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Integrated analysis of multimodal single-cell data
Open accessPreprint — published as 10.1016/j.cell.2021.04.048 · Paper page
Abstract
The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. 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. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. Availability: Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat
Overview
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Written from the paper’s full text.
TL;DR
The paper introduces weighted-nearest neighbor (WNN) analysis, an unsupervised framework that learns cell-specific weights for each modality and integrates multimodal single-cell data into a joint neighbor graph. Applied to CITE-seq and other multimodal datasets, WNN improves cell-state resolution, enables a 210,911-cell multimodal PBMC reference atlas, and supports reference-based mapping of vaccination and COVID-19 immune responses.
Key findings
- WNN integration improved resolution of cell states across datasets: in a CITE-seq dataset of 8,617 cord blood mononuclear cells, it separated CD4+ and CD8+ T cells while retaining cDC identity; in 30,672 human bone marrow mononuclear cells, it resolved T cell and progenitor states not separable by either modality alone.
- The multimodal PBMC atlas contained 210,911 total cells, including 161,764 10x 3′ cells profiled with 228 antibodies and 49,147 10x 5′ cells; WNN clustering identified 57 clusters, grouped into 8 level-1 and 30 level-2 annotations.
- Benchmarking against totalVI and MOFA+ showed WNN had superior or equivalent performance for reconstructing 25 proteins and RNA transcriptome features, with speed improvements up to 15-fold on the full dataset.
- Reference mapping of a COVID-19 scRNA-seq dataset identified MAIT cell depletion, validated by CyTOF with Pearson correlation R = 0.911; vaccination analysis found strong day-3 interferon responses in CD14+ and CD16+ monocytes and cDC2s, including a shared module of 62 interferon-responsive genes and Siglec-1 (CD169) protein induction.
Methods
- Design: WNN constructs independent k-nearest-neighbor graphs per modality, performs within- and cross-modality prediction, converts prediction accuracy to cell-specific modality weights via a softmax, and builds a weighted nearest-neighbor graph for downstream visualization, clustering, and trajectory analysis.
- Data: analyses included CITE-seq datasets of 8,617 cord blood mononuclear cells (10 antibodies), 30,672 bone marrow mononuclear cells (25 antibodies), and 210,911 PBMCs (228 antibodies); plus 11,351 10x Multiome ATAC+RNA PBMCs, 4,725 ASAP-seq PBMCs (227 antibodies), and 34,774 SHARE-seq mouse skin cells.
- Main analysis: WNN was implemented in Seurat v4; clustering used the WNN graph with SLM, differential expression used Wilcoxon tests, and reference mapping used supervised PCA (sPCA) with anchor-based transfer to project query scRNA-seq datasets onto the multimodal reference.
Limitations
- Stated: WNN requires a dimensional reduction to describe neighborhood structure and is not compatible with categorical or low-dimensional data.
- Stated: WNN assumes modalities do not define conflicting sets of cell states, which may be problematic when integrating morphological, functional, and molecular data.
- Stated: the circulating immune atlas was constructed from PBMCs and therefore contains few cells with no nuclei (erythrocytes) or multi-lobed nuclei (granulocytes); evident in the text, developing/differentiated neutrophils absent from the reference could not be successfully mapped, and small marker panels may not isolate truly homogeneous high-resolution subsets.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations.”
§ Integrated analysis of multimodal single-cell data > Abstract
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In the paper
“Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations.”
§ Integrated analysis of multimodal single-cell data > Abstract
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In the paper
“Consistent with our previous example, WNN integration substantially increased our ability to resolve hematopoietic cell states (Figures 2A and S2).”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
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In the paper
“By contrast, protein neighbors were predominantly correctly identified as CD8+ T cells (in the protein KNN graph, 12 CD8+/CD4+ edges were identified).”
§ Integrated analysis of multimodal single-cell data > Results > Quantifying the relative utility of each modality in each cell
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In the paper
“We found that the increasing ADT noise led to a decrease in protein weights for all cell types, in a dose-dependent manner.”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
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In the paper
“This was consistent with UMAP visualization, in which WNN was the only method where these populations were not intermixed with other groups (Figure S2).”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
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In the paper
“We found that each cluster exhibited distinct molecular patterns and biomarkers for both modalities (Figure 4A; additional heatmaps in Figure S4).”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“We obtained highly concordant results when applying WNN analysis to ASAP-seq (Mimitou et al., 2020), a third multimodal technology, that pairs measurements of surface protein abundance with ATAC-seq profiles in single cells (Figure S3).”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
What the sources say
- Not checked yetcites Mimitou et al., 2020
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In the paper
“We found that unsupervised transcriptome-focused clustering was unable to separate these cell states, even if we performed a focused re-clustering using only T cells (Figure S2).”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
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In the paper
“Heatmap displays pseudobulk averages where cells are grouped by cell type, human volunteer, and vaccination time point and demonstrates that markers are repeatedly identified across samples.”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
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- It may state two separate claims as one. 71
- Probably not a substantial claim. 38
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19).”
