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
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].”
Flagged first, then by citations
Research works printed but not linked: 1
- Gayoso, 2017, DoubletDetection: Identifying technical error in single-cell RNA-sequencing data
Cited by
Sorted by citations
Items 401–500 of 1,644
Gerayeli, F. V., Park, H. Y., Milne, S. B., Li, X., Yang, C. X., Tuong, J., Eddy, R. L., Vahedi, S. M., Guinto, E., Cheung, C. Y., et al. (2024) · European Respiratory Journal · doi:10.1183/13993003.01947-2023 · Cited by 23
“Single-cell analysis pipeline Correction for ambient RNA was done using the R package SoupX (version 1.5.2) [13] prior to performing further quality control tasks and downstream analysis.”
InfluentialDivergent FOXA1 mutations drive prostate tumorigenesis and therapy-resistant cellular plasticity
Eyunni, S., Mannan, R., Zhang, Y., Young, E., Zhang, Q., Luo, J., Pang, M., Mahapatra, S., Tien, J. C.-Y., George, J. M., et al. (2025) · Science · doi:10.1126/science.adv2367 · Cited by 30
“After QC steps, the SoupX corrected count matrices of replicates from paired libraries (e.g., case and control) were pooled and normalized.”
Frontiers in single cell analysis: multimodal technologies and their clinical perspectives
Källberg, J., Xiao, W., Assche, D. V., Baret, J., & Taly, V. (2022) · Lab on a Chip · doi:10.1039/d2lc00220e · Cited by 24
Li, L. X., Zhang, X., Zhang, H., Agborbesong, E., Zhou, J. X., Calvet, J. P., & Li, X. (2022) · Cells · doi:10.3390/cells12010045 · Cited by 23
“Once the gene–cell data matrix was generated, poor-quality cells were excluded, such as cells with <100 or >5000 unique expressed genes (as they are potentially cell duplets), and SoupX was performed to remove ambient RNA [28], while DoubletFinder was used to detect and remove doublet cells to improve differential gen…”
MethodDupont, C., Schäffers, O. J. M., Tan, B., Merzouk, S., Bindels, E., Zwijsen, A., Huylebroeck, D., & Gribnau, J. (2023) · Science Advances · doi:10.1126/sciadv.add2913 · Cited by 20
“M. D. Young, S. Behjati, SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data.”
MethodInfluenza virus transcription and progeny production are poorly correlated in single cells
Bacsik, D. J., Dadonaite, B., Butler, A., Greaney, A. J., Heaton, N. S., & Bloom, J. D. (2023) · eLife · doi:10.7554/elife.86852 · Cited by 24
Wang, J., Cheng, X., Liang, Q., Owen, L. A., Lu, J., Zheng, Y., Wang, M., Chen, S., DeAngelis, M. M., Li, Y., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03111-8 · Cited by 21
“To remove the ambient RNA contamination, the read count matrix (gene × cell) was corrected with SoupX for each sample [62].”
MethodDevelopmental role of macrophages modeled in human pluripotent stem cell-derived intestinal tissue
Song, A. T., Sindeaux, R., Li, Y., Affia, H., Agnihotri, T., Leclerc, S., Vliet, P. P. V., Colas, M., Guimond, J.-V., Patey, N., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113616 · Cited by 22
Tangeman, J. A., Rebull, S. M., Grajales‐Esquivel, E., Weaver, J. M., Bendezu-Sayas, S., Robinson, M. L., Lachke, S. A., & Rio‐Tsonis, K. D. (2024) · Development · doi:10.1242/dev.202249 · Cited by 28
Heaton, H., Talman, A. M., Knights, A. J., Imaz, M., Gaffney, D. J., Durbin, R. M., Hemberg, M., & Lawniczak, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/699637 · Cited by 23
“Although a tool for ambient RNA quantification exists 3 , it requires prior knowledge in the form of one or more well expressed genes known to not be expressed in a particular cell type.”
