The DOI matches its Crossref record; 59 of the 60 references it cites were found in a registry or an index; 22 of the 30 sources its claims cite were checked for support.
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
Open accessPublished version of a preprint
Abstract
Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each droplet containing a single cell (singlet) and detect droplets containing two cells (doublets). These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 single-nucleotide polymorphisms (SNPs) per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of eight pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We apply demuxlet to assess cell-type-specific changes in gene expression in 8 pooled lupus patient samples treated with interferon (IFN)-β and perform eQTL analysis on 23 pooled samples.
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22 of 30 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“IFN-β stimulation induces widespread transcriptomic changes observed as a shift in the t-SNE projections of singlets24”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Could not verifycites [24] Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016). sourceExperimental — accuracy not yet measured
The Results section on UPR branch delineation describes t-SNE clustering of cells by UPR perturbation, not a shift following IFN-β stimulation. Searches for IFN-β, interferon beta, and singlets found no passage in our copy addressing the claimed response; page numbers were unavailable. (LLM, conf=0.76)
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In the paper
“a time point we previously found to maximize the expression of interferon-sensitive genes (ISGs) in dendritic cells (DCs) and T cells26, 27.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Could not verifycites [26] Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017). sourceExperimental — accuracy not yet measured
The Methods section on anti-CD3/CD28 stimulation (p. 11) describes stimulating Jurkat T cells for 4 hours, not a comparison of activation time points. Searches for six-hour activation and interferon-sensitive genes found no passage establishing when their expression peaks in dendritic cells or T cells. (LLM, conf=0.75)
- Could not verifycites [27] Farh, K.K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015). sourceExperimental — accuracy not yet measured
The Introduction’s discussion of immune-cell enhancers reports gene induction after T-cell stimulation, but not interferon-sensitive gene expression or its peak time. The Supplementary Methods describe an eight-hour T-cell stimulation; searches for a six-hour ISG maximum and for dendritic-cell results found no passage addressing the claim. (LLM, conf=0.74)
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In the paper
“suggesting multiplexing reduces technical effects due to separate sample processing22, 23.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Supportedcites [22] Cao, J. et al. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. Preprint at bioRxiv https://doi.org/10.1101/104844 (2017). source
The Discussion says that processing multiple samples concurrently within one experiment can potentially reduce batch effects compared with serial processing. This supports the citing sentence’s hedged claim about reducing technical effects from separate sample processing. (LLM, conf=0.92)
- Partly supportedcites [23] Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016). sourceExperimental — accuracy not yet measured
The Introduction distinguishes pooled screens from separately assessed, arrayed screens, and the Results report that Perturb-seq steps were performed in a single pool. Our copy does not state or test whether pooling reduces technical effects caused by separate sample processing, so that attributed benefit remains unsupported. (LLM, conf=0.78)
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In the paper
“While strategies to demultiplex cells from different species1, 10, 17 or host and graft samples17 have been reported, simultaneously demultiplexing and detecting doublets from more than two individuals has not been possible.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Supportedcites [1] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source
The citation is attached to the sentence’s first clause about reported strategies using cells from different species, not to the quoted conclusion about more than two individuals. The Results section (“Cell doublets”; page unavailable) reports a human–mouse mixture used to identify species-mixed cell libraries and estimate doublet rates. The paper does not establish the separate claim that simultaneous demultiplexing and doublet detection from more than two individuals was impossible. (LLM, conf=0.87)
- Could not verifycites [10] Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). sourceExperimental — accuracy not yet measured
The Introduction and Methods sections describe the SAM/BAM alignment format and SAMtools, not cell demultiplexing. Searches for simultaneous demultiplexing, doublet detection, and more than two individuals found no passage addressing the claimed limitation. (LLM, conf=0.76)
- Could not verifycites [17] Ye, C.J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014). sourceExperimental — accuracy not yet measured
The section ‘An experimental and analytic strategy to dissect inter-individual variation’ describes measuring T-cell transcripts from multiple donors, but does not address demultiplexing cells or detecting doublets. Searches for those methods and for the claimed limit of two individuals found no relevant passage in our copy. (LLM, conf=0.76)
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The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The analysis of multiplexed dscRNA-seq data recovers cell type-specific gene regulatory programs affected by interferon stimulation consistent with published IFN-β signatures in mouse and humans29.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Could not verifycites [29] Tung, P.-Y. et al. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7, 39921 (2017). sourceExperimental — accuracy not yet measured
The Abstract describes a study of batch effects in single-cell RNA-seq data from human iPSC lines, and the Results section on regulatory noise compares genes across individuals. Searches for interferon stimulation, IFN-β signatures, and multiplexed dscRNA-seq found no passage in our copy addressing the claimed finding. Page numbers were not provided. (LLM, conf=0.75)
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The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“for each cell type, the variance across individuals22, 30 is also higher than the variance across synthetic replicates (Lin’s concordance = 0.007–0.20)”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Could not verifycites [22] Cao, J. et al. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. Preprint at bioRxiv https://doi.org/10.1101/104844 (2017). sourceExperimental — accuracy not yet measured
The ‘Identifying cell types’ section discusses variation between two C. elegans experiments and suggests developmental timing or preparation, rather than technical variation, as a cause. The ‘Robustness of sci-RNA-seq’ section reports correlations between experiments, but neither section compares variance across individuals with synthetic replicates or reports Lin’s concordance of 0.007–0.20 (searched those terms and the variance comparison). (LLM, conf=0.75)
- Could not verifycites [30] Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017). sourceExperimental — accuracy not yet measured
The section “The mechanism axis: modeling gene regulation” discusses variation between cells, not variance across individuals within each cell type. Searches for synthetic replicates and Lin’s concordance found no comparison or reported range of 0.007–0.20 in our copy. (LLM, conf=0.76)
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In the paper
“t-SNE projections of the pool and each individual are not confounded by well-to-well effects (Supplementary Fig. 11a).”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“we simulated 6,145 cells from a set of 8 related individuals from 1000 Genomes21. In this simulation, 50 SNPs per cell would allow demuxlet to correctly assign over 98% of cells”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Not matchedcites 1000 Genomes21
