The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index; 15 of the 16 sources its claims cite were checked for support.
SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
Open accessPublished version of a preprint
Abstract
Background: Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free RNAs contained within the input solution are also captured by these assays. This sequencing of cell-free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data. Results: We demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics. Conclusions: We present SoupX, a tool for removing ambient RNA contamination from droplet-based single-cell RNA sequencing experiments. This tool has broad applicability, and its application can improve the biological utility of existing and future datasets.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
SoupX is a method and R package that estimates and removes ambient cell-free RNA contamination from droplet-based scRNA-seq by using empty droplets to define a background profile and estimating a contamination fraction. Across species-mixing, PBMC, kidney tumour, and fetal liver datasets, correction improved marker specificity and biological interpretation.
Key findings
- Ambient RNA contamination was ubiquitous: in 10X species-mixing data, ∼1% of transcripts were cross-species contamination, providing a lower bound, with true contamination later estimated at ∼2%; background profiles correlated with cell aggregates at Pearson 0.71–0.96 (median 0.86).
- SoupX reduced cross-species contamination by a factor of ≥2 and usually an order of magnitude, while correct-species expression was effectively unchanged.
- In PBMCs, automated contamination estimation gave 6% and an IG-gene-based estimate gave ∼5%; correction increased marker fold-change contrast and improved LYZ specificity for mononuclear phagocytes.
- In kidney tumour data, correction removed haemoglobin expression from non-red-blood cells, identified collagen expression in T/MNP clusters as contamination, increased cross-batch entropy, and yielded 2 fewer clusters; in fetal liver, HBB was confirmed as erythroid-specific and GYPA expression in EI macrophages as genuine.
Methods
- SoupX has three steps: estimate the ambient mRNA profile from empty droplets with <Nemp UMIs (best correlation at Nemp < 10), estimate or manually set the contamination fraction using genes/cells assumed to have no endogenous expression or an automated marker-based procedure, and correct counts via a multinomial likelihood, optionally redistributing cluster-level corrected counts.
- Datasets included 10X and DropSeq species-mixing experiments, PBMC data, a kidney tumour dataset (7 tumours across 10 channels in the Results; the Introduction describes 12 kidney tumour biopsies), and 40 channels of human fetal liver data.
- Analyses compared corrected and uncorrected data using Seurat tSNE representations, cluster membership, marker gene fold changes, marker sensitivity, and cross-batch entropy.
Limitations
- The paper states that best results require a user-specified set of genes and cells where no endogenous expression can be assumed; when this is not possible, the automated method requires clustering and may not always have an appropriate gene set.
- The paper notes that a constant contamination fraction within a channel is assumed in most experiments, and that manual contamination setting can be used if estimates are deemed inaccurate or overcorrection is desired.
- Evident from the text: the method assumes empty-droplet profiles represent the background and that background composition is shared across cells; evaluation is limited to the datasets analysed, and the paper has no formal limitations section.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Figures
![Figure 4: The application of SoupX to complex, multi-channel data. A, A tSNE representation of the data, with cluster boundaries shown by density contours and shaded according to the cell type they represent. ccRCC: clear-cell renal cell carcinoma cells; pRCC: papillary cell renal cell carcinoma cells; RBC: red blood cells; MNP: mononuclear phagocytes. B, The fraction of cells shared between clusters determined with the same parameters before and after application of SoupX. C, The improvement in marker sensitivity for the gene HBB, which is a marker for red blood cells. The colour scale represents the fraction of HBB expression that has been removed by SoupX. D, Same as C but for COL1A1. E, The cross-batch entropy before and after SoupX has been applied. The entropy measures the level of local mixing (100 nearest neighbours) for 100 cells selected from each cluster [20]. F, The distribution of HBB expression (y-axis, log scale) in the fetal liver data by cell type (x-axis), with the erythroid lineage marked in boldface. For each cell type, the expression distribution is shown before (right) and after (left) application of SoupX. Dots represent individual cells and box plots show the distribution of expression values where the central line is the median, box boundaries are the first and third quartiles, and the whiskers extend to 1.5 times the interquartile range.](/paper-figures/e3f73e96449e4a2ab09f073625ec073c/soupx-removes-ambient-rna-contamination-from-droplet-based-single-cell-rna-figure-4.jpg)
Verification
Claims the paper makes
33 Own result · 26 Own interpretation · 9 No basis given · 8 Supported · 4 Partly supported · 1 Couldn’t confirm · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Violations of this assumption, which may distort the interpretation of scRNA-seq data, are common in practice.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Introduction
What the sources say
- Not matchedcites [11] Gayoso, 2017, DoubletDetection: Identifying technical error in single-cell RNA-sequencing data
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites [12] Ilicic, 2016, Classification of low quality cells from single-cell RNA-seq data, Genome Biol sourceExperimental — accuracy not yet measured
The Background (page unavailable) says some scRNA-seq capture sites, including droplets, may contain multiple cells and that these cells can lead to misinterpretation. The Discussion (page unavailable) reports up to 30% multiple-cell captures in a Fluidigm study, but that figure concerns microfluidic capture sites, not droplets; the paper does not establish that single-cell-per-droplet violations are common. (LLM, conf=0.82)
- Supportedcites [13] Wolock, 2019, Scrublet: Computational identification of cell doublets in single-cell transcriptomic data, Cell Syst source
The Introduction says that at least several percent of capture events in a typical scRNA-seq experiment are multiplets, including events where more than one cell enters a droplet. It also explains that these events can confound interpretation and downstream analyses. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, any cell-free RNAs contained within the input solution are also captured by these assays.”
- Also stated in: § Introduction · boosted
“It is recognized that these contaminating non-endogenous RNAs are present even within datasets of the highest quality [2].”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Background
What the sources say
- Supportedcites [2] Zheng, 2017, Massively parallel digital transcriptional profiling of single cells, Nat Commun source
The Results section (Technical demonstration with cell lines and synthetic RNAs; page unavailable) reports that cell-free RNA contributed background UMI counts. It also reports that synthetic RNAs loaded into droplets in place of cells were detected by sequencing, directly demonstrating capture of RNA from the input solution. (LLM, conf=0.92)
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Introduction · boosted
Every source the paper cites for this claim was checked and supports it.
