The DOI matches its Crossref record; 261 of the 269 references it cites were found in a registry or an index; 6 of the 146 sources its claims cite were checked for support.
Best practices for single-cell analysis across modalities
Open accessAbstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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100 Not checked yet · 19 No basis given · 12 Own interpretation · 3 Supported · 1 Partly supported · 1 Own method · accuracy not yet measured
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In the paper
“For this purpose, Wilcoxon rank-sum tests performed best, but owing to the nature of clustering, P values can be inflated and might lead to false discoveries, as the same data are used to define the labels that we test for differences between60,61.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From clusters to cell identities
What the sources say
- Supportedcites [60] Pullin, J. M. & McCarthy, D. J. A comparison of marker gene selection methods for single-cell RNA sequencing data. bioRxiv https://doi.org/10.1101/2022.05.09.490241 (2022). source
The Discussion identifies methods based on the Wilcoxon rank-sum test as among the best-performing methods for selecting marker genes. It names Student’s t-test and logistic regression alongside Wilcoxon, so the finding does not establish Wilcoxon as the sole best method. (LLM, conf=0.88)
- Partly supportedcites [61] Zhang, J. M., Kamath, G. M. & Tse, D. N. Valid post-clustering differential analysis for single-cell RNA-seq. Cell Syst. 9, 383–392.e6 (2019). sourceExperimental — accuracy not yet measured
The Methods section identifies the Wilcoxon rank-sum test as a differential-expression method, and the Results discuss its use in selecting genes. But the paper does not say it performed best for identifying marker genes; the Results instead warn that the compared tests do not account for clustering-selection bias. No page numbers are available in our copy. (LLM, conf=0.82)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Annotation results obtained with pre-trained classifiers are strongly affected by the classifier type and the quality of the training data used to create the classifier54,55.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From clusters to cell identities
What the sources say
- Supportedcites [54] Abdelaal, T. et al. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol. 20, 194 (2019). source
The Results sections on annotation levels and inter-dataset evaluation show that classification performance varies by classifier and by the data used for training, including its annotation depth and sequencing protocol. The Discussion also identifies technical differences between datasets and mismatched cell-population annotations as challenges for cross-dataset classification. These findings support the claim about annotation results from trained classifiers. (LLM, conf=0.87)
- Supportedcites [55] Pasquini, G., Rojo Arias, J. E., Schäfer, P. & Busskamp, V. Automated methods for cell type annotation on scRNA-seq data. Comput. Struct. Biotechnol. J. 19, 961–969 (2021). source
In “Annotation by supervised classification” (page not provided), the review says that outliers and redundant features in training data harm classification accuracy, and that meaningful features and sufficient label representation matter. Its “Summary and outlook” (page not provided) reports that methods have different advantages and that combining tool types can improve accuracy. Together, these support the paraphrase that annotation results depend on classifier choice and training-data quality. (LLM, conf=0.87)
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- Supported
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In the paper
“Current methods for DGE analysis still show a trade-off between true positive rate (TPR) and precision.”
§ Best practices for single-cell analysis across modalities > Transcriptome > Revealing mechanisms
What the sources say
- Supportedcites [83] Wang, T., Li, B., Nelson, C. E. & Nabavi, S. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinformatics 20, 40 (2019). source
The Discussion’s ‘Detection accuracy’ section says that tools with higher sensitivity also show lower precision. The Conclusions explicitly describe a trade-off between true-positive rates and precision in calling differentially expressed genes. (LLM, conf=0.94)
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- Supported
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In the paper
“Moreover, low numbers of reads in peak versus non-peak regions are indicators for low signal-to-noise ratios similar to TSS scores9.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Feature definition and quality control
What the sources say
- Supportedcites [9] Baek, S. & Lee, I. Single-cell ATAC sequencing analysis: from data preprocessing to hypothesis generation. Comput. Struct. Biotechnol. J. 18, 1429–1439 (2020). This article serves as an introduction to the scATAC-seq analysis workflow. source
The Data preprocessing > Quality control section (page unavailable) lists fraction of reads in peaks (FRiP) and TSS enrichment together as measures used to select scATAC-seq barcodes. This supports the citing sentence’s comparison of peak-read proportion with TSS scores as quality indicators; the paper does not use the phrase “signal-to-noise ratio” there. (LLM, conf=0.84)
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“one should consider removing ambient RNA as an initial analysis step in quality control to improve downstream analyses for many tissues21–23.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [21] Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020). source
- Not checked yetcites [22] Fleming, S. J. et al. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. bioRxiv https://doi.org/10.1101/791699 (2022). source
- Not checked yetcites [23] Yang, S. et al. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol. 21, 57 (2020). source
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In the paper
“The shifted logarithm transformation with size factor s performs well but should not be used with counts per million as an input, as it reflects an unrealistically large overdispersion.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [29] Ahlmann-Eltze, C. & Huber, W. Comparison of transformations for single-cell RNA-seq data. bioRxiv https://doi.org/10.1101/2021.06.24.449781 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“although computational best practices and tool recommendations have previously been outlined for scRNA-seq2,5,6,17, they are either outdated or incomplete.”
