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
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Cites
Statistics
Verification
Claims the paper makes
15 of 16 cited sources checked · 10 supported · 0 flagged · 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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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)
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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)
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“as well as improving quality control metrics”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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)
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- 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)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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)
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Cites
Integrated analysis of single cell transcriptomic data across conditions, technologies, and species
Butler, A., & Satija, R. (2017) · doi:10.1101/164889 · Cited by 13,200 · cited 4×
“We include code to input the resulting cell specific expression profiles into popular downstream analysis packages (Seurat Satija et al., 2015; Butler and Satija, 2017 and SCANPY Wolf, Angerer, and Theis, 2018).”
MethodInfluentialIntegrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · cited 1×
“We used the Seurat package [18, 19] to produce a t -distributed stochastic neighbour embedding (tSNE) representation of the data and annotated clusters of cells based on the expression of canonical marker genes (Fig.”
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · cited 7×
“The 10X species-mixing dataset was the mixture of the human cell line 293T and the mouse cell line 3T3 described in [2].”
InfluentialHighly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · cited 5×
“We next investigate the properties of ambient RNA contamination in data where ground truth is available, the “speciesmixing” experiments combining mouse and human cell lines using 10X [2] and Drop-Seq technologies [14].”
InfluentialSpatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · cited 1×
“We used the Seurat package [18, 19] to produce a t -distributed stochastic neighbour embedding (tSNE) representation of the data and annotated clusters of cells based on the expression of canonical marker genes (Fig.”
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758 · cited 1×
“Attempts to detect and remove doublets are an active area of research [11–13].”
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · cited 1×
“We therefore calculated the cross-batch entropy of the kidney tumour data before and after background correction [20].”
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · cited 1×
“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].”
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Research works printed but not linked: 1
- Gayoso, 2017, DoubletDetection: Identifying technical error in single-cell RNA-sequencing data
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Items 701–800 of 1,644
Choung, S., Kang, G., Kim, T., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68816-3 · Cited by 7
“6.2) with the round-ToInt=TRUE option in the adjustCounts function and other default parameters 104 .”
InfluentialFronto-insular circuit mechanisms of accelerated intermittent theta burst stimulation
Johnson, S. B., Rocks, D., Chalençon, L., Johnson, K., Hassan, U., Arefin, A., Akgül, G., Donatelle, A., Asher, H., Elbau, I., et al. (2026) · Cell · doi:10.1016/j.cell.2026.04.030 · Cited by 8
Boesch, M., Anak, S., Abyad, D. E., Ostyn, T., Antoranz, A., Van, T. M., Newhouse, D., Myers, C., Melkebeke, L. V., Palmer, J. M., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02600-3 · Cited by 13
scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing
Li, K., Sun, Y. H., Ouyang, Z., Negi, S., Gao, Z., Zhu, J., Wang, W., Chen, Y., Piya, S., Hu, W., et al. (2023) · BMC Genomics · doi:10.1186/s12864-023-09332-2 · Cited by 8
“In a recent paper [48], CellBender [49] outperforms other tools including DecontX [50] and SoupX [51].”
MethodDistinct and shared endothermic strategies in the heat producing tissues of tuna and other teleosts
Wu, B., Gao, X., Hu, M., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-022-2312-1 · Cited by 7
“After obtaining the count matrix, SoupX (Young and Behjati, 2020) was used to reduce the ambient RNA noise by using the autoEstCont function with the rho value set to 0.2.”
MethodInfluentialZhang, Y., Tan, J., Yang, K., Fan, W., Yu, B., & Shi, W. (2023) · Journal of Neuroinflammation · doi:10.1186/s12974-023-02831-9 · Cited by 7
Pritchard, J. E., Pearce, J. E., Snoeren, I., Fuchs, S. N. R., Götz, K., Peisker, F., Wagner, S., Benabid, A., Lutterbach, N., Klöker, V., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113608 · Cited by 7
Yang, Y., Zeng, S., & Hu, X. (2024) · APOPTOSIS · doi:10.1007/s10495-023-01933-2 · Cited by 8
“In brief, doublet cells were detected and removed by Scrublet [32], while the suspected ambient RNA in droplet-based sequencing technology was decontaminated in the SoupX algorithm [33].”