§ Integrated analysis of multimodal single-cell data > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The eight volunteers and three time points are representative of the circulating immune system and vaccination response.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, CD8+ and CD4+ T cells were partially blended together when analyzing the transcriptome but separated clearly in the protein data.”
§ Integrated analysis of multimodal single-cell data > Results > Quantifying the relative utility of each modality in each cell
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In the paper
“These data demonstrate that while we can achieve substantial enrichment with small panels, isolating pure and homogeneous populations based on small marker panels remains challenging for some clusters.”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
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In the paper
“Both markers were specifically enriched in the ASDC cells compared to other DC subsets.”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
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In the paper
“WNN analysis recapitulated each of the 23 populations described in the original manuscript where unsupervised clustering was performed on transcriptomic measurements, including three subgroups of Basal cells that could be distinguished from scRNA-seq.”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
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In the paper
“Contrastingly, conventional dendritic cells (cDCs), along with a rare population of erythroid progenitors and spiked-in murine 3T3 controls, formed distinct clusters when analyzing RNA but were intermixed with other cell types based on surface protein abundance.”
§ Integrated analysis of multimodal single-cell data > Results > Quantifying the relative utility of each modality in each cell
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In the paper
“However, in addition to the published findings, WNN analysis identified a novel population of Basal cells that exhibits distinct chromatin accessibility profiles, but does not exhibit unique transcriptomic characteristics (Figure S3).”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
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In the paper
“We found that for CD8+ T cells, protein KNN-based predictions were more accurate compared to RNA KNN-based predictions (Figures 1C and 1D), whereas the converse was true for cDCs (Figure S1).”
§ Integrated analysis of multimodal single-cell data > Results > Quantifying the relative utility of each modality in each cell
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In the paper
“Our clusters could be readily grouped into larger categories, including CD4+ T cells (12 clusters), CD8+ T cells (12 clusters), unconventional T cells (7 clusters), NK cells (6 clusters), B cells, plasma cells, and plasmablasts (8 clusters), dendritic cells and monocytes (8 clusters), and rare clusters of hematopoietic progenitors, platelets, erythrocytes, and circulating innate lymphoid cells (IL”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
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In the paper
“For example, we found that ATAC-seq peaks accessible in MAIT cells were highly enriched for motifs for the pro-inflammatory transcription factor RORγt (Ivanov et al., 2006; Willing et al., 2018), which was also upregulated transcriptionally in these cells (Figure S3).”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
What the sources say
- Not checked yetcites Ivanov et al., 2006
- Not checked yetcites Willing et al., 2018
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In the paper
“B cell states are subdivided by their mutually exclusive expression of kappa or lambda light chain, with distinguishing markers including IGKC, IGLC3, IGLC3.”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
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In the paper
“In contrast to the separate analysis of either modality, our joint integration clearly separated CD4+ and CD8+ T cells, retained the identity of cDCs, and also uncovered additional sources of subtle heterogeneity within natural killer (NK) cells (Figure S1).”
§ Integrated analysis of multimodal single-cell data > Results > Quantifying the relative utility of each modality in each cell
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In the paper
“Independent unsupervised analysis of the RNA and protein data revealed largely consistent cell classifications (Figures 1A, 1B, and S1) but did exhibit some differences.”
§ Integrated analysis of multimodal single-cell data > Results > Quantifying the relative utility of each modality in each cell
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In the paper
“We identified 57 clusters in WNN analysis, encapsulating all major and minor immune cell types and revealing striking cellular diversity particularly within lymphoid lineages.”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
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In the paper
“After NovaSeq sequencing, stringent quality control, and doublet filtration (STAR Methods), our final dataset consists of 210,911 total cells and allows us to profile cellular heterogeneity in both the resting (unvaccinated) and activated (post-vaccination) immune system.”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The eight volunteers and three time points are representative of the circulating immune system and vaccination response.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite the fact that clusters were enriched for both RNA and protein markers, our ability to identify these groups was substantially reduced without WNN analysis, as multiple clusters blended together when performing separate analysis of either RNA or protein data (Figures 3B and 3C).”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
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In the paper
“For example, the WNN graph contained only 20 CD8+/CD4+ edges.”