§ Best practices for single-cell analysis across modalities > Introduction
What the sources say
- Not checked yetcites [2] Amezquita, R. A. et al. Orchestrating single-cell analysis with bioconductor. Nat. Methods 17, 137–145 (2020). source
- Not checked yetcites [5] Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019). This scRNA-seq-focused review and workflow template served as inspiration for the current benchmark-based Expert Recommendation. source
- Not checked yetcites [6] Kharchenko, P. V. The triumphs and limitations of computational methods for scRNA-seq. Nat. Methods 18, 723–732 (2021). The article highlights limitations of computational methods, which analysts should be aware of. source
- Not checked yetcites [17] Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019). A review of the advent of multimodal single-cell data with a focus on the experimental assays and data integration. source
plain- Asserted: passes — The authors put it forward as true.
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In the 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.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [21] Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The shifted logarithm transformation with size factor s performs well but should not be used with counts per million as an input, as it reflects an unrealistically large overdispersion.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [29] Ahlmann-Eltze, C. & Huber, W. Comparison of transformations for single-cell RNA-seq data. bioRxiv https://doi.org/10.1101/2021.06.24.449781 (2022). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“a recent study showed that relying only on 2D embeddings can lead to misinterpretation of the relationships between cells”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [49] Chari, T., Banerjee, J. & Pachter, L. The specious art of single-cell genomics. bioRxiv https://doi.org/10.1101/2021.08.25.457696 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 24
- It may state two separate claims as one. 45
In the paper
“leading to more than 1,400 tools currently being available to analyse scRNA-seq data1”
§ Best practices for single-cell analysis across modalities > Introduction
What the sources say
- Not checked yetcites [1] Zappia, L. & Theis, F. J. Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape. Genome Biol. 22, 301 (2021). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Several benchmarks have highlighted that scDblFinder outperforms other methods in terms of doublet detection accuracy and computational efficiency25–27.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [25] Xi, N. M. & Li, J. J. Protocol for executing and benchmarking eight computational doublet-detection methods in single-cell RNA sequencing data analysis. Star. Protoc. 2, 100699 (2021). source
- Not checked yetcites [26] Xi, N. M. & Li, J. J. Benchmarking computational doublet-detection methods for single-cell RNA sequencing data. Cell Syst. 12, 176–194.e6 (2021). source
- Not checked yetcites [27] Neavin, D. et al. Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods. bioRxiv https://doi.org/10.1101/2022.03.07.483367 (2022). source
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In the paper
“Additionally, it can be beneficial to apply multiple doublet detection methods and compare the results to increase the accuracy of doublet detection27.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [27] Neavin, D. et al. Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods. bioRxiv https://doi.org/10.1101/2022.03.07.483367 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Louvain’s successor Leiden circumvents this issue by yielding guaranteed connected communities and is computationally more efficient52.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From clusters to cell identities
What the sources say
- Not checked yetcites [52] Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“As it is splicing kinetics that are modelled, the observed process must also occur during this time frame70.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From clusters to cell identities
What the sources say
- Not checked yetcites [70] Bergen, V., Soldatov, R. A., Kharchenko, P. V. & Theis, F. J. RNA velocity-current challenges and future perspectives. Mol. Syst. Biol. 17, e10282 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Although Slingshot62 performed better for simple topologies, PAGA63 and RaceID/StemID64 scored better for complex trajectories65.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From clusters to cell identities