Nyirenda, J., Hardy, O. M., Silva‐Filho, J. L., Herder, V., Attipa, C., Ndovi, C., Siwombo, M., Namalima, T. R., Suwedi-Kapesa, L. C., Ilia, G., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03354-3 · Cited by 7
“Finally, cell frequencies in the postmortem lung across all three cohorts were plotted, and differential abundance analysis was performed using scanpro (https://github.com/loosolab/scanpro) and scCODA Python packages 57 and the miloR R package (version 1.4.0) 58 .”
InfluentialSlota, J. A., Lamoureux, L., Frost, K. L., Sajesh, B. V., & Booth, S. A. (2024) · Nature Communications · doi:10.1038/s41467-024-54579-2 · Cited by 8
Tian, Y., Liu, S., Shi, H., Li, J., Wan, X., Sun, Y., Li, H., Cao, N., Feng, Z., Zhang, T., et al. (2025) · Advanced Science · doi:10.1002/advs.202413819 · Cited by 8
Interrupting T-Cell Memory Ameliorates Exaggerated Metabolic Response to Weight Cycling
Garcia, J. N., Cottam, M. A., Rodriguez, A. S., Agha, A. F. H., Caslin, H. L., Winn, N. C., & Hasty, A. H. (2026) · Diabetes · doi:10.2337/db25-0605 · Cited by 12
Sturgill, D. M., Wang, L., & Arda, H. E. (2024) · BMC Genomics · doi:10.1186/s12864-024-09964-y · Cited by 7
“A few groups developed computational approaches to remove or correct the contaminating transcripts from scRNA-Seq datasets [42–44].”
BackgroundQClus: a droplet filtering algorithm for enhanced snRNA-seq data quality in challenging samples
Schmauch, E., Ojanen, J., Galani, K., Jalkanen, J. M., Harju, K., Hollmén, M., Kokki, H. J., Gunn, J. M., Halonen, J., Hartikainen, J. E. K., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1145 · Cited by 8
“After reviewing current meth-ods ( 6 , 9 , 10 , 17–19 , 21 ) and testing them on heart snRNA-seq data, we opted for a new approach to improve data quality by contamination-based droplet filtering.”
Braga, F. A. V., Kar, G. S., Berg, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/527408 · Cited by 7
A Single-Cell RNA Expression Map of Human Coronavirus Entry FactorsPreprint
Singh, M., Bansal, V., & Feschotte, C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3611279 · Cited by 9
Zuela-Sopilniak, N., Morival, J. L. P., Ntekas, I., Elpers, M. A., Agarwal, R., Henretta, S. J., Odell, J. D., Vlaminck, I. D., & Lammerding, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.11.598511 · Cited by 8
A conserved cell-type gradient across the human mediodorsal and paraventricular thalamusPreprint
Schulmann, A., Feng, N., Auluck, P. K., Mukherjee, A., Komal, R., Leng, Y., Gao, C., Williams, S. K., Roy, S., Usdin, T. B., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.03.611112 · Cited by 9
Wang, M., Li, Y., Wang, J., Oh, S. H., Cao, Y., & Chen, R. (2025) · Genome Research · doi:10.1101/gr.279167.124 · Cited by 10
“Contamination from background transcripts in the preserved true cells was mitigated using SoupX (Young and Behjati 2020).”
Yang, Q., Elz, A., Panis, M., Liu, T., Nilsson-Payant, B. E., & Blanco-Melo, D. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1267078 · Cited by 6
Unique challenges and best practices for single cell transcriptomic analysis in toxicology
Filipovic, D., Kana, O. Z., Marri, D., & Bhattacharya, S. (2024) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2024.100475 · Cited by 7
Time- and cell-specific activation of BMP signaling restrains chondrocyte hypertrophy
Gadomski, S. J., Mui, B. W. H., Gorodetsky, R., Paravastu, S. S., Featherall, J. T., Li, L., Haffey, A., Kim, J.-C., Kuznetsov, S. A., Futrega, K., et al. (2024) · iScience · doi:10.1016/j.isci.2024.110537 · Cited by 8
Jag1 insufficiency alters liver fibrosis via T cell and hepatocyte differentiation defects
Mašek, J., Filipovic, I., Hul, N. V., Belicová, L., Jiroušková, M., Oliveira, D. V., Frontino, A. M., Hankeová, S., He, J., Turetti, F., et al. (2024) · EMBO Molecular Medicine · doi:10.1038/s44321-024-00145-8 · Cited by 6
“Prior to transformation into Seurat Objects, matrices were precleared using SoupX (Young and Behjati, 2020) with default settings of setContaminationFraction parameter (sc) for the P3, and (sc, 0.13) for the E16.”