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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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
“Detected doublets form distinct clusters near the periphery of other clusters defined by cell type (Supplementary Fig. 13).”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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 a pool of 8 individuals and a set of uncorrelated SNPs each with 50% minor allele frequency (MAF), 4 reads overlapping SNPs are sufficient to uniquely assign a cell to the donor of origin (Fig. 1b)”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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- Probably not a substantial claim. 11
- It may state two separate claims as one. 28
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Droplet single cell RNA-sequencing (dscRNA-seq) has increased substantially the throughput of single cell capture and library preparation1, 10, enabling the simultaneous profiling of thousands of cells.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Supportedcites [1] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source
The Introduction describes analyzing mRNA expression in thousands of individual cells using droplets for parallel analysis. The Discussion reports that Drop-Seq can prepare 10,000 single-cell libraries in twelve hours, supporting the claim of rapid, large-scale profiling. (LLM, conf=0.94)
- Could not verifycites [10] Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). sourceExperimental — accuracy not yet measured
The Introduction and Methods passages (pages not supplied) describe the SAM alignment format and SAMtools, not droplet single-cell RNA sequencing. Searches for droplet sequencing, cell-capture throughput, and profiling thousands of cells found no passage addressing the claim. (LLM, conf=0.76)
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- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“IFN-β, a potent cytokine that induces genome-scale changes in the transcriptional profiles of immune cells24, 25.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Could not verifycites [24] Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016). sourceExperimental — accuracy not yet measured
The Results section on UPR transcriptional programs describes responses to thapsigargin and tunicamycin, not IFN-β in immune cells. The Discussion mentions dendritic-cell activation only as a parallel study. Searches for IFN-β and interferon-related transcriptional responses found no passage addressing the attributed claim; page numbers were not available in our copy. (LLM, conf=0.75)
- Not checked — no full text of the sourcecites [25] Jaitin, D.A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. Cell 167, 1883–1896.e15 (2016). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Inspired by models and algorithms developed for detecting contamination in DNA sequencing18, demuxlet is fast, accurate, scalable, and compatible with standard input formats17, 19, 20.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Could not verifycites [17] Ye, C.J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014). sourceExperimental — accuracy not yet measured
The section “An experimental and analytic strategy to dissect inter-individual variation” describes Nanostring measurements and eQTL analysis, not demuxlet. Searches for demuxlet’s speed, accuracy, scalability, input-format compatibility, and single-cell demultiplexing found no passage addressing those properties in our copy. (LLM, conf=0.77)
- Could not verifycites [19] Palmer, C., Diehn, M., Alizadeh, A.A. & Brown, P.O. Cell-type specific gene expression profiles of leukocytes in human peripheral blood. BMC Genomics 7, 115 (2006). sourceExperimental — accuracy not yet measured
The Methods sections describe leukocyte gene-expression microarrays and data filtering, not demuxlet. Searches for demuxlet, demultiplexing, scalability, and standard input formats found no passage in our copy addressing the claimed properties. No page numbers were available for these sections. (LLM, conf=0.78)
- Not checked — no full text of the sourcecites [20] Saveanu, L. et al. Concerted peptide trimming by human ERAP1 and ERAP2 aminopeptidase complexes in the endoplasmic reticulum. Nat. Immunol. 6, 689–697 (2005). source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“The capability to demultiplex and identify doublets using natural genetic variation reduces the per-sample and per-cell library preparation cost of single-cell RNA-sequencing, does not require synthetic barcodes or split-pool strategies36–40, and captures biological variability among individual samples while limiting unwanted technical variability.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Could not verifycites [36] Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
- Not matchedcites [37] Dabney, A., Storey, J.D. & Warnes, G.R. qvalue: Q-value estimation for false discovery rate control. R package version 1 (2010).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [38] Falconer, D.S. & Mackay, T.F. Introduction to Quantitative Genetics, 4th edn. (Pearson, 1996).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Could not verifycites [39] Loh, P.R., Palamara, P.F. & Price, A.L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
- Could not verifycites [40] Shabalin, A.A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Among other cell type-specific local eQTLs are CD52, a gene ubiquitously expressed in leukocytes that only has eQTLs in monocyte populations, and DIP2A, a gene with an eQTL only in NK cells that is associated with immune response to vaccination in peripheral blood35.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Could not verifycites [35] Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015). sourceExperimental — accuracy not yet measured
The Results section on dispersed cell populations discusses gene expression in zebrafish embryos, not cell type-specific eQTLs in human immune cells. Searches for CD52, DIP2A, monocytes, NK cells, and vaccination found no passage in our copy addressing the claim. (LLM, conf=0.78)
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- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“For 709 genes, estimates of fold change in response to IFN-β stimulation in myeloid and CD4+ cells are consistent with estimates in monocyte derived dendritic cells28 and CD4+ T cells27, respectively (Supplementary Fig. 17) and correlated with qPCR results of sorted CD4+ T cells (Supplementary Fig. 18).”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
What the sources say
- Could not verifycites [28] Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015). sourceExperimental — accuracy not yet measured
The source’s “Cell cycle variation affects global gene expression” section describes CD4+ T cells stimulated with IL-4 to study TH2 differentiation, not IFN-β responses. Searches for 709 genes, IFN-β fold changes, dendritic-cell comparisons, and sorted-cell qPCR found no passage addressing the claimed agreement or correlation. (LLM, conf=0.77)
- Could not verifycites [27] Farh, K.K.-H. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015). sourceExperimental — accuracy not yet measured
The Supplementary Methods (Cell Isolation and Culture; Analysis of ex vivo Stimulation-dependent Enhancers, pages not provided) describe CD4+ T-cell sorting and stimulation with PMA/ionomycin or anti-CD3/CD28, not IFN-β. Searches for “709 genes,” IFN-β fold changes, and the stated comparisons and qPCR correlation found no passage addressing the claim. (LLM, conf=0.76)
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- Could not verify
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
“We show that 50 SNPs per cell allows demultiplexing of 97% of singlets and identification of 92% of doublets in pools of up to 64 individuals”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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In the paper
“While 6 genes were differentially expressed between wells W1 and W2 (DESeq2 on pseudobulk counts, FDR < 0.05), only 2 genes were differentially expressed between W1 and W2 individuals in well W3 (FDR < 0.05)”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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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
