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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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)
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Could not verify
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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
“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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- 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)
Tests✓ 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
“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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- 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
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
“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
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
“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
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
“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
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
“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
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 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
Tests✓ 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
“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
Flagged first, then by citations
Cited by
Sorted by citations
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0276-y · Cited by 4,984
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
Single-cell reconstruction of the early maternal–fetal interface in humans
Vento-Tormo, R., Efremova, M., Botting, R. A., Turco, M. Y., Vento-Tormo, M., Meyer, K. B., Park, J.-E., Stephenson, E., Polański, K., Goncalves, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0698-6 · Cited by 2,358
A single-cell atlas of the peripheral immune response in patients with severe COVID-19
Wilk, A. J., Rustagi, A., Zhao, N. Q., Roque, J., Martínez-Colón, G. J., McKechnie, J. L., Ivison, G. T., Ranganath, T., Vergara, R., Hollis, T., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0944-y · Cited by 1,493
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460
ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222
GSEApy: a comprehensive package for performing gene set enrichment analysis in Python
Fang, Z., Liu, X., & Peltz, G. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac757 · Cited by 1,244
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046
“[13], we “super-loaded” the 10x Genomics instrument, loading cells at a significantly higher concentration with an expected yield of 20,000 single cells and 5000 multiplets.”
BackgroundMethodResultInfluentialA practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Hie, B., Bryson, B. D., & Berger, B. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0113-3 · Cited by 876
“Ten publicly-available datasets 17,53 involving PBMCs, or cell types found in PBMCs, were processed, aligned, and merged using the procedure described previously to give a total of 47,994 cells.”
MethodConfronting false discoveries in single-cell differential expression
Squair, J. W., Gautier, M., Kathe, C., Anderson, M. A., James, N. D., Hutson, T. H., Hudelle, R., Qaiser, T., Matson, K. J. E., Barraud, Q., et al. (2021) · doi:10.1101/2021.03.12.435024 · Cited by 1,086
scMC learns biological variation through the alignment of multiple single-cell genomics datasets
Zhang, L., & Nie, Q. H. (2021) · Genome biology · doi:10.1186/s13059-020-02238-2 · Cited by 861
Organoid single-cell genomic atlas uncovers human-specific features of brain development
Kanton, S., Boyle, M. J., He, Z., Santel, M., Weigert, A., Sanchís-Calleja, F., Guijarro, P., Sidow, L., Fleck, J. S., Han, D., et al. (2019) · Nature · doi:10.1038/s41586-019-1654-9 · Cited by 778
A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors
Slyper, M., Porter, C. B. M., Ashenberg, O., Waldman, J., Drokhlyansky, E., Wakiro, I., Smillie, C. S., Smith-Rosario, G., Wu, J., Dionne, D. A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0844-1 · Cited by 758
De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes
Hufford, M. B., Seetharam, A. S., Woodhouse, M. R., Chougule, K. M., Ou, S., Liu, J., Ricci, W. A., Guo, T., Olson, A., Qiu, Y., et al. (2021) · Science · doi:10.1126/science.abg5289 · Cited by 747
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643
Doublet identification in single-cell sequencing data using scDblFinder
Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W., & Robinson, M. D. (2022) · F1000Research · doi:10.12688/f1000research.73600.2 · Cited by 684
Single-cell eQTL mapping identifies cell type–specific genetic control of autoimmune disease
Yazar, S., Alquicira-Hernandez, J., Wing, K., Senabouth, A., Gordon, M. G., Andersen, S., Lu, Q., Rowson, A., Taylor, T. R. P., Clarke, L., et al. (2022) · Science · doi:10.1126/science.abf3041 · Cited by 682
“A. Dobin et al., STAR: Ultrafast universal RNA-seq aligner.”
MethodInfluentialSelf-citationscGen predicts single-cell perturbation responses
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0494-8 · Cited by 661
Rozenblatt–Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J., Ashenberg, O., Cerami, E. G., Coffey, R. J., Demir, E., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.053 · Cited by 566
“For snRNA-seq, frozen tissue samples can be accrued over time and then multiplexed for reducing costs and limiting potential batch effects (Gaublomme et al., 2019; Kang et al., 2018).”
