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×
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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
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Items 1,501–1,600 of 1,644
Wang, H., Liu, Y., Mu, X., Ma, J., Xu, W., Liu, H., Yong, Q., Wang, X., Zhai, Y.-F., Liu, S., et al. (2026) · Insect Science · doi:10.1111/1744-7917.70293
Roy, N., & Unckless, R. L. (2026) · BMC Genomics · doi:10.1186/s12864-026-12902-9
“In conclusion, Nora and Drosophila A virus may activate canonical immune path-399 ways (IMD and Toll) in the fat body, and effector genes of the Toll pathway may be downregulated in association with Drosophila A virus infection, which could contribute to its higher viral titer in this tissue.”
Yerrabelli, R., Thibodeau, A., Nehar-Belaid, D., Marcheş, R., Ravichandran, S., Barnouin, Y., Sen, R., Paust, S., Stitzel, M. L., Ferrucci, L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.08.723786
Satellite microglia-like cells in human dorsal root ganglia and changes with diabetic neuropathyPreprint
Mazhar, K., O'Brien, J. A., Wilde, M. A., Srikanth, H., Wangzhou, A., Pastor, V., Maina, C. W., Arefin, N. S., Moreno, M. M., Sankaranarayanan, I., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.12.724479
“Raw sequencing files were aligned (using reference genome GRCh38) with 10x CellRanger to generate count matrices per sample, which were then processed with ambient RNA correction tool SoupX 87 and doublet correction tool scDblFinder 88 .”
Kim, J., Long, A. N., Xie, Y., Carter, K. T., Noehl-Tekorius, S. R., Elz, A., & Yu, M. (2026) · STAR Protocols · doi:10.1016/j.xpro.2026.104581
DeSpotX: Identifiability-Based Decontamination for Spatial TranscriptomicsPreprint
Wang, R. H., & Gentles, A. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.12.724704 · Cited by 1
“We compare DeSpotX against four published decontamination baselines, including SoupX [13], DecontX [12], ResolVI [8], and SpaceBender [10], all run with default settings.”
Tau Ablation Rewires Brain Cell Programs in Health and Restores Function in Disease
Chimal-Juárez, E., Patel, H., Jury-Garfe, N., Dabin, L. C., Vidal, R., Kim, J., & Lasagna-Reeves, C. A. (2026) · bioRxiv · doi:10.64898/2026.05.12.724684
Workload-induced changes to cell state contribute to β-cell failure in diabetesPreprint
Sai, S., Liu, F., Harrington, A. R., Zhu, H., Omar, I., Zeng, C., Mallick, M., Sui, Y., Sander, M., & Wortham, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.13.725004
“5.2) 50 .”
InfluentialWagner, M. R., Pintozzi, N. G., Schoff, B. M., Gold, M. I., Kasper, R. H., Steele, N. G., & Blum, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.15.725534
A surviving beta cell subpopulation enriched in patients with T1DPreprint
Spurrell, M., Tobiásová, Z., Pober, J. S., Tsang, J. S., & Herold, K. C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.15.725449
MTDH-SND1 disruption sensitizes ovarian cancer to ferroptosis and PARP inhibitionPreprint
Esmaeili, P., Nasimian, A., Ernestal, E., Persson, E., Bochis, B., Li, Y., Zamore, M., Gerdtsson, A. S., Kazi, J. U., & Levander, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.18.725896
“Ambient RNA contamination was estimated and corrected at the single-cell level using SoupX (version 1.6.2) 57 .”
InfluentialAggarwal, J., Eeba, & Agarwal, N. (2026) · Springer eBooks · doi:10.1007/978-981-95-9789-5_4
T cell dysregulation and remodeling in pediatric obesity and weight lossPreprint
Hay, C., Sayed, S. U., Espinoza, D. A., Knight, M., Abrams, E. D., Campos, J. S., Nagy, M. Z., Nelson, M. A., Sheetz, S. A., Gunnala, P., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.19.722395
“Marker genes were identified utilizing a one-vs-all approach via the “quickMarker” function from the SoupX package(Young and Behjati, 2020), utilizing the term frequency-inverse document frequency (tf-idf) as a measure for marker gene expression.”