“In wells W1, W2 and W3, demuxlet identified 91% (3332/3645), 91% (3864/4254), and 86% (5348/6205) of droplets as singlets (likelihood ratio test, L(singlet)/L(doublet) > 2), of which 25% (+/− 2.6%), 25% (+/− 4.6%) and 12.5% (+/− 1.4%) mapped to each donor, consistent with equal mixing of individuals in each well.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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In the paper
“The estimated doublet rate of 10.9% in each condition is consistent with predicted rates (Fig. 2C) and the observed and expected frequencies of doublets for each pair of individuals are highly correlated (R=0.98)”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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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
“demuxlet identified 138/3645, 165/4254, and 384/6205 doublets corresponding to doublet rates of 5.0%, 5.2% and 7.1%, consistent with the expected doublet rates estimated from mixed species experiments”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“demuxlet identified 91% (426/466) of synthetic doublets as doublets or ambiguous, correctly recovering the sample identity of both cells in 403/426 (95%) doublets”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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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
“We obtained 14,619 (control) and 14,446 (stimulated) cell-containing droplets, of which demuxlet identified 83% (12,138) and 84% (12,167) as singlets.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“Singlets identified by demuxlet in all three wells cluster into known immune cell types (Fig. 2d) and are correlated with bulk RNA-sequencing of sorted cell populations (R=0.76–0.92)”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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 the same individuals from different wells, t-distributed stochastic neighbor embedding (t-SNE) of dscRNA-seq data are qualitatively consistent, and estimates of cell type proportions are highly correlated (R = 0.99)”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“In CD14+CD16− monocytes, the correlation of mean expression between pairs of synthetic replicates from the same individual (>99%) is greater than from different individuals (~97%)”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“We found between 15 to 827 genes with statistically significant inter-individual variability in control cells and 7 to 613 in stimulated cells (Pearson correlation, FDR < 0.05), with most found in classical monocytes (cM) and CD4+ helper T (Th) cells.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“At these depths, we recovered >90% of captured transcripts in each sequencing experiment.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Methods > Droplet-based capture and sequencing
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 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
“We found a total of 32 local eQTLs (+/− 100kb, FDR < 0.1), 22 of which were detected in only one cell type”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“IFN-β did not affect cell type proportions between control and stimulated cells (Supplementary Fig. 14), and these were consistent with flow cytometry measurements (R=0.88)”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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
“Demuxlet identified 71% (10,766/15,250), 73% (16,618/22,619) and 60% (15,596/25,918) of droplets as singlets, correctly assigning 99% of singlets from the first two pools, W1 and W2 (10,740/10,766 and 16,616/16,618).”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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
“HLA-DQA1 has local eQTLs only in some cell types (P <2.11×10−15, Cochran’s Q) while HLA-DQA2 has local eQTLs in all antigen presentation cells (P < 1.02e10−43, Cochran’s Q).”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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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
“we found that expression variability was determined by variability in cell type proportion (Fig. 4A) and reproducible between batches (Supplementary Fig. 20).”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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
“For example, if a 1,000-cell run without multiplexing results in 990 singlets with a 1% undetected doublet rate, multiplexing 1,570 cells each from 63 samples can theoretically achieve the same rate of undetected doublets, producing up to a 37-fold more singlets (36,600) if the sample identity of every droplet can be perfectly demultiplexed”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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
“Furthermore, if droplets containing multiple cells from different individuals could be detected, pooled cells could be loaded at higher concentrations, enabling additional reduction in per-cell library preparation cost.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✗ Asserted: fails✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: In standard workflows, dscRNA-seq of many samples in parallel remains challenging to implement.
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 optimal multiplexed designs where cells from tens of unrelated individuals should be pooled, loaded at concentrations 2–10x higher than standard workflows, and sequenced to at least 1,000 UMIs per cell.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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
“The ability to demultiplex cells is a function of the number of individuals multiplexed, the depth of sequencing or number of read-overlapping SNPs, and relatedness of multiplexed individuals.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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
“If the genetic identity of each cell could be determined, pooling cells from different individuals in one microfluidic run would result in lower per-sample library preparation cost and eliminate confounding effects.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: In standard workflows, dscRNA-seq of many samples in parallel remains challenging to implement.
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
“Batch information for each sample as well as the first 3 principal components of the expression matrix were used as covariates.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Methods > Quantitative trait mapping in major immune cell types
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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
“we remove all doublets and ambiguous droplets to conservatively estimate singlets.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Methods > Screening for droplets containing multiple samples
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
“Demuxlet enables reliable estimation of cell type proportion, recovers cell type-specific transcriptional response to stimulation, and could facilitate further genetic and longitudinal analyses”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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
“Demultiplexed individuals were used as replicates for differential expression analysis.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Methods > Differential expression analysis
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“the qvalue package was used to calculate FDR < 0.05 55.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Methods > Differential expression analysis
What the sources say
- Not matchedcites [55]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
- Not matched
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 demonstrate the ability of multiplexed dscRNA-seq to characterize inter-individual variation in immune response and when integrated with genetic data, reveal cell type-specific genetic control of gene expression, which would be undetectable when bulk tissues are analyzed.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
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
“While demuxlet could in principle be applied to sequencing solid tissue, standardizing sample processing and preservation remain major challenges.”
§ Multiplexed droplet single-cell RNA-sequencing using natural genetic variation > Abstract
Strength:hedged· 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. It may state two separate claims as one.
The paper relies on this without support.
In standard workflows, dscRNA-seq of many samples in parallel remains challenging to implement.