§ Integrated analysis of multimodal single-cell data > Results > Quantifying the relative utility of each modality in each cell
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In the paper
“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 (Figure 1F).”
§ Integrated analysis of multimodal single-cell data > Results > Quantifying the relative utility of each modality in each cell
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In the paper
“The expression of IFI27 increases within particular myeloid populations at day 3, but dampens at day 7.”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
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In the paper
“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.”
§ Integrated analysis of multimodal single-cell data > Results > Mapping query datasets to multimodal references
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- Rests on an assumption: The protein data held out from mapping can serve as ground truth for assessing annotation accuracy.
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In the paper
“These findings were reproducible in independent analyses of the 3′ and 5′ scRNA-seq experiments and persisted in both day 3 and day 7 samples (Figures 6F and S6).”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
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In the paper
“Each clone typically represented cells from a single volunteer, but could be independently found across multiple time points, including before vaccination (Figure 5G).”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“Contrastingly, closely related populations of CD141+ cDC1, as well as ASDC and pDC, exhibited minimal responses, and we did not detect any DE genes before and after vaccination for these groups (Figure 6A).”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
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In the paper
“We validated this result, as well as the observed return to baseline ratios at day 7, using flow cytometry on the same samples (Figure 6G).”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
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In the paper
“Clones reside in a restricted set of cytotoxic and effector cell states and are shared across vaccination time points.”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“Both populations did not express the canonical resident marker CD69 (Szabo et al., 2019; Walsh et al., 2019) (Figure S5)”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
What the sources say
- Not checked yetcites Szabo et al., 2019
- Not checked yetcites Walsh et al., 2019
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In the paper
“Protein expression of canonical resident lymphocyte marker CD69 in CD8+ CD103+, CD8+ CD49a+ T cell populations. Neither population is positive.”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“As expected, we observed robust responses in a subset of myeloid subpopulations, but only minimal responses in lymphoid groups (Figures 6A and 6B).”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
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In the paper
“We observe concordant trends in both subsets, as well as an independent CITE-seq dataset (Kotliarov et al., 2020).”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
What the sources say
- Not checked yetcites Kotliarov et al., 2020
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In the paper
“We did not observe changes within lymphoid cells with one exception: a small population of NK cells expressing proliferation and cell-cycle genes (NK_proliferation), consistently increased upon vaccination (Figure 6F).”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
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In the paper
“In each case, cells within a clonal population exhibited extremely similar molecular profiles (Figure 5H), representing subgroups of CD8+ T cells (primarily within our previously identified CD8_TEM_4 and CD 8_TEM_5 clusters), as well as cytotoxic CD4+ T cells (CD4 CTL).”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“The response is consistent across all volunteers with one exception (P6), which exhibited signs of a highly activated immune system even prior to vaccination.”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
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In the paper
“CD8+ CD103+ T cells expressed high surface protein levels of the heterodimeric co-binding partner integrin beta-7 (Figure 5B), while expression was absent in CD8+ CD49a+ groups.”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“As expected, we detect the canonical MAIT TRAV1-2-TRAJ33 as the most abundant sequence along with reduced usage of TRAJ12 and TRAJ20.”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“For example, although the overall proportion of monocytes was consistent across time points, there was a strong shift in the ratio between classical and non-classical populations between day 0 and day 3 (Figure 6F).”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
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In the paper
“both MAIT and invariant NKT cells exhibited closely related transcriptional profiles (Huang et al., 2019) and semi-invariant repertoires across multiple volunteers (Figure S5).”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
What the sources say
- Not checked yetcites Huang et al., 2019
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“For example, cells that were annotated as regulatory T cells expressed CD25 in the CITE-seq data, and we observed similar results for MAIT cells (CD161+), memory (CD45RA− CD45RO+) and naive (CD45RA+ CD45RO−) T cells, and circulating ILC (CD117+ CD25+) (Figure S7).”
§ Integrated analysis of multimodal single-cell data > Results > Mapping query datasets to multimodal references
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In the paper
“CD38 and CD16 protein expression define two separate gradients and are uncorrelated in NK cells.”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“Our integrated WNN analysis reveals a rich diversity of T cell states that is not typically captured in scRNA-seq analyses, including CD4+ regulatory T cells, MAIT cells, multiple subpopulations of γδ and double-negative T cells, along with heterogeneous subpopulations of naive, memory, and effector states.”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“Notably, KLRC2, which encodes the NK activating receptor NKG2C was negatively associated with this continuum, while the signaling adaptor FCER1G was positively associated (Figure 5J).”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“Our data show that the traditional division of NK cells into CD56-bright and CD56-dim categories represents a broader continuum defined by CD16 expression, alongside a module of genes that modulate cytotoxicity and correlate both positively and negatively with this marker (Figure 5I).”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“In preliminary experiments designed to test the balance of markers in the panel, reads corresponding to 12 antibodies each took up more than 2% of the total sequencing space, and combined together, accounted for half of the total antibody reads.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“We ran ten rounds of forward selection, allowing us to design panels of one-ten immunophenotypic markers for each cell type.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“Our findings recapitulated the original unsupervised analysis, for example, highlighting increases in plasmablast frequency during COVID-19 response (Figure S7).”