InfluentialZillich, E., Artioli, A., Rossetti, A. C., Avetyan, D., Belschner, H., Frank, J., Stein, F., Schwarz, J., Mechawar, N., Turecki, G., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115332 · Cited by 6
Somites are a source of nephron progenitors in zebrafish
Peng, Z., Vanichapol, T., Nguyen, P. D., Chang, H.-H., Kocha, K. M., O’Brien, L. L., Currie, P. D., Huang, P., & Davidson, A. J. (2025) · Nature Communications · doi:10.1038/s41467-025-62259-y · Cited by 6
“4.5 52 , was used to remove ambient RNA.”
InfluentialAge-associated immune dysregulation and B cell dysfunction drive severe outcomes in SFTSV infection
Kim, E.-H., Jang, H. B., Kim, S., Park, D., Kim, Y., Jang, S., Hwang, S., Kwon, W., Choi, I., Kim, J., et al. (2025) · PLoS Pathogens · doi:10.1371/journal.ppat.1013402 · Cited by 6
“5.0) with the default parameters [60].”
InfluentialKamuro, R., Yoshihara, M., Hara, S., Nemoto, T., Howard, L., Honda, A., Yamamoto, A., Takigawa, T., Sakimoto, S., Baba, K., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08643-2 · Cited by 6
“The output count data were corrected for ambient RNA expression using the R package SoupX (version 1.6.2) 72 , and doublets were identi fi ed using the Python package scrublet (version 0.2.3) 73 .”
InfluentialPB2 and NP of North American H5N1 virus drive immune cell replication and systemic infections
Kim, Y., Jang, S., Kwon, W., Kim, J., Park, D., Choi, I., Choi, J. H., Gil, J., Yu, M., Jeong, B., et al. (2025) · Science Advances · doi:10.1126/sciadv.ady1208 · Cited by 6
Inflammation perturbs hematopoiesis by remodeling specific compartments of the bone marrow niche
Swann, J. W., Zhang, R., Verovskaya, E. V., Calero‐Nieto, F. J., Wang, X., Proven, M. A., Hirakawa, H., Heubel, B. P., Shyu, P. T., Guo, X.-D. E., et al. (2025) · Blood · doi:10.1182/blood.2025029513 · Cited by 8
Sithara, A. A., Kapustová, V., Žihala, D., Venglar, O., Bilek, D., Helal, M., John, M., Radova, E., Broskevicova, L., Vrána, J., et al. (2025) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-25-0170 · Cited by 3
“52) algorithm with a manually defined contamination fraction specific to each sample.”
