phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | cell states that one measurement type cannot separate and another can - CD4 and CD8 T cells blend in the transcriptome and separate in protein; dendritic cells do the reverse— “Although established technologies like single-cell RNA-seq (scRNA-seq) are capable of discovering new cell types and states in heterogeneous tissues, transcriptomics alone is often incapable of separating molecularly similar, but functionally distinct, categories of immune cells.” | |
| claim-type | method— “Our approach consists of four broad steps, as explained in detail below: (1) Constructing independent k-nearest neighbor (KNN) graphs for both modalities. (2) Performing within and across-modality prediction (3) Calculating cell-specific modality weights. (4) Calculating a WNN graph.” | |
| method — evidence | — “We provide a detailed mathematical description for each component of the WNN workflow in the STAR Methods, highlighting three key steps: (1) obtaining within modality and cross-modality predictions, (2) converting these to prediction affinities, based on a cell-specific bandwidth kernel, and (3) calculating modality weights using a softmax… | |
| object of study — evidence | — “Even within the same dataset, the relative utility of each modality to define cell states may vary across individual cells.” | |
| research question — evidence | — “Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities.” | |
| contribution — evidence | — “Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.” | |
| mechanism | prediction accuracy as evidence of informativeness the core move: if a cell's neighbours in one modality predict its measured profile well, that modality is capturing its state; if they predict badly, it is not. The weight is the ratio of those two accuracies— “Next, we independently averaged the molecular profiles of protein neighbors and RNA neighbors (i.e., predicted the molecular contents of a cell from its neighbors), and compared the averages to their original measured values.” | mechanisms[0] |
| mechanism | adaptive kernel bandwidth why rare cells are not penalised: a fixed neighbour count for the kernel bandwidth would hold common and rare states to the same scale, so the bandwidth is set from the 20 cells with the LOWEST non-zero Jaccard similarity - similar enough to matter, unlikely to be the same state— “For each cell , we therefore aim to identify the 20 cells in the dataset with the lowest non-zero Jaccard similarity. We expect that these represent cells that exhibit some similarity with cell , but are unlikely to reside in the same molecular state.” | mechanisms[1] |
| mechanism | shared-neighbour filtering how a spurious edge is told from a real one - the Jaccard metric counts shared nearest neighbours, so local density separates well-supported links from coincidental similarity— “However, it is possible to identify these spurious edges through the use of the Jaccard metric. This identifies the number of shared nearest neighbors between two cells, thereby exploiting the local density of each data point to separate well-supported from spurious edges.” | mechanisms[2] |
| mechanism | compensation between modalities what the weighting buys: one data type is allowed to make up for another's weakness, cell by cell, so the combined analysis is not limited by whichever measurement happens to be poor for that cell— “We confirmed that our results were robust to a range of values for k ( Figure S2), and the incorporation of protein information in the WNN graph does not come at the expense of identifying transcriptomically congruent neighborhoods” | mechanisms[3] |
| distinguishes-from | totalVI and MOFA+, two recently introduced multimodal integration methods based on deep neural networks with a hierarchical Bayesian model and on factor analysis respectively— “We next benchmarked WNN analysis against two recently introduced methods for multimodal integration: multi-omics factor analysis v2 (MOFA+) ( Argelaguet et al., 2020), which uses a statistical framework based on factor analysis, and totalVI ( Gayoso et al., 2019), which combines deep neural networks with a hierarchical Bayesian model.” | claim_relations[0] |
| distinguishes-from | scArches, a method for mapping single-cell RNA-seq queries onto reference datasets— “We benchmarked our method against scArches, a recently developed method for mapping scRNA-seq queries to reference datasets ( Lotfollahi et al., 2020) and observed that our approach yielded substantial improvements in accuracy and performance” | claim_relations[1] |
| critiques | the practice of defining cell states from the transcriptome alone, which cannot separate functionally distinct T cell populations however sensitive the technology— “Although established technologies like single-cell RNA-seq (scRNA-seq) are capable of discovering new cell types and states in heterogeneous tissues, transcriptomics alone is often incapable of separating molecularly similar, but functionally distinct, categories of immune cells.” | claim_relations[2] |
| extends | the unsupervised clustering analysis of Wilk et al.'s COVID-19 PBMC dataset, which found six T cell clusters where reference mapping resolved twelve— “Notably, populations of developing and differentiated neutrophils, which were identified by the original manuscript as being uniquely present in COVID-19 samples but were absent from our reference, could not be successfully mapped” | claim_relations[3] |