“However, in standard workflows, dscRNA-seq of many samples in parallel remains challenging to implement.”
The authors state this as an assumption.
When a base call is incorrect, the probability of the observed base call given the genotype and error follows the distribution in Supplementary Table 4.
“When ecvi = 1, we assume that Pr(bcvi|g, ecvi) follows Supplementary Table 4.”
The authors state this as an assumption.
The latent variable indicating whether a base call is correct follows a Bernoulli distribution with parameter 10−qcvi10.
“ecvi is assumed to follow Bernoulli (10−qcvi10) where qcvi is a phred-scale quality score of the observed base call.”
The paper relies on this without support.
The prior probability of genotype g follows a Binomial(2, pv) distribution, where pv is the population allele frequency of the alternate allele.
“Pr(g) ~ Binomial(2, pv) and pv is the population allele frequency of the alternate allele.”
The authors state this as an assumption.
The expected rate of multiplets for a given number of loaded cells follows an exponential model: d(x)=1−(1−d0)^(x/x0).
“Then the expected multiplet rates when x cells are loaded can be modeled exponentially as d(x)=1−(1−d0)xx0.”
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 cited source doesn’t support the claim as written.
In the citing paper
“However, since the identities of the samples cannot be determined, this approach dissociates other sample information from the sequencing data, thereby reducing the power of the conclusions [9].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [9]
Checked by a reasoning model — may be wrong.
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
“The integration of scRNA-seq with eQTL was first reported in 2018 [94].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [94]
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
“The maturation of single-cell technologies now enables large-scale comparisons of cell states within complex tissues, thus providing the appropriate resolution to dissect cell-type-specific responses to perturbation5,6.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [5]
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
“Failing to account for the intrinsic variability of biological replicates produced hundreds of DE genes between randomly assigned replicates (Fig. 4d and Supplementary Fig. 6a, b).”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [5]
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
“Demuxlet, one of the first bioinformatic tools developed, showed that as few as 50 genetic variants were determined to be required to separate up to 64 samples [9].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [9]
Experimental — accuracy not yet measured
The cited source says what the claim attributes to it.
In the citing paper
“Kang et al. utilized multiplexed droplet scRNA-seq to profile eight immune cell populations from 23 donors and subsequently conducted eQTL analysis, identifying 32 cis-eQTLs, 22 of which were cell-specific.”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [94]
The cited source says what the claim attributes to it.
In the citing paper
“Within our compendium, we identified a total of fourteen studies with control groups that included six or more samples5,6,15,49–59.”
From Confronting false discoveries in single-cell differential expression (2021) · cites this paper as [5]
The cited source says what the claim attributes to it.
In the citing paper
“SNP-based approaches have been benchmarked and shown to be highly effective at separating human samples (Kang et al, 2018; Huang et al, 2019; Xu et al, 2019; Heaton et al, 2020; Weber et al, 2021).”
From Evaluation of genetic demultiplexing of single-cell sequencing data from model species (2023) · cites this paper as Kang et al, 2018
The cited source says what the claim attributes to it.
In the citing paper
“However, while the number of SNPs from RNA-Seq was not as high as from the array, they exceeded the minimum number reported necessary for demultiplexing [9].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [9]
The cited source says what the claim attributes to it.
In the citing paper
“Increasing biological replicates through sample pooling prior to library preparation followed by demultiplexing (i.e., identifying and isolating the sequencing data related to each sample within the pool) to the original biological sample is an effective strategy to overcome these issues as well as reduce batch effects [5–9].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [9]
In the citing paper
“SingleR differentiated cellular subtypes such as memory and naïve B and T cells”
From Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage (2019) · cites this paper as [22]
In the citing paper
“Between IFN-β and control conditions in all cells, the top 25 differentially active TFs, including the top-ranked SREBP (Supplementary Fig. 16d), are consistent with previously reported interferon induction of lipogenesis57, which has not been described in the previous scRNA-seq analysis56.”
From Transformer for one stop interpretable cell type annotation (2023) · cites this paper as [56]
In the citing paper
“The authors identified ground truth multiplets as cell barcodes associated with reads containing polymorphisms from more than one individual (Figure 4A).”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Kang et al., 2018
In the citing paper
“However, we observe in our own data set as well as in published studies47,78 slightly higher doublet rates than shown in77.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [47]
In the citing paper
“Doublet detection methods such as Demuxlet47 and Scrublet76 enable faithful detection of those to exclude the doublets from the downstream analysis.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [47]
In the citing paper
“The default for the doublet rate is based on reference values from 10X Genomics and is a lower bound compared to the doublet rates we estimate for our own data and to rates reported by other studies47,78.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [47]
In the citing paper
“Using strategies such as cell tagging (preprint: Gehring et al, 2018), or via genetic variation (Kang et al, 2018), it is possible to demultiplex cells that were pooled in the experiment.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Kang et al, 2018
In the citing paper
“This set‐up tells us how individual cell identities react transcriptionally under particular experimental conditions (Kang et al, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Kang et al, 2018
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.
W1 and W2 are assigned to different pools
MinorThe initial W1 and W2 wells contain four donors each, but the later passage calls the first two additional pools W1 and W2 even though they contain seven and eight donors. The text does not distinguish these as reused labels.
“W1: patients S1-S4, W2: patients S5-S8 and W3: patients S1-S8”
“we sequenced an additional 15,250 (7 donors), 22,619 (8 donors) and 25,918 cells (15 donors; 8 lupus patients, 5 rheumatoid arthritis patients, and 2 healthy controls). Demuxlet identified 71% (10,766/15,250), 73% (16,618/22,619) and 60% (15,596/25,918) of droplets as singlets, correctly assigning 99% of singlets from the first two pools, W1 and W2”
Citations
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · cited 1×
“Inspired by models and algorithms developed for detecting contamination in DNA sequencing 18 , demuxlet is fast, accurate, scalable, and compatible with standard input formats 17, 19, 20 .”
MethodSTAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Self-citationThe variant call format and VCFtools
Danecek, P., Auton, A., Abecasis, G. R., Albers, C. A., Banks, E., DePristo, M. A., Handsaker, R. E., Lunter, G. A., Marth, G., Sherry, S. T., et al. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr330 · Cited by 16,450
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · cited 1×
“Cellular suspensions were loaded onto the 10x Chromium instrument (10x Genomics) and sequenced as described in Zheng et al 17 .”
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · cited 2×
“Let ecvi ∈ {0, 1} be a latent variable indicating whether the base call is correct (0) or not (1), then given ecvi = 0, bcvi ∈ {R = 0, A = 1} and ∼ Binomial 2 2 , g when g ∈ {0, 1, 2} is the true genotype of the sample corresponding to c-th droplet at v-th variant.”
BackgroundSpatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · cited 1×
“To identify known immune cell populations in PBMCs, we used the Seurat package to perform unbiased clustering on the 2.7k PBMCs from Zheng et al., following the publicly available Guided Clustering Tutorial 17, 53 .”
MethodSingle-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
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txci-ATAC-seq, a massive-scale single-cell technique to profile chromatin accessibilityPreprint
Zhang, H., Mulqueen, R. M., Iannuzo, N., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.11.540245 · Cited by 3
Multi-context genetic modeling of transcriptional regulation resolves novel disease lociPreprint
Thompson, M., Gordon, M. G., Lu, A. C., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.23.461579 · Cited by 3
Li, X., Tang, S., Zhang, Y., et al. (2023) · Angewandte Chemie · doi:10.1002/ange.202315552 · Cited by 3
Yu, J. X., Suh, J. M., Popova, K. D., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.13.628239 · Cited by 3
ROGUE: an entropy-based universal metric for assessing the purity of single cell populationPreprint
Liu, B., Li, C., Li, Z., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/819581 · Cited by 3
Schörnig, M., Ju, X.-C., Fast, L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.21.094912 · Cited by 3
Wilson, G. W., Duong, A., Moshkelgosha, S., Bader, G. D., Keshavjee, S. H., Martinu, T., Juvet, S., & Yeung, J. C. (2023) · Frontiers in Transplantation · doi:10.3389/frtra.2023.1161146 · Cited by 3
Somatic mutations reveal the ontogeny of microglia in human ageing
Belk, J. A., Zhang, Y., Reilly, E., Shi, Q., Liu, D. D., Womack-Gambrel, N., van der Linde, M., Ma, L., Paul, D., Enciso, A. M., et al. (2026) · Nature · doi:10.1038/s41586-026-10939-0 · Cited by 13
Benoit, C. R., Sattler, L. B., Aylward, A. J., Pembridge, O. G., Kim, B., Muratore, C., Liao, M., He, A., Ashour, N., Fancher, S. B., et al. (2025) · Brain · doi:10.1093/brain/awaf221 · Cited by 3
Binned multinomial logistic regression for integrative cell-type annotation
Motwani, K., Bacher, R. L., & Molstad, A. J. (2023) · The Annals of Applied Statistics · doi:10.1214/23-aoas1769 · Cited by 4
Xu, X., & Li, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbac608 · Cited by 3
Tuning hyperparameters of doublet‐detection methods for single‐cell RNA sequencing data
Xi, N. M., & Vasilopoulos, A. (2023) · Quantitative Biology · doi:10.15302/j-qb-022-0324 · Cited by 3
Brown, M. A., Dodd, A., Shi, F., Greenwood, E., Nagpal, S., Kolachala, V. L., Kugathasan, S., & Gibson, G. C. (2024) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjae055 · Cited by 4
Temporal trajectories of human brown and white adipocyte progenitors at single cell resolutionPreprint
Palani, N. P., Timshel, P., Folkertsma, P., Grønning, A. G. B., Henriksen, T., Peijs, L., Jensen, V., Hvid, K., Sun, W., Jespersen, N., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1328581/v1 · Cited by 2
Label-free single-cell RNA multiplexing leveraging genetic variability
Hoeft, K., Bleckwehl, T., Schumacher, D., Kim, H., Meyer, R., Long, Q., Zhang, L., Möller, C., Clahsen-van Groningen, M. C., Babler, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54270-6 · Cited by 3
Cell type-specific associations with Alzheimer’s Disease conserved across racial and ethnic groupsPreprint
Luquez, T., Algoo, J., Chiu, R., Mares, J. A., Yadav, A., Lam, M., Gaur, P., Lai, X., Lee, D. I., Paryani, F., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.14.648597 · Cited by 3
Shared acute phase traits in effector and memory human CD8 T cells
Marraco, S. A. F., Alpern, D., Lofek, S., Lourenço, J., Bovay, A., Hajjami, H. M.-E., Delorenzi, M., Deplancke, B., & Speiser, D. E. (2021) · Current Research in Immunology · doi:10.1016/j.crimmu.2021.12.002 · Cited by 4
Computational approaches for hematopoietic stem cells
Raghav, P. K., Banerjee, B., Kumar, R., Raghav, A., Lathwal, A., & Chadha, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13222-3.00013-7 · Cited by 2
Single-cell multiomics of neuron activation reveals context-specific genetics of brain disorders
Liang, L., Zhang, S., Wang, Z., Zhang, H., Li, C., Thapa, C., Oh, E., Sirkin, D., Sun, X., Barishman, A., et al. (2026) · Science · doi:10.1126/science.adw3949 · Cited by 3
