The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index; 4 of the 5 sources its claims cite were checked for support.
Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes
Open accessAbstract
Methods to deconvolve single-cell RNA-sequencing (scRNA-seq) data are necessary for samples containing a mixture of genotypes, whether they are natural or experimentally combined. Multiplexing across donors is a popular experimental design that can avoid batch effects, reduce costs and improve doublet detection. By using variants detected in scRNA-seq reads, it is possible to assign cells to their donor of origin and identify cross-genotype doublets that may have highly similar transcriptional profiles, precluding detection by transcriptional profile. More subtle cross-genotype variant contamination can be used to estimate the amount of ambient RNA. Ambient RNA is caused by cell lysis before droplet partitioning and is an important confounder of scRNA-seq analysis. Here we develop souporcell, a method to cluster cells using the genetic variants detected within the scRNA-seq reads. We show that it achieves high accuracy on genotype clustering, doublet detection and ambient RNA estimation, as demonstrated across a range of challenging scenarios.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
souporcell clusters single-cell RNA-seq cells by genotype without reference genotypes, using variants detected directly in the reads. It jointly calls genotype clusters, cross-genotype doublets, and ambient RNA, and the paper reports that it outperforms demuxlet, vireo, and scSplit across synthetic, maternal/fetal, and *Plasmodium falciparum* benchmarks.
Key findings
- On a synthetic mixture of five human iPSC lines, souporcell assigned 6612/6622 singletons and 415/451 doublets correctly; four singletons were falsely labeled as doublets, 35 doublets were misidentified as singletons, and one doublet and four singletons were unassigned.
- For doublet detection on a synthetic mixture with 6% doublets and 10% ambient RNA, ROC AUC was 0.98 for souporcell and 0.91 for vireo; demuxlet’s posterior doublet probability was reported as poorly calibrated, with nearly 40% false positive doublets at its default threshold.
- Cell assignment versus demuxlet on three experimental iPSC mixture replicates gave Adjusted Rand Index 1.0 for souporcell and vireo and an average of 0.97 for scSplit.
- Across ambient RNA from 2.5%–50%, souporcell and vireo retained high accuracy, with souporcell more robust at calling doublets in high-ambient-RNA cases; all tools performed well down to a minority cluster of 1.2% (50 cells), but only souporcell and vireo correctly identified all minority sample singletons down to 0.6% of all cells.
- In maternal/fetal samples, souporcell clusters agreed with demuxlet and segregated with expected cell types, while vireo and scSplit had major discordances; in *P. falciparum* datasets, souporcell had superior ARI across the board, with vireo performing poorly on one dataset (ARI versus demuxlet 0.24).
Methods
- Reads are remapped with minimap2 rather than using STAR-aligned BAMs directly, putative SNPs are called with freebayes, and per-cell allele counts are obtained with vartrix.
- Clustering uses a sparse mixture model fit by a deterministic annealing variant of expectation maximization, with cluster centers represented as alternate allele fractions; doublets are detected with a beta-binomial model, and genotype plus ambient RNA are co-inferred in STAN by maximum likelihood/gradient descent.
- Validation used five HipSci human iPSC lines sequenced individually and in mixtures with 10x Chromium, synthetic mixtures with simulated doublets and ambient RNA, maternal/fetal samples from Vento et al., and three *P. falciparum* datasets containing six genetically distinct strains with 1893–2608 cells and median UMIs of ~1000.
Limitations
- The paper states that limitations include low signal-to-noise due to decreased UMI per cell and high numbers of donors causing increased local maxima.
- The text also makes evident that genotype accuracy from scRNA-seq is significantly lower than from whole-genome sequencing because most variants have very low coverage per cluster.
- The paper notes that no good generative models were available for batch effects, allele-specific expression, ambient RNA, and doublets in scRNA-seq, so realistic benchmarking required experimentally generated mixtures rather than fully in silico data.
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Claims the paper makes
20 Own result · 14 Own interpretation · 7 No basis given · 2 Supported · 2 Partly supported · 1 No full text of the sources · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“Demuxlet significantly overestimates doublets versus expectations based on the number of cells loaded11 (Table S2) especially as ambient RNA increases (Fig. 2j).”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
What the sources say
- Supportedcites [11] Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017). source
The citation follows “expectations based on the number of cells loaded,” rather than the Demuxlet result or the ambient-RNA observation. In Results, “Technical demonstration with cell lines and synthetic RNAs” (page unavailable), the source reports that multiplet rates varied with cell load and were consistent with Poisson loading, supporting that expectation. (LLM, conf=0.88)
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In the paper
“The default doublet probability threshold for demuxlet gives nearly 40% false positive doublets.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“With these data we also show that souporcell is able to accurately estimate the amount of ambient RNA in the experiment (Fig. 2k).”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
plainn = 5- Asserted: passes — The authors put it forward as true.