Li, S., Morrison, H., Duta, M. D., Marshall, J. L., Harris, S. A., He, W., Ateere, A., Nassanga, B. R., Satti, I., & McShane, H. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102830
Initial leukemic epigenomic state determines hypomethylating agent response
Gopal, A., Tam, D., Mey, F., Lin, D., May, C., Jiang, J.-H., Bridgers, J., Caswell, B., O’Neill, K., Vizeacoumar, F., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-73334-3
Fiancette, R., Huang, J., Stephens, C., Hibbert, J. E., Hewitt, G., Carlein, C., Shilleh, A. H., Clinton, C., Osório, L. A., Tourigny, D. S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.22.726825 · Cited by 1
Sumera, A., Crichton, A., Kind, P. C., Sibley, C. R., & Booker, S. A. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1819200
“6.2 (Young and Behjati, 2020) was used to estimate and filter out ambient RNA from each sample individually.”
InfluentialShi, C., Wen, Z., Zheng, K., Li, B., Miao, H., Klinkhammer, B. M., Boor, P., Anders, H., & Liu, D. (2026) · Science Translational Medicine · doi:10.1126/scitranslmed.adv8372
Park, J., Kim, K. H., Lee, H. C., et al. (2026) · Genome Medicine · doi:10.1186/s13073-026-01669-z
“6.2) [27].”
InfluentialLange, B., Graceffo, E., Stenzel, W., Biebermann, H., Schuelke, M., & Wilpert, N. (2026) · medRxiv · doi:10.64898/2026.06.04.26354648
Aberrant STAT signaling and T cell dysregulation define a targetable pediatric sepsis endotype
Lindell, R. B., Sayed, S. U., Campos, J. S., Sheetz, S. A., Babu, A., Knight, M., Mauracher, A. A., Hay, C., Conrey, P. E., Fitzgerald, J. C., et al. (2026) · Journal of Clinical Investigation · doi:10.1172/jci202867 · Cited by 2
“After quality control using SoupX (55) and DoubletFinder (56) and integration using RPCA via Seurat v5 (57), cell identities were inferred using ScType (58) and refined using Azimuth (59) and T cell phenotypes from Giles et al. (60).”
Lorenzi, V., Moullet, M., Icoresi-Mazzeo, C., Colligan, R. M., Danks, M. R., Cohen, C. E., Parranga, A., Kim, C. E., Sancho‐Serra, C., Sakata, R. C., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.08.730878
“Cluster marker genes were identified using term frequency–inverse document frequency (TF-IDF) as implemented in SoupX 109 (using 100 cells downsampled per cluster, top 50 markers retained at FDR 0.01).”
InfluentialFu, Z.-C., Zhang, C., Yan, Y., Xu, Y., Yin, X., Tao, T., Lu, P., Liang, Y., Wu, H., Cui, W., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.08.730656 · Cited by 2
LPAR4 mediates resistance to interferon-induced stress in soft tissue sarcoma
Xiao, J.-F., Ko, E. Y., Smith, A., Piras, R., Fowler, A., Ishaya, K., Wang, L., Wang, B., Paulucci-Holthauzen, A. A., Landers, S. M., et al. (2026) · Science Advances · doi:10.1126/sciadv.aef1962
“Resultant gene counts were preprocessed in Seurat v4 ( 71 ) along with R packages for ambient RNA filtering [SoupX ( 72 )] and for doublet detection [scDblFinder ( 73 ) and Seurat::HTODemux].”
Cohen, C. E., Párraga-Leo, A., Rodríguez-Montes, L., Kim, C. E., Moullet, M., Paredes, A., Lorenzi, V., Vilarrasa‐Blasi, R., Predeus, A. V., Polański, K., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.10.731198
Shelton, C. D., de Brito, C. B., Nirello, V. D., Kirchoff, N., Lane, J., Olivas, J., Carroll, D. T., Armstrong, D., Rhee, M. W., James, M. N., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.11.731695
“Ambient RNA contamination was estimated and corrected using the SoupX package (version 1.6.2) by comparing raw and filtered expression matrices ( 78 ).”