What the sources say
- Not checked yetcites [65] Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods: towards more accurate and robust tools. Nat. Biotechnol. 37, 547–554 (2019). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“scANVI40 can incorporate the cell-type labels, which is favourable as it can help to conserve biological variation35.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [35] Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022). source
plainconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“analytical Pearson residuals are better suited for selection of biologically variable genes and identification of rare cell identities31.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [31] Lause, J., Berens, P. & Kobak, D. Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data. Genome Biol. 22, 258 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, if a gene includes multiple, pronounced kinetics, lineage-specific models are more appropriate69.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From clusters to cell identities
What the sources say
- Not checked yetcites [69] Weiler, P., Van den Berge, K., Street, K. & Tiberi, S. A guide to trajectory inference and RNA velocity. Methods Mol. Biol. 2584, 269–292 (2023). source
plainconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“It performed favourably for identifying genes with high variance across subpopulations and thus for selecting informative genes, as shown in an independent comparison20.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
What the sources say
- Not checked yetcites [20] Germain, P.-L., Sonrel, A. & Robinson, M. D. pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools. Genome Biol. 21, 227 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“Under the assumption of constant rates, phase portraits form an almond shape with induction (upper half/arc) and repression (lower half/arc) phases.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From clusters to cell identities
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- The authors state this as an assumption: RNA velocity inference assumes gene independence and constant rates of transcription, splicing and degradation.
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
“Additionally, ranking genes by deviance is performed on raw counts and is therefore not sensitive to normalization.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Gene expression is controlled by a complex interplay of regulatory mechanisms, including epigenetics and chromatin accessibility132.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility
What the sources say
- Not checked yetcites [132] Isbel, L., Grand, R. S. & Schübeler, D. Generating specificity in genome regulation through transcription factor sensitivity to chromatin. Nat. Rev. Genet. 23, 728–740 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Single-cell-specific methods were found to systematically underestimate the variance of gene expression and to be prone to wrongly labelling highly expressed genes as differentially expressed86.”
§ Best practices for single-cell analysis across modalities > Transcriptome > Revealing mechanisms
What the sources say
- Not checked yetcites [86] Squair, J. W. et al. Confronting false discoveries in single-cell differential expression. Nat. Commun. 12, 5692 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“for which tools have not yet been independently benchmarked.”
§ Best practices for single-cell analysis across modalities > Transcriptome > Revealing mechanisms
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Owing to the lack of consensus between tools, we recommend using LIANA, which provides an overall ranking for several combinations of method and database126.”
§ Best practices for single-cell analysis across modalities > Transcriptome > Revealing mechanisms
What the sources say
- Not checked yetcites [126] Dimitrov, D. et al. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-seq data. Nat. Commun. 13, 3224 (2022). source
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) measures genome-wide chromatin accessibility in individual cells133,134”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility
What the sources say
- Not checked yetcites [133] Cusanovich, D. A. et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015). source
- Not checked yetcites [134] Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015). source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Reducing K for the KNN graph or constructing a graph on particular lineages mitigates this issue112.”