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- The authors state this as an assumption: Allele counts for locus i of each cluster j are drawn from a binomial distribution with an alternative allele fraction of (1-ρ)fij+ρ*ai, where fij is 0, 0.5, or 1 (with a haploid mode limited to 0 and 1), ρ is the amount of ambient RNA, and ai is the average allele fraction in the experiment.
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In the paper
“This is similar for the other samples tested (Fig. S2, Table S3).”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“The ARI of scSplit and demuxlet suffered due to poor doublet detection.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“Nevertheless, souporcell surpasses both vireo and scSplit in genotype accuracy on a synthetically mixed sample with 6% doublets and 10% ambient RNA (Fig. S1i).”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“The next set of artifacts is alignment parameter differences between STAR and aligners intended for variant calling.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
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In the paper
“To visualize the five clusters identified by genotype we carried out a Principal Component Analysis (PCA) of the normalized log likelihood matrix, which reveals a clear separation of the clusters, with interspersed doublets (Fig. 2c and d).”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“Comparing souporcell to demuxlet, there are 21 cells that demuxlet labels as maternal or fetal but which appear in the other individual’s cell type clusters.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“The second type of artifact we found was due to the soft clip penalty being higher in STAR and not being exposed as a parameter to the user. This leads to false positive variants due to the lack of soft clipping where other mappers would soft clip poorly matching read ends.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
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- It may state two separate claims as one. 21
- Probably not a substantial claim. 3
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In the paper
“Next, we count alleles per cell with vartrix14(Fig. 1c) which avoids reference bias due to ambiguous support such as alignment end effects and corrects bases with duplicate reads via the UMI.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Clustering model and data preprocessing
What the sources say
- Partly supportedcites [14] Petti, A. A. et al. A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing. Nat. Commun. 10, 3660 (2019). sourceExperimental — accuracy not yet measured
The Methods section (‘Single-cell mutation identification and analysis’; page unavailable) identifies VarTrix as a tool for automated cell-variant assignment. It also describes resolving discordant duplicate reads by UMI, but attributes that procedure to the authors’ Pysam-based workflow, not to VarTrix. Our copy does not say that VarTrix avoids reference bias or alignment-end effects. (LLM, conf=0.85)
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In the paper
“If using known variable sites such as from the 1k genomes project24 or if the genotypes of the donors are known, the remapping process is significantly less important.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
What the sources say
- Supportedcites [24] 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015). source
The citation is attached to the 1000 Genomes Project as an example of a source of known variable sites. The Main section describes its resource as enabling the cataloguing of variants, which supports that identification; the citation does not specifically attribute the claim about reduced importance of remapping to this paper. (LLM, conf=0.91)
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In the paper
“This has been noted by others in the past, and GATK recommendations for variant calling on bulk RNAseq involve removing these regions of the alignments prior to variant calling23.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
What the sources say
- Partly supportedcites [23] Sahraeian, S. M. E. et al. Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis. Nat. Commun. 8, 59 (2017). sourceExperimental — accuracy not yet measured
The Results section (‘Variant calling’; page unavailable) says GATK applies RNA-specific filtering steps to handle splicing in RNA-seq data. It does not say that GATK recommends removing the regions in question from alignments before variant calling, so that specific claim is unsupported. (LLM, conf=0.82)
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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
“Analysis of the expression profile of one of these (see Fig. S3 for the others) reveals that the genotypes are distributed across the Plasmodium intra-erythrocytic cycle (Fig. 3b) while being well separated in normalized loss cluster space (Fig. 3c,d).”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“For these data souporcell assigned 6612/6622 singletons and 415/451 doublets correctly; four singletons were falsely labeled as a doublet, 35 doublets were misidentified as singletons, and one doublet and four singletons were unassigned.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“The first artifact is due to the way STAR handles spliced reads when the read does not match the reference well. STAR will take such a read and introduce multiple splice events that force it to fit the reference in a statistically spurious fashion.”