§ Integrated analysis of multimodal single-cell data > Results > Mapping query datasets to multimodal references
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In the paper
“The plots show that the methods generally reveal similar sets of cell types, but with important differences.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“However, we also observed proportional shifts in cell states that were not detected in unsupervised clustering but were successfully annotated after reference mapping.”
§ Integrated analysis of multimodal single-cell data > Results > Mapping query datasets to multimodal references
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In the paper
“In our final annotations, we considered 57 total clusters.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“Our data also enable us to explore the response of the innate immune system to vaccination, highlighting specific response biomarkers, as well as the heterogeneous responses of conventional DCs.”
§ Integrated analysis of multimodal single-cell data > Discussion
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In the paper
“In Figure S2, we show that we obtain very similar results from the WNN procedure when varying k across a series of values ranging from 10 to 50.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“For example, regulatory T cells, defined by CD25 expression, are only separated in the WNN UMAP. Figure S2B demonstrates that this is due to the fact that CD25+ cells only form a distinct cluster in WNN analysis.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Our current implementation of WNN analysis extends to handle three or more simultaneously measured modalities, as these technologies mature.”
§ Integrated analysis of multimodal single-cell data > Discussion
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- The authors state this as an assumption: WNN assumes that modalities do not define conflicting sets of cell states.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our 57 clusters fall into subsets of these categories (i.e., CD8+ TCM_1, CD8+ TCM_2, etc.), and represent Level 3 annotations with the highest level of granularity (all listed in the legend for Figure 3C).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“We placed clusters into eight broad groups (Level 1 annotations: CD4+ T cells, CD8+ T cells, Unconventional T, B cells, Natural Killer (NK) cells, Monocytes, Dendritic Cells (DC), and Other (consisting of progenitors and additional rare populations expressing erythroid or platelet lineage markers).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“While some clusters (for example, regulatory T cells) were only identified in the WNN-derived clusters, we did identify shared populations across both cluster sets including: CD4 Naive T, CD4 Memory T, CD8 Naive T, B Naive, B Memory, HSC, and LMPP subgroups.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“We leverage this resource to characterize extensive lymphoid heterogeneity that has not been previously observed by scRNA-seq alone, including the heterogeneous expression of integrin proteins on circulating memory T cells, a gradient of adaptive-like responses in NK cells, and tightly clustered clonal populations within effector and cytotoxic groups.”
§ Integrated analysis of multimodal single-cell data > Discussion
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In the paper
“We further subdivided these groups into 30 Level 2 annotation categories representing well-described subtypes of human immune cells: CD4+ T Naive, CD4+ T Central Memory (TCM), CD4+ T Effector Memory (TEM), CD8+ TEM, etc., all thirty subtypes are listed at https://azimuth.hubmapconsortium.org/).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“We obtained productive TCR ⍺/β sequences representing 16,060 distinct clones, where all cells within a clone share the exact same CDR3⍺ and CDR3β sequences.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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In the paper
“We detected significant shifts (p < 0.05) for three clusters, visualized for the 10X 3′ samples in Figure 6F, with independent support for each of the signals in the 10X 5′ datasets (Figure S6).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Quantification and statistical analysis
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In the paper
“For Level 1 annotations, for each of the 24 samples, we calculated the percentage of each cell state in each sample, and ran two paired Wilcoxon tests: day 0 versus day 3, and day 0 versus day7. No cell states exhibited significant changes.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Quantification and statistical analysis
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In the paper
“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.”
§ Integrated analysis of multimodal single-cell data > Abstract
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In the paper
“Notably, basal cell carcinoma, the most common form of skin cancer, is often characterized by mutations in p53 and CTCF binding sites (Poulos et al., 2016) and results in uncontrolled basal cell division.”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
What the sources say
- Not checked yetcites Poulos et al., 2016
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although this causes unvaccinated and vaccinated samples to cluster together initially, it enables us to annotate cell states consistently in all samples, and to learn cell-type-specific responses in downstream analyses.”