splatPop: simulating population scale single-cell RNA sequencing dataPreprint
Azodi, C. B. D., Zappia, L., Oshlack, A., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.17.448806 · Cited by 2
Sun, H., Wu, L., Zhao, X., Huo, Y., Dong, P., Pang, A., Zheng, Y., Han, Y., Ma, S., Jiang, E., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1433091 · Cited by 3
DCATS: differential composition analysis for complex single-cell experimental designsPreprint
Lin, X., Chau, C., Huang, Y., & Ho, J. W. K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.21.485232 · Cited by 2
A novel paradigm for single-cell annotation in stem cell research
Atkeson, T., Wilson, S. B., Ruiz-Morales, E. R., Little, M. H., Vento‐Tormo, R., Neavin, D. R., & Powell, J. E. (2025) · Stem Cell Reports · doi:10.1016/j.stemcr.2025.102707 · Cited by 3
G3DC: a Gene-Graph-Guided selective Deep Clustering method for single cell RNA-seq dataPreprint
He, S., Fan, J., & Yu, T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.15.524109 · Cited by 2
HD Spot: Interpretable Deep Learning Classification of Single Cell Transcript DataPreprint
Prince, E. W., & Hankinson, T. C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/822759 · Cited by 2
Inference of B cell clonal families using heavy/light chain pairing informationPreprint
Ralph, D., & Matsen, F. A., IV (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.22.485213 · Cited by 2
Single-cell and spatial characterization of the human pancreas in cystic fibrosis
Mummey, H. M., Corban, S., Lucero, J. D., Melton, R. L., Pittigher, M., Johnston, V. C., Deutsch, G. H., D’Antonio‐Chronowska, A., Wang, A. S., Hull-Meichle, R. L., et al. (2026) · Cell Reports · doi:10.64898/2025.12.14.694120 · Cited by 2
Zarinsefat, A., Hartoularos, G. C., Chandran, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.11.281782 · Cited by 2
Zenk, F. L., Fleck, J. S., Jansen, S. M. J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.12.557341 · Cited by 2
Li, T., Alvarez, M., Liu, C., Abuhanna, K. D., Sun, Y., Ernst, J., Plath, K., Balliu, B., Luo, C., & Zaitlen, N. (2025) · eLife · doi:10.7554/elife.106769.1 · Cited by 2
Self-citationD’Antonio‐Chronowska, A., Donovan, M. K. R., Benaglio, P., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/644633 · Cited by 2
Liu, W., & Zhao, Z. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf090 · Cited by 3
An expanded role for single-cell chemical genomics profiling in drug discovery
Wyatt, A., Hoffer-Hawlik, K., Giglio, R. M., Azizi, E., & McFaline‐Figueroa, J. L. (2026) · Biochemical Journal · doi:10.1042/bcj20253273 · Cited by 3
Kennedy, V., Roy, R., Peretz, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.07.579345 · Cited by 2
Hung, A., Housman, G., Briscoe, E. A., Cuevas, C., & Gilad, Y. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.22.432314 · Cited by 2
Joint profiling of cell morphology and gene expression during in vitro neurodevelopment
Sundaresh, A., Meistermann, D., Lampela, R., Yang, Z., Woldegebriel, R., Ganna, A., Puigdevall, P., & Kilpinen, H. (2025) · eLife · doi:10.7554/elife.102578 · Cited by 4
Clonal dynamics of SARS-CoV-2-specific T cells in children and adults with COVID-19Preprint
Khoo, W. H., Jackson, K., Phetsouphanh, C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.30.478400 · Cited by 2
Cell-type signatures of Alzheimer’s disease shared across population groups
Luquez, T., Algoo, J., Chiu, R., Mares, J. A., Yadav, A., Lam, M., Gaur, P., Lai, X., Lee, D. I., Paryani, F., et al. (2026) · Nature · doi:10.1038/s41586-026-10793-0 · Cited by 11
Analysis of single-cell RNA sequencing data based on autoencodersPreprint
Tangherloni, A., Ricciuti, F., Besozzi, D., Lió, P., & Cvejic, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/727867 · Cited by 2
Zhu, H., Asiaee, A., Azinfar, L., Li, J., Liang, H., Irajizad, E., Do, K.-A., & Long, J. P. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf426 · Cited by 8
Koh, J., Kim, D. R., Son, S., Park, H., Kim, K.-R., Min, S., Lee, H. S., Jhun, B. W., Kang, E., Jung, I., et al. (2024) · Journal of Clinical Immunology · doi:10.1007/s10875-024-01687-9 · Cited by 3
Baldonado, G. C. L., Kumar, S., Jin, J. Q., Fang, X., Ildardashty, A., Braun, M., Neuhaus, I. M., Mathes, E., Bhutani, T., & Liao, W. (2025) · Journal of Translational Medicine · doi:10.1016/j.jid.2025.06.088 · Cited by 2
Disruption of Cell-Type-Specific Molecular Programs of Medium Spiny Neurons in AutismPreprint
Yuan, G., Suresh, V., Wigdor, E. M., Hao, Y., Leonard, R., Steyert, M. R., Griffiths, M., Evans, C., Rohani, N., Weiss, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.05.686845 · Cited by 4
Sokolowski, E. K., Kursawe, R., Selvam, V., Bhuiyan, R. M., Thibodeau, A., Zhao, C. G., Spracklen, C. N., Ucar, D., & Stitzel, M. L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.17.580646 · Cited by 2
Xin, H., Yan, Q., Jiang, Y., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/828483 · Cited by 2
Scarf: A toolkit for memory efficient analysis of large-scale single-cell genomics dataPreprint
Dhapola, P., Rodhe, J., Olofzon, R., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.02.441899 · Cited by 2
DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing dataPreprint
Simon, L. M., Yan, F., & Zhao, Z. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/864165 · Cited by 2
Amarasinghe, S. L., Yang, P., Voogd, O., Yang, H., Du, M. R. M., Su, S., Brown, D. V., Jabbari, J. S., Bowden, R. J., & Ritchie, M. E. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad105 · Cited by 2
ShinySC: An R/Shiny-based desktop application for seamless analysis of scRNA-Seq data
Huang, P.-J., Tsai, F.-Y., Wu, Y.-J., et al. (2025) · Biomedical Journal · doi:10.1016/j.bj.2025.100885 · Cited by 2
Wang, Y., Lu, T., Chen, X., Yao, Z., & Wong, K. (2025) · Bioengineering · doi:10.3390/bioengineering12080884 · Cited by 4
One for All: A Pooled Approach to Classify Functional Impacts of Multiple Mutations
Jourdon, A., & Vaccarino, F. M. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.06.016 · Cited by 2
Gazzinelli-Guimarães, P. H., Sanku, G., Sette, A., et al. (2022) · medRxiv · doi:10.1101/2022.01.20.22269491 · Cited by 2
Almonte-Loya, A., Wang, W., Houshdaran, S., Tang, X., Flynn, E., Liu, B., Kushnoor, D., NEZHAT, C. R., Vilella, F., Simón, C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.21.666016 · Cited by 2
Single-cell multiomics and chromatin structure reveal gene-regulatory dynamics in heart failure
Xie, Y., Tucciarone, L., Farah, E. N., Chang, L., Yang, Q., Shankar, T. S., Elison, W. S., Tran, S., Djulamsah, J., Lie, A., et al. (2026) · Science · doi:10.1126/science.ady6893 · Cited by 8
Choi, J., Lee, W., Yoon, J.-K., Choi, S. M., Lee, C., Moon, H., Cho, S., Chung, J.-H., Yang, H., & Kim, J. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04676-0 · Cited by 2
Towards the detection of copy number variation from single sperm sequencing in cattle
Yang, L., Gao, Y., Oswalt, A., Fang, L., Boschiero, C., Neupane, M., Sattler, C. G., Li, C., Seroussi, E., Xu, L., et al. (2022) · BMC Genomics · doi:10.1186/s12864-022-08441-8 · Cited by 2
Variational autoencoders learn universal latent representations of metabolomics dataPreprint
Gomari, D. P., Schweickart, A., Cerchietti, L. C. A., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.14.426721 · Cited by 2
Analysing Genetic Programs of Cell Differentiation to Study Cardiac Cell Diversification
Wu, Z., Shen, S., Sun, Y., Werner, T., Bradford, S. T., & Palpant, N. J. (2022) · Springer eBooks · doi:10.1007/978-3-030-86140-7_8 · Cited by 1
SNACS: a tool for demultiplexing single-cell DNA sequencing data
Kennedy, V. E., Roy, R., Peretz, C. A. C., Koh, A., Tran, E., Smith, C. C., & Olshen, A. B. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf265 · Cited by 4
Unbiased integration of single cell transcriptome replicatesPreprint
Loza, M., Teraguchi, S., Standley, D. M., & Díez, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.05.442380 · Cited by 2
Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCoverPreprint
Wang, A., Hicks, S. C., Geman, D., et al. (2025) · Genome Research · doi:10.1101/gr.280539.125 · Cited by 2
Li, T., Ilano, A., Arias-Badia, M., Luong, D., Chang, H., Kwek, S. S., Allaire, K. M., Chumber, A. K., Sakamoto, M., Clark, M., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2506168123 · Cited by 4
Kim, Y. S., Jeong, J., Heo, G., Kwon, Y. W., Lee, J., Jeong, Y. J., Park, G. T., Jeong, S., Hwang, K. W., Choi, M., et al. (2026) · Advanced Science · doi:10.1002/advs.202514365 · Cited by 2
Yang, P., Bao, H., Sun, G., & Zhang, Y. (2025) · Clinical Immunology · doi:10.1016/j.clim.2025.110646 · Cited by 3
Wu, X., Bhatt, V., Liu, J. J., & Hong, L. (2026) · Biochemical and Biophysical Research Communications - BBRC · doi:10.1016/j.bbrc.2026.153273 · Cited by 2
Inferring and perturbing cell fate regulomes in human cerebral organoids
Fleck, J. S., Jansen, S. M., Wollny, D., et al. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.5242913 · Cited by 2
Choi, S. J., Lee, W., Kim, S. C., Jo, H.-Y., Park, H., Kim, H. B., Park, W., Park, S. H., Ko, J., & Lee, J. S. (2024) · Vaccine · doi:10.1016/j.vaccine.2024.07.019 · Cited by 2
Effects of somatic mutations on cellular differentiation in iPSC models of neurodevelopmentPreprint
Puigdevall, P., Jerber, J., Danecek, P., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.04.482992 · Cited by 2
Adelus, M. L., Ding, J., Tran, B. T., Conklin, A. C., Golebiewski, A. K., Stolze, L. K., Whalen, M. B., Cusanovich, D. A., & Romanoski, C. E. (2023) · eLife · doi:10.7554/elife.91729.1 · Cited by 2
Hirota, M., Tamai, M., Yukawa, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.08.503075 · Cited by 1
Ferreira, P., Kuipers, J., & Beerenwinkel, N. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.15.512383 · Cited by 3
Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data
Fu, Y., Youness, M., Virzì, A., Song, X., Tubeeckx, M. R. L., De Keulenaer, G. W., Heidbuchel, H., Segers, V. F. M., Sipido, K. R., Thienpont, B., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf371 · Cited by 2
“However, since the identities of the samples cannot be determined, this approach dissociates other sample information from the sequencing data, thereby reducing the power of the conclusions [9].”