- Also stated in: § Supplementary Methods · plain
“Consequently, for variants in these loci, the variant callers will count these reads as supporting the reference allele, thereby introducing reference bias and noise to the downstream clustering.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
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- Also stated in: § Supplementary Methods · plain
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In the paper
“we calculated a receiver-operator characteristic (ROC) curve of the doublet calls (Fig. 2i) on a synthetic mixture with 6% doublets and 10% ambient RNA that showed the area under the curve values of 0.98 and 0.91 for souporcell and vireo, respectively.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“The Adjusted Rand Index (ARI) of the remaining cell assignments versus demuxlet (Table S2) were 1.0 (fully concordant) for souporcell and vireo across the three replicates and an average of 0.97 for scSplit.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“We found that souporcell and vireo retain high accuracy with souporcell being more robust at accurately calling doublets in high ambient RNA cases (Fig. 2k).”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“We show that souporcell not only outperforms these new methods, but also surpasses demuxlet on both cell assignment and doublet accuracy.”
- Also stated in: § Discussion · plain
“Our benchmarks show that souporcell can outperform all other currently available methods, including those that require genotypes a priori.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Abstract
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In the paper
“Indeed, the most significant driver of expression appears to be the donor of origin, but the donor cells overlap in expression patterns and it is not possible to assign a donor to each cell based solely on expression patterns.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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“Moreover, souporcell is highly accurate for challenging datasets involving closely related maternal/fetal samples, and varying mixtures of Plasmodium falciparum strains.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Discussion
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In the paper
“While souporcell clusters agree with demuxlet and segregate with the expected cell type clusters, vireo and scSplit have major discordances with demuxlet.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“We found that all tools performed well down to the minority cell cluster comprising only 1.2% (50 cells) of total cells (Fig. 2m), but only souporcell and vireo were able to correctly identify all minority sample singletons as their own cluster down to 0.6% of all cells.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“The most common error mode for vireo and scSplit is calling homozygous reference loci as heterozygous variants (Fig. S1j) which is expected when ambient RNA is not accounted for, as it is not in these two tools.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“The ARI for each method (Table S4) on the three Plasmodium data sets show superior performance for souporcell across the board, with scSplit suffering on all datasets and vireo performing poorly on one, which had an ARI versus demuxlet of 0.24.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“Demuxlet’s posterior doublet probability output did not have enough significant digits and is 1.0 until it starts varying with 27% false positives.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“The final issue is that the indel penalty relative to the mismatch penalty is much higher in STAR than other aligners. This causes the alignments to choose many mismatches over a single or few indels when possible and thus create false positive variants.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
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In the paper
“Limitations of souporcell include low signal to noise due to decreased UMI per cell and high numbers of donors causing increased local maxima.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Discussion
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In the paper
“Using more realistic and challenging test cases than previous studies, we show that souporcell is robust across a large range of parameters, and more so than any other currently available method.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Discussion
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In the paper
“The expression PCA (Fig. 2e) and normalized cell-cluster loss PCA (Fig. 2g,h) of the experimental mixture were similar to the synthetic mixture indicating that the synthetic mixtures were an accurate approximation of real mixtures.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Validation and Benchmarking
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In the paper
“We present souporcell, a method to cluster cells by genotype, call doublet-cell barcodes, and infer the amount of ambient RNA in the experiment without the use of a genotype reference.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Abstract
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In the paper
“This provides us with fairly robust SNPs that have a good chance of aiding the clustering process.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
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In the paper
“souporcell also enables valuable genotype information to be extracted and ambient RNA estimation at no additional cost.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Discussion
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It may state two separate claims as one.
The authors state this as an assumption.
Allele counts for locus i of each cluster j are drawn from a binomial distribution with an alternative allele fraction of (1-ρ)fij+ρ*ai, where fij is 0, 0.5, or 1 (with a haploid mode limited to 0 and 1), ρ is the amount of ambient RNA, and ai is the average allele fraction in the experiment.
“we assume that the allele counts for locus i of each cluster j are drawn from a binomial distribution with an alternative allele fraction of (1-ρ)fij+ρ*ai, where fij is 0, 0.5, or 1 (with a haploid mode limited to 0 and 1), ρ is a parameter representing the amount of ambient RNA and ai is the average allele fraction in the experiment.”
The paper relies on this without support.
There are no good generative models available for batch effects, allele-specific expression, ambient RNA, and doublets in scRNAseq that can be used to generate in silico data for testing methods that cluster by genotype.
“Currently, there are no good generative models available for batch effects, allele-specific expression, ambient RNA, and doublets in scRNAseq that can be used to generate in silico data for testing methods that cluster by genotype.”
The paper relies on this without support.
Malaria infections often contain parasites from multiple different genetic backgrounds, and it is not possible to separate the strains prior to sequencing.
“Malaria infections often contain parasites from multiple different genetic backgrounds, and it is not possible to separate the strains prior to sequencing.”
The paper relies on this without support.