Иванович, Г. И., Podshivalova, E. S., Chechekhin, V. I., Zubritskiy, A. V., Matrosova, A. A., Sergeev, N. A., Samsonova, M. D., Dvoryanchikov, Y. V., Nikonova, T. V., Bondarenko, E. V., et al. (2025) · JCI Insight · doi:10.1172/jci.insight.199050 · Cited by 7
Rebalancing NTRK2 isoforms promotes vascular regeneration in bronchopulmonary dysplasia
Zhang, Y., Tan, C., Liu, Z., Mao, X., Jiang, C., Mohammed, A. N., Li, X., Lu, R., Wang, A., Maihemuti, W., et al. (2025) · Cell stem cell · doi:10.1016/j.stem.2025.12.006 · Cited by 6
Sharma, K., Habibey, R., Ribeiro, M. M., Cui, B., Siwicki, R. A., Striebel, J., Pawlick, J. S., Zorn, J., Utz, L., Renner, M., et al. (2026) · Cell stem cell · doi:10.1016/j.stem.2025.12.013 · Cited by 10
Single-cell transcriptomics reveals the impact of sex and age in the healthy human liver
Rahman, R. U., Epstein, E. T., Murphy, S. M., Amir-Zilberstein, L., Aubin, C., Delorey, T. M., Koene, H., Fernandes, L., Tanabe, K. K., Qadan, M., et al. (2026) · JHEP Reports · doi:10.1016/j.jhepr.2026.101773 · Cited by 7
A skeletal muscle atlas shows neuromuscular junction adaptations to growth and atrophy
Campanario, S., Grima-Terrén, M., Rommelfanger, M., Orso, S. D., Feng, X., Cisneros, A., Ramírez‐Pardo, I., Calls, A., Yang, B. A., Ko, K. D., et al. (2026) · Developmental Cell · doi:10.1016/j.devcel.2026.03.010 · Cited by 10
Epigenetic adaptation of beta cells across lifespan and disease
Manduchi, E., Descamps, H. C., Liu, J., Schug, J., Da, T., Lahori, D., El‐Mekkoussi, H., Lee, M., Feleke, E., Bernstein, D. L., et al. (2026) · Nature Metabolism · doi:10.1038/s42255-026-01495-y · Cited by 7
“For each donor islet sample, we obtained counts with CellRanger (v.7.1.0) 70 and used Seurat (v.4.9.9.9041) 71 (complemented by SoupX (v.1.6.2) 72 and scDblFinder (v.1.12.0) 73 ) for clean-up, normalization, pre-processing and data integration.”
InfluentialGut bacteria induce IgA expression in pituitary hormone-secreting cells during aging
Li, Y., Wang, J., Wang, R., Chang, Y., & Wang, X. (2023) · iScience · doi:10.1016/j.isci.2023.107747 · Cited by 6
Rethinking eco‐evo studies of gene expression for non‐model organisms in the genomic era
Freedman, A. H., & Sackton, T. B. (2024) · Molecular Ecology · doi:10.1111/mec.17378 · Cited by 7
“SoupX (Young & Behjati, 2020) and CellBender (Fleming et al., 2023) are two tools that conduct such decontamination and that are not part of scverse or Seurat.”
Lu, I., Cheung, P. F., Heming, M., Thomas, C., Giglio, G., Leo, M., Erdemir, M., Wirth, T., König, S., Dambietz, C. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48195-3 · Cited by 10
Kidney cell response to acute cardiorenal and isolated kidney ischemia-reperfusion injury
Burfeind, K. G., Funahashi, Y., Su, X., Lackey, A. E., Hagen, M. W., Blanche, S., Emathinger, J. M., Hebert, J. F., McDonough, A. A., Gurley, S. B., et al. (2025) · Physiological Genomics · doi:10.1152/physiolgenomics.00161.2024 · Cited by 6
Convergent State-Control of Endogenous Opioid Analgesia
Kimmey, B. A., Ejoh, L. L., Shangloo, L., Wojick, J. A., Chehimi, S. N., McCall, N. M., Oswell, C. S., Poulson, S. J., Wu, J., Ortega, R. A. S., et al. (2026) · Journal of Pain · doi:10.1016/j.jpain.2025.105741 · Cited by 7
Single cell RNA-seq analysis of the flexor digitorum brevis mouse myofibers
Verma, R., Kannan, S., Lin, B. L., Fomchenko, K. M., Nieuwenhuis, T. O., Patil, A. H., Lukban, C., Yang, X., Fox-Talbot, K., McCall, M. N., et al. (2021) · Skeletal Muscle · doi:10.1186/s13395-021-00269-2 · Cited by 6
Integrative Single-Cell Transcriptomic Analysis of Human Fetal Thymocyte Development
Li, Y., Zeng, W., Li, T., Guo, Y., Zheng, G., He, X., Bai, L., Ding, G., Li, J., & Liu, X. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.679616 · Cited by 7
“The GO analysis of DEGs was carried out with the ClusterProfiler R package (version 3.14.0) (Yu et al., 2012), and the cutoffs were set to 0.01 for the p-value and 0.05 for the q-value.”
MethodInfluentialTranscriptomic Profile of the Mouse Postnatal Liver Development by Single-Nucleus RNA Sequencing
Xu, J., Hao, S., Shi, Q., Deng, Q., Jiang, Y., Guo, P., Yuan, Y., Shi, X., Shangguan, S., Zheng, H., et al. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.833392 · Cited by 6
Nguyen, H. T. T., Radwanska, M., & Magez, S. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1051647 · Cited by 7
“Dataset of each sample were adjusted to remove ambient RNAs contamination using SoupX (42).”
