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”
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Sorted by citations
SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
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Single-cell multi-omics analysis of the immune response in COVID-19
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“Both annotation sources (for example, Ensembl or RefSeq) and annotation versions (for example, Ensembl release 84 or Ensembl release 91) contribute to the variation between different gene naming schemes.”
BackgroundCircuits between infected macrophages and T cells in SARS-CoV-2 pneumonia
Grant, R. A., Morales‐Nebreda, L., Markov, N. S., Swaminathan, S., Querrey, M., Rios-Guzman, E., Abbott, D. A., Donnelly, H. K., Donayre, A., Goldberg, I. A., et al. (2021) · Nature · doi:10.1038/s41586-020-03148-w · Cited by 718
SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis
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“Demultiplexing of donors Donors of PBMCs were demultiplexed using the souporcell algorithm (Heaton et al., 2020) based on the BAM files & barcodes exported by CellRanger count.”
Spatially resolved multiomics of human cardiac niches
Kanemaru, K., Cranley, J., Muraro, D., Miranda, A. M. A., Ho, S. Y., Wilbrey-Clark, A., Patrick Pett, J., Polanski, K., Richardson, L., Litvinukova, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06311-1 · Cited by 425
“Part of the SAN samples were mixed with different donors after the nuclei isolation for cost-efficient experimental design (Supplementary Table 8) and computationally demultiplexed (Soupercell, v.2.0) 55 on the basis of genetic variation between the donors.”
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
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Local and systemic responses to SARS-CoV-2 infection in children and adults
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Mapping the developing human immune system across organs
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“0) (78) (for more details, see the supplementary materials).”
BackgroundCRISPR activation and interference screens decode stimulation responses in primary human T cells
Schmidt, R. L. J., Steinhart, Z., Layeghi, M., Freimer, J. W., Bueno, R., Nguyen, V. Q., Blaeschke, F., Ye, C., & Marson, A. (2022) · Science · doi:10.1126/science.abj4008 · Cited by 316
Spatial multiomics map of trophoblast development in early pregnancy
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The single-cell eQTLGen consortium
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Induction of trained immunity by influenza vaccination - impact on COVID-19
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“Next, we demultiplexed and clustered cells within each pool into four samples using souporcell, a genotype-free method [42].”
MethodFibroblast inflammatory priming determines regenerative versus fibrotic skin repair in reindeer
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“Sample identify of each cell was computational demultiplexed based on natural genetic variation using souporcell (v2).(23) Briefly, souporcell analyses were performed with the number of clusters set to k = 3 and without the use of ‘common_variants’ or ‘skip_remap’ options as species-specific variants are not available…”
MethodDiscrete limbal epithelial stem cell populations mediate corneal homeostasis and wound healing
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“Souporcell (v.2.0) 39 was used to demultiplex each pool based on the genotype differences between the mixed samples.”
InfluentialA cell atlas foundation model for scalable search of similar human cells
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“Individual samples were genetically demultiplexing using the singularity container provided with Souporcell (v.2.0) 82 .”
InfluentialGenetic ancestry effects on the response to viral infection are pervasive but cell type specific
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A multi-omic atlas of human embryonic skeletal development
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A prenatal skin atlas reveals immune regulation of human skin morphogenesis
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“Possible maternal contamination (total of 118 cells) was identified using the souporcell pipeline (v.2.4.0) 88 as previously described 5,58 .”
InfluentialWang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 104
“Genotype‐based approaches RNA reads from scRNA-seq provide a unique opportunity to infer SNVs per cell to demultiplex for individual samples [108–110], or cluster cells to trace clonal evolution [107] or genotype distributions [72].”
BackgroundMethodInfluentialYoon, Y., Kim, D., Tak, K. Y., Hwang, S., Kim, J., Sim, N. S., Cho, J.-M., Choi, D., Ji, Y., Hur, J. K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30934-z · Cited by 108
“The processed fi les were demultiplexed with Souporcell 77 .”
A single-cell atlas of Plasmodium falciparum transmission through the mosquito
Real, E., Howick, V. M., Dahalan, F. A., Witmer, K., Cudini, J., Andradi-Brown, C., Blight, J., Davidson, M. S., Dogga, S. K., Reid, A. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23434-z · Cited by 92
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“Seven of those are demultiplexing methods ( Demuxalot [6], Demuxlet [3], Dropulation [5], Freemuxlet [16], ScSplit [7], Souporcell [4], and Vireo [2]) which leverage the common genetic variation between individuals to identify cells that came from each individual and to identify heterogenic…”
BackgroundMethodSelf-citationLifelong restructuring of 3D genome architecture in cerebellar granule cells
Tan, L., Shi, J., Moghadami, S., Wright, C. P., Parasar, B., Seo, Y., Vallejo, K., Cobos, I., Duncan, L. E., Chen, R., et al. (2023) · Science · doi:10.1126/science.adh3253 · Cited by 81
“We found increasing interactions among certain human chromosomes—most prominently within a hub of Chr 1, 9, 11, 14, 15, 16, 17, 21, and 22, and between chromosome pairs such as Chr 2/9, 4/14, 8/11, 13/20; meanwhile, Chr 12 weakened its interactions with the hub (Fig.”