§ Best practices for single-cell analysis across modalities > Transcriptome > Revealing mechanisms
What the sources say
- Not checked yetcites [112] Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40, 245–253 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Most workflows use a cell-by-peak or cell-by-bin matrix as a basis for analysis, which performs better than matrices of gene or TF motif features135”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Feature definition and quality control
What the sources say
- Not checked yetcites [135] Chen, H. et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data. Genome Biol. 20, 241 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Although the performance on scATAC-seq data has not been benchmarked yet, existing benchmarks on bulk ATAC-seq data recommend edgeR for the determination of DARs when sample size is limited and DESeq2 in the case of large sample sizes146.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Annotating cell identities based on accessible regions
What the sources say
- Not checked yetcites [146] Gontarz, P. et al. Comparison of differential accessibility analysis strategies for ATAC-seq data. Sci. Rep. 10, 10150 (2020). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“This approach can be extended by inferring active TFs using SCENIC followed by matching the corresponding motifs with peak regions to add additional interpretability156.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Linking single-cell chromatin accessibility and transcriptomics
What the sources say
- Not checked yetcites [156] Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Notably, the cell-by-peak matrix is even more sparse than scRNA-seq data, with only 1–10% of peaks called in each cell owing to the presence of only two copies of assayable chromatin in cells of a diploid organism135.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Feature definition and quality control
What the sources say
- Not checked yetcites [135] Chen, H. et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data. Genome Biol. 20, 241 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“TF-motif enrichment facilitates the characterization of cell identity and can be conducted on a cluster level using a hypergeometric test on cluster-specific DARs140.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Unravelling identities with TF motifs and footprinting
What the sources say
- Not checked yetcites [140] Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Identifying peaks requires a sufficient number of cells and therefore may fail in rare cell types136.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Feature definition and quality control
What the sources say
- Not checked yetcites [136] Fang, R. et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Although advancements in experimental assays allow for the paired measurements of many modality combinations246, different modalities are still commonly measured independently, resulting in unpaired data247.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Linking single-cell chromatin accessibility and transcriptomics
What the sources say
- Not checked yetcites [246] Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. 39, 1246–1258 (2021). A review on multimodal data integration that further categorizes methods into different types of data integration task as for example used here. source
- Not checked yetcites [247] Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“However, this may also remove biological information and therefore modelling of scATAC counts directly has been suggested142.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Learning a low-dimensional representation
What the sources say
- Not checked yetcites [142] Martens, L. D., Fischer, D. S., Theis, F. J. & Gagneur, J. Modeling fragment counts improves single-cell ATAC-seq analysis. bioRxiv https://doi.org/10.1101/2022.05.04.490536 (2022). source
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In the paper
“The sensitivity of peak detection can be improved by calling them within clusters, which reduces the risk of missing peaks in rare cell types masked by the noise of other highly abundant cell types.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Feature definition and quality control
What the sources say
- Not checked yetcites [136] Fang, R. et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“To guide cell-type annotation, simple models are often sufficient, and visualization can be enhanced by smoothing gene activity scores among neighbouring cells, which is often performed using MAGIC152.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Annotating cell identities based on accessible regions
What the sources say
- Not checked yetcites [152] van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Low-quality cells often form a cluster combining low counts and low TSS enrichment scores that should be removed137.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Feature definition and quality control
What the sources say
- Not checked yetcites [137] Ou, J. et al. ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data. BMC Genomics 19, 169 (2018). source
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In the paper
“Although totalVI and MultiVI can also be used for mosaic integration, they are both applicable only to CITE-seq and Multiome data, respectively.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Linking single-cell chromatin accessibility and transcriptomics
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“It has been shown to work well for the integration of more than two modalities and is the winner of the NeurIPS 2021 multimodal single-cell data integration challenge252.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Linking single-cell chromatin accessibility and transcriptomics
What the sources say
- Not checked yetcites [252] Lance, C. et al. Multimodal single cell data integration challenge: results and lessons learned. in Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track (eds Kiela, D., Ciccone, M. & Caputo, B.) vol. 176 162–176 (PMLR, 2022). source
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In the paper
“DSB uses background droplets that represent protein background noise to correct values in cells while removing cell-to-cell variation by combining isotype control levels with the specific background level of the respective cell.”
§ Best practices for single-cell analysis across modalities > Surface protein expression > Accounting for ADT composition biases
What the sources say
- Not checked yetcites [161] Mulè, M. P., Martins, A. J. & Tsang, J. S. Normalizing and denoising protein expression data from droplet-based single cell profiling. Nat. Commun. 13, 2099 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“To capture the vast range of antigens, somatic V(D)J recombination generates highly diverse AIR sequences across the population of B and T cells in an individual”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“However, cells with more than two assignments for either VJ or VDJ chains are always indicative of doublets.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Filtering for functional adaptive immune receptors
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Probably not a substantial claim. The paper may give no reason why it holds.
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
“By contrast, private clonotypes represent patient-specific clonal responses that might be valuable for personalized medicine.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Identifying and classifying clonotypes
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Clonotypes can be identified by identical V gene and identical nucleic acid sequences for VJ and VDJ CDR3 for TCRs or based on distance as implemented in the analysis frameworks for lineage reconstruction of BCRs accounting for somatic hypermutation”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Identifying and classifying clonotypes
plain- Asserted: passes — The authors put it forward as true.