BackgroundMethodJAK-STAT1 as therapeutic target for EGFR deficiency-associated inflammation and scarring alopecia
Strobl, K., Klufa, J., Jin, R., Artner-Gent, L., Krauß, D., Novoszel, P., Strobl, J., Stary, G., Vujic, I., Griss, J., et al. (2024) · EMBO Molecular Medicine · doi:10.1038/s44321-024-00166-3 · Cited by 25
“First, ambient RNA contamination, which is commonly observed in droplet-based scRNA-seq methods, was corrected by using the SoupX package (Young and Behjati, 2020).”
Chen, K., Wang, Y., Huang, Y., Liu, X., Tian, X., Yang, Y., & Dong, A. (2023) · Genomics · doi:10.1016/j.ygeno.2023.110644 · Cited by 22
Alveolar repair following LPS-induced injury requires cell-ECM interactions
Sucre, J. M. S., Bock, F. M., Negretti, N. M., Benjamin, J. T., Gulleman, P. M., Dong, X., Ferguson, K. T., Jetter, C. S., Han, W., Liu, Y., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.167211 · Cited by 23
“Data were integrated with non-SoupX processed data using the ‘IntegrateData’ function in Seurat, following the workflow for data normalized with SCTransform.”
InfluentialBreathing fresh air into respiratory research with single-cell RNA sequencing
Alexander, M. J., Budinger, G. R. S., & Reyfman, P. A. (2020) · European Respiratory Review · doi:10.1183/16000617.0060-2020 · Cited by 23
Cellular development and evolution of the mammalian cerebellumPreprint
Sepp, M., Leiss, K., Sarropoulos, I., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.20.473443 · Cited by 21
Comparative analysis of multiplexed in situ gene expression profiling technologiesPreprint
Hartman, A., & Satija, R. (2024) · eLife · doi:10.7554/elife.96949 · Cited by 24
Current and future perspectives of single-cell multi-omics technologies in cardiovascular research
Tan, W. L. W., Seow, W. Q., Zhang, A., Rhee, S., Wong, W. H., Greenleaf, W. J., & Wu, J. C. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00205-7 · Cited by 22
He, H., Bell, S. M., Davis, A. K., Zhao, S., Sridharan, A., Na, C.-L., Guo, M., Xu, Y. J., Snowball, J. M., Swarr, D. T., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52154-3 · Cited by 21
“SoupX-corrected data from individual samples were integrated using Seurat (version 4.1.0) 77 reciprocal principal component analysis (RPCA) pipeline with SCTransform normalization.”
InfluentialOrigin and stepwise evolution of vertebrate lungs
Li, Y., Hu, M., Zhang, Z., Wu, B., Zheng, J., Zhang, F., Hao, J., Xue, T., Li, Z., Zhu, C., et al. (2025) · Nature Ecology & Evolution · doi:10.1038/s41559-025-02642-6 · Cited by 24
Chen, S., Liu, R., Mo, C.-K., Wendl, M. C., Houston, A., Lal, P., Zhao, Y., Caravan, W., Shinkle, A. T., Do, A. A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02161-x · Cited by 23
Qin, Y., Liu, Z., Gao, S., Martínez-Vasallo, C., Long, Y., Zhu, X., Liu, B., Gao, Y., Xu, X., Nohales, M. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59424-8 · Cited by 27
“Next, we use scdbl fi nder 83 to eliminate potential doublets and SoupX 84 to remove ambient RNA contamination, thereby obtaining an initially fi ltered matrix.”
Mimicking opioid analgesia in cortical pain circuits
Oswell, C. S., Rogers, S. A., James, J. G., McCall, N. M., Hsu, A. I., Salimando, G. J., Mahmood, M., Wooldridge, L. M., Wachira, M., Jo, A. Y., et al. (2026) · Nature · doi:10.1038/s41586-025-09908-w · Cited by 23
Deep single-cell decoding of human pancreatic islets reveals T2D β-cell gene expression defects
Bandesh, K., Motakis, E., Nargund, S., Kursawe, R., Selvam, V., Ansarullah, Bhuiyan, R. M., Eryilmaz, G. N., Willett, A., White, J. K., et al. (2026) · The EMBO Journal · doi:10.1038/s44318-026-00744-w · Cited by 20
“We estimated and removed contamination in individual cells by the SoupX model (Young and Behjati, 2020) using the soupX-1.”