BackgroundLee, A., Kim, C., Park, S.-W., Joo, J., Choi, B., Yang, D., Jun, K., Eom, J., Lee, S., Chung, S. J., et al. (2023) · Science Advances · doi:10.1126/sciadv.abo2467 · Cited by 90
Evolutionary cell type mapping with single-cell genomics
Tanay, A., & Sebé-Pedrós, A. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.04.008 · Cited by 76
DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data
Muskovic, W., & Powell, J. E. (2021) · Genome biology · doi:10.1186/s13059-021-02547-0 · Cited by 82
“problem, several methods have been developed to estimate and remove its contribution to gene expression [2 – 4].”
MethodGuo, C., Liu, Q., Zong, D., Zhang, W., Zuo, Z.-Q., Yu, Q., Sha, Q., Zhu, L., Gao, X., Fang, J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111606 · Cited by 86
Immune responses in checkpoint myocarditis across heart, blood and tumour
Blum, S. M., Zlotoff, D. A., Smith, N. P., Kernin, I., Ramesh, S., Zubiri, L., Caplin, J., Samanta, N., Martin, S., Wang, M., et al. (2024) · Nature · doi:10.1038/s41586-024-08105-5 · Cited by 104
“Next, the pooled scRNA-seq data were demultiplexed using Souporcell (v.2.5) 63 , and the resulting VCF files for each predicted donor were matched to the VCF files from our bulk RNA-seq data using Vireo (v.0.5.8, Python) 64 .”
InfluentialShannon, M. J., McNeill, G. L., Koksal, B., Baltayeva, J., Wächter, J., Castellana, B., Peñaherrera, M. S., Robinson, W. P., Leung, P. C. K., & Beristain, A. G. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.01.023 · Cited by 88
Vértesy, Á., Eichmüller, O. L., Naas, J., Novatchkova, M., Esk, C., Balmaña, M., Ladstätter, S., Bock, C., von Haeseler, A., & Knoblich, J. A. (2022) · The EMBO Journal · doi:10.15252/embj.2022111118 · Cited by 70
“For pooled 10 × GEX libraries, the donor cell-line of the assayed cells was determined by genotype-based demultiplexing using souporcell (Heaton et al , 2020).”
MethodEarly human lung immune cell development and its role in epithelial cell fate
Barnes, J. L., Yoshida, M., He, P., Worlock, K. B., Lindeboom, R. G. H., Suo, C., Pett, J. P., Wilbrey-Clark, A., Dann, E., Mamanova, L., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.adf9988 · Cited by 79
Garcia‐Flores, V., Romero, R. J., Tarca, A. L., Peyvandipour, A., Xu, Y., Galaz, J., Miller, D., Chaiworapongsa, T., Chaemsaithong, P., Berry, S. M., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adh8335 · Cited by 76
A village in a dish model system for population-scale hiPSC studies
Neavin, D. R., Steinmann, A. M., Farbehi, N., Chiu, H. S., Daniszewski, M., Arora, H., Bermudez, Y., Moutinho, C., Chan, C., Bax, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38704-1 · Cited by 77
“ThehiPSCvillageswerecapturedonDays 0, 1, 2, 3, 4, 5, 7, and 15 to capture the transcriptional changes that occur early during differentiation.”
Kim, S., Leem, G., Choi, J., Koh, Y., Lee, S., Nam, S., Kim, J. S., Park, C. H., Hwang, H. K., Min, K. I., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01287-7 · Cited by 81
“To demultiplex the samples in a single 10X scRNA-seq library, we utilized Souporcell [22] (v2.”