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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
“Public clonotypes appear in more than one donor and can represent shared immunological response.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Identifying and classifying clonotypes
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Lymphocytes can express dual AIRs173 with ~10% expressing multiple VJ chains paired with a single VDJ chain.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Filtering for functional adaptive immune receptors
What the sources say
- Not checked yetcites [173] Schuldt, N. J. & Binstadt, B. A. Dual TCR T cells: identity crisis or multitaskers? J. Immunol. 202, 637–644 (2019). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“As antibody efficacy is variable, the integration of ADT data across several studies can lead to strong batch effects that should be corrected for160.”
§ Best practices for single-cell analysis across modalities > Surface protein expression > Correcting ADT counts
What the sources say
- Not checked yetcites [160] Zheng, Y., Jun, S.-H., Tian, Y., Florian, M. & Gottardo, R. Robust normalization and integration of single-cell protein expression across CITE-seq datasets. Preprint at https://doi.org/10.1101/2022.04.29.489989. source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The recombination of V(D)J gene segments and the imprecise junction of V and J segments produce the CDR3 region in VJ and VDJ chains that is mainly responsible for AIR–antigen binding.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Decoding AIRR sequence characteristics
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
“Whereas BCRs directly recognize soluble or membrane-bound epitopes, TCRs interact with linear peptides bound to cell-surface major histocompatibility complex (MHC) molecules.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“TCRs and BCRs are transmembrane surface protein complexes that constitute the adaptive immune receptor repertoire (AIRR)”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Adding information extracted from the imaging data can enhance, for example, cell identification193,194 or the resolution of the molecular features195, or can help to identify spatial patterns of variation196.”
§ Best practices for single-cell analysis across modalities > Single-cell data resolved in space
What the sources say
- Not checked yetcites [193] Tan, X., Su, A., Tran, M. & Nguyen, Q. SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. Bioinformatics 36, 2293–2294 (2020). source
- Not checked yetcites [194] He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020). source
- Not checked yetcites [195] Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40, 476–479 (2022). source
plainconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“BALDR169, BASIC170 and BraCer171 were shown to robustly recover BCR sequences172 but are no longer maintained.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Decoding AIRR sequence characteristics
What the sources say
- Not checked yetcites [169] Upadhyay, A. A. et al. BALDR: a computational pipeline for paired heavy and light chain immunoglobulin reconstruction in single-cell RNA-seq data. Genome Med. 10, 20 (2018). source
- Not checked yetcites [170] Canzar, S., Neu, K. E., Tang, Q., Wilson, P. C. & Khan, A. A. BASIC: BCR assembly from single cells. Bioinformatics 33, 425–427 (2017). source
- Not checked yetcites [171] Lindeman, I. et al. BraCeR: B-cell-receptor reconstruction and clonality inference from single-cell RNA-seq. Nat. Methods 15, 563–565 (2018). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Alternatively, Multigrate learns a joint latent space of paired and unpaired measurements. Combined with transfer learning, Multigrate can map unimodal and multimodal query data sets to multi-omic references while imputing missing modalities254.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Linking single-cell chromatin accessibility and transcriptomics
What the sources say
- Not checked yetcites [254] Lotfollahi, M., Litinetskaya, A. & Theis, F. J. Multigrate: single-cell multi-omic data integration. bioRxiv https://doi.org/10.1101/2022.03.16.484643 (2022). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Although no benchmarks exist for TCR sequence reconstruction, MiXCR167 and TRUST4 (ref. 168) are frequently used.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Decoding AIRR sequence characteristics
What the sources say
- Not checked yetcites [167] Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015). source
- Not checked yetcites [168] Song, L. et al. TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data. Nat. Methods 18, 627–630 (2021). source
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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
“All three approaches suffer from reliance on public databases that contain data primarily from commonly researched diseases and a lack of information on MHCs to decipher T cell antigen specificity.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Determining cell specificity
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Alternatively, weighted nearest-neighbour (WNN)3 analysis learns cell-specific modality weights that reflect the modality information content to determine the importance of modalities in downstream analyses in the form of a neighbour graph.”
§ Best practices for single-cell analysis across modalities > Chromatin accessibility > Linking single-cell chromatin accessibility and transcriptomics
What the sources say
- Not checked yetcites [3] Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Contrary to the negative binomial distribution of gene counts, ADT data are less sparse.”
§ Best practices for single-cell analysis across modalities > Surface protein expression > Correcting ADT counts
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Cellular organization bridges the gap between tissue biology and pathology, which enables the discovery of new cellular functionalities and creates new computational challenges for which distinct analysis methods are required190–192.”