In the paper
“A similar effect is seen in the Drop-Seq–based species-mixing data (Supplementary Fig. S3).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Properties of Ambient RNA
What the sources say
- Supportedcites [14] Macosko, 2015, Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets, Cell source
The Results section (“The single cell accuracy and sensitivity of Drop-Seq libraries”; page not provided) reports human–mouse species-mixing experiments and species-mixed cell barcodes. It also reports single-cell impurity in those libraries, with ambient RNA appearing to be the largest source. This supports the claim that cross-species contamination is seen in Drop-Seq species-mixing data. (LLM, conf=0.90)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Droplets containing human (top right) and mouse (bottom right) cells show that ∼1% of observed transcripts are cross-species contamination.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Properties of Ambient RNA
What the sources say
- Supportedcites [2] Zheng, 2017, Massively parallel digital transcriptional profiling of single cells, Nat Commun source
The Results section (‘Technical demonstration with cell lines and synthetic RNAs’) reports that the mean fraction of UMI counts from the other species was 0.9% in both human and mouse GEMs. It describes this as a low level of cross-talk between cell barcodes, consistent with the claim’s approximately 1% figure. (LLM, conf=0.93)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This analysis shows that the batch-mixing entropy is increased after background correction, indicating better mixing between samples (Fig. 4E).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Ambient RNA Confounds Interpretation in Complex Experiments
What the sources say
- Partly supportedcites [20] Haghverdi, 2018, Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors, Nat Biotechnol sourceExperimental — accuracy not yet measured
The Results section on the pancreas data set reports higher batch-mixing entropy after MNN batch correction than in uncorrected data. The Online Methods section also specifies that entropy was assessed in pancreas data sets. Neither passage supports the claim that entropy increased in kidney tumour data after background correction; no page numbers are available in our copy. (LLM, conf=0.88)
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This is likely a consequence of the fact that the capture efficiency of molecules in droplet-based experiments varies by as much as an order of magnitude [17].”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Properties of Ambient RNA
What the sources say
- Partly supportedcites [17] Svensson, 2017, Power analysis of single-cell RNA-sequencing experiments, Nat Methods sourceExperimental — accuracy not yet measured
The Results section on technical sensitivity reports substantial variation across scRNA-seq protocols, including a droplet-based protocol, and high within-protocol variability. But it does not establish that molecular capture efficiency in droplet-based experiments varies by an order of magnitude. The Discussion’s one-order-of-magnitude figure compares endogenous RNA with spike-ins, not variation among droplet experiments; page numbers were unavailable in our copy. (LLM, conf=0.83)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
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 demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude.”
- Also stated in: § Introduction · plain
“Here, we show that this "soup" of cell-free mRNAs is ubiquitous and non-negligible in magnitude.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Results
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Introduction · plain
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
“Here, we show that this "soup" of cell-free mRNAs is ubiquitous and non-negligible in magnitude.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“as well as improving quality control metrics”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Results
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“with experiment-specific variations in composition and magnitude”
- Also stated in: § Introduction · plain
“Because the character and extent of ambient mRNA contamination varies by experiment”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Introduction · plain
- It may state two separate claims as one. 11
- Probably not a substantial claim. 7
Every source the paper cites for this claim was checked and supports it.
In the paper
“We first investigate 2 “species mixing” datasets run on the Chromium 10X [2] and DropSeq [14] platforms, which allow us to directly identify contaminating mRNAs and test our method’s accuracy.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Introduction
What the sources say
- Supportedcites [2] Zheng, 2017, Massively parallel digital transcriptional profiling of single cells, Nat Commun source
The Results section (“Technical demonstration with cell lines and synthetic RNAs”) describes a human–mouse mixture run on the cited system and reports UMIs from the other species in each cell type. The Methods section (“Alignment, barcode assignment and UMI counting”) explains how reads from the other species were used to assess barcode cross-talk. The citation identifies the Chromium species-mixing dataset; testing SoupX is the citing authors’ proposed use of it, not a test attributed to this paper. Page numbers are not provided in our copy. (LLM, conf=0.90)
- Supportedcites [14] Macosko, 2015, Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets, Cell source
The Results section describes Drop-Seq species-mixing experiments using human and mouse cells, with transcripts scored by species and cell barcode. It also reports using those experiments to measure single-cell impurity, including impurity attributed to ambient RNA. The citation identifies the Drop-Seq dataset; testing SoupX’s accuracy is the citing authors’ stated use of it, not a result attributed to Macosko et al. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Finally, DecontX [23] relies on accurate clustering of the data to estimate and remove the background without the need for gene counts from empty droplets.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Supportedcites [23] Yang, 2003, Decontamination of ambient RNA in single-cell RNA-seq with DecontX, Genome Biol source
The Discussion (page unavailable) says DecontX needs cell-cluster labels, generates them with Celda if none are supplied, and uses other clusters to derive each population’s contamination distribution. The Background’s Fig. 1 description (page unavailable) lists an expression count matrix and cluster labels as inputs for estimating native and contamination counts, supporting the point that empty-droplet gene counts are not required. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“For example, solid-tissue experiments are frequently highly contaminated with red blood cells and red cell lysis is used to prepare the samples [15].”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Could not verifycites [15] Young, 2018, Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors, Science sourceExperimental — accuracy not yet measured
The paper’s tissue-processing description (Abstract; page not provided) says kidney samples were made into viable single-cell solutions, but does not mention red-blood-cell contamination or red-cell lysis. Searches for red blood cells, erythrocyte lysis, and lysis buffer found no passage addressing either part of the claim. (LLM, conf=0.72)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This deep generative model comes with a heavy computational cost compared to other tools, and the output of the model (which in effect estimates mg, c for each cell type) provides an imputed cell profile rather than raw counts with the background “subtracted off,” which SoupX provides.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Partly supportedcites [22] Fleming, 2019, CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets, bioRxiv sourceExperimental — accuracy not yet measured
Section 3.3.2 describes the output as a model-inferred, background-free expression-rate estimate that is subsequently quantized, supporting the distinction from simply subtracting background counts from raw counts. Section 3.2.1 reports runtimes of 30 minutes to several hours on a GPU, but does not compare computational cost with other tools; that comparative claim is unsupported. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Cell Bender [22] uses a deep generative model to estimate shared expression patterns likely to represent distinct cell types while simultaneously removing contamination.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Supportedcites [22] Fleming, 2019, CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets, bioRxiv source
The Introduction describes a neural network that learns gene-expression patterns across droplets, combines information from similar cells, and estimates background contamination simultaneously. Section 3 explains that the model lets similar cells share statistical power; the paper also identifies it as a deep generative model. (LLM, conf=0.91)