MethodSingle-cell RNA sequencing of mouse lower respiratory tract epithelial cells: A meta-analysis
Martins, L. R., Glimm, H., & Scholl, C. (2023) · Cells and Development · doi:10.1016/j.cdev.2023.203847 · Cited by 8
ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems
Thomas, R. A., Fiorini, M. R., Amiri, S., Fon, E. A., & Farhan, S. M. K. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05935-y · Cited by 7
“The R package SoupX is used to account for ambient RNA, providing users the option to correct the gene expression matrices for RNA contamination [21].”
MethodSingle-cell RNA sequencing to study vascular diversity and function
Ma, F., Hernandez, G. E., Romay, M. C., & Iruela‐Arispe, M. L. (2021) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000651 · Cited by 9
Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomicsPreprint
Subramanian, A., Alperovich, M., Yang, Y., & Li, B. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.27.466176 · Cited by 6
Larcombe, S. D., Briggs, E. M., Savill, N. J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.06.535835 · Cited by 6
“As the day 7 -dox sample showed higher than expect number of droplets with low total RNA, we controlled for potential free RNA in the samples with SoupX (Young and Behjati, 2020).”
MethodA single-cell atlas of the aging murine ovaryPreprint
Isola, J. V. V., Ocañas, S. R., Hubbart, C. R., Ko, S., Mondal, S. A., Hense, J. D., Carter, H. N. C., Schneider, A., Kovats, S., Alberola‐Ila, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.29.538828 · Cited by 8
“The resultant out files were loaded into R using the “load10X” function of the SoupX package 72 .”
CRMetrics - an R package for Cell Ranger Filtering and Metrics VisualisationPreprint
Kick, F. L., Holze, H., Rydbirk, R., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2853524/v1 · Cited by 6
Epigenetic reprogramming driving successful and failed repair in acute kidney injuryPreprint
Muto, Y., Dixon, E. E., Yoshimura, Y., Ledru, N., Kirita, Y., Wu, H., & Humphreys, B. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.20.576421 · Cited by 6
Preeclampsia in mice carrying fetuses with APOL1 risk variantsPreprint
Yoshida, T., Latt, K. Z., Shrivastav, S., Lü, H., Reidy, K. J., Charlton, J. R., Zhao, Y., Winkler, C. A., Reznik, S. E., Rosenberg, A. Z., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.20.586039 · Cited by 7
Peng, F., Nordgren, E., & Murray, J. I. (2024) · Genome Research · doi:10.1101/gr.278980.124 · Cited by 6
“Ambient RNA contamination was removed using the SoupX algorithm (Young and Behjati 2020) with default settings.”
Single cell decomposition of multicellular aging programs associated with impaired lung regenerationPreprint
Gote-Schniering, J., Melo-Narváez, M. C., Boosarpu, G., Lauer, D., Prakki, S. R. S., Wang, H., Brunner, M., Khan, S., Ansari, M., Ammeter, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.24.666371 · Cited by 7
“Ambient RNA contamination was corrected with SoupX 63 , where the background contamination fraction was manually set to 0.3.”
InfluentialMedina-Feliciano, J. G., Valentín‐Tirado, G., Luna-Martínez, K., Beltrán-Rivera, A., Miranda-Negrón, Y., & García‐Arrarás, J. E. (2024) · eLife · doi:10.7554/elife.100796.3 · Cited by 10
Saddala, M. S., Chittineni, M. S., Hariharan, N., Rias, A. L., & Nagaraju, G. P. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.217693 · Cited by 7
Habitual Exercise Modulates Neuroimmune Interaction to Mitigate Aortic Stiffness
Cho, J. M., Vu, K., Park, S., Zhu, E., Li, Y.-R., Zhao, P., Yokota, T., Yang, L., Lu, R., Xiang, Y. K., et al. (2025) · Circulation Research · doi:10.1161/circresaha.124.325656 · Cited by 5
Gleitz, H. F. E., Fuchs, S. N. R., Snoeren, I., Boys, C., Nagai, J. S., Mora, H. T., Klöker, V., Pritchard, J. E., Bakker, I. A. J. M., Garasa, M. G., et al. (2025) · HemaSphere · doi:10.1002/hem3.70179 · Cited by 5
Human deep subcutaneous adipose tissue is enriched for inflammatory and tissue remodeling pathways
Yamada, K., Kubota, Y., Kosaka, K., Yamaji, Y., Akita, S., Tokumoto, H., Kuroda, M., & Mitsukawa, N. (2025) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00463.2025 · Cited by 5
Zadegan, S. B., Li, P., Sultana, M. S., Abbas, H. M. K., Coffey, N., Öztürk, C., Elwasif, M., Rice, J. H., Krishnan, H. B., & Hewezi, T. (2025) · Horticulture Research · doi:10.1093/hr/uhaf223 · Cited by 7
“5.0) [60].”