§ Best practices for single-cell analysis across modalities > Single-cell data resolved in space
What the sources say
- Not checked yetcites [190] Palla, G., Fischer, D. S., Regev, A. & Theis, F. J. Spatial components of molecular tissue biology. Nat. Biotechnol. 40, 308–318 (2022). This review highlights both the importance of different spatial profiling technologies and computational challenges for single-cell data resolved in space. source
- Not checked yetcites [191] Dries, R. et al. Advances in spatial transcriptomic data analysis. Genome Res. 31, 1706–1718 (2021). source
- Not checked yetcites [192] Asp, M., Bergenstråhle, J. & Lundeberg, J. Spatially resolved transcriptomes-next generation tools for tissue exploration. Bioessays 42, e1900221 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“The most influencing positions of the AIR–antigen interaction, reflecting specificity, are contained in the CDR3 of the VDJ chain and to a lesser degree the CDR3 in the VJ chain176.”
§ Best practices for single-cell analysis across modalities > Adaptive immune receptor repertoires > Determining cell specificity
What the sources say
- Not checked yetcites [176] Springer, I., Tickotsky, N. & Louzoun, Y. Contribution of T cell receptor alpha and beta CDR3, MHC typing, V and J genes to peptide binding prediction. Front. Immunol. 12, 664514 (2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“An alternative approach is to use CiteFuse163, which normalizes ADTs using CLR and combines both modality matrices with a similarity network fusion algorithm.”
§ Best practices for single-cell analysis across modalities > Surface protein expression > Jointly analysing transcriptomics and ADT data
What the sources say
- Not checked yetcites [163] Kim, H. J., Lin, Y., Geddes, T. A., Yang, J. Y. H. & Yang, P. CiteFuse enables multi-modal analysis of CITE-seq data. Bioinformatics 36, 4137–4143 (2020). source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“For simulated data sets, cell2location outperformed other approaches for cell-type deconvolution, but requires more computational resources, whereas for real data sets, SpatialDWLS and RCTD performed best in terms of the overall accuracy score based on four different accuracy metrics215,216.”
§ Best practices for single-cell analysis across modalities > Single-cell data resolved in space > Obtaining count matrices and spatial coordinates of cells
What the sources say
- Not checked yetcites [215] Li, B. et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods 19, 662–670 (2022). source
- Not checked yetcites [216] Yan, L. & Sun, X. Benchmarking and integration of methods for deconvoluting spatial transcriptomic data. Bioinformatics 39, btac805 (2023). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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
“Processing pipelines such as Giotto and squidpy allow the addition of tailored segmentation methods to the analysis pipeline, which simplifies the comparison, choice and evaluation of the chosen method.”
§ Best practices for single-cell analysis across modalities > Single-cell data resolved in space > Obtaining count matrices and spatial coordinates of cells
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Tools for their exploratory analysis, the integration with omics data sets and the mapping of omics measurements to phenotype information are lacking, and we expect further developments in this direction.”
§ Best practices for single-cell analysis across modalities > Conclusions and future perspectives
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
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
“They simplify the analysis of single-cell data by skipping manual quality control steps.”
§ Best practices for single-cell analysis across modalities > Conclusions and future perspectives
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“These events are commonly referred to as extrinsic effects on gene expression variation and should be taken into consideration in efforts to describe cellular organization and tissue niches236.”
§ Best practices for single-cell analysis across modalities > Single-cell data resolved in space > Spatially dependent communication events across cells
What the sources say
- Not checked yetcites [236] Fischer, D. S., Schaar, A. C. & Theis, F. J. Modeling intercellular communication in tissues using spatial graphs of cells. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01467-z (2022). source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All recommendations are based on independent benchmarks, which inevitably lag behind the latest method developments.”
§ Best practices for single-cell analysis across modalities > Conclusions and future perspectives
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Genes are typically selected on the basis of prior biological knowledge obtained from scRNA-seq (probe selection) and might not be suited to the identification of rare cell subpopulations, which results in bias towards known cell types223.”