| builds-on | Barshan et al.'s supervised principal component analysis, applied here with the WNN graph as the supervising structure— “Instead of seeking to identify a low-dimensional projection that maximizes total variance as in PCA, sPCA identifies a projection of the transcriptome dataset that maximally captures the structure defined in the WNN graph.” | claim_relations[4] |
| sense | modality weight - A PER-CELL NUMBER, NOT A PER-DATASET ONE, AND THE PAPER SAYS IT HAS TWO POSSIBLE MEANINGS. It is a proxy for the TECHNICAL QUALITY of a measurement in that cell, and it `may also reflect the biological importance of each modality in determining cellular identity` - progenitors and differentiated cells came out with divergent weights. The same figure can mean `this instrument worked here` or `this axis is what this cell is`, and nothing in the number distinguishes them— “Last, we note that the modality weights learned in our procedure serve not only as a proxy for the technical quality of a measurement type, but may also reflect the biological importance of each modality in determining cellular identity.” | senses[0] |
| sense | weight, as computed here - EARNED BY A PREDICTION TEST, not assigned. Each cell is predicted from its neighbours within each modality and across them, the predictions are compared with the cell's actual measurements, and the RATIO of those accuracies becomes the weight after a softmax. Non-negative, unique to each cell, summing to 1— “The RNA and protein modality weights are non-negative, unique to each cell, and sum to 1.” | senses[1] |
| sense | unsupervised, of this procedure - MEANS UNAWARE OF CELL-TYPE LABELS, AND THAT IS WHAT MAKES THE AGREEMENT EVIDENCE. CD8 T cells got high protein weights and dendritic cells high RNA weights, `despite the fact that the calculation of modality weights was unsupervised and unaware of cell-type labels`. The recapitulation of a known biology is the check, and it only counts because the labels were withheld— “We observed that cells classified as CD8+ T cells were assigned higher protein modality weights, whereas DCs were assigned higher RNA modality weights, recapitulating our biological expectations despite the fact that the calculation of modality weights was unsupervised and unaware of cell-type labels” | senses[2] |
| sense | spurious edge - A LINK BETWEEN CELLS THAT RESEMBLE EACH OTHER WITHOUT BEING IN THE SAME STATE, and the paper counts them rather than describing them: 944 wrong CD8/CD4 edges in the RNA graph, 12 in the protein graph, 20 in the combined one. A quality claim with an integer behind it— “Recent work has clearly demonstrated that KNN-graphs are prone to the formation of spurious edges, which represent links between cells that share some similarity molecular profiles, but are not in a matched molecular state” | senses[3] |
| sense | annotation, in this atlas - THREE NESTED TIERS AND A CELL CARRIES ALL THREE. Level 1 has 8 categories, level 2 has 30, level 3 has 57. A result reported at one tier is not comparable with a result reported at another, and the granularity is a choice the reader has to make rather than one the data settles— “To assist in the interpretation of our clusters, we assign each cell three annotations with increasing granularity (level 1, 8 categories; level 2, 30 categories; level 3, 57 categories).” | senses[4] |
| sense | supervised, in `supervised principal component analysis` - SUPERVISED BY THE WNN GRAPH, NOT BY A HUMAN. sPCA finds the transcriptomic projection that best captures the structure the multimodal analysis already found, so the supervision comes from the other modality rather than from labels. `Supervised` and `unsupervised` are both used in this paper about the same pipeline, meaning different things— “Instead of seeking to identify a low-dimensional projection that maximizes total variance as in PCA, sPCA identifies a projection of the transcriptome dataset that maximally captures the structure defined in the WNN graph.” | senses[5] |
| control | adding increasing amounts of random Gaussian noise to the protein data, to mimic nonspecific antibody binding, and watching what the learned weights do — held— “We found that the increasing ADT noise led to a decrease in protein weights for all cell types, in a dose-dependent manner. Moreover, protein modality weights were assigned to 0 after a sufficient amount of protein noise was added, correctly instructing downstream analyses to focus only on scRNA-seq data.” — rules out the modality weights being an artefact of the method rather than a measurement of information content - THE WEIGHTS FELL DOSE-DEPENDENTLY AND REACHED ZERO, which is the behaviour the method claims and could have failed to show— “We confirmed this using a simulation experiment, where we added increasing amounts of random Gaussian noise to the ADT data, in order to mimic increases in nonspecific binding” | controls[0] |
| control | calculating the modality weights with no knowledge of cell-type labels, then checking whether they line up with known biology — held— “We observed that cells classified as CD8+ T cells were assigned higher protein modality weights, whereas DCs were assigned higher RNA modality weights, recapitulating our biological expectations despite the fact that the calculation of modality weights was unsupervised and unaware of cell-type labels” — rules out the weights being read off the answer - CD8 T cells got high protein weights and dendritic cells high RNA weights, and the labels were not available to the calculation— “We observed that cells classified as CD8+ T cells were assigned higher protein modality weights, whereas DCs were assigned higher RNA modality weights, recapitulating our biological expectations despite the fact that the calculation of modality weights was unsupervised and unaware of cell-type labels” | controls[1] |
