The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index; 15 of the 16 sources its claims cite were checked for support.
SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data
Open accessPublished version of a preprint
Abstract
Background: Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free RNAs contained within the input solution are also captured by these assays. This sequencing of cell-free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data. Results: We demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics. Conclusions: We present SoupX, a tool for removing ambient RNA contamination from droplet-based single-cell RNA sequencing experiments. This tool has broad applicability, and its application can improve the biological utility of existing and future datasets.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
SoupX is a method and R package that estimates and removes ambient cell-free RNA contamination from droplet-based scRNA-seq by using empty droplets to define a background profile and estimating a contamination fraction. Across species-mixing, PBMC, kidney tumour, and fetal liver datasets, correction improved marker specificity and biological interpretation.
Key findings
- Ambient RNA contamination was ubiquitous: in 10X species-mixing data, ∼1% of transcripts were cross-species contamination, providing a lower bound, with true contamination later estimated at ∼2%; background profiles correlated with cell aggregates at Pearson 0.71–0.96 (median 0.86).
- SoupX reduced cross-species contamination by a factor of ≥2 and usually an order of magnitude, while correct-species expression was effectively unchanged.
- In PBMCs, automated contamination estimation gave 6% and an IG-gene-based estimate gave ∼5%; correction increased marker fold-change contrast and improved LYZ specificity for mononuclear phagocytes.
- In kidney tumour data, correction removed haemoglobin expression from non-red-blood cells, identified collagen expression in T/MNP clusters as contamination, increased cross-batch entropy, and yielded 2 fewer clusters; in fetal liver, HBB was confirmed as erythroid-specific and GYPA expression in EI macrophages as genuine.
Methods
- SoupX has three steps: estimate the ambient mRNA profile from empty droplets with <Nemp UMIs (best correlation at Nemp < 10), estimate or manually set the contamination fraction using genes/cells assumed to have no endogenous expression or an automated marker-based procedure, and correct counts via a multinomial likelihood, optionally redistributing cluster-level corrected counts.
- Datasets included 10X and DropSeq species-mixing experiments, PBMC data, a kidney tumour dataset (7 tumours across 10 channels in the Results; the Introduction describes 12 kidney tumour biopsies), and 40 channels of human fetal liver data.
- Analyses compared corrected and uncorrected data using Seurat tSNE representations, cluster membership, marker gene fold changes, marker sensitivity, and cross-batch entropy.
Limitations
- The paper states that best results require a user-specified set of genes and cells where no endogenous expression can be assumed; when this is not possible, the automated method requires clustering and may not always have an appropriate gene set.
- The paper notes that a constant contamination fraction within a channel is assumed in most experiments, and that manual contamination setting can be used if estimates are deemed inaccurate or overcorrection is desired.
- Evident from the text: the method assumes empty-droplet profiles represent the background and that background composition is shared across cells; evaluation is limited to the datasets analysed, and the paper has no formal limitations section.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
15 of 16 cited sources checked · 10 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Violations of this assumption, which may distort the interpretation of scRNA-seq data, are common in practice.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Introduction
What the sources say
- Not matchedcites [11] Gayoso, 2017, DoubletDetection: Identifying technical error in single-cell RNA-sequencing data
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites [12] Ilicic, 2016, Classification of low quality cells from single-cell RNA-seq data, Genome Biol sourceExperimental — accuracy not yet measured
The Background (page unavailable) says some scRNA-seq capture sites, including droplets, may contain multiple cells and that these cells can lead to misinterpretation. The Discussion (page unavailable) reports up to 30% multiple-cell captures in a Fluidigm study, but that figure concerns microfluidic capture sites, not droplets; the paper does not establish that single-cell-per-droplet violations are common. (LLM, conf=0.82)
- Supportedcites [13] Wolock, 2019, Scrublet: Computational identification of cell doublets in single-cell transcriptomic data, Cell Syst source
The Introduction says that at least several percent of capture events in a typical scRNA-seq experiment are multiplets, including events where more than one cell enters a droplet. It also explains that these events can confound interpretation and downstream analyses. (LLM, conf=0.94)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, any cell-free RNAs contained within the input solution are also captured by these assays.”
- Also stated in: § Introduction · boosted
“It is recognized that these contaminating non-endogenous RNAs are present even within datasets of the highest quality [2].”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Background
What the sources say
- Supportedcites [2] Zheng, 2017, Massively parallel digital transcriptional profiling of single cells, Nat Commun source
The Results section (Technical demonstration with cell lines and synthetic RNAs; page unavailable) reports that cell-free RNA contributed background UMI counts. It also reports that synthetic RNAs loaded into droplets in place of cells were detected by sequencing, directly demonstrating capture of RNA from the input solution. (LLM, conf=0.92)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Introduction · boosted
Every source the paper cites for this claim was checked and supports it.
In the paper
“A similar effect is seen in the Drop-Seq–based species-mixing data (Supplementary Fig. S3).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Properties of Ambient RNA
What the sources say
- Supportedcites [14] Macosko, 2015, Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets, Cell source
The Results section (“The single cell accuracy and sensitivity of Drop-Seq libraries”; page not provided) reports human–mouse species-mixing experiments and species-mixed cell barcodes. It also reports single-cell impurity in those libraries, with ambient RNA appearing to be the largest source. This supports the claim that cross-species contamination is seen in Drop-Seq species-mixing data. (LLM, conf=0.90)
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Droplets containing human (top right) and mouse (bottom right) cells show that ∼1% of observed transcripts are cross-species contamination.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Properties of Ambient RNA
What the sources say
- Supportedcites [2] Zheng, 2017, Massively parallel digital transcriptional profiling of single cells, Nat Commun source
The Results section (‘Technical demonstration with cell lines and synthetic RNAs’) reports that the mean fraction of UMI counts from the other species was 0.9% in both human and mouse GEMs. It describes this as a low level of cross-talk between cell barcodes, consistent with the claim’s approximately 1% figure. (LLM, conf=0.93)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This analysis shows that the batch-mixing entropy is increased after background correction, indicating better mixing between samples (Fig. 4E).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Ambient RNA Confounds Interpretation in Complex Experiments
What the sources say
- Partly supportedcites [20] Haghverdi, 2018, Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors, Nat Biotechnol sourceExperimental — accuracy not yet measured
The Results section on the pancreas data set reports higher batch-mixing entropy after MNN batch correction than in uncorrected data. The Online Methods section also specifies that entropy was assessed in pancreas data sets. Neither passage supports the claim that entropy increased in kidney tumour data after background correction; no page numbers are available in our copy. (LLM, conf=0.88)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This is likely a consequence of the fact that the capture efficiency of molecules in droplet-based experiments varies by as much as an order of magnitude [17].”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Properties of Ambient RNA
What the sources say
- Partly supportedcites [17] Svensson, 2017, Power analysis of single-cell RNA-sequencing experiments, Nat Methods sourceExperimental — accuracy not yet measured
The Results section on technical sensitivity reports substantial variation across scRNA-seq protocols, including a droplet-based protocol, and high within-protocol variability. But it does not establish that molecular capture efficiency in droplet-based experiments varies by an order of magnitude. The Discussion’s one-order-of-magnitude figure compares endogenous RNA with spike-ins, not variation among droplet experiments; page numbers were unavailable in our copy. (LLM, conf=0.83)
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude.”
