined in this paper's own words; in-silico admixtures of purified profiles - where every method performs better than it does on tissue
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concepts[5]
conceptreal mixtures — not defined in this paper's own words; actual tissue with cytometry counts beside it - the harder test, and the one whose correlations the authors call far from perfect
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concepts[6]
concepttumor microenvironment — not defined in this paper's own words; the non-malignant cells around a tumour; the object all these methods are trying to enumerate, and richer than the immune subsets most of them cover
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concepts[7]
conceptCD8A — not defined in this paper's own words; the single gene that beats the whole method on CD8+ T cells, and which the authors note may not be a reliable biomarker in cancer
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concepts[8]
conceptssGSEA — not defined in this paper's own words; single-sample gene set enrichment analysis - the scoring step, and the reason the output cannot be read as a fraction
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concepts[9]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
ssGSEA
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content_tags.statistics[0]
p value
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content_tags.statistics[1]

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