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“SoupOrCell [21] uses the identification of conflicting genotypes to identify ambient RNA contamination, limiting its application to mixed-genotype experiments.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Supportedcites [21] Heaton, 2020, souporcell: Robust clustering of single-cell RNA-seq data by genotype without reference genotypes, Nat Methods source
The Abstract says that cross-genotype variant contamination can be used to estimate ambient RNA and frames souporcell as a method for samples containing a mixture of genotypes. The Clustering model and data preprocessing section describes estimating ambient RNA from allele fractions shifted away from each cluster’s genotype, supporting the stated limitation to mixed-genotype experiments. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This allows DecontX to be applied when empty droplet counts are not available but also means that the results are potentially heavily dependent on the accuracy of the clustering provided.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Supportedcites [23] Yang, 2003, Decontamination of ambient RNA in single-cell RNA-seq with DecontX, Genome Biol source
The Background (Fig. 1 caption; page unavailable) describes DecontX as taking an expression count matrix and cell-cluster labels, without requiring empty-droplet counts. The Discussion (page unavailable) identifies cluster labels as a limitation and explains that the choice of labels can affect the inferred contamination distribution, supporting the stated potential dependence on clustering. (LLM, conf=0.87)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
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 species-mixing experiment allows us to compare how accurately bg recapitulates the true background expression found within each cell, revealing that any value of Nemp < 100 produces a good correlation, with the best correlation given when Nemp < 10 (Supplementary Fig. S2).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > The SoupX Method
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“Applying SoupX and re-analysing the kidney tumour data revealed that, in contrast to the PBMC data, many cells changed cluster and with the same clustering parameters 2 fewer clusters were identified in the corrected data (Fig. 4B).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Ambient RNA Confounds Interpretation in Complex Experiments
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“These 2 profiles were highly correlated in the 10X species experiment (Fig. 2B) with a high correlation found in all other datasets considered (Pearson correlation 0.71–0.96, median 0.86; Supplementary Table S2).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Properties of Ambient RNA
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Droplets with ≤10 UMIs contain only ambient mRNAs.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The output of the SoupX package is a corrected table of counts, which can be used as input for standard workflows, and running SoupX does not add appreciably to the computational cost of standard single-cell analyses.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
“Comparing cluster membership before and after correction revealed that the same number of clusters was identified, but some cells changed which cluster they belonged to (Fig. 3C).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Application of SoupX to PBMC Data
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The genes for which mg, c = 0 for a given cell are those genes that are strong negative markers of the cell type c.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > The SoupX Method
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We present SoupX, a method for quantifying the extent of ambient mRNA contamination whilst purifying the true, cell-specific signal from the observed mixture of cellular and exogenous mRNAs.”
- Also stated in: § Results · plain
“We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Results · plain
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
“SoupX produces a modified table of counts, which can be used in place of the original count matrix in any downstream analysis tool.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > The SoupX Method
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, we recognize that selecting an appropriate set of genes to estimate contamination will not always be possible.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This tool has broad applicability”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By contrast, SoupX can be applied generally, is computationally inexpensive, and does not depend heavily on accurate pre-annotation of input data.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Ambient mRNA contamination is increased in necrotic or complex samples.
“with increased contamination in necrotic or complex samples”
The authors state this as an assumption.
Droplets with fewer than Nemp UMIs unambiguously do not contain cells.
“To estimate the background expression profile we consider all droplets with <Nemp UMIs, which we assume unambiguously do not contain cells.”
The authors state this as an assumption.
The relative abundance of genes that make up the background does not differ between cells.
“We assume that the relative abundance of genes that make up the background does not differ between cells”
The authors state this as an assumption.
There is a combination of genes and cells for which mg,c = 0 exists.
“To proceed, we assume that there is a combination of genes and cells for which mg, c = 0 exists.”
The authors state this as an assumption.
If it can be shown for any cell c in cluster P that mg,c > 0, then mg,c > 0 for all c ∈ P.
“If it can be shown for any cell c in cluster P that mg, c > 0, then it is assumed that mg, c > 0 for all c ∈ P (see Supplementary Fig. S1).”
The authors state this as an assumption.
For each strong marker, mg,c = 0 for all cells in clusters where the gene is not a marker.
“For each strong marker, it is assumed that mg, c = 0 for all cells in clusters where the gene is not a marker and the contamination fraction is estimated (Supplementary Fig. S1).”
The authors state this as an assumption.
Inaccurate estimates of the contamination fraction will have no preferred value while true estimates will cluster around the true value.
“To obtain a final value, it is assumed that inaccurate estimates will have no preferred value while true estimates will cluster around the true value.”
The authors state this as an assumption.
Droplets with ≤10 UMIs contain only ambient mRNAs.
“which we assumed to contain only ambient mRNAs”
The authors state this as an assumption.
For species-mixing genes/cells (human transcripts in mouse cells and vice versa), mg,c = 0.
“For these genes/cells it is assumed that mg, c = 0 and the contamination fraction is calculated using Equation 4.”
The authors state this as an assumption.
SoupX assumes a constant contamination fraction within a channel.
“SoupX assumes a constant contamination fraction within a channel.”
The paper relies on this without support.
In most experiments there is less power to determine cell-specific contamination fractions.
“In most experiments there is less power to determine cell-specific contamination fractions and so SoupX assumes a constant contamination fraction within a channel.”
The authors state this as an assumption.
Some genes can be assumed to be unexpressed in some cells (mg,c = 0).
“To confirm the accuracy of this estimate, we also calculated the background contamination rate using a set of genes that could be assumed to be unexpressed in some cells (i.e., where mg, c = 0).”
The paper relies on this without support.
Haemoglobin genes are highly specific to red blood cells.
“together with the knowledge that haemoglobin genes are highly specific to red blood cells.”
The paper relies on this without support.
The ambient mRNA expression profile is experiment specific.
“Because the ambient mRNA expression profile is experiment specific, we reasoned that background contamination likely creates batch effects.”
The paper relies on this without support.
Red blood cells are the only cells that produce haemoglobin under normal physiological conditions.
“Furthermore, red blood cells are the only cells that produce haemoglobin under normal physiological conditions, so for the set of haemoglobin genes, it is safe to assume that there is no cell endogenous expression for cells that are not red blood cells.”
The authors state this as an assumption.
For haemoglobin genes, there is no cell endogenous expression for cells that are not red blood cells.
“Furthermore, red blood cells are the only cells that produce haemoglobin under normal physiological conditions, so for the set of haemoglobin genes, it is safe to assume that there is no cell endogenous expression for cells that are not red blood cells.”