InfluentialPyruvate Dehydrogenase and Cellular Metabolism in Calcineurin Inhibitor–Induced Kidney Fibrosis
Oda, Y., Nishi, H., Hamano, F., Yoshida, T., Kita, Y., Kopp, J. B., & Nangaku, M. (2025) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000855 · Cited by 5
Nirello, V. D., Araújo, N. V. P., de Assis, H. C., Moreno‐Gonzalez, M., Ruiz, P. S. B., Castro, P. R., Shealy, N. G., Shelton, C. D., Fernandes, M. F., de Oliveira, S., et al. (2025) · Gut Microbes · doi:10.1080/19490976.2025.2573045 · Cited by 8
“First, we estimated cell-free mRNA contamination and removed it with the SoupX library 27 by comparing the raw and filtered matrices obtained with Cell Ranger.”
Splice modulation of COL4A5 reinstates collagen IV assembly in an organoid model of Alport syndrome
Saei, H., Estebe, B., Goudin, N., Esmailpour, M., Haure, J., Gribouval, O., Arrondel, C., Morinière, V., Tian, P., Lennon, R., et al. (2025) · JCI Insight · doi:10.1172/jci.insight.194759 · Cited by 5
The Heterogeneity and Function of Stromal Cells in the Tumor Microenvironment
Wang, X., Li, S., Xue, Y., Liang, X., Ma, X., Zhang, K., & Wang, Y. (2026) · Research · doi:10.34133/research.1183 · Cited by 7
“Droplet-based assays are additionally affected by ambient RNA contamination, which can create misleading marker detection in stromal and immune compartments unless explicitly modeled and removed [ 13 ].”
Toward informed batch correction for single-cell transcriptome integration
Li, S., Luecken, M. D., Marioni, J. C., Teichmann, S. A., & He, P. (2026) · Nature Computational Science · doi:10.1038/s43588-025-00943-1 · Cited by 7
“These include procedures such as data normalization 4 , ambient RNA correction 5 and feature selection 6 .”
InfluentialYi, J., Chen, Y., Tian, S., et al. (2026) · The Plant Cell · doi:10.1093/plcell/koag035 · Cited by 5
Fisher, K., Garcia, M. A., Frattari, G. S., Naasz, C. M., Zhuo, J., Rosás-Umbert, M., Dietz, L. L., Juhl, A. K., Iversen, E. F., Olesen, R., et al. (2026) · Nature Immunology · doi:10.1038/s41590-026-02448-z · Cited by 6
“Ambient RNA contamination was assessed using SoupX (v.1.6.2) 81 , no correction was applied (levels <5%; Supplementary File 1).”
InfluentialMulticellular origins of murine ovarian inflammaging
Galligos, A. M., Varberg, J. M., Yueh, W.-T., Converse, A., Malloy, S., Aljubran, F., Duncan, F. E., & Gerton, J. L. (2026) · Communications Biology · doi:10.1038/s42003-026-09826-1 · Cited by 9
Jassowicz, L., Feng, F., Warta, R., Heinicke, F., Wöllner, A., Briesch, H., Sor, F., Soller, J., Lotsch, C., Wong, J. K. L., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.03.016 · Cited by 6
Xian, W., Asad, M., Wu, S., Bai, Z., Li, F., Lu, J., Zu, G., Brintnell, E., Chen, H., Mao, Y. Q., et al. (2023) · Frontiers of Medicine · doi:10.1007/s11684-023-1017-7 · Cited by 5
Xie, G., Toledo, M. P., Hu, X., Yong, H. J., Sanchez, P. S., Liu, C., Naji, A., Irianto, J., & Wang, Y. J. (2024) · BMC Genomics · doi:10.1186/s12864-024-10335-w · Cited by 5
“The raw gene expression matrices were input to Soup X 1.6.2 [33] for ambient RNA removal using autoEstCont to automatically estimate the contamination fraction.”