- Also stated in: § Introduction · plain
“Here, we show that this "soup" of cell-free mRNAs is ubiquitous and non-negligible in magnitude.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Results
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Introduction · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here, we show that this "soup" of cell-free mRNAs is ubiquitous and non-negligible in magnitude.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“as well as improving quality control metrics”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“with experiment-specific variations in composition and magnitude”
- Also stated in: § Introduction · plain
“Because the character and extent of ambient mRNA contamination varies by experiment”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Introduction · plain
- It may state two separate claims as one. 11
- Probably not a substantial claim. 7
Every source the paper cites for this claim was checked and supports it.
In the paper
“We first investigate 2 “species mixing” datasets run on the Chromium 10X [2] and DropSeq [14] platforms, which allow us to directly identify contaminating mRNAs and test our method’s accuracy.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Introduction
What the sources say
- Supportedcites [2] Zheng, 2017, Massively parallel digital transcriptional profiling of single cells, Nat Commun source
The Results section (“Technical demonstration with cell lines and synthetic RNAs”) describes a human–mouse mixture run on the cited system and reports UMIs from the other species in each cell type. The Methods section (“Alignment, barcode assignment and UMI counting”) explains how reads from the other species were used to assess barcode cross-talk. The citation identifies the Chromium species-mixing dataset; testing SoupX is the citing authors’ proposed use of it, not a test attributed to this paper. Page numbers are not provided in our copy. (LLM, conf=0.90)
- Supportedcites [14] Macosko, 2015, Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets, Cell source
The Results section describes Drop-Seq species-mixing experiments using human and mouse cells, with transcripts scored by species and cell barcode. It also reports using those experiments to measure single-cell impurity, including impurity attributed to ambient RNA. The citation identifies the Drop-Seq dataset; testing SoupX’s accuracy is the citing authors’ stated use of it, not a result attributed to Macosko et al. (LLM, conf=0.90)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Finally, DecontX [23] relies on accurate clustering of the data to estimate and remove the background without the need for gene counts from empty droplets.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Supportedcites [23] Yang, 2003, Decontamination of ambient RNA in single-cell RNA-seq with DecontX, Genome Biol source
The Discussion (page unavailable) says DecontX needs cell-cluster labels, generates them with Celda if none are supplied, and uses other clusters to derive each population’s contamination distribution. The Background’s Fig. 1 description (page unavailable) lists an expression count matrix and cluster labels as inputs for estimating native and contamination counts, supporting the point that empty-droplet gene counts are not required. (LLM, conf=0.90)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“For example, solid-tissue experiments are frequently highly contaminated with red blood cells and red cell lysis is used to prepare the samples [15].”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Could not verifycites [15] Young, 2018, Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors, Science sourceExperimental — accuracy not yet measured
The paper’s tissue-processing description (Abstract; page not provided) says kidney samples were made into viable single-cell solutions, but does not mention red-blood-cell contamination or red-cell lysis. Searches for red blood cells, erythrocyte lysis, and lysis buffer found no passage addressing either part of the claim. (LLM, conf=0.72)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This deep generative model comes with a heavy computational cost compared to other tools, and the output of the model (which in effect estimates mg, c for each cell type) provides an imputed cell profile rather than raw counts with the background “subtracted off,” which SoupX provides.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Partly supportedcites [22] Fleming, 2019, CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets, bioRxiv sourceExperimental — accuracy not yet measured
Section 3.3.2 describes the output as a model-inferred, background-free expression-rate estimate that is subsequently quantized, supporting the distinction from simply subtracting background counts from raw counts. Section 3.2.1 reports runtimes of 30 minutes to several hours on a GPU, but does not compare computational cost with other tools; that comparative claim is unsupported. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Cell Bender [22] uses a deep generative model to estimate shared expression patterns likely to represent distinct cell types while simultaneously removing contamination.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Supportedcites [22] Fleming, 2019, CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets, bioRxiv source
The Introduction describes a neural network that learns gene-expression patterns across droplets, combines information from similar cells, and estimates background contamination simultaneously. Section 3 explains that the model lets similar cells share statistical power; the paper also identifies it as a deep generative model. (LLM, conf=0.91)
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“SoupOrCell [21] uses the identification of conflicting genotypes to identify ambient RNA contamination, limiting its application to mixed-genotype experiments.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Supportedcites [21] Heaton, 2020, souporcell: Robust clustering of single-cell RNA-seq data by genotype without reference genotypes, Nat Methods source
The Abstract says that cross-genotype variant contamination can be used to estimate ambient RNA and frames souporcell as a method for samples containing a mixture of genotypes. The Clustering model and data preprocessing section describes estimating ambient RNA from allele fractions shifted away from each cluster’s genotype, supporting the stated limitation to mixed-genotype experiments. (LLM, conf=0.90)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This allows DecontX to be applied when empty droplet counts are not available but also means that the results are potentially heavily dependent on the accuracy of the clustering provided.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
What the sources say
- Supportedcites [23] Yang, 2003, Decontamination of ambient RNA in single-cell RNA-seq with DecontX, Genome Biol source
The Background (Fig. 1 caption; page unavailable) describes DecontX as taking an expression count matrix and cell-cluster labels, without requiring empty-droplet counts. The Discussion (page unavailable) identifies cluster labels as a limitation and explains that the choice of labels can affect the inferred contamination distribution, supporting the stated potential dependence on clustering. (LLM, conf=0.87)
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The species-mixing experiment allows us to compare how accurately bg recapitulates the true background expression found within each cell, revealing that any value of Nemp < 100 produces a good correlation, with the best correlation given when Nemp < 10 (Supplementary Fig. S2).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > The SoupX Method
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Applying SoupX and re-analysing the kidney tumour data revealed that, in contrast to the PBMC data, many cells changed cluster and with the same clustering parameters 2 fewer clusters were identified in the corrected data (Fig. 4B).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Ambient RNA Confounds Interpretation in Complex Experiments
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These 2 profiles were highly correlated in the 10X species experiment (Fig. 2B) with a high correlation found in all other datasets considered (Pearson correlation 0.71–0.96, median 0.86; Supplementary Table S2).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Properties of Ambient RNA
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: Droplets with ≤10 UMIs contain only ambient mRNAs.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The output of the SoupX package is a corrected table of counts, which can be used as input for standard workflows, and running SoupX does not add appreciably to the computational cost of standard single-cell analyses.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Comparing cluster membership before and after correction revealed that the same number of clusters was identified, but some cells changed which cluster they belonged to (Fig. 3C).”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Application of SoupX to PBMC Data
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The genes for which mg, c = 0 for a given cell are those genes that are strong negative markers of the cell type c.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > The SoupX Method
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We present SoupX, a method for quantifying the extent of ambient mRNA contamination whilst purifying the true, cell-specific signal from the observed mixture of cellular and exogenous mRNAs.”