The authors state this as an assumption.
Inaccurate estimates of the contamination fraction are not strongly correlated (i.e., there is no preferred, incorrect estimate).
“By using all high-quality marker genes identified in the data to independently estimate the contamination fraction, this method estimates the true contamination fraction by assuming that inaccurate estimates of the contamination fraction are not strongly correlated (i.e., there is no preferred, incorrect estimate).”
The authors state this as an assumption.
For a chosen set of genes and cells, the only source of expression for these genes is background contamination.
“The best case for applying SoupX occurs when the user can specify a set of genes and cells where there is no cell endogenous expression, i.e., a set of genes and cells where it is safe to assume that the only source of expression for these genes is from background contamination.”
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
“This contamination may distort transcriptomic profiles, leading to the underrepresentation of specific cell types or misclassification of cellular states[34, 37].”
From Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing (2026) · cites this paper as [37]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Ambient RNA contamination was removed using the SoupX v1.6.2 package67 with default parameters, except for samples from Miedema et al.14 where tfidfMin was adjusted to 0.8 (default 1) due to the lower cellular complexity of the dataset (prior FACS).”
From CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis (2024) · cites this paper as [67]
The cited source says what the claim attributes to it.
In the citing paper
“Contamination of cell-free RNA for each sample group was removed using SoupX.”
From Cholesterol 25-hydroxylase mediates neuroinflammation and neurodegeneration in a mouse model of tauopathy (2024) · cites this paper as Young and Behjati, 2020
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Accurate chromatin accessibility analysis depends on precise peak calling, as calling peaks across the entire cell population can inadvertently exclude those specific to smaller subpopulations (85).”
From Molecular and cellular processes disrupted in the early postnatal Down syndrome prefrontal cortex (2026) · cites this paper as [85]
Experimental — accuracy not yet measuredIn the citing paper
“Ambient RNA contamination can lead to cell-type-specific marker gene transcripts being detectable also in other cell populations, which can blend different cell populations together21.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [21]
In the citing paper
“one should consider removing ambient RNA as an initial analysis step in quality control to improve downstream analyses for many tissues21–23.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [21]
Citations
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MethodInfluentialIntegrating single-cell transcriptomic data across different conditions, technologies, and species
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Scrublet: computational identification of cell doublets in single-cell transcriptomic data
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“We therefore calculated the cross-batch entropy of the kidney tumour data before and after background correction [20].”
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“Moreover, large-scale efforts to create a ”Human Cell Atlas” critically depend on the accuracy and cellular specificity of the transcriptional readout produced by droplet-based scRNA-seq [9, 10].”
Research works printed but not linked: 1
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Cited by
Sorted by citations
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Zawieja, S. D., Pea, G. A., Broyhill, S. E., Patro, A., Bromert, K. H., Norton, C. E., Kim, H. J., Sivasankaran, S. K., Li, M., Castorena-Gonzalez, J. A., et al. (2023) · eLife · doi:10.7554/elife.90679 · Cited by 10
“Ambient RNA was removed from the Cell Ranger output with SoupX ( Young and Behjati, 2020 ).”
A nociceptive amygdala-striatal pathway for chronic pain aversionPreprint
Wojick, J. A., Paranjapye, A., Chiu, J. K., Mahmood, M., Oswell, C. S., Kimmey, B. A., Wooldridge, L. M., McCall, N. M., Han, A., Ejoh, L. L., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.12.579947 · Cited by 10
“Initial dimensionality reduction and clustering was performed prior to processing to enable batch correction and removal of cell free mRNA using SoupX (92).”
MethodVornholt, E., Liharska, L. E., Cheng, E., Hashemi, A., Park, Y. J., Ziafat, K., Wilkins, L., Silk, H., Linares, L. M., Thompson, R. C., et al. (2024) · medRxiv · doi:10.1101/2024.05.01.24306590 · Cited by 11
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InfluentialTabula Glycine: The whole-soybean single-cell resolution transcriptome atlasPreprint
Cervantes-Pérez, S. A., Thibivilliers, S., Amini, S., Pelletier, J. M., Meyer, I., Xu, H., Tennant, S., Ma, P., Sprueill, C. M., Farmer, A. D., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.08.602332 · Cited by 9
“The background contamination was subtracted after the alignment of the reads using SoupX [77], and doublets were filtered out using the DoubletDetection prediction method 70 .”
MethodMummey, H. M., Elison, W. S., Korgaonkar, K., Elgamal, R. M., Kudtarkar, P., Griffin, E. N., Benaglio, P., Miller, M., Jha, A., Fox, J. E. M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.03.606460 · Cited by 10
De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing dataPreprint
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“Calling mtSNVs remains challenging, ashigh levels ofambi-ent mtRNA, released by dead or dying cells (Young and Behjati 2020), can cause false-negative calls on the bulk level (loci wrongly excluded as germline) and false-positive calls on the single-cell level (contaminated noncancer cells called…”
Spatiotemporal cellular map of the developing human reproductive tract
Lorenzi, V., Icoresi-Mazzeo, C., Cassie, C., Yayon, N., Ruiz-Morales, E. R., Sancho‐Serra, C., Colligan, R. M., Wong, F. C. K., Marečková, M., Tuck, E. J., et al. (2025) · Nature · doi:10.1038/s41586-025-09875-2 · Cited by 19
Human haematopoietic stem cells remember inflammatory stress
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“6.2) 79 with default parameters.”
InfluentialGerayeli, F. V., Milne, S. B., Yang, C. X., Li, X., Guinto, E., Yang, J. S.-W., Cheung, C. Y., Stach, T. R., Shaipanich, T., Leung, J. M., et al. (2023) · BioTechniques · doi:10.2144/btn-2023-0017 · Cited by 9
Rotenone impairs brain glial energetics and locomotor behavior in bumblebees
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“In scRNA-seq data, ambient RNA from the cell suspension can be counted along with a cell’s native RNA and result in contamination of gene markers between cell types ( 10 ,11 ) .”
Callebaut, A., Guyer, P., Derua, R., et al. (2024) · Diabetes · doi:10.2337/db23-0588 · Cited by 9
Single-Cell Informatics for Tumor Microenvironment and Immunotherapy
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“If not removed, they are likely misidentified in other cell types, obscuring the distinction between different cell populations during the clustering step [50].”