Decontamination of ambient RNA in single-cell RNA-seq with DecontX
Yang, S., Corbett, S. E., Koga, Y., Wang, Z., Johnson, W. E., Yajima, M., & Campbell, J. D. (2020) · Genome Biology · doi:10.1186/s13059-020-1950-6 · Cited by 635
“Other approaches such as demuxlet [12] and scds [13] model gene expression from the original data, then assign doublet to barcodes that have observed expression from genes that are likely to not occur simultaneously.”
MethodLineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics
Ludwig, L. S., Lareau, C. A., Ulirsch, J. C., Christian, E., Muus, C., Li, L. H., Pelka, K., Ge, W. H., Oren, Y., Brack, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.022 · Cited by 554
“greater sensitivity in conjunction with currently employed approaches (Kang et al., 2018; Zheng et al., 2017b).”
Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529
Single-cell RNA-seq reveals cell type–specific molecular and genetic associations to lupus
Perez, R. K., Gordon, M. G., Subramaniam, M., Kim, M. C., Hartoularos, G. C., Targ, S., Sun, Y., Ogorodnikov, A., Bueno, R., Lu, A. C., et al. (2022) · Science · doi:10.1126/science.abf1970 · Cited by 535
“After quality control and doublet removal using freemuxlet (8) (mean doublet rate 22.”
MethodResultInfluentialSelf-citationA test metric for assessing single-cell RNA-seq batch correction
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-018-0254-1 · Cited by 511
“To estimate pure biological variability with kBET, we studied a pooled dataset of human PBMCs from eight unrelated individuals for which donor identity was reconstructed for each cell with demux-let 26 .”
BackgroundMethodCycling cancer persister cells arise from lineages with distinct programs
Oren, Y., Tsabar, M., Cuoco, M. S., Amir-Zilberstein, L., Cabanos, H. F., Hütter, J.-C., Hu, B., Thakore, P. I., Tabaka, M., Fulco, C. P., et al. (2021) · Nature · doi:10.1038/s41586-021-03796-6 · Cited by 527
Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472
Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes
Heaton, H., Talman, A. M., Knights, A., Imaz, M., Gaffney, D. J., Durbin, R., Hemberg, M., & Lawniczak, M. K. N. (2020) · Nature Methods · doi:10.1038/s41592-020-0820-1 · Cited by 506
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
Mereu, E., Lafzi, A., Moutinho, C., Ziegenhain, C., McCarthy, D. J., Álvarez-Varela, A., Batlle, E., Sagar, Grün, D., Lau, J. K., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0469-4 · Cited by 499
Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity
Kinker, G. S., Greenwald, A. C., Tal, R., Orlova, Z., Cuoco, M. S., McFarland, J. M., Warren, A., Rodman, C., Roth, J. A., Bender, S. A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00726-6 · Cited by 445
A clinically applicable integrative molecular classification of meningiomas
Nassiri, F., Liu, J., Patil, V. S., Mamatjan, Y., Wang, J. Z., Hugh-White, R., Macklin, A. M., Khan, S., Singh, O., Karimi, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03850-3 · Cited by 474
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs
Study, L. C., van der Wijst, M. G. P., BIOS Consortium, Brugge, H., de Vries, D. H., Deelen, P., Swertz, M. A., & Franke, L. (2018) · Nature Genetics · doi:10.1038/s41588-018-0089-9 · Cited by 399
“Further details of the algorithm can be found in Kang et al.33 Using the Demuxlet algorithm, we could confidently assign the majority (99.8”
InfluentialSingle-nucleus cross-tissue molecular reference maps toward understanding disease gene function
Eraslan, G., Drokhlyansky, E., Anand, S. K., Fiškin, E., Subramanian, A., Slyper, M., Wang, J., Wittenberghe, N. V., Rouhana, J. M., Waldman, J., et al. (2022) · Science · doi:10.1126/science.abl4290 · Cited by 406
Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
Li, X., Wang, K., Lyu, Y., Pan, H., Zhang, J., Stambolian, D. E., Suszták, K., Reilly, M. P., Hu, G., & Li, M. (2020) · Nature Communications · doi:10.1038/s41467-020-15851-3 · Cited by 419
Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments
Tian, L., Dong, X., Freytag, S., Cao, K. L., Su, S., JalalAbadi, A., Amann‐Zalcenstein, D., Weber, T. S., Seidi, A., Jabbari, J. S., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0425-8 · Cited by 355
Single‐cell RNA sequencing in cancer research
Zhang, Y., Wang, D., Miao, P., Tang, L. M., Ouyang, J., Xiong, F., Guo, C. J., Tang, Y., Zhou, Y., Liao, Q., et al. (2021) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-021-01874-1 · Cited by 412
“Microfluidic technology considerably increases the single-cell catch and library capacity, thereby enabling thousands of cells to be analyzed simultaneously; therefore, highlighting a great advantage of this method to screen large numbers of cells for sequencing [38, 39].”