- Also stated in: § Results · plain
“We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § Results · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“SoupX produces a modified table of counts, which can be used in place of the original count matrix in any downstream analysis tool.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > The SoupX Method
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, we recognize that selecting an appropriate set of genes to estimate contamination will not always be possible.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This tool has broad applicability”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Abstract > Conclusions
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By contrast, SoupX can be applied generally, is computationally inexpensive, and does not depend heavily on accurate pre-annotation of input data.”
§ SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data > Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
Ambient mRNA contamination is increased in necrotic or complex samples.
“with increased contamination in necrotic or complex samples”
The authors state this as an assumption.
Droplets with fewer than Nemp UMIs unambiguously do not contain cells.
“To estimate the background expression profile we consider all droplets with <Nemp UMIs, which we assume unambiguously do not contain cells.”
The authors state this as an assumption.
The relative abundance of genes that make up the background does not differ between cells.
“We assume that the relative abundance of genes that make up the background does not differ between cells”
The authors state this as an assumption.
There is a combination of genes and cells for which mg,c = 0 exists.
“To proceed, we assume that there is a combination of genes and cells for which mg, c = 0 exists.”
The authors state this as an assumption.
If it can be shown for any cell c in cluster P that mg,c > 0, then mg,c > 0 for all c ∈ P.
“If it can be shown for any cell c in cluster P that mg, c > 0, then it is assumed that mg, c > 0 for all c ∈ P (see Supplementary Fig. S1).”
The authors state this as an assumption.
For each strong marker, mg,c = 0 for all cells in clusters where the gene is not a marker.
“For each strong marker, it is assumed that mg, c = 0 for all cells in clusters where the gene is not a marker and the contamination fraction is estimated (Supplementary Fig. S1).”
The authors state this as an assumption.
Inaccurate estimates of the contamination fraction will have no preferred value while true estimates will cluster around the true value.
“To obtain a final value, it is assumed that inaccurate estimates will have no preferred value while true estimates will cluster around the true value.”
The authors state this as an assumption.
Droplets with ≤10 UMIs contain only ambient mRNAs.
“which we assumed to contain only ambient mRNAs”
The authors state this as an assumption.
For species-mixing genes/cells (human transcripts in mouse cells and vice versa), mg,c = 0.
“For these genes/cells it is assumed that mg, c = 0 and the contamination fraction is calculated using Equation 4.”
The authors state this as an assumption.
SoupX assumes a constant contamination fraction within a channel.
“SoupX assumes a constant contamination fraction within a channel.”
The paper relies on this without support.
In most experiments there is less power to determine cell-specific contamination fractions.
“In most experiments there is less power to determine cell-specific contamination fractions and so SoupX assumes a constant contamination fraction within a channel.”
The authors state this as an assumption.
Some genes can be assumed to be unexpressed in some cells (mg,c = 0).
“To confirm the accuracy of this estimate, we also calculated the background contamination rate using a set of genes that could be assumed to be unexpressed in some cells (i.e., where mg, c = 0).”
The paper relies on this without support.
Haemoglobin genes are highly specific to red blood cells.
“together with the knowledge that haemoglobin genes are highly specific to red blood cells.”
The paper relies on this without support.
The ambient mRNA expression profile is experiment specific.
“Because the ambient mRNA expression profile is experiment specific, we reasoned that background contamination likely creates batch effects.”
The paper relies on this without support.
Red blood cells are the only cells that produce haemoglobin under normal physiological conditions.
“Furthermore, red blood cells are the only cells that produce haemoglobin under normal physiological conditions, so for the set of haemoglobin genes, it is safe to assume that there is no cell endogenous expression for cells that are not red blood cells.”
The authors state this as an assumption.
For haemoglobin genes, there is no cell endogenous expression for cells that are not red blood cells.
“Furthermore, red blood cells are the only cells that produce haemoglobin under normal physiological conditions, so for the set of haemoglobin genes, it is safe to assume that there is no cell endogenous expression for cells that are not red blood cells.”
The authors state this as an assumption.
Inaccurate estimates of the contamination fraction are not strongly correlated (i.e., there is no preferred, incorrect estimate).
“By using all high-quality marker genes identified in the data to independently estimate the contamination fraction, this method estimates the true contamination fraction by assuming that inaccurate estimates of the contamination fraction are not strongly correlated (i.e., there is no preferred, incorrect estimate).”
The authors state this as an assumption.
For a chosen set of genes and cells, the only source of expression for these genes is background contamination.
“The best case for applying SoupX occurs when the user can specify a set of genes and cells where there is no cell endogenous expression, i.e., a set of genes and cells where it is safe to assume that the only source of expression for these genes is from background contamination.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This contamination may distort transcriptomic profiles, leading to the underrepresentation of specific cell types or misclassification of cellular states[34, 37].”
From Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing (2026) · cites this paper as [37]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Ambient RNA contamination was removed using the SoupX v1.6.2 package67 with default parameters, except for samples from Miedema et al.14 where tfidfMin was adjusted to 0.8 (default 1) due to the lower cellular complexity of the dataset (prior FACS).”
From CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis (2024) · cites this paper as [67]
The cited source says what the claim attributes to it.
In the citing paper
“Contamination of cell-free RNA for each sample group was removed using SoupX.”
From Cholesterol 25-hydroxylase mediates neuroinflammation and neurodegeneration in a mouse model of tauopathy (2024) · cites this paper as Young and Behjati, 2020
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Accurate chromatin accessibility analysis depends on precise peak calling, as calling peaks across the entire cell population can inadvertently exclude those specific to smaller subpopulations (85).”
From Molecular and cellular processes disrupted in the early postnatal Down syndrome prefrontal cortex (2026) · cites this paper as [85]
Experimental — accuracy not yet measuredIn the citing paper
“Ambient RNA contamination can lead to cell-type-specific marker gene transcripts being detectable also in other cell populations, which can blend different cell populations together21.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [21]
In the citing paper
“one should consider removing ambient RNA as an initial analysis step in quality control to improve downstream analyses for many tissues21–23.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [21]
Citations
Cites
Integrated analysis of single cell transcriptomic data across conditions, technologies, and species
Butler, A., & Satija, R. (2017) · doi:10.1101/164889 · Cited by 13,200 · cited 4×
“We include code to input the resulting cell specific expression profiles into popular downstream analysis packages (Seurat Satija et al., 2015; Butler and Satija, 2017 and SCANPY Wolf, Angerer, and Theis, 2018).”
MethodInfluentialIntegrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · cited 1×
“We used the Seurat package [18, 19] to produce a t -distributed stochastic neighbour embedding (tSNE) representation of the data and annotated clusters of cells based on the expression of canonical marker genes (Fig.”
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · cited 7×
“The 10X species-mixing dataset was the mixture of the human cell line 293T and the mouse cell line 3T3 described in [2].”
InfluentialHighly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · cited 5×
“We next investigate the properties of ambient RNA contamination in data where ground truth is available, the “speciesmixing” experiments combining mouse and human cell lines using 10X [2] and Drop-Seq technologies [14].”
InfluentialSpatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · cited 1×
“We used the Seurat package [18, 19] to produce a t -distributed stochastic neighbour embedding (tSNE) representation of the data and annotated clusters of cells based on the expression of canonical marker genes (Fig.”
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758 · cited 1×
“Attempts to detect and remove doublets are an active area of research [11–13].”
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · cited 1×
“We therefore calculated the cross-batch entropy of the kidney tumour data before and after background correction [20].”
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · cited 1×
“Moreover, large-scale efforts to create a ”Human Cell Atlas” critically depend on the accuracy and cellular specificity of the transcriptional readout produced by droplet-based scRNA-seq [9, 10].”
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Research works printed but not linked: 1
- Gayoso, 2017, DoubletDetection: Identifying technical error in single-cell RNA-sequencing data
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Items 301–400 of 1,644
Single-cell atlas of the transcriptome and chromatin accessibility in the human retina
Li, J., Wang, J., Ibarra, I. L., Cheng, X., Luecken, M. D., Lu, J., Monavarfeshani, A., Yan, W., Zheng, Y., Zuo, Z., et al. (2026) · Nature Genetics · doi:10.1038/s41588-025-02454-1 · Cited by 77
“To correct for the background transcript measurements derived from ambient RNAs that are not endogenous to cells, SoupX (version 1.6.2) was used to estimate a global contamination fraction across cells and to correct gene expression profiles by subtracting the contaminations 77 .”
InfluentialProbabilistic machine learning ensures accurate ambient denoising in droplet-based single-cell omicsPreprint
Sheng, C., Lopes, R., Li, G., Schuierer, S., Waldt, A., Cuttat, R., Dimitrieva, S., Kauffmann, A., Durand, E., Galli, G. G., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.14.476312 · Cited by 37
Soelter, T. M., Howton, T. C., Clark, A. D., Oza, V. H., & Lasseigne, B. N. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01686-8 · Cited by 34
“Since Lau et al. had increased levels of ambient RNA after alignment, we performed ambient RNA removal using SoupX [39] v1.”
MethodVinnakota, J. M., Adams, R. C., Athanassopoulos, D., Schmidt, D., Biavasco, F., Zähringer, A., Erny, D., Schwabenland, M., Langenbach, M., Wenger, V., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adj9672 · Cited by 35
An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases
Oguchi, A., Suzuki, A., Komatsu, S., Yoshitomi, H., Bhagat, S., Son, R., Bonnal, R. J. P., Kojima, S., Koido, M., Takeuchi, K., et al. (2024) · Science · doi:10.1126/science.add8394 · Cited by 40
Matera, A., Compagnion, A.-C., Pedicone, C., Kotah, J. M., Ivanov, A., Monsorno, K., Labouèbe, G., Leggio, L., Pereira-Iglesias, M., Beule, D., et al. (2025) · Immunity · doi:10.1016/j.immuni.2024.11.003 · Cited by 34
“Integration and pre-processing After pre-processing with CellRanger7, nuclei were processed per sample to adjust for ambient RNA using SoupX, 92 and then to remove predicted doublets with Scrublet.”
Maternal circadian rhythms during pregnancy dictate metabolic plasticity in offspring
Yao, N., Kinouchi, K., Katoh, M., Ashtiani, K. C., Abdelkarim, S., Morimoto, H., Torimitsu, T., Kozuma, T., Iwahara, A., Kosugi, S., et al. (2025) · Cell Metabolism · doi:10.1016/j.cmet.2024.12.002 · Cited by 39
Nguyen, R., Zhang, X., Sun, M., Abbas, S., Seibert, C., Kelly, M. C., Shern, J. F., & Thiele, C. J. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-0717 · Cited by 34
A spatial multi-omics atlas of the human lung reveals a novel immune cell survival nichePreprint
Madissoon, E., Oliver, A., Kleshchevnikov, V., Wilbrey-Clark, A., Polański, K., Orsi, A., Mamanova, L., Bolt, L., Richoz, N., Elmentaite, R., et al. (2021) · bioRxiv · doi:10.1101/2021.11.26.470108 · Cited by 30
MBD2 couples DNA methylation to transposable element silencing during male gametogenesis
Wang, S., Wang, M., Ichino, L., Boone, B. A., Zhong, Z., Papareddy, R. K., Lin, E. K., Yun, J., Feng, S., & Jacobsen, S. E. (2024) · Nature Plants · doi:10.1038/s41477-023-01599-3 · Cited by 34
Smith, J. J., Taylor, S. R., Blum, J. A., Feng, W., Collings, R., Gitler, A. D., Miller, D. M., & Kratsios, P. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113857 · Cited by 35
Spatiotemporal immune atlas of a clinical-grade gene-edited pig-to-human kidney xenotransplant
Cheung, M. D., Asiimwe, R., Erman, E. N., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47454-7 · Cited by 32
Flexiplex: a versatile demultiplexer and search tool for omics data
Cheng, O., Ling, M. H., Wang, C., Wu, S., Ritchie, M. E., Göke, J., Amin, N., & Davidson, N. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae102 · Cited by 34
“We first show Flexiplex is a reliable and general sequence searching tool using an Illumina short-read single-cell RNA-Seq dataset from Chen et al. (2021).”
BackgroundInfluentialWischnewski, S., Thäwel, T., Ikenaga, C., Kocharyan, A., Lerma-Martin, C., Zulji, A., Rausch, H.-W., Brenner, D., Thomas, L., Kutza, M., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00645-9 · Cited by 34
“After excluding all low-quality nuclei, background mRNA contamination was quantified and removed using the R package SoupX (v.1.5.2) 56 with default parameters.”
InfluentialBeliën, J., Swinnen, S., D’hondt, R., Verdú de Juan, L., Dedoncker, N., Matthys, P., Bauer, J., Vens, C., Moylett, S., & Dubois, B. (2024) · Nature Communications · doi:10.1038/s41467-024-49312-y · Cited by 32
Private information leakage from single-cell count matrices
Walker, C. R., Li, X., Chakravarthy, M., Lounsbery-Scaife, W., Choi, Y. A., Singh, R., & Gürsoy, G. (2024) · Cell · doi:10.1016/j.cell.2024.09.012 · Cited by 33
Sokolowski, E. K., Kursawe, R., Selvam, V., Bhuiyan, R. M., Thibodeau, A., Zhao, C. G., Spracklen, C. N., Ucar, D., & Stitzel, M. L. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.09.006 · Cited by 37
Montserrat-Ayuso, T., & Esteve‐Codina, A. (2024) · BMC Genomics · doi:10.1186/s12864-024-11015-5 · Cited by 37
“While there are tools [10–12] that aim to remove the ambient RNA contained in droplets, any of these methods account for the presence of nuclear content in the cells.”
de Oliveira, E. A., Milite, S., Fernández-Mateos, J., Cresswell, G. D., Romero, E. Y., Vlachogiannis, G., Chen, B., James, C. T., Patruno, L., Ascolani, G., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-25-0999 · Cited by 46
“The matrices produced from the alignment were corrected for ambient RNA using the adjustCounts function of SoupX (21).”