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Novel Arf1 Inhibitors Drive Cancer Stem Cell Aging and Potentiate Anti‐Tumor Immunity
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Sleeve gastrectomy reveals the plasticity of the human gastric epithelium
Elad, A., Moalem, B., Sender, D., Bardugo, A., Kim, K., Arad, Y., Benhayon, H., Etzyoni, A. G., Greenstein, N., Halfon, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56135-y · Cited by 9
“SoupX (1.6.2) 62 (8) was applied to mitigate potential extracellular mRNA contamination during droplet-based library generation.”
InfluentialLiu, S., Hamilton, M. C., Cowart, T., Barrera, A., Bounds, L. R., Nelson, A. C., Dornbaum, S. F., Riley, J. W., Doty, R. W., Allen, A. S., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100766 · Cited by 10
“13 However, ambient gRNAs in perturb-seq libraries have yet to be systematically characterized to understand the abundance and source of ambient gRNA contamination.”
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Transcriptomic and morphologic vascular aberrations underlying FCDIIb etiology
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“…aberration in response to disease status, we carried out pseudo-bulk RNA sequencing (RNA-seq) differential expression gene (DEG) analysis using an R package Muscat: multi-sample multi-group scRNA-seq analysis tools 54 , which aggregates single nucleus signals across cell type and clinical factors.”
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“Sequencing was performed on an Illumina Novaseq 6000 instrument. scRNA-Seq data analysis .”
InfluentialA nociceptive amygdala-striatal pathway modulating affective-motivational pain
Wojick, J. A., Paranjapye, A., Chiu, J. K., Oswell, C. S., Mahmood, M., Wooldridge, L. M., Kimmey, B. A., Ortega, R. A. S., McCall, N. M., Han, S., et al. (2025) · Science Advances · doi:10.1126/sciadv.ado2837 · Cited by 19
“Initial dimensionality reduction and clustering were performed before processing to enable batch correction and the removal of cell-free mRNA using SoupX ( 89 ).”
A mesothelial differentiation gateway drives fibrosis
Kadri, S., Fischer, A., Mück-Häusl, M., Han, W., Kadri, A., Lin, Y., Yang, L., Hu, S., Ye, H., Ramesh, P., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63990-2 · Cited by 10
“Ambient RNA analysis was performed using SoupX (Version 1.6.0) 100 , and doublet detection was conducted with DoubletDetection (Version 4.0) 101 .”
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“Brie fl y, ambient mRNA adjustment was performed using SoupX (20% contamination estimation) 75 .”
Luo, H., Hussain, A., Abbas, M., Yuan, L., Shen, Y., Zhang, Z., Sun, G., Yin, X., & HUANG, S. H. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06996-0 · Cited by 12
“…the field continues to lack robust biological reference standards that adequately represent the complexity of single-cell transcriptomes [63] Oil droplet-based single-cell sequencing methods, including Drop-seq, inDrops, and 10× Genomics platforms, have revolutionized single-cell…”
In vivo transcriptomic, functional, circuit-based, and translational analyses of enteric neurons
Millett, C. J., Shaver, J. J., Bracken, B., Jones, S. J., Lovelett, R. J., Rubinow, D. A., Singhal, R., Charlton, C., Piazza, N., Hauck, Q., et al. (2025) · Cell · doi:10.1016/j.cell.2025.11.024 · Cited by 11
Nociceptive innervation limits tertiary lymphoid structures to promote lung cancer
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“To address this issue, we implemented the SoupX algorithm (SoupX 1.6.0) (Young and Behjati, 2020), to remove cell-free RNA contamination.”
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Specific photoreceptor cell fate pathways are differentially altered in NR2E3-associated diseases
Aísa-Marín, I., Rovira, Q., Díaz, N., Calvo‐López, L., Vaquerizas, J. M., & Marfany, G. (2024) · Neurobiology of Disease · doi:10.1016/j.nbd.2024.106463 · Cited by 9
Regeneration in the absence of canonical neoblasts in an early branching flatworm
Gąsiorowski, L., Chai, C., Rozanski, A., Purandare, G., Ficze-Schmidt, F., Mizi, A., Wang, B., & Rink, J. C. (2025) · Nature Communications · doi:10.1038/s41467-024-54716-x · Cited by 11
“Ambient RNA contamination was then removed using SoupX 68 with default parameters.”
Gopalan, V., Wong, C. W., Leshem, R., Owen, L., Vallius, T., Shi, Y., Jiang, Y., Pérez‐Guijarro, E., Wu, E., Chin, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.14.512297 · Cited by 9
“We ran SoupX on each embryonic sample to remove any contamination from ambient RNA in each cell[50].”
Trevisan, A. J., Han, K., Chapman, P., Kulkarni, A. S., Hinton, J. M., Ramirez, C., Klein, I., Gatto, G., Gabitto, M. I., Menon, V., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.18.613279 · Cited by 9
Su, Z., Liu, Z., Lei, W., Xia, K., Xiao, A., Hu, Z., Zhou, M., Zhu, F., Tian, J., Yang, M., et al. (2023) · Cell Biology and Toxicology · doi:10.1007/s10565-023-09819-4 · Cited by 9
Lung, H., Wentworth, K. L., Moody, T. A., Zamarioli, A., Ram, A., Ganesh, G., Kang, M., Ho, S. P., & Hsiao, E. C. (2024) · JBMR Plus · doi:10.1093/jbmrpl/ziae011 · Cited by 9
Jordan, C. Z., Tunbridge, M. J., Husain, I., Kitai, H., Dilts, M., Fay, O. K., Abe, K., Xiang, C., Kwun, J., Souma, T., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.178502 · Cited by 8
Bhagavan, H., Wei, A. D., Oliveira, L. M., Aldinger, K. A., & Ramirez, J. (2024) · American Journal of Physiology-Lung Cellular and Molecular Physiology · doi:10.1152/ajplung.00320.2023 · Cited by 8
Swisa, A., Kieckhaefer, J. E., Daniel, S. G., El‐Mekkoussi, H., Kolev, H. M., Tigue, M., Jin, C., Assenmacher, C., Dohnalová, L., Thaiss, C. A., et al. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.05.006 · Cited by 8
Manduchi, E., Descamps, H. C., Schug, J., et al. (2024) · Diabetes · doi:10.2337/db24-0076 · Cited by 8
Konger, R. L., Xuei, X., Derr-Yellin, E. C., Fang, F., Gao, H., & Liu, Y. (2024) · Cells · doi:10.3390/cells13161356 · Cited by 8
“The R package SoupX version 1.5.2 [18] was used to remove cell-free contaminating RNA from the data.”