InfluentialManuel, M. N., Tan, K. B., Kozić, Z., Molinek, M., Marcos, T. S., Razak, F. M. A., Dobolyi, D., Dobie, R., Henderson, B. E. P., Henderson, N. C., et al. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001563 · Cited by 20
“All data were subjected to SoupX (Young and Behjati, 2020) to remove ambient RNA captured in droplets.”
MethodChen, M., Wan, Y., Li, X., Xiang, J., Chen, X., Jiang, J., Han, X., Zhong, L., Xiao, F., Liu, J., et al. (2023) · Cell & Bioscience · doi:10.1186/s13578-023-00971-2 · Cited by 20
“Young MD, Behjati S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data.”
MethodGaydosik, A. M., Stonesifer, C. J., Tabib, T., Lafyatis, R., Geskin, L. J., & Fuschiotti, P. (2023) · JCI Insight · doi:10.1172/jci.insight.170015 · Cited by 22
“The default parameters were used to calculate the contamination fraction as per reference (17).”
Sun, J., Daniels, R. R., Balic, A., Andresen, A. M. S., Bjørgen, H., Dobie, R., Henderson, N. C., Koppang, E. O., Martín, S., Fosse, J. H., et al. (2024) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2024.109358 · Cited by 21
“Background RNA contamination was estimated and removed using SoupX [41].”
MethodTubular CPT1A deletion minimally affects aging and chronic kidney injury
Hammoud, S. H., Ivanova, A. V., Osaki, Y., Funk, S. D., Yang, H., Viquez, O. M., Delgado, R., Lu, D., Mignemi, M. P., Tonello, J. M., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.171961 · Cited by 22
“Count matrices produced for each sample with the CellRanger package were processed in the SoupX package (v. 1.4.2) to remove ambient background signals (56), using a contamination rate of 0.3, and then filtered for cell-cell doublets twice with the DoubletFinder package (v. 2.0.3) (57).”
MethodInfluentialHsu, K. S., Goodale, B. C., Ely, K. H., Hampton, T. H., Stanton, B. A., & Enelow, R. I. (2020) · Toxicological Sciences · doi:10.1093/toxsci/kfaa080 · Cited by 21
Wang, W., Cen, Y., Lu, Z., Xu, Y., Sun, T., Xiao, Y., Liu, W., Li, J. J., & Wang, C. (2024) · Genome Biology · doi:10.1186/s13059-024-03284-w · Cited by 19
“Previous research has used RNA abundance in empty droplets to estimate ambient RNA contamination [5, 11].”
MethodInfluentialMuralidharan, C., Huang, F., Enriquez, J. R., Wang, J. E., Nelson, J. B., Nargis, T., May, S. C., Chakraborty, A., Figatner, K. T., Navitskaya, S., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci176136 · Cited by 21
“After initial processing, raw RNA matrices from each sample were then analyzed for quality control, “ambient” mRNA was removed using SoupX, version 1.6.1 (76), and clustering was performed utilizing Seurat (77), version 4.3.0, in R, version 4.2.2.”