A single-cell RNA expression map of human coronavirus entry factorsPreprint
Singh, M., Bansal, V., & Feschotte, C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.08.084806 · Cited by 33
Gene networks and systems biology in Alzheimer's disease: Insights from multi‐omics approaches
Rahimzadeh, N., Srinivasan, S. S., Zhang, J., & Swarup, V. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.13790 · Cited by 39
“60 Technical variances in sample and library preparation, biological phenomena, or a mix of the two, can result in some cells having a low number of detected genes, a high fraction of mitochondrial counts, or low count depth.”
BackgroundWu, B., Xu, W., Wu, K., Li, Y., Hu, M., Feng, C., Zhu, C., Zheng, J., Cui, X., Li, J., et al. (2024) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02510-9 · Cited by 31
Multiplex generation and single-cell analysis of structural variants in mammalian genomes
Pinglay, S., Lalanne, J., Daza, R. M., Kottapalli, S., Quaisar, F., Koeppel, J., Garge, R. K., Li, X., Lee, D. S., & Shendure, J. (2025) · Science · doi:10.1126/science.ado5978 · Cited by 33
“First, becausewepermeabilizecellsforT7IST,our protocolmaybemoresusceptibletoambient RNAcontamination,apervasiveissueinscRNA-seq( 53 , 54 ).”
Extraction and sequencing of single nuclei from murine skeletal muscles
Santos, M. D., Gioftsidi, S., Backer, S., Machado, L., Relaix, F., Maire, P., & Mourikis, P. (2021) · STAR Protocols · doi:10.1016/j.xpro.2021.100694 · Cited by 34
“It is advised to use SoupX software (Young and Behjati, 2020) to reduce the level of back- ground of ambient RNA contamination.”
MethodCosta-da-Silva, A. C., Aure, M. H., Dodge, J. T., Martı́n, D., Dhamala, S., Cho, M., Rose, J. J., Bassim, C. W., Ambatipudi, K., Hakim, F. T., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103592 · Cited by 31
Zhang, A. M. Y., Chu, K., Daly, B. F., Ruiter, T. J. J., Dou, Y., Yang, J. C. C., de Winter, T. J. J., Chhuor, J., Wang, S., Flibotte, S., et al. (2022) · Cancer & Metabolism · doi:10.1186/s40170-022-00282-z · Cited by 32
“The ambient RNA contamination was cleaned by R package SoupX (29).”
MethodQuinn, J., Salman, A., Paluch, C., Jackson-Wood, M., McClements, M. E., Luo, J., Davis, S. J., Cornall, R. J., MacLaren, R. E., Dendrou, C. A., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-68401-y · Cited by 32
Regional epithelial cell diversity in the small intestine of pigs
Wiarda, J. E., Becker, S. R., Sivasankaran, S. K., & Loving, C. L. (2022) · Journal of Animal Science · doi:10.1093/jas/skac318 · Cited by 33
Diverse bat organoids provide pathophysiological models for zoonotic viruses
Kim, H., Heo, S.-Y., Kim, Y., Park, D., Monford Paul Abishek, N., Hwang, S., Lee, Y.-K., Jang, H. B., Ahn, J., Ha, J., et al. (2025) · Science · doi:10.1126/science.adt1438 · Cited by 31
The promise of single-cell genomics in plants
McFaline‐Figueroa, J. L., Trapnell, C., & Cuperus, J. T. (2020) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2020.04.002 · Cited by 31
Quality control and evaluation of plant epigenomics data
Schmitz, R. J., Marand, A. P., Zhang, X., Mosher, R. A., Turck, F., Chen, X., Axtell, M. J., Zhong, X., Brady, S. M., Megraw, M. S., et al. (2021) · The Plant Cell · doi:10.1093/plcell/koab255 · Cited by 28
“DNA methylation is highly enriched at transcriptionally silent regions of plant genomes such as TEs and repeats (Cokus et al., 2008; Lister et al., 2008; Feng et al., 2010; Zemach et al., 2010; Niederhuth et al., 2016).”
BackgroundMcNamara, J. W., Parker, B. L., Voges, H. K., Mehdiabadi, N. R., Bolk, F., Ahmad, F., Chung, J. D., Charitakis, N., Molendijk, J., Zech, A. T. L., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00219-9 · Cited by 30
The angiopoietin receptor Tie2 is atheroprotective in arterial endothelium
Anisimov, A., Fang, S., Hemanthakumar, K. A., Örd, T., Avondt, K. V., Chèvre, R., Toropainen, A., Singha, P., Gilani, H., Nguyen, S. D., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00224-y · Cited by 33
Transcriptional and post-transcriptional control of odorant receptor choice in ants
Brahma, A., Frank, D. D., Pastor, P. D. H., Piekarski, P., Wang, W., Luo, J., Carroll, T. S., & Kronauer, D. J. C. (2023) · Current Biology · doi:10.1016/j.cub.2023.11.025 · Cited by 30
Strunz, M., Simon, L. M., Ansari, M., Mattner, L. F., Angelidis, I., Mayr, C. H., Kathiriya, J. J., Yee, M., Ogar, P., Sengupta, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/705244 · Cited by 30
Single-Cell Transcriptomic Profiling of Human Dental Pulp in Sound and Carious Teeth: A Pilot Study
Opasawatchai, A., Nguantad, S., Sriwilai, B., Matangkasombut, P., Matangkasombut, O., Srisatjaluk, R. L., & Charoensawan, V. (2022) · Frontiers in Dental Medicine · doi:10.3389/fdmed.2021.806294 · Cited by 29
Single-Nuclei RNA-Sequencing of the Gastrocnemius Muscle in Peripheral Artery Disease
Pass, C. G., Palzkill, V. R., Tan, J., Kim, K., Thome, T., Yang, Q., Fazzone, B. J., Robinson, S. T., O’Malley, K. A., Yue, F., et al. (2023) · Circulation Research · doi:10.1161/circresaha.123.323161 · Cited by 31
Reduced adipocyte glutaminase activity promotes energy expenditure and metabolic health
Lecoutre, S., Maqdasy, S., Rizo‐Roca, D., Renzi, G., Vlassakev, I., Alaeddine, L. M., Higos, R., Jalkanen, J., Zhong, J., Zareifi, D., et al. (2024) · Nature Metabolism · doi:10.1038/s42255-024-01083-y · Cited by 33
Molecular pathways and diagnosis in spatially resolved Alzheimer’s hippocampal atlas
Wang, P., Han, L., Wang, L., Tao, Q., Guo, Z., Luo, T., He, Y., Xu, Z. P., Yu, J., Liu, Y., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.03.002 · Cited by 36
Interleukin-34-dependent perivascular macrophages promote vascular function in the brain
Hove, H. V., Glück, C., Mildenberger, W., Petrova, E., Maheshwari, U., Häne, P., Kreiner, V., Bijnen, M., Mussak, C., Utz, S. G., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.04.003 · Cited by 35
Aisenberg, W. H., O’Brien, C. A., Sangster, M., Yaqoob, F., Zhang, Y., Temsamrit, B., Thom, S., Gosse, L., Chaluvadi, V. S., Elfayomi, B., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.03.019 · Cited by 31
Melton, R. L., Jiménez, S., Elison, W. S., Tucciarone, L., Howell, A., Wang, G., Berti, D. A., Beebe, E., Miller, M., Zeng, C., et al. (2025) · Science Advances · doi:10.1126/sciadv.ady0080 · Cited by 36
Rondini, E. A., Ramseyer, V. D., Burl, R. B., Piqué-Regi, R., & Granneman, J. G. (2021) · Molecular Metabolism · doi:10.1016/j.molmet.2021.101307 · Cited by 28
ELF5 is a potential respiratory epithelial cell-specific risk gene for severe COVID-19
Pietzner, M., Chua, R. L., Wheeler, E., Jechow, K., Willett, J., Radbruch, H., Trump, S., Heidecker, B., Zeberg, H., Heppner, F. L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31999-6 · Cited by 30
Probing cell identity hierarchies by fate titration and collision during direct reprogramming
Hersbach, B. A., Fischer, D. S., Masserdotti, G., Deeksha, Mojžišová, K., Waltzhöni, T., Rodriguez‐Terrones, D., Heinig, M., Theis, F. J., Götz, M., et al. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211129 · Cited by 26
“Furthermore, between 1 and 10 PiggyBac vectors typically integrate, leading to a broad range of transcript expression between different cells (Yusa et al , 2009).”