§ Integrated analysis of multimodal single-cell data > Results > A multimodal atlas of the human PBMCs
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- Rests on an assumption: The eight volunteers and three time points are representative of the circulating immune system and vaccination response.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
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In the paper
“As basal cells in the skin are continually replenished (Epstein, 2008), cells that exhibit a primed chromatin state preceding transcriptomic shifts may differ in their proliferative and regenerative potential.”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
What the sources say
- Not checked yetcites Epstein, 2008
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Other panels may omit antibodies for key or previously undiscovered markers, or contain antibodies with low binding specificity, in which case the unsupervised nature of scRNA-seq may be most informative.”
§ Integrated analysis of multimodal single-cell data > Results > Quantifying the relative utility of each modality in each cell
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In the paper
“For example, we recently introduced CITE-seq (Stoeckius et al., 2017), which leverages oligonucleotide-conjugated antibodies to simultaneously quantify RNA and surface protein abundance in single cells via the sequencing of antibody-derived tags (ADTs).”
§ Integrated analysis of multimodal single-cell data > Introduction
What the sources say
- Not checked yetcites Stoeckius et al., 2017
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results suggest that integrated WNN analysis can provide necessary flexibility and allow one data type to compensate for weaknesses in another.”
§ Integrated analysis of multimodal single-cell data > Results > WNN analysis is a robust and flexible approach for multimodal analysis
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that although WNN integration improves the ability to discover distinct cell subpopulations, it can also improve the characterization of cellular trajectories and continuous sources of heterogeneity.”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“This finding may reflect an early step in the development and maturation of NK cells, a key component of the NK cell-mediated innate immune response (Abel et al., 2018).”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
What the sources say
- Not checked yetcites Abel et al., 2018
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results indicate that this phenotype does not represent a strictly binary phenomenon and may not be specific to CMV response.”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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In the paper
“Taken together, these results demonstrate that our dataset represents a powerful resource to enumerate cell states in the immune system, identify optimal reagents for cell-type-specific enrichment, and to understand the molecular heterogeneity in clonally related or antigen-specific cell groups.”
§ Integrated analysis of multimodal single-cell data > Results > Multimodal heterogeneity within lymphoid populations
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Response patterns were also largely consistent across samples with the exception of one volunteer that exhibited a highly activated immune system in advance of vaccination and was removed from further analysis (Figure S6).”
§ Integrated analysis of multimodal single-cell data > Results > Characterizing the initial innate response to vaccination
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The WNN graph can be used as input for common downstream analytical tasks including tSNE or UMAP visualization, graph-based clustering, and the identification of developmental trajectories.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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None of the sources cited for this claim have been checked yet.
In the paper
“HVTN 087 (NCT01578889) was a phase 1a HIV vaccine trial that tested intramuscular electroporation of a DNA vaccine with or without IL-12 adjuvant delivered as a plasmid at months 0, 1 and 3 followed by boosting with VSV-vectored HIV gag vaccine at month 6 (Elizaga et al., 2018; Li et al., 2017).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Experimental model and subject details
What the sources say
- Not checked yetcites Elizaga et al., 2018
- Not checked yetcites Li et al., 2017
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As described for scRNA-seq analysis, while we use Seurat and Signac functions in this manuscript, any analytical workflow that computes a KNN graph for surface protein or chromatin accessibility data can also be used in the first step of WNN analysis.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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None of the sources cited for this claim have been checked yet.
In the paper
“This change in abundance may reflect these cells exiting circulation to play protective roles in barrier tissues during the antiviral immune response (Grimaldi et al., 2014; Hinks and Zhang, 2020; Provine and Klenerman, 2020).”
§ Integrated analysis of multimodal single-cell data > Results > Mapping query datasets to multimodal references
What the sources say
- Not checked yetcites Grimaldi et al., 2014
- Not checked yetcites Hinks and Zhang, 2020
- Not checked yetcites Provine and Klenerman, 2020
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To minimize batch effects, for each experiment we processed frozen PBMCs from four different patients at 3 different time points (day 0, day 3, and day 7).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we developed WNN analysis, a computational method that learns the information content of each modality and generates an integrated representation of multimodal data.”
§ Integrated analysis of multimodal single-cell data > Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This experiment includes a small proportion of spiked-in murine 3T3 cells as negative controls.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This enables the generation of a WNN graph: for each cell, this graph denotes the most similar cells in the dataset based on a weighted combination of protein and RNA similarities.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, WNN requires a dimensional reduction to describe the neighborhood structure between cells.”