MethodThe genetic architecture of cell type–specific cis regulation in maize
Marand, A. P., Jiang, L., Gómez-Cano, F., Minow, M. A. A., Zhang, X., Mendieta, J. P., Luo, Z., Bang, S., Yan, H., Meyer, C. A., et al. (2025) · Science · doi:10.1126/science.ads6601 · Cited by 79
Generating human bone marrow organoids for disease modeling and drug discovery
Olijnik, A.-A., Rodriguez-Romera, A., Wong, Z. C., Shen, Y., Reyat, J. S., Jooss, N. J., Rayes, J., Psaila, B., & Khan, A. O. (2024) · Nature Protocols · doi:10.1038/s41596-024-00971-7 · Cited by 77
Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC
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Senabouth, A., Daniszewski, M., Lidgerwood, G. E., Liang, H. H., Hernández, D., Mirzaei, M., Keenan, S. N., Zhang, R., Han, X., Neavin, D. R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31707-4 · Cited by 75
Bailey, J. I., Puritz, C. H., Senkow, K. J., Markov, N. S., Diaz, E., Jonasson, E., Yu, Z., Swaminathan, S., Lu, Z., Fenske, S. W., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01975-x · Cited by 71
“41) for that library and two libraries that were prepared from these samples separately.”
BackgroundMMR vaccination induces trained immunity via functional and metabolic reprogramming of γδ T cells
Röring, R. J., Debisarun, P. A., Botey-Bataller, J., Suen, T. K., Bulut, O. P., Kılıç, G., Koeken, V. A. C. M., Sarlea, A., Bahrar, H., Dijkstra, H., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci170848 · Cited by 72
SCITO-seq: single-cell combinatorial indexed cytometry sequencing
Hwang, B., Lee, D. S., Tamaki, W., Sun, Y., Ogorodnikov, A., Hartoularos, G. C., Winters, A. F., Yeung, B. Z., Nazor, K. L., Song, Y. S., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01222-3 · Cited by 65
The effect of background noise and its removal on the analysis of single-cell expression data
Janssen, P., Kliesmete, Z., Vieth, B., Adiconis, X., Simmons, S., Marshall, J. L., Aubin, C., Heyn, H., Levin, J. Z., Enard, W., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02978-x · Cited by 64
Moerkens, R., Mooiweer, J., Ramírez-Sánchez, A. D., Oelen, R., Franke, L., Wijmenga, C., Barrett, R. J., Jonkers, I. H., & Withoff, S. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114247 · Cited by 68
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Single-cell immune profiling reveals distinct immune response in asymptomatic COVID-19 patients
Zhao, X.-N., You, Y., Cui, X., Gao, H., Wang, G., Zhang, S., Lin, Y., Duan, L., Zhu, K.-L., Wang, Y., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00753-7 · Cited by 64
Johansen, M. D., Mahbub, R. M., Idrees, S., Nguyen, D. H., Miemczyk, S., Pathinayake, P. S., Nichol, K., Hansbro, N. G., Gearing, L. J., Hertzog, P. J., et al. (2022) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202108-1901oc · Cited by 61
A single-cell view on host immune transcriptional response to in vivo BCG-induced trained immunity
Li, W., Moorlag, S. J. C. F. M., Koeken, V. A. C. M., Röring, R. J., de Bree, L. C. J., Mourits, V. P., Gupta, M. K., Zhang, B., Fu, J., Zhang, Z., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112487 · Cited by 56
Age-specific nasal epithelial responses to SARS-CoV-2 infection
Woodall, M. N. J., Cujba, A.-M., Worlock, K. B., Case, K., Masonou, T., Yoshida, M., Polański, K., Huang, N., Lindeboom, R. G. H., Mamanova, L., et al. (2024) · Nature Microbiology · doi:10.1038/s41564-024-01658-1 · Cited by 61
“Cells that were identified as heterotypic doublets by Souporcell were discarded.”
Boháčová, P., Terekhova, M., Tsurinov, P., Mullins, R. D. Z., Husarcikova, K., Shchukina, I., Antonova, A. U., Echalar, B., Kössl, J., Saidu, A., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.08.019 · Cited by 71
Cheng, J., Liao, J., Shao, X., Lu, X., & Fan, X. (2021) · Advanced Science · doi:10.1002/advs.202101229 · Cited by 56
Guo, C., Wu, M., Huang, B., Zhao, R., Jin, L., Fu, B., Wang, P., Wang, D., Zheng, M., Fang, J., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01049-3 · Cited by 55
“Heaton H, Talman AM, Knights A, Imaz M, Gaffney DJ, Durbin R, et al. Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes.”