Sen, T., Ju, W., Nair, V., Ladd, P. E., Menon, R., Otto, E. A., Pyle, L. L., Vigers, T., Nelson, R. G., Arnott, C. G., et al. (2023) · Kidney International · doi:10.1016/j.kint.2023.07.007 · Cited by 31
Single-cell immune profiling reveals immune responses in oral lichen planus
Li, Q., Wang, F., Shi, Y., Zhong, L., Duan, S., Kuang, W., Liu, N., Luo, E., Zhou, Y., Jiang, L., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1182732 · Cited by 31
A practical handbook on single-cell RNA sequencing data quality control and downstream analysis
Kim, G. D., Lim, C., & Park, J. (2024) · Molecules and Cells · doi:10.1016/j.mocell.2024.100103 · Cited by 33
Lay, A. C., Tran, V. D. T., Nair, V., Betin, V. M. S., Hurcombe, J., Barrington, A. F., Pope, R. J. P., Burdet, F., Mehl, F., Kryvokhyzha, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54089-1 · Cited by 31
Modeling early gastrulation in human blastoids with DNA methylation patterns of natural blastocysts
Xie, H., An, C., Bai, B., Luo, J., Sun, N., Ci, B., Jin, L., Mo, P., Lu, Y., Zhong, K., et al. (2025) · Cell stem cell · doi:10.1016/j.stem.2024.12.010 · Cited by 32
Aarrestad, I. K., Cameron, L. P., Fenton, E. M., Casey, A. B., Rijsketic, D. R., Patel, S. D., Sambyal, R., Johnson, S. B., Ly, C., Viswanathan, J., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02021-1 · Cited by 33
“Reads attributed to contamination from nuclei preparation were computationally removed with the SoupX v.1.5.0 algorithm 95 .”
InfluentialLiu, N. Q., Lin, Y., Li, L., Lu, J., Geng, D., Zhang, J., Jashashvili, T., Buser, Z., Magallanes, J., Tassey, J., et al. (2022) · Communications Biology · doi:10.1038/s42003-021-02944-y · Cited by 28
The astroglial and stem cell functions of adult rat folliculostellate cells
Fletcher, P. A., Smiljanić, K., Prévide, R. M., Constantin, S., Sherman, A., Coon, S. L., & Stojilković, S. S. (2022) · Glia · doi:10.1002/glia.24267 · Cited by 28
“Cell type identities were used as input clustering for SoupX and used the automatic estimation mode on each sample.”
MethodInfluentialBerg, M., Petoukhov, I., van den Ende, I., Meyer, K. B., Guryev, V., Vonk, J. M., Carpaij, O. A., Banchero, M., Hendriks, R. W., van den Berge, M., et al. (2023) · BMC Genomics · doi:10.1186/s12864-023-09822-3 · Cited by 35
“This despite having corrected for ambient RNA using SoupX [4].”
MethodResultKim, B., Dabin, L. C., Tate, M. D., Karahan, H., Sharify, A. D., Acri, D. J., Al-Amin, M. M., Philtjens, S., Smith, D. C., Wijeratne, H. R. S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48484-x · Cited by 28
Hermans, F., Bueds, C., Hemeryck, L., Lambrichts, I., Bronckaers, A., & Vankelecom, H. E. J. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1021459 · Cited by 27
Toker, I. A., Ripoll-Sánchez, L., Geiger, L. T., Sussfeld, A., Saini, K. S., Beets, I., Vértes, P. E., Schafer, W. R., Ben‐David, E., & Hobert, O. (2025) · Current Biology · doi:10.1016/j.cub.2025.05.036 · Cited by 36
“6.2) 109 to correct for background contamination by ambient RNAs, reprocessing the datasets after each step.”
InfluentialMajane, A. C., Cridland, J. M., & Begun, D. J. (2021) · Genetics · doi:10.1093/genetics/iyab213 · Cited by 28
Arora, J. K., Opasawatchai, A., Poonpanichakul, T., Jiravejchakul, N., Sungnak, W., Sakuntabhai, A., Singhasivanon, P., Suraamornkul, S., Yingtaweesak, T., Manopwisedjaroen, K., et al. (2022) · iScience · doi:10.1016/j.isci.2022.104034 · Cited by 26
“0.1 (Young and Behjati, 2020) https://github.com/constantAmateur/SoupX DoubletFinder v2.”
MethodInfluentialEmbryonic and neonatal waves generate distinct populations of hepatic ILC1s
Sparano, C., Solís-Sayago, D., Vijaykumar, A., Rickenbach, C., Vermeer, M., Ingelfinger, F., Litscher, G., Fonseca, A., Mussak, C., Mayoux, M., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abo6641 · Cited by 29
“As a first step, ambient RNA was removed using the SoupX algorithm (71).”
MethodSlota, J. A., Sajesh, B. V., Frost, K. F., Medina, S. J., & Booth, S. A. (2022) · Acta Neuropathologica Communications · doi:10.1186/s40478-022-01450-4 · Cited by 28
Adrenergic signaling coordinates distant and local responses to amputation in axolotl
Payzin‐Dogru, D., Froitzheim, T., Blair, S. J., Jena, S. G., Singer, H. D., Paoli, J. C., Kim, R. T., Kriukov, E., Wilson, S., Hou, R., et al. (2025) · Cell · doi:10.1016/j.cell.2025.09.025 · Cited by 26
Kolmogorov, M., Billingsley, K. J., Mastoras, M., Meredith, M., Monlong, J., Lorig-Roach, R., Asri, M., Jerez, P. Á., Malik, L., Dewan, R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.12.523790 · Cited by 27
Comparative analysis of multiplexed in situ gene expression profiling technologiesPreprint
Hartman, A., & Satija, R. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.11.575135 · Cited by 36
“We therefore anticipate that new computational methods, analogous to those that control for ambient RNA in scRNA-seq 38,42 , will be able to account for and correct for non-specific biases that arise in downstream analyses.”