InfluentialEvolutionary Recoding of Olfactory Sensory NeuronsPreprint
Bontonou, G., Baticle, T., Hume, S., Kafle, T., Mahmoud, B. M., Vlachou, V., Day, M. W., & Arguello, J. R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.12.731899
Wang, Q., Wang, X., Yan, H., & Yuan, J. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1820459
Single-cell chromatin tracing reveals multimodal molecular programs during memory formationPreprint
Itoh, K., Khalil, V., Faress, I., & Kitazawa, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.16.732522
JAK–STAT3–MYC axis defines pathogenic stem cell-like memory CD4+ T cells in rheumatoid arthritis
Yang, S., Kim, M., Park, K.-L., Kim, H., Park, J. W., Park, J. K., & Lee, E. B. (2026) · Clinical Immunology · doi:10.1016/j.clim.2026.110733
Developmentally dynamic, non-convergent transcriptomic profiles in CNV models for schizophrenia risk
French, H., Yao, S., Bast, L., Adam, A. R., Stümpges, P., Memic, F., Mullé, J. G., Sullivan, P. F., & Hjerling‐Leffler, J. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03679-0
Uddin, F., Pandit, A., Kudpaje, A., Notani, D., & Sabarinathan, R. (2026) · STAR Protocols · doi:10.1016/j.xpro.2026.104640
Сергеевна, Ч. Е., Shcherbakova, L. N., Vigovskiy, M. A., Ильдарович, Т. А., Logunov, D. Y., Tychinin, D. I., Svetlichnyy, D. V., Kulebyakin, K. Y., Tkachuk, V., & Chechekhin, V. I. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag612
“6.2 [ 20 ].”
InfluentialCircadian rhythms of metabolite abundance in Drosophila are largely driven by time of feeding
Saurabh, S., Guerrero, C. Y. P., Cusick, M. R., Samaras, A. J., Stephenson, T. M., Kirkpatrick, S. L., Matthews, G. J., & Cavanaugh, D. J. (2026) · FEBS Journal · doi:10.1111/febs.70631 · Cited by 1
Flow-Induced Yap/Taz Signaling Balances Endothelial and Hematopoietic Stem Cell FatesPreprint
Sugden, W. W., George, S., LeBlanc, Z. C., Walcheck, M. T., Meader, E., Goldstein, J., Molnar, E., Zhu, W., Mout, R., Li, C., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.20.733393
Rodríguez, C., Edinger, R. S., Diehl, G., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102884
Dedifferentiation unlocks keratinocyte competence for metaplasia and tumorigenesis in the foregutPreprint
Drubbel, A. V., Pirard, S., & Beck, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.24.734259
Spatiotemporal cellular landscape of the human utricle sensory epitheliumPreprint
Liang, W., Yamamoto, R., Luca, E., & Dabdoub, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.25.734616
From candidate genes to omics: Unbiased approaches reshaping arthropod Evo-Devo
Vieira, J., & da Fonseca, R. N. (2026) · Genetics and Molecular Biology · doi:10.1590/1678-4685-gmb-2025-0217 · Cited by 1
“Technical issues such as dropout, ambient RNA, doublets, stress responses, and systematic differences between nuclei-and cell-based measurements can further bias inferred trajectories if not carefully controlled (Denisenko et al ., 2020; Ding et al ., 2020; Young and Behjati, 2020).”
Fuchs, T., Menzel, M., Mößinger, K., Kazdal, D. N., Kahles, A., Budczies, J., & Stenzinger, A. (2026) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2026.06.005
Niwa, T., Kikuchi, M., & Tanaka, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.26.731870
“6.2 ; Young and Behjati, 2020) for ambient RNA contamination and normalized by total UMI (unique molecular identifier) counts or by SCTransform (v0.”