§ Best practices for single-cell analysis across modalities > Single-cell data resolved in space > Characterization of cell identity and cellular microenvironments
What the sources say
- Not checked yetcites [223] Kuemmerle, L. B. et al. Probe set selection for targeted spatial transcriptomics. Preprint at https://doi.org/10.1101/2022.08.16.504115. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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
“Coupling genome-scale Perturb-seq with further modalities enables the systematic exploration of the genetic landscape to unveil context-specific gene regulatory networks.”
§ Best practices for single-cell analysis across modalities > Conclusions and future perspectives
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
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
“Methods for the analysis of these measurements are sparse, selectively benchmarked, and best practices have yet to be developed.”
§ Best practices for single-cell analysis across modalities > Conclusions and future perspectives
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“Gene expression matrices obtained by array-based spatial transcriptomics followed by cell-type deconvolution, or by image-based spatial transcriptomics followed by segmentation, can be filtered, normalized and visualized in a similar way to scRNA-seq data.”
§ Best practices for single-cell analysis across modalities > Single-cell data resolved in space > Obtaining count matrices and spatial coordinates of cells
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Methods for this purpose vary broadly with respect to their assumptions and their definition of SVGs, and there is no consensus on how to best identify SVGs.”
§ Best practices for single-cell analysis across modalities > Single-cell data resolved in space > Identification of spatial patterns linked to cellular organization and tissue structure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
scRNA-seq captures only one layer of the complex regulatory machinery that governs cellular function and signalling.
“scRNA-seq captures only one layer of the complex regulatory machinery that governs cellular function and signalling.”
The paper relies on this without support.
Heterotypic doublets are hard to annotate and can lead to wrong cell-type labels.
“Doublets formed by different cell types (heterotypic doublets) are hard to annotate and can lead to wrong cell-type labels.”
The authors state this as an assumption.
Scaling all genes by a common factor assumes that differences in count depth due to cell size are negligible.
“By scaling all genes by a common factor, one assumes that differences in count depth due to cell size are negligible.”
The authors state this as an assumption.
RNA velocity inference assumes gene independence and constant rates of transcription, splicing and degradation.
“RNA velocity inference assumes gene independence and constant rates of transcription, splicing and degradation.”
The authors state this as an assumption.
Most analysis tasks assume that each droplet contains RNA from an intact single cell.
“Most analysis tasks assume that each droplet contains RNA from an intact single cell.”
The paper relies on this without support.
Cell–cell communication is essential for organismal development and homeostasis, and its impairment leads to disease.
“Cells are in constant interaction with each other for organismal development and homeostasis. If this interaction is impaired, disease ensues.”
The paper relies on this without support.
Not all generated AIR chains produced during allelic rearrangements form a functional AIR.
“Not all generated AIR chains produced during allelic rearrangements form a functional AIR.”
The paper relies on this without support.
Public clonotypes represent shared immunological responses.
“Public clonotypes appear in more than one donor and can represent shared immunological response.”
The paper relies on this without support.
Including the VJ CDR3 sequence in database queries decreases the false discovery rate compared with using the VDJ CDR3 sequence alone.
“Similarly to clonotype assignment, database queries can be conducted with varying strictness by considering either the VDJ CDR3 sequence alone, or additionally the VJ CDR3 sequence, which decreases the FDR.”
The paper relies on this without support.
The presence of AIRs can guide cell-type annotation by separating immune cell clusters and facilitating detailed T cell annotations.
“The presence of AIRs can guide cell-type annotation by separating immune cell clusters and facilitating detailed T cell annotations.”
The paper relies on this without support.
Analysing spatial data sets requires analysis tools specifically tailored to this modality.
“Analysing spatial data sets requires analysis tools specifically tailored to this modality, which can be conducted with frameworks such as Squidpy209, Giotto210, Seurat45 or SpatialExperiment211.”
The paper relies on this without support.
In tissue, cells interact through direct contact, surface-bound ligands and receptors, long-range paracrine effects, bio-mechanical forces, and indirect mechanisms such as metabolite exchange.
“In tissue, cells have direct contact and can interact through surface-bound ligands and receptors, long-range paracrine effects, bio-mechanical forces and indirect mechanisms such as metabolite exchange.”
The paper relies on this without support.
Array-based spatial transcriptomics assays do not capture single-cell resolution, so spot gene expression reflects cell-type composition rather than distinct cell types.