MethodSingle-cell atlas of human pancreatic islet and acinar endothelial cells in health and diabetes
Craig‐Schapiro, R., Li, G., Chen, K., Gómez-Salinero, J. M., Nachman, R., Kopacz, A., Schreiner, R. P., Chen, X., Zhou, Q., Rafii, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55415-3 · Cited by 34
“Ambient RNAadjustmentwasperformedusingR packageSoupX 19 usingdefault settings (20% contamination estimation).”
InfluentialO’Neill, M. B., Quach, H., Pothlichet, J., Aquino, Y., Bisiaux, A., Zidane, N., Deschamps, M., Libri, V., Hasan, M., Zhang, S., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.768189 · Cited by 27
“com/constantAmateur/ SoupX, accessed November 28, 2019) (21), using estimated contamination fractions (per 10X library) from SoupOrCell (53).”
BackgroundMethodInfluentialChen, K., Zhang, J., Huang, Y., Tian, X., Yang, Y., & Dong, A. (2022) · iScience · doi:10.1016/j.isci.2022.105366 · Cited by 26
“Nevertheless, ambient RNA in the cell suspension will be counted along with a cell’s native mRNA, resulting in cross-contamination between different cell populations.(40,41) We observed obvious ambient RNA contamination in endocrine cells, instead of in other cell types, in scRNA-seq datasets from open databases.”
Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection
Heimli, M., Flåm, S. T., Hjorthaug, H. S., Trinh, D., Frisk, M., Dumont, K.-A., Ribarska, T., Tekpli, X., Saare, M., & Lie, B. A. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1092028 · Cited by 27
“2 (99) and doublet scores were calculated by scDblFinder v.”
MethodIntegrated multi-omics single cell atlas of the human retinaPreprint
Li, J., Wang, J., Ibarra, I. L., Cheng, X., Luecken, M. D., Lu, J., Monavarfeshani, A., Yan, W., Zheng, Y., Zuo, Z. C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.07.566105 · Cited by 30
Neutrophil metalloproteinase driven spleen damage hampers infection control of trypanosomiasis
Phạm, H. T. T., Magez, S., Choi, B., Baatar, B., Jung, J., & Radwanska, M. (2023) · Nature Communications · doi:10.1038/s41467-023-41089-w · Cited by 25
“…CellRanger with the default SoupX (version 1.5.2, Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK) work fl ow (autoEstCounts and adjustCounts) 70 in RStudio (version 1.3.1093, RStudio, Inc., Boston, Massachusetts, USA), before being processed using Seurat (version 4.0.5, New York Genome…”
InfluentialSingle-cell transcriptomics in tissue engineering and regenerative medicine
Ruta, A., Krishnan, K., & Elisseeff, J. H. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00132-7 · Cited by 25
“Ambient RNA removal algorithms, such as SoupX 24 , can further reduce noise from RNA of the cell suspension, which may contaminate individual cell droplets.”
Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification
Zhong, H., Zhou, X., Uhm, H., Jiang, Y., Cao, H., Chen, Y., Mak, T. T. W., Lo, R. M. N., Wong, B. W. Y., Cheng, E. Y. L., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.13691 · Cited by 25
E-cadherin loss drives diffuse-type gastric tumorigenesis via EZH2-mediated reprogramming
Zou, G., Huang, Y., Zhang, S., Ko, K.-P., Kim, B.-J., Zhang, J., Venkatesan, V., Pizzi, M. P., Fan, Y., Jun, S., et al. (2024) · The Journal of Experimental Medicine · doi:10.1084/jem.20230561 · Cited by 28
Balakumar, A., Das, D., Datta, A., Mishra, A., Bryak, G., Ganesh, S. M., Netea, M. G., Kumar, V., Lionakis, M. S., Arora, D., et al. (2024) · PLOS Pathogens · doi:10.1371/journal.ppat.1012699 · Cited by 25
“To address the ambient RNA contamination SoupX version 1.5.2[54] was utilized to identify and remove ambient RNA background noise from the data.”
InfluentialHOX gene expression in the developing human spine
Lawrence, J. E. G., Roberts, K., Tuck, E. J., Li, T., Mamanova, L., Balogh, P., Usher, I., Piapi, A., Mazin, P., Anderson, N. D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54187-0 · Cited by 29
Self-citationCellular and molecular architecture of submucosal glands in wild-type and cystic fibrosis pigs
Yu, W., Moninger, T. O., Thurman, A. L., Xie, Y., Jain, A., Zarei, K., Powers, L. S., Pezzulo, A. A., Stoltz, D. A., & Welsh, M. J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2119759119 · Cited by 28
“Matrix data were subjected to ambient RNA correction using the SoupX R package (72), doublet filtering using the Scrublet Python package (73), and dead and low-quality cellfiltering in Seurat (74).”
MethodSingle-Cell Analysis of the Transcriptome and Epigenome
Mazan-Mamczarz, K., Ha, J., De, S., & Sen, P. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1831-8_3 · Cited by 27
A contamination focused approach for optimizing the single-cell RNA-seq experiment
Arceneaux, D., Chen, Z., Simmons, A. J., Heiser, C. N., Southard-Smith, A. N., Brenan, M. J., Yang, Y., Chen, B., Xu, Y., Choi, E.-Y. K., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107242 · Cited by 29
“DecontX, SoupX, and CellBender are tools for factoring out the contribution of ambient RNA in the resultant counts data matrix.(12,21,22) However, it cannot be assumed that post-processing algorithms can be relied on to turn all poor-quality data to high-quality data, because algorithms are built on certain assumption…”
BackgroundMethodInfluentialDissecting the cellular architecture and genetic circuitry of the soybean seed
Pelletier, J. M., Chen, M., Lin, J.-Y., Le, B. H., Kirkbride, R. C., Hur, J., Wang, T., Chang, S.-H., Olson, A., Nikolov, L. A., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2416987121 · Cited by 26
Oncogene aberrations drive medulloblastoma progression, not initiation
Okonechnikov, K., Joshi, P. K., Körber, V., Rademacher, A., Bortolomeazzi, M., Mallm, J., Vaillant, J., da Silva, P. B. G., Statz, B., Sepp, M., et al. (2025) · Nature · doi:10.1038/s41586-025-08973-5 · Cited by 27
“Filtered cells were corrected for background signature using the SoupX pipeline 41 .”