MethodInfluentialMathews, J. A., Borovsky, D. T., Reid, K. T., et al. (2024) · iScience · doi:10.1016/j.isci.2024.111416 · Cited by 8
Saito, S., Nakamura, Y., Miyashita, S., Sato, T., Hoshina, K., Okada, M., Hasegawa, H., Oishi, M., Fujii, Y., Körbelin, J., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.179729 · Cited by 9
Katsoula, G., Lawrence, J. E. G., Arruda, A. L., Tutino, M., Balogh, P., Southam, L., Swift, D., Behjati, S., Teichmann, S. A., Wilkinson, J. M., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.10.019 · Cited by 8
“49,50 In addition, each run was processed with the Scrublet pipeline, 51 with cells called as doublets subsequently removed from analysis.”
Self-citationEom, T.-Y., Schmitt, J. E., Li, Y., Davenport, C. M., Steinberg, J. D., Bonnan, A., Alam, S., Ryu, Y. S., Paul, L., Hansen, B. S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54837-3 · Cited by 9
HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling
Otoničar, J., Lazareva, O., Mallm, J., Simovic-Lorenz, M., Philippos, G., Sant, P., Parekh, U., Hammann, L., Li, A., Yildiz, U., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03450-0 · Cited by 7
“Next, ambient RNA correction was performed with SoupX (version 1.6.2) [105], followed by doublet detection with scD-blFinder (version 1.14.0) [106].”
InfluentialAli, S. A., Perera, G., Laird, J., Batorsky, R., Maron, M. S., Rivas, V. N., Stern, J. A., Harris, S. P., & Chin, M. T. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.124.035780 · Cited by 8
“…quality control filtering was performed using SoupX version 1.6.1, an R-package used for the quantification and removal of ambient mRNA contamination in droplet-based single-cell RNA sequence data 19 and DoubletFinder version 2.0.3, an R-package that identifies doublets via gene expression.”
InfluentialZhang, J., Ding, Q., Wang, A. X., Lin, M., Yu, N., MOSS, K. L., Williamson, M., Miao, D., Marchesan, J. T., Zeng, E., et al. (2025) · Life Sciences · doi:10.1016/j.lfs.2025.123559 · Cited by 12
“6.2) [39]; (2) doublet detection and removal by DoubletFinder (v2.”
InfluentialSchaefer, N. K., Pavlovic, B. J., Schmitz, M. T., & Pollen, A. A. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101275 · Cited by 9
“For SoupX, 27 we used the clusters inferred by DecontX for the sake of consistency, extracted the ambient expression profile from the soupProfile field in the result object, and measured each cell’s contamination rate as 1 – colSums(after-Matrix)/colSums(beforeMatrix), where beforeMatrix was the…”
InfluentialCellular landscape of avian intestinal organoids revealed by single cell transcriptomics
Sun, J., Borowska, D., Furniss, J. J., Sutton, K., Macqueen, D. J., & Vervelde, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-95721-4 · Cited by 8
Cavagnero, K. J., Albright, J., Li, F., et al. (2025) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2025.05.029 · Cited by 8
Single‐nucleus analysis reveals oxidative stress in Down syndrome basal forebrain neurons at birth
West, N. R., Arachchilage, K. H., Knaack, S., MacGregor, S., Hosseini, M., Risgaard, R. D., Kumarage, P., Martinez, J. L., Zhang, S., Wang, D., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70445 · Cited by 8
“6.2) 38 package.”
InfluentialRegion-specific gene expression and sex inform about disease susceptibility in the aorta
Romay, M. C., Ma, F., Mompeón, A., Silvestro, M., Hernandez, G. E., Salvador, J., Wang, A. L., Vandestienne, M., Bardin, N., Blot‐Chabaud, M., et al. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00692-4 · Cited by 9
“To mitigate the potential role of ambient RNA in our libraries, we applied two algorithms: EmptyDrops 56 and SoupX 57 .”
Arnoux, G., Serre, J., Verissimo, T., Tihy, M., Davidson, S. M., Placier, S., Verney, C., Sangla, F., Paolucci, D., Fernandez, M., et al. (2025) · Kidney International · doi:10.1016/j.kint.2025.07.021 · Cited by 9
Tran, H., Wasikowski, R., Zhang, H., Xing, X., Gharaee‐Kermani, M., Raducu, M., Fox, J. P., Jiang, R., Plazyo, O., Harms, P. W., et al. (2025) · JCI Insight · doi:10.1172/jci.insight.193038 · Cited by 8
Dábilla, N., Maya, S., McNinch, C. M., Eddens, T. J., Dolan, P., & Freeman, M. C. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1698639 · Cited by 8
“To classify cells as “Infected” or “Non Infected”, a Poisson test was applied to these percentages using the “Estimate Non Expressing Cells” function from the Soup X R package (Young and Behjati, 2020), using an FDR of 0.05.”
InfluentialChen, H., Fan, L., Guo, Q., Wong, M. Y., Zhu, J., Foxe, N. R., Wang, W. T., Nessim, A., Carling, G., Liu, B., et al. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-025-00903-3 · Cited by 10
“Subsequently, this file was read into Seurat, where the raw data underwent further cleaning with DoubletFinder and SoupX [29].”
Renal PIEZO2 is an essential regulator of renin
Hill, R. Z., Nelson, J. W., Gyarmati, G., Medrano, S., Shirvan, S., McCormick, J. A., Burquez, S., Ahmed, J., Eng, D. G., Wysocki, J. A., et al. (2025) · Cell · doi:10.1016/j.cell.2025.11.013 · Cited by 10
Johnson, M. J., Crotteau, M., Ghosh, D., Vu, T., Trinity, L., Marcheş, R., Ucar, D., Levin, M. J., & Weinberg, A. (2025) · PLoS Pathogens · doi:10.1371/journal.ppat.1013759 · Cited by 10
“The gene-cell expression matrices were corrected for ambient RNA contamination using SoupX [74] prior to downstream analyses.”