The decidua is primarily composed of maternal cells with some invading fetal trophoblasts, while the placenta is largely composed of cells of fetal origin with the exception of maternal macrophages.
“The decidua is primarily composed of maternal cells with some invading fetal trophoblasts, while the placenta is largely composed of cells of fetal origin with the exception of maternal macrophages.”
The paper relies on this without support.
In scRNAseq data most variants have very low coverage per cluster compared to what would be generated from WGS data, so genotype accuracy is significantly lower than one would attain with genome sequencing.
“In scRNAseq data most variants have very low coverage per cluster compared to what would be generated from WGS data, thus the genotype accuracy is significantly lower than one would attain with genome sequencing.”
The authors state this as an assumption.
The majority of barcodes contain a single cell.
“It is important for some assumptions in this model that the majority of barcodes contain a single cell.”
The authors state this as an assumption.
Only biallelic variants are considered.
“We will assume only biallelic variants.”
The authors state this as an assumption.
Ploidy is limited to 1 or 2.
“We assume ploidy is limited to 1 or 2.”
The authors state this as an assumption.
Each cluster is independent and each locus is independent.
“We treat each cluster as independent and each locus as independent, before marginalizing across the possible genotypes.”
The authors state this as an assumption.
The observed allele fractions are drawn from a binomial distribution with a probability skewed away from p = g/P by the level of ambient RNA ρ.
“The observed allele fractions are assumed to have been drawn from a binomial distribution with a probability that was skewed away from p = g/P by the level of ambient RNA ρ.”
The authors state this as an assumption.
Allele counts of each cell at each locus are drawn from a beta-binomial distribution from either a single cluster or a pair of clusters.
“We treat the allele counts of each cell at each locus as random variables drawn from a beta-binomial distribution from either a single cluster or a pair of clusters.”
The authors state this as an assumption.
The expected allele fractions of a doublet coming from cluster i and cluster j is the average of the allele fractions of the two clusters.
“The expected allele fractions of a doublet coming from cluster i, and cluster j is the average of the allele fractions of the two clusters.”
The authors state this as an assumption.
The beta-binomial is used to model uncertainty in the binomial parameter p.
“The beta-binomial is used to model our uncertainty in the binomial parameter p.”
The authors state this as an assumption.
Each locus in each cluster comes from one of three genotypes for diploid (0/0, 0/1, 1/1) and two genotypes for haploid (0, 1).
“The model treats each locus in each cluster as coming from one of three genotypes for diploid (0/0, 0/1, 1/1, here denoted by g = 0, 1, or 2) and two genotypes from haploid (0, 1).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Outside of human samples, SNP-based demultiplexing has been applied to demultiplex other species: plasmodium samples, and across mouse strains (Heaton et al, 2020; Mylka et al, 2022).”
From Evaluation of genetic demultiplexing of single-cell sequencing data from model species (2023) · cites this paper as Heaton et al, 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“SoupOrCell [21] uses the identification of conflicting genotypes to identify ambient RNA contamination, limiting its application to mixed-genotype experiments.”
From SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2020) · cites this paper as [21]
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
“The expected reference allele rate without ambient RNA is g and g is an integer value ∈ [0..P].”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
“The observed allele fractions are assumed to have been drawn from a binomial distribution with a probability that was skewed away from p = g/P by the level of ambient RNA ρ.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
“This is a real number representing the fraction of ref alleles in this cluster at this locus.”
§ souporcell: Robust clustering of single cell RNAseq by genotype without reference genotypes > Online Methods > Supplementary Methods
“Equation 1: Cluster model Likelihood function ℒ(A)=∏c∈C∑k∈K1K∏l∈Lc(Al,c,0+Al,c,1Al,c,1)ϕk,lAl,c,1(1−ϕk,l)Al,c,0”
Citations
Cites
STAR: 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-citationMinimap2: Pairwise alignment for nucleotide sequences
Li, H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty191 · Cited by 14,807
Stan : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754 · cited 2×
“This model is implemented in the domain-specific language for probabilistic models, STAN 16 , and it solves for the maximum-likelihood soup fraction with gradient descent.”
MethodMassively 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
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
Haplotype-based variant detection from short-read sequencing
Garrison, E., & Marth, G. (2012) · arXiv · doi:10.48550/arxiv.1207.3907 · Cited by 3,700 · cited 1×
“Until recently, a genotype reference obtained via whole-genome or exome sequencing has been required for each multiplexed individual before cell-sample categorization 6 .”