InfluentialMorales, P. E., Tong, W., Vishvanath, L., Leander, D. C., Wade, T. E., Hallaron, D. S., EL, K. M., HOLLANDER, R. A., Truong, A., WOTHE, D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.23.734002
A surviving beta cell subpopulation enriched in patients with T1DPreprint
Spurrell, M., Tsang, J. S., & Herold, K. C. (2026) · eLife · doi:10.7554/elife.112006.1
A surviving beta cell subpopulation enriched in patients with T1DPreprint
Spurrell, M., Tsang, J. S., & Herold, K. C. (2026) · eLife · doi:10.7554/elife.112006
Bhagavan, H., Wei, A. D., Oliveira, L. M., & Ramirez, J. (2026) · Molecular Neurobiology · doi:10.1007/s12035-026-06162-7
An integrated skin cell atlas decodes the pilosebaceous unit
Düz, T., Törőcsik, D., Momnougui, S. M. E., Oubounyt, M., Al, B., Gallinat, S., Baumbach, J., & Holzscheck, N. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2026.06.1282 · Cited by 1
Lu, D., Osgood, T., Ma, S., Zhang, H., Kanke, M., Tan, J. A., Paudel, S. N., Zhou, H., Estrada, J., Yamawaki, T., et al. (2026) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-25-0794 · Cited by 1
Mahajan, A. S., Ravichandran, S., Marcheş, R., Yazıcı, Y. Y., Nelson, S., Aydillo, T., Kshitija, K., Rojo-Fernandez, A., Nehar-Belaid, D., Kenyon‐Pesce, L., et al. (2026) · medRxiv · doi:10.64898/2026.07.02.26357118
Single-cell multiome profiling reveals neuronal bias of modulatory role of MeCP2 phosphorylation
Zhang, Z., Huang, X., Arachchilage, K. H., Chandrashekar, P. B., Dong, Q., Wang, R., Wang, D., & Chang, Q. (2026) · Neurobiology of Disease · doi:10.1016/j.nbd.2026.107521
Lazzari, E., Rozera, G., Pierfederici, L., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27136025
“Raw count matrices from S1 and S2 were independently corrected for ambient RNA contamination using SoupX (version 1.6.2) [30] in R, then imported into AnnData objects [31] for downstream analysis using scanpy (version 1.10.2) [32].”
InfluentialHighly efficient and low-mosaicism piggyBac transgenesis platform for rapid founder phenotyping
Okamura, E., Matsumoto, S., Mizutani, E., Murata, K., Tanimoto, Y., Dinh, T. T. H., Suzuki, H., Kuno, A., Kang, W., Mikami, N., et al. (2026) · iScience · doi:10.1016/j.isci.2026.116648
D’Agostino, L. W., Ghose, K., Yong‐Villalobos, L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.07.737096
Tanaka, T., Kerever, A., Suzuki, Y., Kunitomi, T., Kato, K., Yamashita, Y., Higashi, K., Akazawa, C., Saitow, F., Suzuki, H., et al. (2026) · Glia · doi:10.1002/glia.70188
“Doublets were predicted using Scrublet (Wolock et al. 2019), and ambient RNA levels were estimated separately using SoupX (Young and Behjati 2020).”
Wang, Z., Modave, E., Remoortel, S. V., Pajk, N., Berghe, P. V., Chen, Y., Vandeput, M., Appeltans, I., Quan, R., Matteoli, G., et al. (2026) · Gut · doi:10.1136/gutjnl-2025-336369
Wang, L., Roskams-Hieter, B., Hussain, N., Nulsen, J., Aggarwal, A., Sallam, M., Wang, L., Rai, L. J., Shi, H., Ahmed-Ebbiary, A., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-74599-4 · Cited by 1
Vessel Organoids Reveal FOXF1 Variant-Specific Regulation of Mesoderm and Capillary DevelopmentPreprint
Pek, N. M. Q., Thorner, K., Guo, M., Dennison, T. S. H., Rajaguru, T., Stan, G., Kishimoto, K., Rottier, R. J., Kotton, D. N., Zorn, A. M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.12.737936
Peripheral somatostatin-expressing neurons are analgesic targets for refractory occipital neuralgia
Wang, Q., Li, C., Zhu, Q., Liu, T.-F., Zhao, G.-D., Tang, Y.-F., Jiang, Y., Chen, Z.-X., Feng, J., Yu, Y., et al. (2026) · Acta Pharmacologica Sinica · doi:10.1038/s41401-026-01865-y
“SoupX (version 1.6.1) was utilized to eliminate ambient RNA bias [30].”