MethodInfluentialEngel, A., Ludwig, N., Grandke, F., Wagner, V., Kern, F., Fehlmann, T., Schmartz, G. P., Aparicio‐Puerta, E., Henn, D., Walch‐Rückheim, B., et al. (2024) · RNA Biology · doi:10.1080/15476286.2024.2361571 · Cited by 5
“For quality-control, SoupX [54] (v.1.6.1) was used to remove ambient cell-free mRNA contamination, cells with more than 10% mitochondrial reads, <200 features or > 2,500 features were removed using Seurat [55] (v.4.3.0).”
InfluentialSchupp, P. G., Shelton, S. J., Brody, D. J., Eliscu, R., Johnson, B., Mazor, T., Kelley, K. W., Potts, M. B., McDermott, M. W., Huang, E. J., et al. (2024) · Cancers · doi:10.3390/cancers16132429 · Cited by 5
“However, single-cell data are confounded by technical factors related to tissue dissociation, sampling bias, noise, contamination, and sparsity [26–30], which muddle the relationships between malignant cell genotypes and molecular phenotypes, particularly for cancers that lack consistent CNVs.”
Nakamura, J., Yamamoto, T., Takabatake, Y., Namba‐Hamano, T., Takahashi, A., Matsuda, J., Minami, S., Sakai, S., Yonishi, H., Maeda, S., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.184451 · Cited by 7
Alghamdi, B., Liu, M., Huang, X., Debnath, R., Afzali, H., Troka, M. E., Hasuike, A., Easter, Q. T., Zhou, M., Byrd, K. M., et al. (2025) · Mucosal Immunology · doi:10.1016/j.mucimm.2025.06.008 · Cited by 7
Jaszczak, R. G., Zussman, J. W., Wagner, D. E., et al. (2025) · eLife · doi:10.7554/elife.103074.3 · Cited by 5
Nguyen, H. T. T., Magez, S., & Radwanska, M. (2023) · Frontiers in Tropical Diseases · doi:10.3389/fitd.2023.1127022 · Cited by 5
“Prior to analyzing by Seurat package, cellranger count outputs, including expression matrices before and after empty droplets filtering, were utilized by the SoupX software to estimate and remove ambient mRNA contamination for each sample (44).”
MethodZhang, X., Gopalan, V., Syed, N., Hannenhalli, S. S., & Shern, J. F. (2023) · STAR Protocols · doi:10.1016/j.xpro.2023.102297 · Cited by 5
Inferring regulators of cell identity in the human adult pancreas
Vanheer, L., Fantuzzi, F., To, S. K., Schiavo, A. A., Haele, M. V., Ostyn, T., Haesen, T., Yi, X., Janiszewski, A., Chappell, J., et al. (2023) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqad068 · Cited by 10
Perera, G., Power, L. H., Larson, A., Codden, C. J., Awata, J., Batorsky, R., Strathdee, D., & Chin, M. T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241411594 · Cited by 5
“2) [24] and reduce doublets (DoubletFinder version 2.”
BackgroundWang, K., & Adler, C. E. (2023) · BMC Genomics · doi:10.1186/s12864-023-09724-4 · Cited by 5
“These contaminants often appear as ambient RNA, a computational challenge for cell calling algorithms that can introduce bias into normalization [6, 7].”