MethodPenter, L., Liu, Y., Wolff, J. O., Yang, L., Taing, L., Jhaveri, A., Southard, J., Patel, M., Cullen, N. M., Pfaff, K. L., et al. (2023) · Blood · doi:10.1182/blood.2022018246 · Cited by 55
“33 By applying souporcell 34 (Figure 2D, left) on 62 673 myeloid cells from the post-HSCT baseline time point from 8 patients (median 296 days post-HSCT, range 151-1290), we identi fi ed 1422 cells (2.3”
InfluentialMethods and Insights from Single-Cell Expression Quantitative Trait Loci
Kang, J. B., Raveane, A., Nathan, A., Soranzo, N., & Raychaudhuri, S. (2023) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-101422-100437 · Cited by 59
“Since then, advancements in the throughput of single-cell technologies and sample multiplexing (60, 70, 136) have enabled the sequencing of larger cohorts amenable to eQTL analyses.”
BackgroundLatent human herpesvirus 6 is reactivated in CAR T cells
Lareau, C. A., Yin, Y., Maurer, K., Sándor, K., Dániel, B., Yagnik, G., Peña, J., Crawford, J. C., Spanjaart, A. M., Gutierrez, J. C., et al. (2023) · Nature · doi:10.1038/s41586-023-06704-2 · Cited by 58
Cong, J., Wu, J., Fang, Y., Wang, J., Kong, X., Wang, L., & Duan, Z. (2024) · Environment International · doi:10.1016/j.envint.2024.108744 · Cited by 56
Rodríguez‐Ubreva, J., Arutyunyan, A., Bonder, M. J., Molina, L. D. P., Clark, S. J., de la Calle‐Fabregat, C., García‐Alonso, L., Handfield, L., Ciudad, L., Andrés‐León, E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29450-x · Cited by 59
Recruitment of highly cytotoxic CD8+ T cell receptors in mild SARS-CoV-2 infection
Wagner, K. I., Mateyka, L. M., Jarosch, S., Grass, V., Weber, S., Schober, K., Hammel, M., Burrell, T., Kalali, B. N., Poppert, H., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110214 · Cited by 47
“…et al., 2020 https://doi.org/10.1093/bioinformatics/ btaa611 scSplit 1.0 Xu et al., 2019 https://doi.org/10.1186/ s13059-019-1852-7 Souporcell 2.0 Heaton et al., 2020 https://doi.org/10.1038/ s41592-020-0820-1 uType software Invitrogen/ThermoFisher N/A Other Äkta pureSuperdeso 200 10/300GL GE…”
InfluentialRobust temporal map of human in vitro myelopoiesis using single-cell genomics
Alsinet, C., Primo, M. N., Lorenzi, V., Bello, E., Kelava, I., Jones, C. P., Vilarrasa‐Blasi, R., Sancho‐Serra, C., Knights, A. J., Park, J.-E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30557-4 · Cited by 45
Self-citationHuman conjunctiva organoids to study ocular surface homeostasis and disease
Bannier-Hélaouët, M., Korving, J., Ma, Z., Begthel, H., Giladi, A., Lamers, M. M., van de Wetering, W. J., Yawata, N., Yawata, M., LaPointe, V. L. S., et al. (2024) · Cell stem cell · doi:10.1016/j.stem.2023.12.008 · Cited by 49
A single cell atlas of sexual development in Plasmodium falciparum
Dogga, S. K., Rop, J., Cudini, J., Farr, E., Dara, A., Ouologuem, D., DJIMDÉ, A. A., Talman, A. M., & Lawniczak, M. (2024) · Science · doi:10.1126/science.adj4088 · Cited by 45
Self-citationGeyer, M. B., DeWolf, S., Mi, X., Weis, K., Shaffer, B. C., Cadzin, B., McAvoy, D. P., Katsamakis, Z., Lorenc, R., Lewis, A. M., et al. (2025) · Blood · doi:10.1182/blood.2025029532 · Cited by 48
Landscape and age dynamics of immune cells in the Egyptian rousette bat
Friedrichs, V., Toussaint, C., Schäfer, A., Rissmann, M., Dietrich, O., Mettenleiter, T. C., Pei, G., Balkema‐Buschmann, A., Saliba, A., & Dorhoi, A. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111305 · Cited by 44
Sample-multiplexing approaches for single-cell sequencing
Zhang, Y., Xu, S., Wen, Z., Gao, J., Li, S., Weissman, S. M., & Pan, X. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04482-0 · Cited by 40
“More recently, the computational tool, Souporcell, was designed for clustering scRNA-seq data by genotype without reference genotypes [31].”