Recent Advances in Droplet Microfluidics
Ding, Y., Howes, P. D., & deMello, A. J. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b05047 · Cited by 374
Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation
Jerber, J., Seaton, D. D., Cuomo, A. S. E., Kumasaka, N., Haldane, J., Steer, J., Patel, M. J., Pearce, D., Andersson, M. H. L., Bonder, M. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00801-6 · Cited by 293
Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference
Huang, Y., McCarthy, D. J., & Stegle, O. (2019) · Genome Biology · doi:10.1186/s13059-019-1865-2 · Cited by 348
“Existing barcoding strategies include molecular labelling prior to analysis [9, 10, 11, 12] as well as exploiting natural genetic barcodes of germline variants that segregate between pooled individuals [13].”
BackgroundMethodInfluentialA blood atlas of COVID-19 defines hallmarks of disease severity and specificity
Ahern, D. J., Ai, Z., Ainsworth, M., Allan, C., Allcock, A., Angus, B., Ansari, M. A., Arancibia-Cárcamo, C. V., Aschenbrenner, D., Attar, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.012 · Cited by 328
Brown, C. E., Hibbard, J. C., Alizadeh, D., Blanchard, M. S., Natri, H. M., Wang, D., Ostberg, J. R., Aguilar, B., Wagner, J. R., Paul, J. A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02875-1 · Cited by 374
Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells
Gupta, I., Collier, P. G., Haase, B., Mahfouz, A., Joglekar, A., Floyd, T., Koopmans, F., Barres, B., Smit, A. B., Sloan, S. A., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4259 · Cited by 343
Somatic mutations and cell identity linked by Genotyping of Transcriptomes
Nam, A. S., Kim, K.-T., Chaligne, R., Izzo, F., Ang, C., Taylor, J., Myers, R. M., Abu-Zeinah, G., Brand, R., Omans, N. D., et al. (2019) · Nature · doi:10.1038/s41586-019-1367-0 · Cited by 333
Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia-Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14457-z · Cited by 331
Keren-Shaul, H., Kenigsberg, E., Jaitin, D. A., David, E., Paul, F., Tanay, A., & Amit, I. (2019) · Nature Protocols · doi:10.1038/s41596-019-0164-4 · Cited by 292
Single cell transcriptional signatures of the human placenta in term and preterm parturition
Piqué-Regi, R., Romero, R. J., Tarca, A. L., Sendler, E. D., Xu, Y., Garcia‐Flores, V., Leng, Y., Luca, F., Hassan, S. S., & Gomez‐Lopez, N. (2019) · eLife · doi:10.7554/elife.52004 · Cited by 322
Dream: powerful differential expression analysis for repeated measures designs
Hoffman, G. E., & Roussos, P. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa687 · Cited by 319
Predicting cellular responses to complex perturbations in high‐throughput screens
Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I. L., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211517 · Cited by 307
“We first demonstrated the performance and functionality of the CPA on three small single-cell datasets: a dataset of PBMCs stimulated with IFN-β (Kang et al , 2018), a dataset of human lung cancer cells perturbed by four drugs (Srivatsan et al , 2020), and a longitudinal cross-species dataset of lipopolysaccharide (LP…”
MethodInfluentialInferring and perturbing cell fate regulomes in human brain organoids
Fleck, J. S., Jansen, S. M. J., Wollny, D., Zenk, F., Seimiya, M., Jain, A., Okamoto, R., Santel, M., He, Z., Camp, J. G., et al. (2023) · Nature · doi:10.1038/s41586-022-05279-8 · Cited by 315
“Cells pooled from different stem cell lines were demul-tiplexed using demuxlet 54 .”
Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning
Bernstein, N. J., Fong, N. L., Lam, I., Roy, M. A., Hendrickson, D. G., & Kelley, D. R. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.010 · Cited by 280
scds: computational annotation of doublets in single-cell RNA sequencing data
Bais, A. S., & Kostka, D. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz698 · Cited by 308
Functional interpretation of single cell similarity maps
DeTomaso, D., Jones, M. G., Subramaniam, M., Ashuach, T., Ye, C., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-12235-0 · Cited by 259
Self-citationDatlinger, P., Rendeiro, A. F., Boenke, T., Senekowitsch, M., Krausgruber, T., Barreca, D., & Bock, C. (2021) · Nature Methods · doi:10.1038/s41592-021-01153-z · Cited by 266
Fan, J., Lee, H., Lee, S., Ryu, D., Lee, S., Xue, C., Kim, S. J., Kım, K., Barkas, N., Park, P. J., et al. (2018) · Genome Research · doi:10.1101/gr.228080.117 · Cited by 228
“99.1) (Kharchenko et al. 2014; Fan et al. 2016) with the same gene sets.”
InfluentialCell type prioritization in single-cell data
Skinnider, M. A., Squair, J. W., Kathe, C., Anderson, M. A., Gautier, M., Matson, K. J. E., Milano, M., Hutson, T. H., Barraud, Q., Phillips, A. A., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0605-1 · Cited by 274
“Initial simulation parameters were estimated from the Kang et al. dataset 4 using the ‘splatEstimate’ function, and populations of 100–1,000 cells from two experimental conditions were generated, in increments of 100.”
BackgroundMethodInfluentialSingle-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer
Clarke, J. D., Panwar, B., Madrigal, A., Singh, D. K., Gujar, R., Wood, O., Chee, S., Eschweiler, S., King, E. V., Awad, A. S., et al. (2019) · The Journal of Experimental Medicine · doi:10.1084/jem.20190249 · Cited by 255
“Some notable examples known or likely to have such functions are GPR25, whose closest homologue, GPR15 (Kim et al., 2013), enables homing of T cell subsets to and retention in the colon; AMICA1 (Witherden et al., 2010), encoding JAML (junctional adhesion molecule-like), which contributes to the…”
BackgroundCheroni, C., Caporale, N., & Testa, G. (2020) · Molecular Autism · doi:10.1186/s13229-020-00370-1 · Cited by 290
Mapping the single-cell transcriptomic response of murine diabetic kidney disease to therapies
Wu, H., González-Villalobos, R. A., Yao, X., Reilly, D. F., Chen, T., Rankin, M. M., Myshkin, E., Breyer, M. D., & Humphreys, B. D. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.05.010 · Cited by 254
An entropy-based metric for assessing the purity of single cell populations
Liu, B., Li, C., Li, Z., Wang, D., Ren, X., & Zhang, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-16904-3 · Cited by 217
“The fi fth cluster, B_C4_MX1, predominantly composed of interferon-induced B cells 38 , expressed high levels of MX1 , IFI6 , and IFI44L .”
BackgroundMethodTime-resolved systems immunology reveals a late juncture linked to fatal COVID-19
Liu, C., Martins, A. J., Lau, W. W., Rachmaninoff, N., Chen, J., Imberti, L., Mostaghimi, D., Fink, D. L., Burbelo, P. D., Dobbs, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.018 · Cited by 214
“We used the software package demuxlet (Kang et al., 2018) to thenmatch single cells in the 10x RNaseq data to each donor and identify doublets.”
Development and future of droplet microfluidics
Nan, L., Zhang, H., Weitz, D. A., & Shum, H. C. (2024) · Lab on a Chip · doi:10.1039/d3lc00729d · Cited by 274
van der Wijst, M. G. P., Vazquez, S. E., Hartoularos, G. C., Bastard, P., Grant, T., Bueno, R., Lee, D. S., Greenland, J. R., Sun, Y., Perez, R. K., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abh2624 · Cited by 226
Self-citationLIANA+ Provides an All-in-One Framework for Cell–Cell Communication Inference
Dimitrov, D., Schäfer, P. S. L., Farr, E., Rodriguez-Mier, P., Lobentanzer, S., Badia-i-Mompel, P., Dugourd, A., Tanevski, J., Ramirez Flores, R. O., & Saez-Rodriguez, J. (2024) · Nature Cell Biology · doi:10.1038/s41556-024-01469-w · Cited by 263
“We used a pre-processed dataset of 29 single-nucleotide samples from the same publication 89 .”