InfluentialChen, S., Wang, Y., Chen, J., Xie, W., Chen, J., Liao, G., Ren, X., Lei, K., Zhang, Y., Wang, S., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.06.010 · Cited by 1
Micheli, A. J. D., de Biagi, C. A. O., Cascio, C. L., Machaalani, C., Postlmayr, A., Katiyar, S., Maas, R. R., Kancherla, V., Reimann, R., Zápotocký, M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.16.738862
Alkouz, Z., Zhang, L., Suwait, A. A., Alhejairi, R., Liu, C., xiangyu, G., & Yang, B. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1751725
From stasis to systematics: deciphering the pathophysiology of secondary lymphedema through omics
Stull-Lane, A. R., Chen, X., Book, A. J., Kratchmarov, R., Pal, S., Shin, J. Y., Ashokan, G., Hespe, G. E., Mehrara, B. J., & Kataru, R. P. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1850725
Salomon, M. P., Golden‐Mason, L., Vorobyova, I., Kanel, G. C., Wang, Y., Wong, D., & Maretti-Mira, A. C. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1817738
“Count matrices were corrected for ambient RNA contamination using SoupX with default parameters (14).”
DepthDiff: Restoring Low-Depth Single-Cell RNA-Seq Signals via Diffusion Denoising
Hou, X., Sun, J., Liu, Y., & Wang, G. (2026) · Biology · doi:10.3390/biology15151223
“This design provides controllability and paired supervision, but it cannot fully capture all sources of low-quality scRNA-seq data in real experiments, such as library preparation failure, batch effects, platform differences, ambient RNA [26], or degraded samples.”
Hjorthaug, H. S., Heimli, M., Flåm, S. T., & Lie, B. A. (2026) · iScience · doi:10.1016/j.isci.2026.116916
Miller, S. J., Zhong, H., Wu, W., Cordova, A. M., Yashchenko, M. E., Yashchenko, A., Li, Z., Jafree, D. J., Zimmerman, C. N., DeVette, C. I., et al. (2026) · JCI Insight · doi:10.1172/jci.insight.207157
“The resulting matrix files were loaded into Seurat version 5.2.0 followed by removal of doublets (DoubletFinder) (37) and ambient RNA (Soupx) (38).”
InfluentialSecor, E. J., Wang, J., Dou, Z., Yan, J., Majano, C. F., Woodman, M. J., Ruiz, O., Azaat, J. A., Crosby, D., Cela, R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.27.740924
Stephens, G. S., Alcantara‐Gonzalez, D., & Scharfman, H. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.28.741316
“After correcting for ambient RNA, we used the CreateSeuratObject() function to generate Seurat Objects from the SoupX-adjusted RNA read count datasets (1 object/mouse or 1 object/sample).”
InfluentialCornelius, J. A., Shivakumar, M., Kim, T., Cervantes, O., Kapur, R. P., Huang, H., Zhao, H., Rosario, B. D., Li, A., Li, R., et al. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1785001
“6.2) (Young and Behjati, 2020), with preliminary cluster assignments derived from a standard Seurat workflow (NormalizeData, FindVariableFeatures, ScaleData, RunPCA, FindNeighbors, FindClusters at resolution 0.5); samples with fewer than 100 cells were excluded from SoupX correction.”
InfluentialHoyt, E. A., Moonens, K., Rapisarda, C., Salvador, A. M., Hammond, T. R., Kharade, S. V., Mondragón-González, R., Moran, F. E. Y., Ramkumar, S., Thummapudi, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.29.741483
Kotah, J. M., Trombetta-Lima, M., Dragt, E. C., Voulgaroglou, A., Brouwer, N., Holtman, I. R., Kooistra, S. M., & Eggen, B. J. L. (2026) · Glia · doi:10.1002/glia.70210 · Cited by 2
“6.2 (Young and Behjati 2020) with default settings, and potential doublets were identified using Scrublet v0.”