MethodMapping the developing human immune system across organsPreprint
Suo, C., Dann, E., Goh, I., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.17.476665 · Cited by 40
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BCG vaccination induces innate immune memory in γδ T cells in humans
Suen, T. K., Moorlag, S. J. C. F. M., Li, W., de Bree, L. C. J., Koeken, V. A. C. M., Mourits, V. P., Dijkstra, H., Lemmers, H., Bhat, J., Xu, C., et al. (2023) · Journal of Leukocyte Biology · doi:10.1093/jleuko/qiad103 · Cited by 42
Integrative genotyping of cancer and immune phenotypes by long-read sequencing
Penter, L., Borji, M., Nagler, A., Lyu, H., Lu, W. S., Cieri, N., Maurer, K., Oliveira, G., Al’Khafaji, A. M., Garimella, K. V., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44137-7 · Cited by 43
“Distinguishing normal and malignant hematopoiesis using expressed donor-and recipient-speci fi c single nucleotide polymorphisms (SNPs) ( souporcell ) 25 revealed two additional AML-derived ECs, mega-karyopoiesis and erythropoiesis, to be almost entirely recipient-derived in 6 of 8 analyzed cases…”
BackgroundGibson, A., Ram, R., Gangula, R., Li, Y., Mukherjee, E. M., Palubinsky, A. M., Campbell, C. N., Thorne, M., Konvinse, K. C., Choshi, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52990-3 · Cited by 44
Single cell profiling of COVID-19 patients: an international data resource from multiple tissuesPreprint
Consortia, C. Z. I. S.-C. C.-1., Ballestar, E., Färber, D. L., Glover, S. C., Horwitz, B. H., Meyer, K. B., Nikolić, M., Ordovás-Montañés, J., Sims, P. A., Shalek, A. K., et al. (2020) · medRxiv · doi:10.1101/2020.11.20.20227355 · Cited by 38
MALAT1 expression indicates cell quality in single-cell RNA sequencing dataPreprint
Clarke, Z. A., & Bader, G. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.14.603469 · Cited by 43
“For droplet-based methods, cells may release RNA transcripts into the solution of the experiment during cell processing creating ambient RNA which makes it difficult to tell which transcripts come from which cells (Young and Behjati 2018; Heaton et al. 2020).”
Krakow, E. F., Brault, M., Summers, C. N., Cunningham, T. M., Biernacki, M. A., Black, G., Woodward, K. B., Vartanian, N., Kanaan, S. B., Yeh, A. C., et al. (2024) · Blood · doi:10.1182/blood.2024024105 · Cited by 45
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Modeling human embryo implantation in vitro
Molè, M. A., Elderkin, S., Zorzan, I., Penfold, C. A., Horsley, N., Pokhilko, A. V., Polanek, M., Palomar, A., Sinha, M., Wang, Y., et al. (2025) · Cell · doi:10.1016/j.cell.2025.10.027 · Cited by 50
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Murrow, L. M., Weber, R. J., Caruso, J. A., McGinnis, C. S., Phong, K. T., Gascard, P. D., Rabadam, G., Borowsky, A. D., Desai, T. A., Thomson, M., et al. (2022) · Cell Systems · doi:10.1016/j.cels.2022.06.005 · Cited by 36
“We used MULTI-seq lipid-based barcoding and in silico genotyping for sample multiplexing to minimize technical variability between samples (Figure S1C; Table S2; STAR Methods; Heaton et al., 2020; McGinnis et al., 2019b).”
SMARCB1 loss activates patient-specific distal oncogenic enhancers in malignant rhabdoid tumors
Liu, N.-Q., Paassen, I., Custers, L., Zeller, P., Teunissen, H., Ayyildiz, D., He, J., Buhl, J. L., Hoving, E., van Oudenaarden, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43498-3 · Cited by 36
“Reads were mapped to GRCh38 and cell genotypes were annotated by souporcell 75 .”
MethodCaporale, N., Castaldi, D., Rigoli, M. T., Cheroni, C., Valenti, A., Stucchi, S., Lessi, M., Bulgheresi, D., Trattaro, S., Pezzali, M., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02555-5 · Cited by 37
“1b) were demultiplexed using three different state-of-the-art algorithms available for genetic demultiplexing (demuxlet 27 , souporcell 28 , Vireo 29 ).”
Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility
Wang, K., Xiao, Z., Yan, Y., Ye, R., Hu, M. S., Bai, S., Sei, E., Qiao, Y., Chen, H., Lim, B., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.09.026 · Cited by 33
deMULTIplex2: robust sample demultiplexing for scRNA-seq
Zhu, Q., Conrad, D. N., & Gartner, Z. J. (2024) · Genome biology · doi:10.1186/s13059-024-03177-y · Cited by 32
“For multi-donor datasets, we ran SNP-based sample classification using vireo [11] or souporcell [12] when such genotype-based classification were not provided.”
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
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