A high-resolution atlas of the brain predicts lineage and birth order underlying neuronal identity
Allen, A. M., Neville, M. C., Nojima, T., Alejevski, F., Agarwal, D., Sims, D., & Goodwin, S. F. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101103 · Cited by 12
Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing
Kersey, H. N., Acri, D. J., Dabin, L. C., Hartigan, K. A., Mustaklem, R., Park, J. H., & Kim, J. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101337 · Cited by 8
Wang, Y., Afshari, K., Haddadi, N., Lopes, C. S., Eng, C.-H. L., Whiteman, L. M., Martinez, N., Kyawe, P. P., Anufrieva, K. S., Wei, K., et al. (2026) · Nature Immunology · doi:10.1038/s41590-026-02502-w · Cited by 9
A Novel MIP-1–Expressing Macrophage Subtype in BAL Fluid from Healthy Volunteers
Reyfman, P. A., Malsin, E. S., Khuder, B., Joshi, N., Gadhvi, G. T., Flozak, A. S., Carns, M. A., Aren, K., Goldberg, I. A., Kim, S.-J., et al. (2022) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2021-0123oc · Cited by 9
Drake, R. S., Villanueva, M. A., Vilme, M., Russo, D. D., Navia, A. W., Love, J. C., & Shalek, A. K. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2756-3_3 · Cited by 8
An integrative approach for exploring the nature of fibroepithelial neoplasms
Yun, J., Heo, W., Lee, E.-S., Na, D., Kang, W., Kang, J., Chae, J., Lee, D., Lee, W., Hwang, J., et al. (2022) · British Journal of Cancer · doi:10.1038/s41416-022-02064-2 · Cited by 8
Vicary, A. C., Mendes, M., Swaminath, S., et al. (2023) · PLoS Pathogens · doi:10.1371/journal.ppat.1010943 · Cited by 8
“[31] This was to focus on influenza as a determining factor, and not cell-type.”
BackgroundMethodFlynn, P., Long, M. D., Kosaka, Y., Long, N., Mulkey, J. S., Coy, J. L., Agarwal, A., & Lind, E. F. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1297338 · Cited by 8
Ouwendijk, W. J. D., Roychoudhury, P., Cunningham, A. L., Jerome, K. R., Koelle, D. M., Kinchington, P. R., Mohr, I., Wilson, A. C., Verjans, G. M. G. M., & Depledge, D. P. (2024) · Journal of Virology · doi:10.1128/jvi.01858-23 · Cited by 8
“Such RNA is easily bound on the surface of other cell types and thus carried into the droplets in which cell lysis and the initial steps of scRNA-Seq library preparation take place (36, 37).”
BackgroundYin, K., Zhao, M., Xu, Y., Zheng, Z., Huang, S., Liang, D., Dong, H., Guo, Y., Lin, L., Song, J. T., et al. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c05785 · Cited by 8
Pronounced early differentiation underlies zebra finch gonadal germ cell development
Biegler, M. T., Belay, K., Wang, W., Szialta, C., Collier, P. G., Luo, J., Haase, B., Gedman, G. L., Sidhu, A. V., Harter, E., et al. (2024) · Developmental Biology · doi:10.1016/j.ydbio.2024.08.006 · Cited by 8
Arora, J. K., Opasawatchai, A., Teichmann, S. A., Matangkasombut, P., & Charoensawan, V. (2023) · STAR Protocols · doi:10.1016/j.xpro.2023.102387 · Cited by 9
Lung, spleen and oesophagus tissue remains stable for scRNAseq in cold preservationPreprint
Madissoon, E., Wilbrey-Clark, A., Miragaia, R. J., Saeb‐Parsy, K., Mahbubani, K. T. A., Georgakopoulos, N., Harding, P., Polański, K., Nowicki-Osuch, K., Fitzgerald, R. C., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/741405 · Cited by 8
Ma, F.-Y., Hughes, T., Teles, R. M. B., Andrade, P., Silva, B. J. D. A., Plazyo, O., Tsoi, L., Tran, D., Wadsworth, M., Oulee, A., et al. (2020) · bioRxiv · doi:10.1101/2020.12.01.406819 · Cited by 43
A Single Cell Atlas of Lung DevelopmentPreprint
Negretti, N. M., Plosa, E. J., Benjamin, J. T., Schuler, B. A., Habermann, A. C., Jetter, C. S., Gulleman, P. M., Taylor, C. J., Nichols, D. S., Matlock, B. K., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.21.427641 · Cited by 7
“Analysis of single cell sequencing data Ambient background RNA were cleaned from the scRNA-seq data with “SoupX” (Young and Behjati, 2020) as described previously (Schuler et al.”
MethodSpatial lipidomics reveals demyelination and remyelination dynamics in the mouse brain
Opielka, M., Urbanowicz, K., Konieczna-Wolska, K., Müller, E., Krajewski, O., Feucherolles, M., Zhou, Q., Bienkowski, M., Frache, G., Hopf, C., et al. (2025) · Journal of Lipid Research · doi:10.1016/j.jlr.2025.100912 · Cited by 8
“Subse-quently, ambient RNA contamination was reduced using the SoupX package ver.1.6.2, and doublets were removed with the scDblFinder package (46, 47).”
InfluentialModulation of recovery from neonatal hyperoxic lung injury by sex as a biological variable
Cantu, A., Gutierrez, M. E. C., Dong, X., Leek, C. C., Anguera, M. C., & Lingappan, K. (2023) · Redox Biology · doi:10.1016/j.redox.2023.102933 · Cited by 8
“SoupX version 1.6.1 [27] was run with a contamination factor of 5 % to reduce mRNA background contamination.”
InfluentialResolving Resident Colonic Muscularis Macrophage Diversity and Plasticity During Colitis
Ohishi, K., Dóra, D., Han, C. Y., Guyer, R. A., Ohkura, T., Kazimierczyk, S., Picard, N., Leavitt, A. R., Ott, L. C., Rahman, A. A., et al. (2024) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izae155 · Cited by 7
Dynamic regulation of innate lymphoid cell development during ontogeny
Wu, T., Chen, S., Zhu, X., Ma, J., Luo, M., Wang, Y., Tian, Y., Sun, Q., Guo, X., Zhang, J., et al. (2024) · Mucosal Immunology · doi:10.1016/j.mucimm.2024.08.009 · Cited by 7
Wang, X., Shi, F.-L., Zhou, J., Xiao, Z., Tu, T., Xiong, X.-M., Nie, C., Wan, S., & Gao, Z. (2024) · BMC Biology · doi:10.1186/s12915-024-02103-9 · Cited by 7
Shree, T., Czerwinski, D. K., Haebe, S., Sathe, A., Grimes, S. M., Martin, B. A., Ozawa, M. G., Hoppe, R. T., Ji, H. P., & Levy, R. (2025) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-2770 · Cited by 8
Li, Y., Wei, H., Dai, X., et al. (2025) · BMC Genomics · doi:10.1186/s12864-025-11266-w · Cited by 7
“The data were then subjected to removal of ambient RNA contamination using the SoupX software [28].”