InfluentialCellDemux: coherent genetic demultiplexing in single-cell and single-nuclei experimentsPreprint
Zoodsma, M., Zhan, Q., Kumar, S., Botey-Bataller, J., Li, W., Zhou, L., Alaswad, A., Liu, Z., Zhang, Z., Zhang, B., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.18.576186 · Cited by 5
Rheaume, B. A., Xing, J., Theune, W. C., DeRosa, S., & Trakhtenberg, E. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.15.464552 · Cited by 2
Xiong, K.-X., Zhou, H., Yin, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.07.442884 · Cited by 2
Sutcliffe, M. A., Wong, E., Wingett, S., Morris, C. A. J., Stachelscheid, H., Schoenfelder, S., & Lancaster, M. A. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03254-6 · Cited by 4
Winter, S. D., Mannens, C. C. A., Christiaens, V., Vandepoel, R., Hu, L., Linnarsson, S., & Aerts, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.14.699402 · Cited by 5
Zhang, N., Yue, C., Liu, J., Zhan, X., Cheng, Z., Li, C., Du, Y., & Tian, F. (2022) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-022-04451-3 · Cited by 2
Lin, X., Chen, Y., Lin, L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.16.537058 · Cited by 2
Sousa, A. G. G., Smolander, J., Junttila, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.07.637023 · Cited by 2
Wang, L., Nie, R., Zhang, Z., Gu, W., Wang, S., Wang, A., Zhang, J., & Cai, J. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100558 · Cited by 2
Sparks, R., Lau, W. W., Liu, C., et al. (2022) · medRxiv · doi:10.1101/2022.02.17.22271138 · Cited by 2
Improving doublet cell removal efficiency through multiple algorithm runs
She, Y., Wang, C., & Zhao, Q. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.01.009 · Cited by 2
Phase I Trial Evaluating Locoregionally-delivered IL13Rα2-targeting CAR T Cells in High-Grade GliomaPreprint
Brown, C. E., Hibbard, J. C., Alizadeh, D., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3117663/v1 · Cited by 2
PrePR-CT: Predicting Perturbation Responses in Unseen Cell Types Using Cell-Type-Specific GraphsPreprint
Alsulami, R., Lehmann, R., Khan, S. A., Lagani, V., Gómez-Cabrero, D., Kiani, N. A., & Tegner, J. N. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.24.604816 · Cited by 2
Sample multiplexing for retinal single-cell RNA-sequencingPreprint
Ma, J., Chu, T. K., Prieto, M. P., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.23.589797 · Cited by 2
Keenan, B. P., McCarthy, E. E., Ilano, A., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3985187 · Cited by 2
Self-citationAnalysis of a Single Cell RNA-seq Workflow by Random Matrix Theory Methods
Leviyang, S. (2024) · Bulletin of Mathematical Biology · doi:10.1007/s11538-024-01376-z · Cited by 4
Lawlor, N. T., Nehar-Belaid, D., Grassmann, J. D. S., Stoeckius, M., Smibert, P., Stitzel, M. L., Pascual, V., Banchereau, J., Williams, A., & Ucar, D. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.23.424147 · Cited by 2
scMODD: A model-driven algorithm for doublet identification in single-cell RNA-sequencing data
Zhao, X., Du, A., & Qiu, P. (2023) · Frontiers in Systems Biology · doi:10.3389/fsysb.2022.1082309 · Cited by 4
Inference of heterogeneous effects in single-cell genetic perturbation screensPreprint
Fu, Z., & Hou, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596224 · Cited by 2
Shen, H., Shen, X., Feng, M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.23.457305 · Cited by 2
Zhang, Z., Cheng, W. S., Taniguchi‐Ponciano, K., Marrero–Rodríguez, D., Smith, G. R., Pinças, H., Sagendorf, T. J., Mendelev, N., Strupinsky, G., Ge, Y., et al. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101532 · Cited by 2
Cell villages and Dirichlet modeling map human cell fitness genetics
Hanson, C., Derebenskiy, T., Vega, A. R., Kamte, Y. S., Fox, R. G., Sathe, L., Lambing, H., Dietterich, T. E., Hawes, D., Nehme, R., et al. (2026) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2026.07.005 · Cited by 3
Xu, C., Pouyabahar, D., Voisin, V., Heydari, H., & Bader, G. D. (2026) · Nature Communications · doi:10.1038/s41467-026-69063-2 · Cited by 5
A molecular cell atlas of endocrine signalling in human neural organoidsPreprint
Matassa, G., Rigoli, M. T., Castaldi, D., Valenti, A., Lessi, M., Caporale, N., Stucchi, S., Muda, B., Nagni, R., Mainardi, L. T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.14.669814 · Cited by 2
Gupta, I., Collier, P. G., Haase, B., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/364950 · Cited by 2
Fiorini, M. R., Amiri, S., Dilliott, A. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.17.599314 · Cited by 2
Schott, B. H., Wang, L., Zhu, X., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.30.478319 · Cited by 2
Metadata
- Authors
- Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., Gate, R. E., Mostafavi, S., Marson, A., Zaitlen, N., Criswell, L. A., & Ye, C. J.
- Affiliations
- University of Michigan (US)
- University of California, San Francisco (US)
- University of British Columbia (CA)
- Corresponding author
- Kang, Hyun Min
- Ye, Chun Jimmie
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2017-12-11
- Volume
- 36
- Issue
- 1
- Pages
- 89-94
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC5784859
- OpenAlex
- W2774307122
- Semantic Scholar
- 829fff0f1bab67a4bb98a0c77120ff80311d7da4
- Cited by
- 1,101
- References
- 59
- Citations by year
- 2026: 163 · 2025: 142 · 2024: 182 · 2023: 134 · 2022: 116 · 2021: 153 · 2020: 105 · 2019: 65 · 2018: 37 · 2017: 3
- FWCI
- 20.46
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-10-14)
- Open access
- green
- doi.org
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K25HL121295, U01HL137182)
- National Institute of General Medical Sciences (T32GM007618, T32GM067547)
- National Institute of Allergy and Infectious Diseases (R38AI140299, U19AI090023, R21AI133337)
- National Institute of Diabetes and Digestive and Kidney Diseases (F30DK115167)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR071522, P30AR070155)
- National Institute of Dental and Craniofacial Research (R03DE025665)
- National Center for Chronic Disease Prevention and Health Promotion (U01 DP006486, U01 DP005120)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Chemical Engineering › Fluid Flow and Transfer Processes › Innovative Microfluidic and Catalytic Techniques Innovation
- Keywords
- natural genetic variation, single nucleotide polymorphisms, doublet detection, pooled samples, batch effects, cell type-specific gene expression, expression quantitative trait loci, interferon-beta, lupus
- MeSH terms
- Genotype, Humans, Interferons, Lupus Erythematosus, Systemic, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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