| control | counting incorrect CD8/CD4 edges in each graph as a hard number rather than judging the UMAP pictures — held— “For example, the WNN graph contained only 20 CD8+/CD4+ edges.” — rules out a visual impression of better separation standing in for a measurement - 944 wrong edges in RNA, 12 in protein, 20 in the combined graph— “We found that for CD8+ T cells, the most similar RNA neighbors often reflected a mix of CD8+ and CD4+ T cells (in the RNA KNN graph, there are a total of 944 incorrect edges that connect CD8+ to CD4+ T cells). By contrast, protein neighbors were predominantly correctly identified as CD8+ T cells (in the protein KNN graph, 12 CD8+/CD4+ edges… | controls[2] |
| control | varying k from 10 to 50 and re-running the whole procedure — held— “We confirmed that our results were robust to a range of values for k ( Figure S2), and the incorporation of protein information in the WNN graph does not come at the expense of identifying transcriptomically congruent neighborhoods” — rules out the result depending on the neighbourhood size, which is the one free parameter a reader would suspect first— “We confirmed that our results were robust to a range of values for k ( Figure S2), and the incorporation of protein information in the WNN graph does not come at the expense of identifying transcriptomically congruent neighborhoods” | controls[3] |
| control | benchmarking against two named alternative methods, totalVI and MOFA+, on a stated metric - how well each integrated space reconstructs a cell's molecular state from its neighbours — held— “We found that for each of the 25 proteins ( Figure 2 D), as well as the RNA transcriptome ( Figure S2), WNN analysis exhibited superior or equivalent performance to alternative approaches.” — rules out 'our method is better' resting on the authors' own preferred view of the output; the metric is declared before the comparison and applies equally to all three— “We reasoned that we could quantify the performance of the different methods by comparing the similarity of each cell’s molecular state to its closest neighbors in the integrated latent space.” | controls[4] |
| control | HOLDING THE QUERY DATASET'S PROTEIN MEASUREMENTS OUT of the reference mapping, then using them to check the annotations the transcriptome alone produced — held— “We verified the accuracy of our predictions using the query protein data, which was held out of the reference mapping procedure, yet revealed expression patterns based on our predicted annotations that were fully concordant with our reference dataset.” — rules out the mapping being scored on the data it was given - the protein data is a true held-out test, and the predicted cell types carried the protein markers they should— “We verified the accuracy of our predictions using the query protein data, which was held out of the reference mapping procedure, yet revealed expression patterns based on our predicted annotations that were fully concordant with our reference dataset.” | controls[5] |
| control | validating a computational prediction on a different instrument - CyTOF on the original samples plus 16 more, against the scRNA-seq-predicted MAIT abundance — held— “To validate our findings, we performed CyTOF on both the original samples and a validation cohort of 16 additional samples. We observed strong quantitative agreement (R = 0.911) in the fraction of MAIT cells predicted by scRNA-seq and measured by CyTOF in the original cohort” — rules out a depletion that exists only in the pipeline; the correlation between the two methods was 0.911 and the CyTOF cohort reproduced the depletion independently— “To validate our findings, we performed CyTOF on both the original samples and a validation cohort of 16 additional samples. We observed strong quantitative agreement (R = 0.911) in the fraction of MAIT cells predicted by scRNA-seq and measured by CyTOF in the original cohort” | controls[6] |
| control | sorting cells by the predicted marker panels and sequencing them in bulk, using THE EXACT ANTIBODY CLONES from the CITE-seq panel — held— “Notably, our flow cytometry experiments utilized the exact antibody clones represented in the CITE-seq experiment, which can help to ensure that the two assays will return concordant results.” — rules out a discrepancy between the two assays caused by different antibodies binding different epitopes - the authors name this as the reason for using the same clones— “For this population, as well as a subgroup of highly cytotoxic CD8+ T cells (CD8_TEM_5), we successfully validated our enrichment panels in an independent set of PBMCs from healthy donors by conventional flow cytometry followed by bulk RNA-seq” | controls[7] |
| note | the unsupervised clustering analysis of Wilk et al.'s COVID-19 PBMC dataset, which found six T cell clusters where reference mapping resolved twelve — and the extension is not one-directional - the same comparison shows what the reference CANNOT do: neutrophils that the original unsupervised analysis found, because they were absent from the reference, could not be mapped at all— “Notably, populations of developing and differentiated neutrophils, which were identified by the original manuscript as being uniquely present in COVID-19 samples but were absent from our reference, could not be successfully mapped” | claim_relations[3] |