§ Integrated analysis of multimodal single-cell data > Discussion > Limitations of the study
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although we have not observed this when using molecular data such as chromatin state, gene expression, and surface protein abundance, this assumption may be problematic when integrating morphological, functional, and molecular data.”
§ Integrated analysis of multimodal single-cell data > Discussion > Limitations of the study
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- The authors state this as an assumption: WNN assumes that modalities do not define conflicting sets of cell states.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This requirement is not compatible with categorical or low-dimensional data.”
§ Integrated analysis of multimodal single-cell data > Discussion > Limitations of the study
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In the paper
“sPCA is a generalization of PCA that can be used not only for unsupervised learning, but also for regression and classification problems (Barshan et al., 2011).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
What the sources say
- Not checked yetcites Barshan et al., 2011
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“Our approach is inspired by the concept of large margin nearest neighbors, which aims to identify kernel bandwidths that separate data points in the same class from those in different classes, even if the classes are closely related (Weinberger and Saul, 2009).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
What the sources say
- Not checked yetcites Weinberger and Saul, 2009
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By calculating cell-specific modality weights, WNN analysis solves an important technical challenge for the analysis of multimodal datasets and allows for flexible application across a range of modalities and data types.”
§ Integrated analysis of multimodal single-cell data > Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We emphasize that WNN analysis can leverage any scRNA-seq preprocessing workflow that generates a KNN graph.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“sPCA will then estimate a set of principal components that have maximal dependence on the response variable (Barshan et al., 2011).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
What the sources say
- Not checked yetcites Barshan et al., 2011
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that transcriptomic correlations were also much lower, likely due to the substantial technical noise inherent to scRNA-seq data.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For each cell, the procedure learns a set of modality weights, which reflect the relative information content for each data type in that cell.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A large ratio suggests that the local neighborhood of the cell, as defined by its RNA neighbors, better reflects its molecular state.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This step ‘upweights’ the contribution of highly variable peaks and down-weights peaks that are accessible in all cells.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We note that our supervised mapping is not limited to scRNA-seq and can be extended to other modalities.”
§ Integrated analysis of multimodal single-cell data > Discussion
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These marker panels can be used for flow cytometry with the same antibody clones in our CITE-seq panel, facilitating rapid enrichment and downstream analysis of these groups, and broadening the value of our resource.”
§ Integrated analysis of multimodal single-cell data > Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“This metric and approach are inspired by scmap (Kiselev et al., 2018).”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
What the sources say
- Not checked yetcites Kiselev et al., 2018
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In Stuart et al. (2019), we also provide methods to leverage an existing set of anchors in order to modify the underlying gene-expression levels, allowing shared cell types to cluster together across experiments.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
What the sources say
- Not checked yetcites Stuart et al. (2019)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We emphasize that sPCA takes as input a cell-cell similarity kernel, but does not require cells to be labeled or placed into discrete clusters.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To facilitate fair comparisons between our reference mapping workflow and scArches, we forced both methods to return the most likely annotation for each query cell.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“For example, the Wilk et al. (2020) dataset contains subsets of neutrophils, activated granulocytes that were not captured in our reference, as well as subpopulations of monocytes whose expression profiles are heavily perturbed in COVID-19 samples.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
What the sources say
- Not checked yetcites Wilk et al. (2020)
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This provides a starting point to integrate the two datasets, which can assist in the visualization and annotation of the query as described below.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“These anchors can be used to transfer discrete or continuous data from the reference onto the query.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
What the sources say
- Not checked yetcites Stuart et al. (2019)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“In Stuart et al. (2019), we demonstrate a workflow to identify reference-based transfer ‘anchors’ between reference and query datasets.”
§ Integrated analysis of multimodal single-cell data > STAR★Methods > Method details
What the sources say
- Not checked yetcites Stuart et al. (2019)
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper relies on this without support.
The eight volunteers and three time points are representative of the circulating immune system and vaccination response.
“We obtained PBMC samples from eight volunteers enrolled in an HIV vaccine trial (Elizaga et al., 2018; Li et al., 2017), with ages spanning from 20–49 years.”
“For each subject, PBMCs were collected at three time points: immediately before (day 0), 3 days, and 7 days following administration of a VSV-vectored HIV vaccine (Figure 3A).”
The paper relies on this without support.
The 24 human volunteers sampled are representative of the healthy immune system.
“Because these results are consistent in both pre- and post-vaccination time points, they likely describe general characteristics of the healthy immune system.”
The paper relies on this without support.
The reference dataset's cell states are sufficiently comprehensive to serve as a reference for mapping query PBMC datasets.