InfluentialIdentifying Pulmonary Endothelial Cell Zonation From Single-Cell Transcriptomics
Sveiven, S., Knutsen, C., Zanini, F., Cornfield, D. N., & Alvira, C. M. (2026) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.126.324377
Mycosis Fungoides‐Like Atopic Dermatitis Represents a Th22‐Dominant Inflammatory Endotype
Lee, J. H., Lim, S. H., Kook, H., Kang, S., Lee, G., Lee, B. H., Nam, H., Kim, Y., Joh, C. S.-Y., Jeong, S., et al. (2026) · Allergy · doi:10.1111/all.70440
Nrl expression and promoter activity in developing cone photoreceptorsPreprint
Webley, B. P., Grebe, A. V., Mohammed, B., Lopez, J., Zaman, A., Tegla, M. G. G., & Emerson, M. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.06.743042
Characterizing and mitigating cross-library PCR chimeras in Perturb-seq using Perturb-Audit
Song, X., Lu, J., & Zhu, P. (2026) · Blood Science · doi:10.1097/bs9.0000000000000306
“This architecture is conceptually analogous to mixed-species “barnyard” experiments, 10,27 in which reference-aware mapping distinguishes reads by their origin and enables auditing of cross-library contamination between GEX and sgRNA molecules within the same sequencing run.”
Craig, J. M., Mowlaei, M. E., Miura, S., Shi, X. M., & Kumar, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.03.742620
“For gene-expression analysis, ambient RNA contamination, doublets or multiplets, and low-quality cells with elevated mitochondrial RNA content were removed using SoupX (Young and Behjati, 2020), DoubletFinder (McGinnis et al., 2019), and miQC (Hippen et al., 2021), respectively.”
Transcriptomic analysis of spinal V1 interneurons informs their multifunctional role in motor output
Trevisan, A. J., Han, K., Chapman, P., Kulkarni, A. S., Hinton, J. M., Klein, I., Ramirez, C., Lavado, A., Gatto, G., Gabitto, M. I., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-76522-3 · Cited by 8
Mapping tumor microenvironment heterogeneity of human melanoma metastases across distant organsPreprint
Pulido-Vicuña, C. A., Piprek, M., Demaerel, P. G., Bechter, O. E., Vermeulen, P., Bosisio, F. M., Marine, J., & Poźniak, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.11.743891
“Ambient RNA removal with SoupX [24] was evaluated by per-sample estimation from the raw and filtered CellRanger matrices with Leiden clustering at resolution 0.5 (estimated contamination fractions 1–9%, median 3%).”
InfluentialSmith, J. J., Taylor, S. R., Destain, H., Kim, G., Hall, D. H., White, J. G., Miller, D. M., & Kratsios, P. (2026) · Nature Communications · doi:10.1038/s41467-026-76320-x
“Background RNA correction wasperformed with the SoupX R package 150 , applying a 25-UMIs cutoff for the background expression pro fi le.”
Tourneur, A., Vitória, J. R., Saiselet, M., Craciun, L., Larsimont, D., Lefort, A., Libert, F., Maenhaut, C., Costagliola, S., Tarabichi, M., et al. (2026) · Molecular Oncology · doi:10.1002/1878-0261.70289 · Cited by 2
“Briefly, raw counts from Cell Ranger were loaded and the ‘background soup’ was removed using SoupX [72].”