| audit | transcriptome —contrasted-with→ modalities — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span questions defining cell states from the transcriptome alone; contrasted-with 'modalities' is loose.— “More broadly, this exhibits the challenge of defining cell states based on the transcriptome alone, because important sources of cellular heterogeneity may not correlate strongly with transcriptomic features despite being identifiable in other modalities.” | concept_edges[2] |
| note | transcriptome —contrasted-with→ modalities — the paper's whole framing: the transcriptome is one modality among several and has been treated as the definition of cell state— “More broadly, this exhibits the challenge of defining cell states based on the transcriptome alone, because important sources of cellular heterogeneity may not correlate strongly with transcriptomic features despite being identifiable in other modalities.” | concept_edges[2] |
| weight | transcriptome —contrasted-with→ modalities — 0.8— “More broadly, this exhibits the challenge of defining cell states based on the transcriptome alone, because important sources of cellular heterogeneity may not correlate strongly with transcriptomic features despite being identifiable in other modalities.” | concept_edges[2] |
| audit | CITE-seq —distinct-from→ CyTOF — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but CITE-seq provides a 'unique opportunity' beside flow cytometry and CyTOF; distinct-from is loose.— “We conclude that WNN analysis is capable of sensitively and robustly characterizing populations that cannot be identified by a single modality, exhibits best-in-class performance, and can be flexibly applied to multiple data types for integrative and multimodal analysis. A multimodal atlas of the human PBMCs Although flow cytometry and… | concept_edges[3] |
| note | CITE-seq —distinct-from→ CyTOF — and the distinctness is what makes the agreement worth something - the MAIT depletion was found in one and confirmed in the other at R = 0.911— “We conclude that WNN analysis is capable of sensitively and robustly characterizing populations that cannot be identified by a single modality, exhibits best-in-class performance, and can be flexibly applied to multiple data types for integrative and multimodal analysis. A multimodal atlas of the human PBMCs Although flow cytometry and… | concept_edges[3] |
| weight | CITE-seq —distinct-from→ CyTOF — 0.9— “We conclude that WNN analysis is capable of sensitively and robustly characterizing populations that cannot be identified by a single modality, exhibits best-in-class performance, and can be flexibly applied to multiple data types for integrative and multimodal analysis. A multimodal atlas of the human PBMCs Although flow cytometry and… | concept_edges[3] |
| outcome | control — held— “We confirmed this using a simulation experiment, where we added increasing amounts of random Gaussian noise to the ADT data, in order to mimic increases in nonspecific binding”— “We found that the increasing ADT noise led to a decrease in protein weights for all cell types, in a dose-dependent manner. Moreover, protein modality weights were assigned to 0 after a sufficient amount of protein noise was added, correctly instructing downstream analyses to focus only on scRNA-seq data.” | controls[0] |
| outcome | control — held— “We observed that cells classified as CD8+ T cells were assigned higher protein modality weights, whereas DCs were assigned higher RNA modality weights, recapitulating our biological expectations despite the fact that the calculation of modality weights was unsupervised and unaware of cell-type labels” | controls[1] |
| outcome | control — held— “We found that for CD8+ T cells, the most similar RNA neighbors often reflected a mix of CD8+ and CD4+ T cells (in the RNA KNN graph, there are a total of 944 incorrect edges that connect CD8+ to CD4+ T cells). By contrast, protein neighbors were predominantly correctly identified as CD8+ T cells (in the protein KNN graph, 12 CD8+/CD4+ edges…— “For example, the WNN graph contained only 20 CD8+/CD4+ edges.” | controls[2] |
| outcome | control — held— “We confirmed that our results were robust to a range of values for k ( Figure S2), and the incorporation of protein information in the WNN graph does not come at the expense of identifying transcriptomically congruent neighborhoods” | controls[3] |
| outcome | control — held— “We reasoned that we could quantify the performance of the different methods by comparing the similarity of each cell’s molecular state to its closest neighbors in the integrated latent space.”— “We found that for each of the 25 proteins ( Figure 2 D), as well as the RNA transcriptome ( Figure S2), WNN analysis exhibited superior or equivalent performance to alternative approaches.” | controls[4] |
| outcome | control — held— “We verified the accuracy of our predictions using the query protein data, which was held out of the reference mapping procedure, yet revealed expression patterns based on our predicted annotations that were fully concordant with our reference dataset.” | controls[5] |
| outcome | control — held— “To validate our findings, we performed CyTOF on both the original samples and a validation cohort of 16 additional samples. We observed strong quantitative agreement (R = 0.911) in the fraction of MAIT cells predicted by scRNA-seq and measured by CyTOF in the original cohort” | controls[6] |
| outcome | control — held— “For this population, as well as a subgroup of highly cytotoxic CD8+ T cells (CD8_TEM_5), we successfully validated our enrichment panels in an independent set of PBMCs from healthy donors by conventional flow cytometry followed by bulk RNA-seq”— “Notably, our flow cytometry experiments utilized the exact antibody clones represented in the CITE-seq experiment, which can help to ensure that the two assays will return concordant results.” | controls[7] |