“having constructed a multimodal reference of the immune system, we sought to leverage this dataset to assist in the analysis and interpretation of additional single-cell experiments profiling human PBMCs (queries)”
The paper relies on this without support.
The protein data held out from mapping can serve as ground truth for assessing annotation accuracy.
“We verified the accuracy of our predictions using the query protein data, which was held out of the reference mapping procedure”
The authors state this as an assumption.
WNN assumes that modalities do not define conflicting sets of cell states.
“Second, WNN assumes that modalities do not define conflicting sets of cell states.”
The paper relies on this without support.
Cells originating from rare states should not have the same bandwidth constraint as cells originating from abundant states.
“We considered that cells originating from rare states should not have the same bandwidth constraint as cells originating from abundant states, and therefore considered a modified approach to select kernel bandwidths.”
The paper relies on this without support.
The 20 cells with the lowest non-zero Jaccard similarity to cell i represent cells that exhibit some similarity with cell i but are unlikely to reside in the same molecular state.
“We expect that these represent cells that exhibit some similarity with cell i, but are unlikely to reside in the same molecular state.”
The paper relies on this without support.
The first LSI dimension is typically correlated with technical metrics in ATAC-seq data.
“excluding the first dimension as this is typically correlated with technical metrics in ATAC-seq data”
The paper relies on this without support.
If the WNN procedure is performing well, gene expression levels within the examined neighborhoods should represent a homogeneous population with low levels of variability.
“If the WNN procedure is performing well, gene expression levels within these neighborhoods should represent a ‘homogeneous’ population with low levels of variability.”
The paper relies on this without support.
The statistical power of per-gene differential expression tests across time points within a cell state is heavily dependent on the abundance of the cell state.
“We note that when calculating DE genes and proteins within a cell state, across experimental time points, the statistical power of these per-gene tests is heavily dependent on the abundance of the cell state.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Batch correction using canonical correlation analysis (CCA) with ‘project_id’ as the integrated variable was performed prior to principal component analysis (PCA), UMAP, and clustering [28].”
From Ensemble of Time-Evolving SASP Gene Sets Identifies IGFBP7 and CDKN1A as a Potential Marker Pair for Senescent Fibroblast Subpopulations Across Tissues (2026) · cites this paper as [28]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Batch effects were corrected with the FindIntegrationAnchor() and the IntegrateData() function in the reciprocal PCA approach of the Seurat workflow.”
From TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration (2023) · cites this paper as [79]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Integration with single-cell proteomics (CITE-seq) enriches the information with proteomics that is both similarities and discrepancies with the transcriptome [7].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“In some contexts, inconsistencies in information can occur among omics data, although the measurement is conducted simultaneously within the same cells [7, 129].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“Developing robust and advanced computational methods to integrate and analyze multi-dimensional single-cell data is a pressing challenge to be addressed for the maturation of single-cell multi-omics [7].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“the hdWGCNA R package extends the data structures and functionality of the widely used Seurat package,5,6,7”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“To maximize usability among the genomics community, the hdWGCNA R package extends the data structures and functionality of the widely used Seurat package,5,6,7 and we developed an extensive documentation website for hdWGCNA demonstrating its use on new datasets.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [7]
The cited source says what the claim attributes to it.
In the citing paper
“To validate the cell clustering results, we performed reference mapping to the reference snRNA-seq dataset60 using the Azimuth software61 (Extended Data Fig. 5b, c).”
From Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability (2025) · cites this paper as [61]
The cited source says what the claim attributes to it.
In the citing paper
“To validate the annotation results, reference-mapping61 of Paired-Damage-seq RNA profile to published snRNA-seq dataset60 was performed.”
From Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability (2025) · cites this paper as [61]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“We specifically focused on the lumbar spinal cord, since this region undergoes multifaceted changes that lead to the irreversible degradation of neuronal function25,26.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [26]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“NR4A1/2 have been demonstrated to maintain HSC quiescence 32-34 .”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [32]
Experimental — accuracy not yet measuredIn the citing paper
“Alternatively, weighted nearest-neighbour (WNN)3 analysis learns cell-specific modality weights that reflect the modality information content to determine the importance of modalities in downstream analyses in the form of a neighbour graph.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [3]
In the citing paper
“Recently, Seurat v4 used a weighted nearest-neighbour analysis to integrate multiple modalities measured from a single cell and collectively define a cellular state174.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [174]
In the citing paper
“This unsupervised strategy learns cell-specific modality ‘weights’ that contain the information from each modality to determine its significance in subsequent analysis steps.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [174]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Flow-cytometry validation uses a different post-vaccination day
MajorThe results say flow cytometry validated the shift between day 0 and day 3, but the methods say whole blood for that assay was collected before vaccination and on days 1 and 7-not day 3.