The single-cell eQTLGen consortium
van der Wijst, M. G. P., de Vries, D., Groot, H. E., Trynka, G., Hon, C.-C., Bonder, M. J., Stegle, O., Nawijn, M. C., Idaghdour, Y., van der Harst, P., et al. (2020) · eLife · doi:10.7554/elife.52155 · Cited by 218
Self-citationTian, L., Jabbari, J. S., Thijssen, R., Gouil, Q. A., Amarasinghe, S. L., Voogd, O., Kariyawasam, H., Du, M. R. M., Schuster, J., Wang, C., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02525-6 · Cited by 215
“Louvain clustering on leading principal components generated similar results to the cell type assignment obtained from running Demuxlet [34] on the short-read data (98.7% and 99.4% concordance for scmixology1 and scmixology2 , respectively), which indicated that we can successfully capture variants in these different…”
ResultInfluentialA Map of Transcriptional Heterogeneity and Regulatory Variation in Human Microglia
Young, A. M. H., Kumasaka, N., Calvert, F., Hammond, T. R., Knights, A., Panousis, N., Park, J. S., Schwartzentruber, J., Liu, J., Kundu, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00875-2 · Cited by 213
Wilk, A. J., Lee, M. J., Wei, B., Parks, B. E., Pi, R., Martínez-Colón, G. J., Ranganath, T., Zhao, N. Q., Taylor, S., Becker, W. R., et al. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20210582 · Cited by 214
Maternal-fetal immune responses in pregnant women infected with SARS-CoV-2
Garcia‐Flores, V., Romero, R. J., Xu, Y., Theis, K. R., Arenas‐Hernandez, M., Miller, D., Peyvandipour, A., Bhatti, G., Galaz, J., Gershater, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27745-z · Cited by 234
The effect of low-dose IL-2 and Treg adoptive cell therapy in patients with type 1 diabetes
Dong, S., Hiam-Galvez, K. J., Mowery, C. T., Herold, K. C., Gitelman, S. E., Esensten, J. H., Liu, W., Lares, A., Leinbach, A., Lee, M., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.147474 · Cited by 232
“Resulting VCF genotype files and single-cell RNA-Seq BAM files were used to run Demuxlet (31) with default parameters.”
MethodInfluentialSelf-citationJointly defining cell types from multiple single-cell datasets using LIGER
Liu, J., Gao, C., Sodicoff, J., Kozareva, V., Macosko, E. Z., & Welch, J. D. (2020) · Nature Protocols · doi:10.1038/s41596-020-0391-8 · Cited by 203
Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data
Xi, N. M., & Li, J. J. (2021) · Cell Systems · doi:10.1016/j.cels.2020.11.008 · Cited by 208
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195
“Single-cell multiplexing 77 could help mitigate the impact of batch effects by pooling single-cell samples from different time points and sequencing them together.”
BackgroundTutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218
“This handicap can be partially overcome by jointly capturing samples from different individuals, where genotype differences allow the user to distinguish between donors and thereby reliably identify doublets 69 .”
BackgroundNuclei multiplexing with barcoded antibodies for single-nucleus genomics
Gaublomme, J. T., Li, B., Aubin, C., Knecht, A., Yang, Y., Drokhlyansky, E., Wittenberghe, N. V., Waldman, J., Dionne, D. A., Nguyễn, L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10756-2 · Cited by 186
Sasson, S. C., Slevin, S. M., Cheung, V., Nassiri, I., Olsson-Brown, A. C., Fryer, E., Ferreira, R. C., Trzupek, D., Gupta, T., Al‐Hillawi, L., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.06.025 · Cited by 213
Tran, M. N., Maynard, K. R., Spangler, A., Huuki, L. A., Montgomery, K. D., Sadashivaiah, V., Tippani, M., Barry, B. K., Hancock, D. B., Hicks, S. C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.001 · Cited by 219
Single-cell transcriptomic analysis of allergen-specific T cells in allergy and asthma
Seumois, G., Ramírez-Suástegui, C., Schmiedel, B. J., Liang, S. Y., Peters, B., Sette, A., & Vijayanand, P. (2020) · Science Immunology · doi:10.1126/sciimmunol.aba6087 · Cited by 212
“Doublet cell filtering Barcoded single-cell RNA-seq was demultiplexed patient-wise using Demuxlet (123) with the following parameters: alpha = 0, 0.5 and --geno-error = 0.05.”
MethodInfluentialDoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data
DePasquale, E. A. K., Schnell, D. J., Van Camp, P.-J., Valiente-Alandí, Í., Blaxall, B. C., Grimes, H. L., Singh, H., & Salomonis, N. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.09.082 · Cited by 207
“A common source of confounding gene expression in single-cell RNA-sequencing (scRNA-seq) is the occurrence of multiplet cell profiles that result from the simultaneous capture of multiple cells in a single well or droplet (Kang et al., 2018).”
InfluentialKotliarov, Y., Sparks, R., Martins, A. J., Mulè, M. P., Lu, Y. C., Goswami, M., Kardava, L., Banchereau, R., Pascual, V., Biancotto, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0769-8 · Cited by 184
Genetic determinants of co-accessible chromatin regions in activated T cells across humans
Gate, R. E., Cheng, C. S., Aiden, A. P., Siba, A., Tabaka, M., Lituiev, D., Machol, I., Gordon, M. G., Subramaniam, M., Shamim, M., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0156-2 · Cited by 181
“With the recent advancement of single cell epigenomic 65 and transcriptomic 66,67,68 profiling, it should be possible to more directly detect context-specific genetic effects in a heterogeneous cell population.”
Self-citationGeometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape
Hie, B., Cho, H., DeMeo, B., Bryson, B. D., & Berger, B. (2019) · Cell Systems · doi:10.1016/j.cels.2019.05.003 · Cited by 170
Single-cell transcriptomics in cancer: computational challenges and opportunities
Fan, J., Slowikowski, K., & Zhang, F. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0422-0 · Cited by 195
Normalizing and denoising protein expression data from droplet-based single cell profiling
Mulè, M. P., Martins, A. J., & Tsang, J. S. (2022) · Nature Communications · doi:10.1038/s41467-022-29356-8 · Cited by 197
Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data
Freytag, S., Tian, L., Lönnstedt, I., Ng, M., & Bahlo, M. (2018) · F1000Research · doi:10.12688/f1000research.15809.1 · Cited by 220
Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189
Learning single-cell perturbation responses using neural optimal transport
Bunne, C., Stark, S. G., Gut, G., del Castillo, J. S., Levesque, M., Lehmann, K.-V., Pelkmans, L., Krause, A., & Rätsch, G. (2023) · Nature Methods · doi:10.1038/s41592-023-01969-x · Cited by 202
Cellular communities reveal trajectories of brain ageing and Alzheimer’s disease
Green, G. S., Fujita, M., Yang, H.-S., Taga, M., Cain, A., McCabe, C., Comandante-Lou, N., White, C. C., Schmidtner, A. K., Zeng, L., et al. (2024) · Nature · doi:10.1038/s41586-024-07871-6 · Cited by 204
Transcriptional Programs of Circuit Assembly in the Drosophila Visual System
Kurmangaliyev, Y. Z., Yoo, J., Valdes-Aleman, J., Sanfilippo, P., & Zipursky, S. L. (2020) · Neuron · doi:10.1016/j.neuron.2020.10.006 · Cited by 166
McFarland, J. M., Paolella, B. R., Warren, A., Geiger-Schuller, K., Shibue, T., Rothberg, M., Kuksenko, O., Colgan, W. N., Jones, A., Chambers, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17440-w · Cited by 173
Single-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 222
“The integration of scRNA-seq with eQTL was first reported in 2018 [94].”
MethodBiomarkers of inflammation and repair in kidney disease progression
Puthumana, J., Thiessen‐Philbrook, H., Xu, L., Coca, S. G., Garg, A. X., Himmelfarb, J., Bhatraju, P. K., Ikizler, T. A., Siew, E. D., Ware, L. B., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci139927 · Cited by 185
Cell subtype-specific effects of genetic variation in the Alzheimer’s disease brain
Fujita, M., Gao, Z., Zeng, L., Aubin, C., White, C. C., Ng, B., Green, G. S., Rozenblatt–Rosen, O., Phillips, D., Amir-Zilberstein, L., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01685-y · Cited by 196
Self-citationIdentification of cell barcodes from long-read single-cell RNA-seq with BLAZE
You, Y., Prawer, Y. D. J., Paoli‐Iseppi, R. D., Hunt, C. P. J., Parish, C. L., Shim, H., & Clark, M. B. (2023) · Genome biology · doi:10.1186/s13059-023-02907-y · Cited by 179
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
About these checks
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.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