MethodInfluentialThayer, R. C., Polston, E. S., Xu, J., & Begun, D. J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2409850121 · Cited by 19
Dong, Y., Saglietti, C., Bayard, Q., Espín-Pérez, A., Carpentier, S., Buszta, D., Tissot-Renaud, S., Dubois, R., Kamburov, A., Kang, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59005-9 · Cited by 19
Single-cell transcriptomics identifies chondrocyte differentiation dynamics in vivo and in vitro
Lawrence, J. E. G., Woods Jon, S., Roberts, K., Sumanaweera, D., Balogh, P., Li, T., Predeus, A. V., He, P., Polański, K., Prigmore, E., et al. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.06.031 · Cited by 19
Self-citationSingle-Cell Splicing Isoform Atlas of the Adult Human Heart and Heart Failure
Pan, T., Lu, L., Youker, K. A., Shiau, C.-K., Wang, M., Lin, H., Nguyen, A., He, Y., Tong, E., Zhu, P., et al. (2025) · Circulation · doi:10.1161/circulationaha.125.074959 · Cited by 20
Konkimalla, A., Konishi, S., Kobayashi, Y., Murthy, P. K. L., Macadlo, L., Mukherjee, A., Elmore, Z. C., Kim, S., Pendergast, A. M., Lee, P. J., et al. (2022) · iScience · doi:10.1016/j.isci.2022.105114 · Cited by 19
“Cells were filtered using the SoupX pipeline (Young and Behjati, 2020).”
MethodTumor-derived interleukin-1α and leukemia inhibitory factor promote extramedullary hematopoiesis
Barisas, D. A. G., Kim, M., Subramanian, M., Kabir, A. U., Krchma, K., Wu, J., Zinselmeyer, B. H., Stewart, C. L., & Choi, K. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3001746 · Cited by 19
“SoupX preprocessing was used to remove ambient RNA contamination at a contamination fraction of 10% [75].”
MethodMartins, L. R., Sieverling, L., Michelhans, M., Schiller, C., Erkut, C., Grünewald, T. G. P., Triana, S., Fröhling, S., Velten, L., Glimm, H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46469-4 · Cited by 23
Littleton, S. H., Trang, B. K., Volpe, C. M., Cook, K., DeBruyne, N., Maguire, J. A., Weidekamp, M. A., Hodge, K. M., Boehm, K. D., Lu, S., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100556 · Cited by 20
Single-cell analysis of upper airway cells reveals host-viral dynamics in influenza infected adultsPreprint
Cao, Y., Guo, Z., Vangala, P., Donnard, E., Liu, P., McDonel, P. E., Ordovas-Montanes, J., Shalek, A. K., Finberg, R. W., Wang, J. P., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.15.042978 · Cited by 17
Molecular and cellular pathology of monogenic Alzheimer’s disease at single cell resolutionPreprint
Marinaro, F., Haneklaus, M., Zhang, Z., Strano, A., Evans, L., Handfield, L., Ryan, N. S., Fox, N. C., Hemberg, M., Ramanathan, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.14.202317 · Cited by 18
Multimodal profiling of lung granulomas reveals cellular correlates of tuberculosis controlPreprint
Gideon, H. P., Hughes, T. K., Tzouanas, C. N., Wadsworth, M. H., Tu, A. A., Gierahn, T. M., Peters, J. M., Hopkins, F. F., Wei, J.-R., Kummerlowe, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.24.352492 · Cited by 20
Chen, Z., Ye, L., Zhu, M., Xia, C., Fan, J., Chen, H., Li, Z., & Mou, S. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05118-1 · Cited by 17
“Then we make use of R package SoupX to correct ambient RNA [10], then the cells, which were less than 50% mitochondria transcripts, with more than 200 and less than 3000 genes were retained.”
MethodThe use of single-cell RNA-seq to study heterogeneity at varying levels of virus–host interactions
Swaminath, S., & Russell, A. B. (2024) · PLoS Pathogens · doi:10.1371/journal.ppat.1011898 · Cited by 19
Fuchs, M. A. A., Grabner, A., Shi, M., Murray, S. L., Burke, E. J., Latic, N., Thiriveedi, V., Roper, J., Ide, S., Abe, K., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci179882 · Cited by 20
“SoupX and DoubletFinder were used (61, 62).”