Park, J. H., Kim, B., Al‐Amin, M. M., Tate, M. D., Sharify, A. D., John, S. K., Karahan, H., Lu, H., Dabin, L. C., & Kim, J. (2026) · Alzheimer's & Dementia · doi:10.1002/alz.71742
Oder, B., Rabbani, L., Santos, T. D. P., Parikh, S. A., Plevová, K., Mansouri, L., Chatzidimitriou, A., Pospisilova, S., Kay, N. E., Stamatopoulos, K., et al. (2026) · Blood Neoplasia · doi:10.1016/j.bneo.2026.100282
Rotondo, C., Rossi, A., Properzi, M., et al. (2026) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2026.1911804
Youth-associated protein TIMP2 regulates microglial state and function in healthy and aged mice
Hemmer, B. M., Philippi, S. M., Ferreira, A. C., Petridis, S. F., Phan, A., & Castellano, J. M. (2026) · Nature Communications · doi:10.1038/s41467-026-74906-z · Cited by 1
LeeBae, J., Bopp, V., Moehrle, B., Kühlwein, J. K., Grozdanov, V., Kiechle, M., Mayer, B., Geiger, H., & Danzer, K. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.07.742974
Liu, W., Zhang, S., Zhang, J., et al. (2026) · Advanced Science · doi:10.1002/advs.77168
“Ambient RNA Correction by Soupx Ambient RNA contamination was evaluated and corrected using SoupX [ 75 ], (V.1.6.2).”
InfluentialGuo, L., Tong, B., 高晓敏, Jia, X., Liu, X., Li, L., Xu, M., Zhang, Y., Wang, J., Liu, Q., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101335
MLL1 Regulates T-Cell Phenotype via Notch Signaling During Abdominal Aortic Aneurysm Development
He, X., Juriga, N., Callow, B., et al. (2026) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.126.324552
Abatacept Reduces CD319 + ( SLAMF7 ) Cytotoxic T Cells and Cytokine Production in Systemic Sclerosis
Gurrea‐Rubio, M., Cooney, L. A., Maeda, K., Campbell, P. L., Wu, Q., Amarista, C. I., Stinson, A., Ohara, R. A., Mao‐Draayer, Y., Whitfield, M. L., et al. (2026) · Arthritis & Rheumatology · doi:10.1002/art.70301
Fan, X., Liu, J., Huang, S., Wang, X., Liu, T., Ma, X., Zhou, J., Guo, P., & Wu, Y. (2026) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2026.143348
The role of adipose tissue macrophages in adipose tissue fibrosis
Zhang, D., & Wu, N. (2026) · Metabolism · doi:10.1016/j.metabol.2026.156752
Hopkins, B. R., Barmina, O. Y., Wang, X., Situ, M., Bolanos, H. A., Nie, S., & Kopp, A. (2026) · PLoS Biology · doi:10.1371/journal.pbio.3003964
“A consequence of including the full complement of cells identified by CellRanger (i.e., without use of ‘force-cells’) was, in some datasets, a marked increase in the degree of ambient RNA contamination that we estimated using SoupX [93].”
InfluentialBeale, D., DiStefano, I. A., Brosius, S., et al. (2026) · Acta Neuropathologica Communications · doi:10.1186/s40478-026-02408-6
Pathania, R., Papas, B. N., Kosak, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.25.747019
A distinct effector B cell population drives autoantibody production in SARS-CoV-2 infection
Yuan, D., Li, S., Zhang, R., Ng, R. H., Su, Y., Lan, L., Shome, M., Smith, B., Troisch, P., Xie, J., et al. (2026) · Immunity · doi:10.1016/j.immuni.2026.08.002
wang, Z., Han, C., Huang, Y., Fu, Y., Hu, K., Huang, Z., meng, W., Chen, Q., Wu, L., Wang, Q., et al. (2026) · Cell stem cell · doi:10.1016/j.stem.2026.08.004
From Cluster to Claim: Calibrating Interpretation in Single‐Cell Transcriptomics
Lin, S., & Yu, G. (2026) · Advanced Genetics · doi:10.1002/ggn2.70045 · Cited by 1
Yasmin, F., Podder, N. K., Jahan, I., & Mitu, J. K. (2026) · International Conference on Power, Electronics, Communications, Computing, and Intelligent Infrastructure (PECCII) · doi:10.1109/peccii70991.2026.11662040
Coordinated dysregulation of modular gene activity in human neuropathologiesPreprint
Kang, G., & Oldham, M. C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.25.747130
Wang, L. D., Oill, A. M. T., Lindner, S. E., Stiller, T., Blanchard, M. S., Mudunuri, R. V., Wu, M., Hibbard, J. C., Egelston, C. A., Sepulveda, S. M., et al. (2026) · medRxiv · doi:10.64898/2026.08.27.26361261
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.
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