“there was a strong shift in the ratio between classical and non-classical populations between day 0 and day 3 (Figure 6F). We validated this result, as well as the observed return to baseline ratios at day 7, using flow cytometry on the same samples (Figure 6G).”
“Whole blood collected immediately before the first VSV gag administration and then 1 and 7 days after was stained in TruCOUNT tubes”
NK-cell cluster count changes from six to five
MinorThe atlas results count six NK-cell clusters, but the subsequent description of NK-cell heterogeneity says NK cells were subdivided into five clusters.
“NK cells (6 clusters)”
“Similarly, NK cells were subdivided into five clusters, representing variation across a continuous landscape.”
Figure 5B names a CD103-positive, CD49-positive group despite mutually exclusive expression
MinorThe caption describes CD103 and CD49a expression as mutually exclusive and elsewhere names the comparison group as CD103-negative/CD49-positive. Its description of Figure 5B instead calls that group CD103-positive/CD49-positive.
“Differential expression of integrin-7 between CD103+ CD49a− and CD103+ CD49+ populations as measured by CITE-seq.”
“Differentially expressed genes, and enriched gene ontology terms, between CD103+ CD49a− and CD103− CD49+ populations.”
Targeted marker panels are assigned to different supplementary tables
MinorThe results direct readers to Table S2 for the targeted panels, whereas the methods say the panels are reported in Table S3.
“We report each of these panels in Table S2 to facilitate similar experiments for additional clusters in our dataset.”
“These panels are reported in Table S3.”
Conflicting percentages for the same annotation benchmark
MinorFor query cells receiving different annotations from Seurat and scArches, the Figure S7 caption reports stronger support for Seurat in 73.8% of cases, while the benchmarking methods report 79.4%.
“In 73.8% of cases, we observe stronger support for the Seurat annotation.”
“In 79.4% of cases, we observe stronger support for the Seurat annotation (Figure S7E).”
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Metadata
- Authors
- Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., Hoffman, P., Stoeckius, M., Papalexi, E., Mimitou, E. P., Jain, J., Srivastava, A., Stuart, T., Fleming, L. B., Yeung, B., Rogers, A. J., McElrath, J. M., Blish, C. A., Gottardo, R., Smibert, P., & Satija, R.
- Affiliations
- New York Genome Center (US)
- New York University (US)
- Stanford Medicine (US)
- BioLegend (United States) (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Smibert, Peter
- Satija, Rahul
- ORCID
- Hao, Yuhan
- Hao, Stephanie
- Andersen-Nissen, Erica
- Mauck, William M.
- Zheng, Shiwei
- Butler, Andrew
- Wilk, Aaron J.
- Darby, Charlotte
- Hoffman, Paul
- Stoeckius, Marlon
- Papalexi, Efthymia
- Mimitou, Eleni P.
- Jain, Jaison
- Srivastava, Avi
- Stuart, Tim
- Fleming, Lamar B.
- Yeung, Bertrand
- Rogers, Angela J.
- McElrath, Juliana M.
- Blish, Catherine A.
- Gottardo, Raphael
- Smibert, Peter
- Satija, Rahul
- Venue
- Cell
- Publisher
- openRxiv
- Published
- 2020-10-12
- Volume
- 184
- Issue
- 13
- Pages
- 3573-3587.e29
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC8238499
- OpenAlex
- W3164692211
- Cited by
- 15,508
- References
- 91
- Citations by year
- 2026: 2908 · 2025: 3703 · 2024: 3990 · 2023: 2850 · 2022: 1684 · 2021: 345 · 2020: 12
- FWCI
- 731.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Bill and Melinda Gates Foundation (OPP 1113682)
- Chan Zuckerberg Initiative (HCA-A1704-01895, EOSS-0000000082, U19AI128914)
- National Institutes of Health (1dp2hg009623-01, OT2OD026673, AI068618)
- National Human Genome Research Institute (DP2-HG-009623, R21HG009748, RM1HG011014)
- National Institute of Allergy and Infectious Diseases (U01AI068618, U19AI089986, UM1AI068618, UM1AI068614, UM1AI068635, U19AI128914)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- CITE-seq, peripheral blood mononuclear cells, cell identity, antibody profiling
- MeSH terms
- COVID-19, SARS-CoV-2, Animals, Cell Line, Humans, Immunity, Leukocytes, Mononuclear, Lymphocytes, Vaccination, 3T3 Cells, Sequence Analysis, RNA, Gene Expression Profiling, Mice, Single-Cell Analysis, Transcriptome
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