Zhang, S., Zhou, Y., Liu, Z., Wang, Y., Zhou, X., Chen, H., Zhang, X., Chen, Y., Feng, Q., Ye, X.-P., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.04.009 · Cited by 21
Stalling of the endometrial decidual reaction determines the recurrence risk of miscarriage
Muter, J., Kong, C., Nebot, M. T., Tryfonos, M., Vrljicak, P., Brighton, P. J., Dimakou, D. B., Vickers, M., Yoshihara, H., Ott, S., et al. (2025) · Science Advances · doi:10.1126/sciadv.adv1988 · Cited by 21
Fagiani, F., Pedrini, E., Martire, M. S., Gastoldi, G., Vanden Bulcke, C., Lin, J.-P., Maric, D., Brambilla, E., Ruffini, F., Peri, C., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63371-9 · Cited by 19
Fedak, R., Ruggerie, R. N., Shan, Y., Curvino, E. J., de Sousa, J. F., Shaji, D., Ngo-Casi, M., Kamboh, H., Vu, T., Durmuş, H., et al. (2026) · Nature Medicine · doi:10.1038/s41591-025-04170-z · Cited by 23
“To ensure data integrity, ambient RNA contamination was estimated and removed using SoupX 65 , and potential cellular doublets were identified and excluded using scDblFinder 66 .”
Transcriptomic architecture of nuclei in the marmoset CNS
Lin, J., Kelly, H. M., Song, Y., Kawaguchi, R., Geschwind, D. H., Jacobson, S., & Reich, D. S. (2022) · Nature Communications · doi:10.1038/s41467-022-33140-z · Cited by 16
Yang, W., Cerier, E. J., Nunez-Santana, F., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci157975 · Cited by 18
Henthorn, C. R., Airs, P. M., Neumann, E. K., et al. (2023) · eLife · doi:10.7554/elife.83100 · Cited by 18
Lee, H., Kim, S. Y., Kwon, N., Jo, S. J., Kwon, O., & Kim, J. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.11.008 · Cited by 20
Hersperger, F., Meyring, T., Weber, P., Chhatbar, C., Monaco, G., Dionne, M., Paeschke, K., Prinz, M., Groß, O., Classen, A.-K., et al. (2023) · eLife · doi:10.7554/elife.86700 · Cited by 19
A cell atlas of thoracic aortic perivascular adipose tissue: a focus on mechanotransducers
Thompson, J., Watts, S. W., Terrian, L., Contreras, G. A., Rockwell, C. E., Rendon, C. J., Wabel, E., Lockwood, L. R., Bhattacharya, S., & Nault, R. (2024) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00040.2024 · Cited by 18
Xu, H., Yue, M., Zhou, R., Wang, P., Wong, M. Y.-C., Wang, J., Huang, H., Chen, B., Mo, Y., Tam, R. C.-Y., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-3257 · Cited by 21
“5.2 (28), and Scrublet (29) was used to remove doublets.”
InfluentialYin, K., Xu, Y., Guo, Y., Zheng, Z., Lin, X., Zhao, M., Dong, H., Liang, D., Zhu, Z., Zheng, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54202-4 · Cited by 19
“The raw gene count matrices based on total UMI for each sample were fi rst processed using the R packages SoupX 49 and DoubletFinder 50 to remove ambient RNA and doublets.”
Single cell RNA-sequencing: replicability of cell types
Crow, M., & Gillis, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.12.002 · Cited by 17
Poonpanichakul, T., Shiao, M., Jiravejchakul, N., Matangkasombut, P., Sirachainan, E., Charoensawan, V., & Jinawath, N. (2021) · Bioscience Reports · doi:10.1042/bsr20212093 · Cited by 20
“SoupX [27] was applied to regress out the ambient RNAs.”
Wang, L. D., Oill, A. M. T., Blanchard, M., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3454977/v1 · Cited by 18
Coordinated immune dysregulation in juvenile dermatomyositis revealed by single-cell genomics
Rabadam, G., Wibrand, C., Flynn, E., Hartoularos, G. C., Sun, Y., Madubata, C. J., Fragiadakis, G. K., Ye, C., Kim, S., Gartner, Z. J., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.176963 · Cited by 21
A single-cell atlas to map sex-specific gene-expression changes in blood upon neurodegeneration
Grandke, F., Fehlmann, T., Kern, F., Gate, D., Wolff, T. W., Leventhal, O., Channappa, D., Hirsch, P., Wilson, E. N., Meese, E. U., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56833-7 · Cited by 20
Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications
Arya, A., Tripathi, P., Dubey, N., Aier, I., & Varadwaj, P. K. (2025) · Genomics & Informatics · doi:10.1186/s44342-025-00044-5 · Cited by 20
“Since these ambient mRNAs are found everywhere, they add extra background noise and can greatly muddle the quality of the data and the true biological signals we are trying to capture [68].”