Prummel, K. D., Woods, K., Kholmatov, M. M., Schmitt, E. C., Vlachou, E. P., Labyadh, M., Wehner, R., Poschmann, G., Stühler, K., Winter, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65803-y · Cited by 30
Hobson, R., Levy, S. H. S., Singal, C. M. S., Flaherty, D., Xiao, H., Ciener, B., Reddy, H., Zabinyakov, N., Kim, C. Y., Teich, A. F., et al. (2026) · Nature Immunology · doi:10.1038/s41590-025-02401-6 · Cited by 42
Natural killer cells act as an extrinsic barrier for in vivo reprogramming
Melendez, E., Chondronasiou, D., Mosteiro, L., de Villarreal, J. M., Fernández‐Alfara, M., Lynch, C. J., Grimm, D., Real, F. X., Alcamı́, J., Climent, N., et al. (2022) · Development · doi:10.1242/dev.200361 · Cited by 26
In VivoProfiling of Individual Multiciliated Cells during Acute Influenza A Virus Infection
Hamele, C. E., Russell, A. B., & Heaton, N. S. (2022) · Journal of Virology · doi:10.1128/jvi.00505-22 · Cited by 24
“The raw and filtered output files from the CellRanger pipeline were read into SoupX (71) (v1.”
MethodPractical Considerations for Single‐Cell Genomics
Regan, C., & Preall, J. B. (2022) · Current Protocols · doi:10.1002/cpz1.498 · Cited by 27
“Several statistical and machine learning–based tools have been published that perform subtraction of the estimated ambient RNA fraction (Fleming, Marioni, & Babadi, 2019; Yang et al., 2020; Young & Behjati, 2020), and produce improved differential expression analysis and data integration results.”
BackgroundSmirnova, N. F., Riemondy, K. A., Bueno, M., Collins, S., Suresh, P., Wang, X., Patel, K., Cool, C. D., Königshoff, M., Sharma, N., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.156648 · Cited by 28
“The R package SoupX was used to remove free RNA contamination (68), using Hbb-bs, Hba-a1, Hba-a2, Hbb-bt, H2-Ab1, H2-Aa, H2-Ab1, and H2-Eb1 as markers of erythrocytes; Scgb1a1 as a club cell marker; Cd74, Ighg2c, Igkc, Ighm, and Ighg3 as markers of B cells; Sftpc as a marker of type II cells; Apoe, Lyz2, and Psap as m…”
Wei, M., Sun, Y., Li, S., Chen, Y., Li, L., Fang, M., Shi, R., Tong, D., Chen, J., Ma, Y., et al. (2023) · Protein & Cell · doi:10.1093/procel/pwad007 · Cited by 25
“5.0) (Young and Behjati, 2020).”
InfluentialTransplanted human intestinal organoids: a resource for modeling human intestinal development
Singh, A., Poling, H. M., Chaturvedi, P., Thorner, K., Sundaram, N., Kechele, D. O., Childs, C. J., McCauley, H. A., Fisher, G. W., Brown, N. E., et al. (2023) · Development · doi:10.1242/dev.201416 · Cited by 22
CXCL12 defines lung endothelial heterogeneity and promotes distal vascular growth
Chandrasekaran, P., Negretti, N. M., Sivakumar, A., Liberti, D. C., Wen, H., de Nieuwburgh, M. P., Wang, J. Y., Michki, S. N., Chaudhry, F. N., Kaur, S., et al. (2022) · Development · doi:10.1242/dev.200909 · Cited by 26
Enriched Single-Nucleus RNA-Sequencing Reveals Unique Attributes of Distal Convoluted Tubule Cells
Su, X., Reyes, J. V., Lackey, A. E., Demirci, H., Bachmann, S., Maeoka, Y., Cornelius, R. J., McCormick, J. A., Yang, C., Jung, H. J., et al. (2024) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000297 · Cited by 22
Chen, S., Chu, C., Wang, C., Yang, Y., Xu, Z.-Y., Qu, Y., & Man, Y. (2024) · Biomaterials · doi:10.1016/j.biomaterials.2024.122685 · Cited by 23
Are we there yet? Exploring astrocyte heterogeneity one cell at a time
O’Dea, M. R., & Hasel, P. (2024) · Glia · doi:10.1002/glia.24621 · Cited by 27
Qadir, M. M. F., Elgamal, R. M., Song, K., Kudtarkar, P., Sakamuri, S. S. V. P., Katakam, P. V. G., El-Dahr, S. S., Kolls, J. K., Gaulton, K. J., & Mauvais‐Jarvis, F. (2024) · The EMBO Journal · doi:10.1038/s44318-024-00313-z · Cited by 25
“We used SoupX 1.6.1 (Young and Behjati, 2020) on raw feature barcode matrices correcting for ambient RNA across all 52 donors.”
InfluentialGinkgolide B increases healthspan and lifespan of female mice
Lee, C., Wang, B. Y., Wong, S. H., Chen, Y., Cao, Q., Hsiao, A. W., Fung, S.-H., Chen, Y.-F., Wu, H.-H., Cheng, P.-Y., et al. (2025) · Nature Aging · doi:10.1038/s43587-024-00802-0 · Cited by 26
Lee, D., Shahandeh, M. P., Abuin, L., & Benton, R. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003120 · Cited by 22
“6.2, default parameters) [63] to remove ambient RNA contamination.”
InfluentialSimon, F., & Konstantinides, N. (2021) · Developmental Biology · doi:10.1016/j.ydbio.2021.08.001 · Cited by 19
Evaluating microglial phenotypes using single-cell technologies
Sankowski, R., Monaco, G., & Prinz, M. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.11.001 · Cited by 25
Yeung, K., Raja, K. K. B., Shim, Y.-K., Li, Y., Chen, R., & Mardon, G. (2022) · Communications Biology · doi:10.1038/s42003-022-04337-1 · Cited by 23
Intratumoral erythroblastic islands restrain anti-tumor immunity in hepatoblastoma
Wang, Y., Xiang, X., Chen, H., Zhou, L., Chen, S., Zhang, G., Liu, X., Ren, X., Liu, J., Kuang, M., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101044 · Cited by 26
Read, D. F., Booth, G. T., Daza, R. M., Jackson, D. L., Gladden, R. G., Srivatsan, S. R., Ewing, B., Franks, J. M., Spurrell, C. H., Gomes, A. R., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06582-y · Cited by 22
“Additionally, in order to reduce background RNA levels that commonly contribute noise to single-cell RNA-Seq data 25 we included a FACS sorting step following ligation.”
Metadata
- Authors
- Young, M. D., & Behjati, S.
- Affiliations
- Wellcome Sanger Institute (GB)
- University of Cambridge (GB)
- Corresponding author
- Young, Matthew D
- Venue
- GigaScience
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-12
- Volume
- 9
- Issue
- 12
- ISSN
- 2047-217X (ISSN-L)
- PMC
- PMC7763177
- OpenAlex
- W3114078084
- Semantic Scholar
- e2af27bc9dc56dffc8b7cdf12c4bf22787407d0e
- Cited by
- 1,619
- References
- 30
- Citations by year
- 2026: 358 · 2025: 388 · 2024: 347 · 2023: 211 · 2022: 149 · 2021: 86 · 2020: 54
- FWCI
- 35.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- GigaScience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (submittedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- Funders
- Wellcome Trust
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- cell-free RNA, background correction, single-cell transcriptomics, quality control, contamination quantification
- MeSH terms
- Base Sequence, RNA, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, Transcriptome
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