Interpretable single-cell factor decomposition using sciRED
Pouyabahar, D., Andrews, T. S., & Bader, G. D. (2025) · Nature Communications · doi:10.1038/s41467-025-57157-2 · Cited by 8
“SoupX 31 software (version: 1.6.2) was used to identify genes with the greatest contribution to the ambient RNA.”
InfluentialWiarda, J. E., Davila, K. M. S., Trachsel, J., Loving, C. L., Boggiatto, P. M., Lippolis, J. D., & Putz, E. J. (2025) · Scientific Reports · doi:10.1038/s41598-025-96657-5 · Cited by 12
“6.2 38 as previously described 20,21,24,39 .”
InfluentialMartin, C. A., Cogdill, K. R., Pusey, A. L., & Gehring, M. A. (2026) · Nature Plants · doi:10.1038/s41477-026-02295-8 · Cited by 8
“R’ script, which implements Seurat v.5.0.0 for object generation and SoupX v.1.6.2 for background RNA correction 108,109 .”
InfluentialTranscriptional Diversity in Response to Aging Across Skeletal Muscles
Liu, C., Zheng, D., Zhang, R., Li, H., Tong, X., Wu, Y., Zhang, G., Wang, S., Chen, H., Ren, Z., et al. (2025) · Aging Cell · doi:10.1111/acel.70164 · Cited by 9
“The environmental RNA background for each library was assessed and corrected using the R package SoupX (Young and Behjati 2020).”
Avila, J. A., Benthal, J. T., Schafer, J. C., Flaherty, D. K., & Southard‐Smith, E. M. (2025) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2025.101590 · Cited by 7
Single-cell profiling of blood and cerebrospinal fluid in tuberculous meningitis
Tram, T. T. B., Garner, L. C., Thai, L. N. H., et al. (2025) · The Journal of Immunology · doi:10.1093/jimmun/vkaf186 · Cited by 7
Ediriwickrema, A., Nakauchi, Y., Fan, A. C., Köhnke, T., Hu, X., Luca, B. A., Kim, Y., Ramakrishnan, S., Nakamoto, M. A., Karigane, D., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116236 · Cited by 10
“…applied stringent quality control that incorporates the basic Seurat workflow for multimodal data processing and integration, 89,90 ambient RNA and ADT removal, 94,103 doublet discrimination using DoubletFinder, 93 and elimination of cells expressing mutually exclusive lineage markers (Figure S1).”
Identification of a neural basis for energy expenditure in the mouse arcuate hypothalamus
Wang, T., Han, S., Wang, Y., Li, Y., Li, Y., Xing, Z., Zhang, S., Xia, Y., Zhu, F., Chen, Z., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.021 · Cited by 8
Arora, J. K., James, L. K., & Charoensawan, V. (2025) · PLoS ONE · doi:10.1371/journal.pone.0332440 · Cited by 11
Hegarty, L. M., Jones, G., Biram, A., Adams, C. E., Gentek, R., Ho, G., Emmerson, E., & Bain, C. C. (2025) · Mucosal Immunology · doi:10.1016/j.mucimm.2025.11.007 · Cited by 7
Functional Genomics Link REST to Endothelial Plasticity and Atherosclerosis
Viskadourou, M., Chaudhry, S. A., Scalco, A., Reventún, P., Toledano-Sanz, P., An, R., Alcharani, N., Delgado-Marín, M., Fennell, A., Zhao, Q., et al. (2025) · Circulation Research · doi:10.1161/circresaha.125.326628 · Cited by 8
Transcriptomic Plasticity Is a Hallmark of Metastatic Pancreatic Cancer
Jiménez-Sánchez, A., Persad, S., Hayashi, A., Umeda, S., Sharma, R., Xie, Y., Mehta, A., Park, W., Masilionis, I., Chu, T., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-25-1117 · Cited by 9
Hepatic leukemia factor directs tissue residency of proinflammatory memory CD4 + T cells
Kiuchi, M., Nemoto, M., Yagyu, H., Aoki, A., Iwamura, C., Sugimoto, H., Masuo, Y., Morita, H., Ma, S., Okuno, Y., et al. (2025) · Science · doi:10.1126/science.adp0714 · Cited by 9
Microglia and Myeloid Cell Populations of the Developing Mouse Retina
Martineau, S., Valdez‐Lopez, J. C., Zarnick, S., & Kay, J. N. (2025) · Glia · doi:10.1002/glia.70115 · Cited by 11
The stroke risk gene Foxf2 maintains brain endothelial cell function via Tie2 signaling
Völgyi, K., González-Gallego, J., Müller, S. A., Todorov, M. I., Şeker, F. B., Frerich, S., Cernilogar, F. M., Schröger, L., Malik, R., Cao, J., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02136-5 · Cited by 8
“Next, background contamination (ambient RNA) was corrected using SoupX 103 , and genes not detected in at least 20 cells were filtered out.”
Leveraging Single-Cell Technologies to Advance Understanding of Myocardial Disease
Gardner, R. S., Tucker, N. R., & Amancherla, K. V. (2026) · Circulation Research · doi:10.1161/circresaha.125.326002 · Cited by 9
Jalkanen, J., Zhong, J., Nankam, P. A. N., Bhalla, N., Elmastas, M., Luo, J., Weinbrenner, S., Frendo‐Cumbo, S., Pesti, B., GOURASH, W. F., et al. (2026) · Cell Metabolism · doi:10.1016/j.cmet.2025.12.008 · Cited by 9
Metadata
- Authors
- Young, M. D., & Behjati, S.
- Affiliations
- Wellcome Sanger Institute (GB)
- University of Cambridge (GB)
- Corresponding author
- Young, Matthew D
- Venue
- GigaScience
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-12
- Volume
- 9
- Issue
- 12
- ISSN
- 2047-217X (ISSN-L)
- PMC
- PMC7763177
- OpenAlex
- W3114078084
- Semantic Scholar
- e2af27bc9dc56dffc8b7cdf12c4bf22787407d0e
- Cited by
- 1,619
- References
- 30
- Citations by year
- 2026: 358 · 2025: 388 · 2024: 347 · 2023: 211 · 2022: 149 · 2021: 86 · 2020: 54
- FWCI
- 35.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- GigaScience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (submittedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- Funders
- Wellcome Trust
- 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
- cell-free RNA, background correction, single-cell transcriptomics, quality control, contamination quantification
- MeSH terms
- Base Sequence, RNA, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, Transcriptome
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