Miao, G., Fortier, T. M., Liu, H., Schafer, D. P., Fitzgerald, K. A., Mao, J., & Baehrecke, E. H. (2025) · Nature Structural & Molecular Biology · doi:10.1038/s41594-025-01602-9 · Cited by 18
“Ambient RNA contamination was removed from each cell using SoupX (version 1.6.3) 53 .”
InfluentialWidespread presence of bone marrow–like hematopoietic stem cell niche in invertebrate skeletons
Lian, S., Hu, N., Chen, X., Dai, X., Zhu, X., Qiao, R., Liu, S., Lu, Y., Zhang, F., Sun, F., et al. (2025) · Science Advances · doi:10.1126/sciadv.adw0958 · Cited by 19
Wang, Y., Singh, K., Lokman, A. B., Deng, S., Sunitha, B., Lima, J. C., Jr., Beglov, J., Kelly, M., Blease, A., Fung, J. C. K., et al. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.adq3516 · Cited by 18
Cell atlas of CCl4-induced progressive liver fibrosis reveals stage-specific responses
Guo, P.-C., 吉林大学动物医学学院人兽共患病研究所人兽共患病研究教育部重点实验室, 人., Zuo, J., Huang, K.-K., Lai, G., Zhang, X., An, J., Li, J., Li, L., Wu, L. L., et al. (2023) · 动物学研究 · doi:10.24272/j.issn.2095-8137.2023.031 · Cited by 19
Considerations for reproducible omics in aging research
Singh, P. P., & Benayoun, B. A. (2023) · Nature Aging · doi:10.1038/s43587-023-00448-4 · Cited by 20
Transgene-free direct conversion of murine fibroblasts into functional muscle stem cells
Qabrati, X., Kim, I., Ghosh, A., Bundschuh, N., Noé, F., Palmer, A. S., & Bar‐Nur, O. (2023) · npj Regenerative Medicine · doi:10.1038/s41536-023-00317-z · Cited by 18
Trinks, A., Milek, M., Beule, D., Kluge, J., Florian, S., Sers, C., Horst, D. A., Morkel, M., & Bischoff, P. (2024) · Cellular Oncology · doi:10.1007/s13402-024-00922-0 · Cited by 20
“First, signal from ambient RNA was removed using the SoupX package version 1.6.2 [12], assuming a contamination fraction of up to 0.2.”
MethodInfluentialHwang, A., Kechter, J. A., Do, T. H., Hughes, A. N., Zhang, N., Li, X., Wasikowski, R., Brumfiel, C. M., Patel, M. H., Boudreaux, B. W., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci179436 · Cited by 19
“Doublets were detected and removed using scDblfinder (40, 41).”
Gao, Y., Dong, Q., Arachchilage, K. H., Risgaard, R. D., Syed, M., Sheng, J., Schmidt, D. K., Jin, T., Liu, S., Sandoval, S. O., et al. (2023) · Neuron · doi:10.1016/j.neuron.2025.04.025 · Cited by 19
Multi-Omics Profiling of the Tumor Microenvironment
Oekelen, O. V., & Laganà, A. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-91836-1_16 · Cited by 13
Zhang, W., Spotbeen, X., Vanuytven, S., Kint, S., Sarretto, T., Socciarelli, F., Vandereyken, K., Dehairs, J., Idkowiak, J., Wouters, D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.28.555056 · Cited by 15
Hansen, D., Jensen, J. E. R., Andersen, C. A. T., Jakobsgaard, P. R., Havelund, J. F., Lauritsen, L., Mandacaru, S. C., Siersbæk, M. S., Shackleton, O. L., Inoue, H., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2025.01.022 · Cited by 16
Ectoderm barcoding reveals neural and cochlear compartmentalization
de Haan, S., He, J., Corbat, A. A., Belicová, L., Ratz, M., Vinsland, E., Frisén, J., Kelley, M. W., & Andersson, E. R. (2025) · Science · doi:10.1126/science.adq9248 · Cited by 17
Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlas
Klötzer, K. A., Abedini, A., Shen, L., Balzer, M. S., Liang, X., Levinsohn, J. L., Ha, E., Dumoulin, B., Hogan, J., Quinn, G. Z., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02285-0 · Cited by 16
“5) 86 .”