| no type for this | modality weights → cell states — A NUMBER WHOSE MEANING IS UNDECIDED, AND THE PAPER SAYS SO. The weight is a proxy for the technical quality of a measurement AND may reflect the biological importance of that modality to the cell's identity. Those are different claims about different things and the same float carries both. There is no relation type for 'this measure has two candidate interpretations and we cannot separate them' - the nearest available, 'operationalised-as', would pick one and hide the other— “Last, we note that the modality weights learned in our procedure serve not only as a proxy for the technical quality of a measurement type, but may also reflect the biological importance of each modality in determining cellular identity.” | unhoused_relations[0] |
| no type for this | modalities → cell states — AN ASSUMPTION OF NON-CONFLICT, WHICH IS THE METHOD'S PREMISE AND ITS SECOND STATED LIMITATION. 'WNN assumes that…', and the authors flag that the assumption may fail for morphological and functional data. Every one of the eight relation types asserts something HOLDS between two concepts; none can say 'these two are assumed not to disagree, and the method has no behaviour if they do'— “Second, WNN assumes that modalities do not define conflicting sets of cell states. Although we have not observed this when using molecular data such as chromatin state, gene… | unhoused_relations[1] |
| limitation | WNN needs a dimensional reduction to describe neighbourhood structure, so it does not work on categorical or low-dimensional data | limitations[0] |
| limitation | IT ASSUMES THE MODALITIES DO NOT DEFINE CONFLICTING SETS OF CELL STATES - and the authors say the assumption may be problematic when integrating morphological, functional and molecular data. An integration method whose premise is that its inputs agree | limitations[1] |
| limitation | the atlas is built from peripheral blood mononuclear cells, so it contains almost no erythrocytes or granulocytes - and any query containing them will have nowhere to map them | limitations[2] |
| limitation | demonstrated on exactly that: neutrophil populations present in a COVID-19 query and absent from the reference could not be mapped | limitations[3] |
| limitation | the enrichment panels achieve high fold-enrichment and still cannot sort a truly homogeneous population - the precision and recall figures are published alongside the enrichment ones | limitations[4] |
| limitation | heterogeneity in monocyte inflammatory genes was left undivided BECAUSE it varied across volunteers - a cluster the authors declined to call a cell state Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | limitations[5] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | per-cell modality weights, learned unsupervised, recover known biology: CD8 T cells weight protein highly, dendritic cells weight RNA highly Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[0] |
| finding | The RNA KNN graph had 944 incorrect CD8+/CD4+ edges, the protein graph 12. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[1] |
| finding | adding Gaussian noise to the protein data drove protein weights down dose-dependently and to zero, correctly redirecting the analysis to RNA alone Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[2] |
| finding | The final multimodal dataset has 210,911 cells covering resting and post-vaccination immune states. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[3] |
| finding | MAIT-cell fractions predicted by scRNA-seq agreed with CyTOF measurements at R = 0.911. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[4] |
| finding | WNN matched or beat alternative approaches in reconstructing each of 25 proteins. Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.findings[5] |
| definition | modality weights Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[0] |
| definition | weighted nearest neighbor analysis Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[1] |
| definition | spurious edges Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[2] |
| definition | sPCA Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.definitions[3] |
| concept | modality weights Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[0] |
| concept | modalities Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[1] |
| concept | spurious edges Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[2] |
| concept | Jaccard metric Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[3] |
| concept | cell states Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[4] |
| concept | transcriptome Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[5] |
| concept | scRNA-seq Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[6] |
| concept | CITE-seq Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[7] |
| concept | CyTOF Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[8] |
| concept | totalVI Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[9] |
| concept | sPCA Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[10] |
| concept | neutrophils Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[11] |
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[0] | |
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[1] | |
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | content_tags.statistics[2] |
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