BackgroundDistinct functions and transcriptional signatures in orally induced regulatory T cell populations
Biswas, M., So, K., Bertolini, T. B., Krishnan, P., Rana, J., Muñoz-Melero, M., Syed, F., Kumar, S. R. P., Gao, H., Xuei, X., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1278184 · Cited by 5
Zeng, J., Gao, W., Tang, Y., Wang, Y., Liu, X., Yin, J., Su, X., Zhang, M., Kang, E., Tian, Y., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113442 · Cited by 5
Single-Cell Transcriptional Signatures of Glomerular Disease in Transgenic Mice with APOL1 Variants
Yoshida, T., Latt, K. Z., Santo, B. A., Shrivastav, S., Zhao, Y., Fenaroli, P., Chung, J., Hewitt, S. M., Tutino, V. M., Sarder, P., et al. (2024) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000370 · Cited by 6
“Removal of ambient RNA was conducted by SoupX (version 1.5.2), 33 following the default protocol using “ autoEst-Cont ” and “ adjustCounts ” functions.”
InfluentialMcElliott, M. C., Telang, A. C., Ference-Salo, J. T., Al-Suraimi, A., Chowdhury, M., Otto, E. A., Soofi, A. A., Dressler, G. R., & Beamish, J. A. (2024) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00289.2024 · Cited by 6
Detecting gene expression in Caenorhabditis elegans
Calarco, J. A., Taylor, S. R., & Miller, D. M. (2024) · Genetics · doi:10.1093/genetics/iyae167 · Cited by 8
“2020; Young and Behjati 2020; Caglayan et al .”
Ammons, D. T., Harris, R. A., Chow, L., & DOW, S. W. (2025) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-025-03950-3 · Cited by 6
“1) [20].”
Muto, Y., Li, H., & Humphreys, B. D. (2022) · Springer eBooks · doi:10.1007/978-3-031-11570-7_5 · Cited by 6
Ruan, Z., Chi, D., Wang, Q., Jiang, J., Quan, Q., Bei, J., & Peng, R. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-5684 · Cited by 5
Ma, X., Chen, L., Chen, T., et al. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.10.044 · Cited by 5
“The mRNA expression profiles of these patients have underwent quality control to remove environmental RNA contamination and doublets via the SoupX R [18] and Scrublet packages [19], respectively.”
MethodTranscriptional and Anatomical Diversity of Medium Spiny Neurons in the Primate StriatumPreprint
He, J., Kleyman, M., Chen, J., Alikaya, A., Rothenhoefer, K. M., Öztürk, B. E., Wirthlin, M. E., Bostan, A. C., Fish, K. N., Byrne, L. C., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3904348 · Cited by 5
Fast multi-resolution consensus clusteringPreprint
van Dongen, S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.09.511493 · Cited by 5
“Marker gene analysis was applied for each cluster at the most fine-grained level separately, using the quickMarkers function from the SoupX software [38].”
MethodInfluenza virus transcription and progeny production are poorly correlated in single cellsPreprint
Bacsik, D. J., Dadonaite, B., Butler, A., Greaney, A. J., Heaton, N. S., & Bloom, J. D. (2023) · eLife · doi:10.7554/elife.86852.1 · Cited by 5
Devakinandan, G., Terasaki, M., & Dani, A. (2024) · eLife · doi:10.7554/elife.98250 · Cited by 5
A developmental cell atlas of the human thyroid glandPreprint
Massalha, H., Trinh, M. K., Armingol, E., Tuck, E. J., Predeus, A. V., Mazin, P., Sancho‐Serra, C., Oszlánczi, Á., Wood, Y., Parks, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.22.609152 · Cited by 7
Self-citationIFN-α Induces Heterogenous ROS Production in Human β-CellsPreprint
Wagner, L. E., Melnyk, O., Turner, A., Duffett, B. E., Muralidharan, C., Irizarry, M. M. M., Arvin, M. C., Orr, K. S., Manduchi, E., Kaestner, K. H., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.19.639120 · Cited by 5
A Multi-omic Atlas of Human Choroid Plexus in Alzheimer’s DiseasePreprint
Philippe, T. J., Avey, D. R., Kearns, N. A., Vyas, H., Tissera, S. L. D., Xu, J., Ng, B., Saunders, D. M., Lagrimas, A. K. B., Duong, D. M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.23.690014 · Cited by 8
Dai, D., Si, J., Jiang, L., Han, B., Wang, K., Wang, X., Yan, S., Yin, Y., Chen, W., Mao, H., et al. (2025) · Advanced Science · doi:10.1002/advs.202508847 · Cited by 5
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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