Sensitive CAR T cells redefine targetable CD70 expression in solid tumors
Hanina, S., Park, T., Lopez, M. A., Rajasekhar, V. K., Mansilla‐Soto, J., Haubner, S., Zhao, H., Kogel, F., Nataraj, S. E., Banerjee, P., et al. (2026) · Science · doi:10.1126/science.adv7378 · Cited by 19
Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing
Dudchenko, O., Ordovás-Montañés, J., & Bingle, C. D. (2023) · Biochemical Journal · doi:10.1042/bcj20220572 · Cited by 18
Forston, M. D., Wei, G. Z., Chariker, J. H., Stephenson, T., Andres, K. R., Glover, C., Rouchka, E. C., Whittemore, S. R., & Hetman, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-48425-6 · Cited by 17
SARS-CoV-2 variant of concern fitness and adaptation in primary human airway epithelia
Meganck, R. M., Edwards, C. E., Mallory, M. L., Lee, R. E., Dang, H., Bailey, A. B., Wykoff, J. A., Gallant, S. C., Zhu, D. R., Yount, B. L., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114076 · Cited by 17
“Cytotoxicity assay— Apical washes from viral growth curves were analyzed for LDH release using the LDH-Glo Cytotoxicity Assay following the manufacturer’s instructions. v4 95 using recommended quality control parameters after ambient RNA decontamination with the R package SoupX 96 , and doublet removal using the Bioco…”
Mueller, J. L., Leavitt, A. R., Rahman, A. A., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114919 · Cited by 16
Ressler, J. M., Plaschka, M., Silmbrod, R., Bachmayr, V., Shaw, L. E., Silly, T., Zila, N., Stepan, A., Kusienicka, A., Tschandl, P., et al. (2025) · Nature Cancer · doi:10.1038/s43018-024-00879-x · Cited by 17
“We removed cell-free messenger RNA contamination for each sample independently using SoupX (v.1.5.2) 63 .”
InfluentialZhao, Y. N., Zhou, R. R., Xie, B., Liu, C. Y., Kalski, M., Cham, C. M., Jiang, Z., Koval, J., Weber, C. R., Rubin, D. T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56212-2 · Cited by 16
“SoupX was used with default parameters for the estimation and removalofcell-freemRNAcontaminationforeachsampleseparately 38 .”
DePasquale, E. A. K., Ssozi, D., Ainciburu, M., Good, J., Noel, J., Villanueva, M. A., Couturier, C., Shalek, A. K., Aranki, S. F., Mallidi, H. R., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.809414 · Cited by 16
“Ambient RNAs were estimated and removed from the datasets using SoupX with default parameters (28).”