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
SoupX removes ambient RNA contamination from droplet based single-cell RNA sequencing data
Young, M. D., & Behjati, S. (2018) · doi:10.1101/303727 · Cited by 1,648
SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
Young, M. D., & Behjati, S. (2020) · GigaScience · doi:10.1093/gigascience/giaa151 · Cited by 1,619
Cited by
Sorted by citations
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Luo, X., Li, J., Qi, J., Lin, C., Wu, J., Jin, O., & Gu, J. (2026) · Lara D. Veeken · doi:10.1093/rheumatology/keag429
Interstitial macrophages drive chronic lung allograft dysfunctionPreprint
Suzuki, A., Schleck, M. J., Wu, Q., Nafikova, R. A., Cusick, L., Kaiho, T., Abdala‐Valencia, H., Yu, Z., Sokolenko, Y. V., Lu, Z., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.21.746267
Eyal-Lubling, Y., Vias, M., Kania, K., Kaludova, D., Hall, J., Crawford, R., Nyagumbo, R., Ward, S., Khoronenkova, S., Aparicio, S. A. J. R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.26.746998 · Cited by 1
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Machine learning–driven decoding of maternal immune signatures in repeated pregnancy loss
Ko, T. L., Park, J., Leem, D., Kim, J., Han, J. W., Park, J. S., Lee, S. K., & Paik, H. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014806
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Boháčová, P., Terekhova, M., Shpynov, O., Francis, T., Husarcikova, K., Tsurinov, P., Kössl, J., Kleverov, M., Keppel, M. P., Harridge, S. D. R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.15.751807
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Cell atlas of the developing human meninges reveals a dura-like nature of meningiomas
Vinsland, E., Salas, S. M., Kapustová, I., Hu, L., Webb, S., Li, X., He, X., Nilsson, M. F., Haniffa, M. A., Barker, R. A., et al. (2026) · Nature Cell Biology · doi:10.1038/s41556-026-02074-9 · Cited by 7
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RNA splicing factor mutations drive myeloid neoplasm oncogenesis through protein complex poisoningPreprint
Moura, P. L., Branca, R. M., Kaminskiy, Y., Siavelis, I., Shrung, K. R., Hofmann, S., Perraki, C. M., Argyriou, A., Fazeli, S., Nakagawa, M. M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.29.754980 · Cited by 1
Blum, S. M., Chan, T. L., Smith, N. P., Ambrose, C. T., Xu, K. H., Tirard, A. J., Samanta, N., Martin, S., Best, R., Tuttle, E., et al. (2026) · Science Immunology · doi:10.1126/sciimmunol.aeb9068
Vértesy, Á., Najm, R., Doleschall, B., Isaev, S. V., Zabolocki, M., Novatchkova, M., Burkard, T. R., Ziegler, C., Krautberger, S., Li, C. V., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.01.755792 · Cited by 1
Temporal and Cellular Organization of Immune Responses to Live Attenuated Herpes Zoster Vaccination
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Terekhova, M., Kleverov, M., Francis, T., Usoltsev, D., Boháčová, P., Arthur, L., Tsurinov, P., Kleverov, D., Trinity, L., Husarcikova, K., et al. (2026) · Immunity · doi:10.1016/j.immuni.2026.09.012
Metadata
- Authors
- Heaton, H., Talman, A. M., Knights, A., Imaz, M., Gaffney, D. J., Durbin, R., Hemberg, M., & Lawniczak, M. K. N.
- Affiliations
- Wellcome Sanger Institute (GB)
- Centre National de la Recherche Scientifique (FR)
- University of Cambridge (GB)
- Corresponding author
- Heaton, Haynes
- Hemberg, Martin
- Lawniczak, Mara K. N.
- ORCID
- Venue
- Nature Methods
- Published
- 2020-05-04
- Volume
- 17
- Issue
- 6
- Pages
- 615-620
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7617080
- OpenAlex
- W3023290106
- Cited by
- 506
- References
- 25
- Citations by year
- 2026: 95 · 2025: 88 · 2024: 126 · 2023: 77 · 2022: 69 · 2021: 38 · 2020: 11
- FWCI
- 13.53
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- HAL (Le Centre pour la Communication Scientifique Directe) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Wellcome Trust (206194/Z/17/Z)
- British Heart Foundation (RG/13/13/30194; RG/18/13/33946)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell RNA sequencing, genotype clustering, cell lysis, batch effects
- MeSH terms
- RNA-Seq, Algorithms, Base Sequence, Cell Line, Genotype, Humans, RNA, Sensitivity and Specificity, Software, Cluster Analysis, Polymorphism, Single Nucleotide, Single-Cell Analysis
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 2026-10-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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Statistics and arithmetic are recomputed, not judged.
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