MethodDefining the architecture of cerebrospinal fluid cellular communities in neuroinflammatory diseasesPreprint
Roostaei, T., Diaconu, C. I., Touil, H., Harbison, C., Zhang, Y., Epstein, S., Tuddenham, J. F., Thakur, K. T., Bryois, J., Wiendl, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.01.466797 · Cited by 14
Aberrant basal cell clonal dynamics shape early lung carcinogenesis
Gómez‐López, S., Alhendi, A. S. N., Przybilla, M. J., Bordeu, I., Whiteman, Z. E., Butler, T. M., Rouhani, M. J., Kalinke, L., Uddin, I., Otter, K. E. J., et al. (2025) · Science · doi:10.1126/science.ads9145 · Cited by 21
Transforming microfluidics for single-cell analysis with robotics and artificial intelligence
Cheng, J., Anne, R., & Chen, Y. (2025) · Lab on a Chip · doi:10.1039/d5lc00216h · Cited by 21
Zhang, J., Zhang, H., Zhang, L., Li, D., Qi, M., Zhang, L., Yu, H., Wang, D., Jiang, G., Wang, X., et al. (2022) · Advanced Genetics · doi:10.1002/ggn2.202100060 · Cited by 15
Staupe, R. P., Lodge, K. E., Thambi, N., Toole, D., Tamburino, A. M., Chang, D., Howell, B. J., Hazuda, D. J., Vora, K. A., & Sullivan, N. (2022) · Communications Biology · doi:10.1038/s42003-022-04216-9 · Cited by 14
“To correct for ambient mRNA contamination, SoupX (v 1.5.2) was used to generate correct RNA count matrices for downstream analysis 69 .”
InfluentialBan, M., Bredikhin, D., Huang, Y., Bonder, M. J., Katarzyna, K., Oliver, A. J., Wilson, N. K., Coupland, P. G., Hadfield, J., Göttgens, B., et al. (2023) · Brain · doi:10.1093/brain/awad404 · Cited by 15
“The raw count matrix was corrected for ambient RNA using the soupX tool 17 in R using standard settings.”
MethodChromatin Remodeling in Patient‐Derived Colorectal Cancer Models
Xiang, K., Wang, E., Mantyh, J. B., Rupprecht, G., Negrete, M., Sanati, G., Hsu, C., Randon, P. M., Dohlman, A. B., Kretzschmar, K., et al. (2024) · Advanced Science · doi:10.1002/advs.202303379 · Cited by 15
“The expression matrices were also subjected to SoupX to remove any predicted ambient RNA contamination with default parameters(72).”
MethodJiang, A., Han, K., Wei, J., Su, X., Wang, R., Zhang, W., Liu, X., Qiao, J., Liu, P., Liu, Q., et al. (2024) · Science Advances · doi:10.1126/sciadv.adi9035 · Cited by 15
Divergent landscapes of A-to-I editing in postmortem and living human brain
Santos, M. R. D. L., Kopell, B. H., Grice, A. B., Ganesh, G., Yang, A., Amini, P., Liharska, L. E., Vornholt, E., Fullard, J. F., Dong, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49268-z · Cited by 16
Concepts and new developments in droplet-based single cell multi-omics
Chow, A., & Lareau, C. A. (2024) · Trends in biotechnology · doi:10.1016/j.tibtech.2024.07.006 · Cited by 16
Fu, Q., Dong, C., Liu, Y., Xia, X., Liu, G., Zhong, F., & Liu, L. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae392 · Cited by 18
“Additionally, cell rupture introduces RNA from lysed cells into the solution, complicating initial data processing [38–40].”
MethodDeng, M., Odhiambo, W. O., Qin, M., To, T., Brewer, G. M., Kheshvadjian, A. R., Cheng, C. E., & Agak, G. W. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01725-4 · Cited by 18
“Briefly, to mitigate batch effects, we used SoupX v.0.3.1 [84] to remove ambient RNA contamination and cell clustering as previously described.”
MethodInfluentialZeng, Y., Luo, C.-L., Lin, G., Li, F., Bai, X., Ko, J. M. Y., Xiong, L., Liu, Y., He, S., Jiang, J., et al. (2025) · Journal of Clinical Investigation · doi:10.1172/jci182768 · Cited by 16
Sklavenitis‐Pistofidis, R., Konishi, Y., Heilpern-Mallory, D., Wu, T., Tsakmaklis, N., Aranha, M. P., Hunter, Z. R., Ali, A. K., Tsuji, J., Haradhvala, N. J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56323-w · Cited by 16
Comparative analysis of multiplexed in situ gene expression profiling technologiesPreprint
Hartman, A., & Satija, R. (2024) · eLife · doi:10.7554/elife.96949.1 · Cited by 23
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