“As array-based assays do not capture single-cell resolution, the gene expression profile of spots reflects cell-type composition rather than distinct cell types.”
The paper relies on this without support.
Imaging-based spatial transcriptomics technologies commonly read out only a predefined set of transcripts.
“These technologies commonly read out only a predefined set of transcripts.”
The paper relies on this without support.
Alignment of spatially naive scRNA-seq data and targeted spatially resolved data enables imputation of the whole transcriptome in a spatially resolved manner.
“Alignment of standard spatially naive scRNA-seq data and targeted spatially resolved data enables imputation of the whole transcriptome (measured in standard scRNA-seq) in a spatially resolved manner and attempts to resolve the limitations of targeted feature spaces.”
The paper relies on this without support.
The identification of cellular microenvironments across different samples can be hindered by differences in image orientation.
“The identification of cellular microenvironments across different samples can be hindered by differences with respect to image orientation.”
The paper relies on this without support.
Cell communication events can be identified in dissociated scRNA-seq data.
“Cell communication events can be identified in dissociated scRNA-seq data as described above.”
The paper relies on this without support.
Models built on latent spaces become predictive with query-to-reference mapping approaches, creating a shift from unsupervised exploratory analysis to supervised predictions.
“Models built on these latent spaces become predictive with query-to-reference mapping approaches, which will create a shift from the unsupervised, exploratory analysis approach to single-cell analysis complemented by supervised predictions.”
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
“there is no consensus as to which of these methods represents the “state of the art”; these methods have not undergone rigorous statistical validation and comparison [18]”
From Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection (2024) · cites this paper as [18]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The development of single-cell multi-omics methods and the growth of single-cell datasets created a well-suited scenario for applying deep learning methods in single-cell analysis, which could further lead to advancements in the field of drug discovery, therapeutic targets, and digital health [2,131].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [2]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Corrected count matrices for each sample were processed using Scanpy tools following recommended best practices for retaining high-quality cells63.”
From A comparative transcriptomic analysis of mouse demyelination models and multiple sclerosis lesions (2026) · cites this paper as [63]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“there is no commonly accepted framework for quantifying the statistical validity of single-cell CRISPR screen association testing methods”
From Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection (2024) · cites this paper as [18]
Experimental — accuracy not yet measuredIn the citing paper
“Hence, a common task in single-cell analysis is to eliminate batch effects without removing meaningful biological differences, allowing for data integration [13, 14, 15].”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [15]
In the citing paper
“However, much of the work in single-cell biology is inherently exploratory, where labels may not be available a priori .”
From Assessing the limits of zero-shot foundation models in single-cell biology (2023) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Best practices for single-cell analysis across modalities > Conclusions and future perspectives
“All recommendations are based on independent benchmarks, which inevitably lag behind the latest method developments.”
§ Best practices for single-cell analysis across modalities > Transcriptome > From raw count matrices to high-quality cellular data
“Even in the absence of a systematic benchmark, one should consider removing ambient RNA as an initial analysis step in quality control to improve downstream analyses for many tissues21–23.”
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · cited 1×
“3).”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · cited 1×
“3).”
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890 · cited 1×
“…, for example, leverage spatial correlation testing, BayesSpace 227 uses Markov random fields, spaGCN 229 uses graph neural networks to integrate gene expression data, spatial information and histology images, and sepal 235 utilizes diffusion-based modelling to identify genes with spatial patterns.”
MethodThe neighbor-joining method: a new method for reconstructing phylogenetic trees.
Saitou, N., & Nei, M. (1987) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040454 · Cited by 59,959 · cited 1×
“Moreover, tools such as Nichenet 130 or Cytotalk 131 , which provide complementary estimates of intracellular activities, such as induced gene expression changes or spatial information, can be used to increase the confidence in predicted interactions.”
BackgroundSubramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · cited 1×
“3).”
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · cited 1×
“3).”
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · cited 1×
“3).”
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · cited 1×
“3).”
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508
Self-citationGSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440 · cited 1×
“3).”
Research works printed but not linked: 2
- Ahlmann-Eltze, C. & Huber, W. Comparison of transformations for single-cell RNA-seq data. bioRxiv https://doi.org/10.1101/2021.06.24.449781 (2022). · DOI · Paper page
- Sokal, R. & Michener, C. A statistical method for evaluating systematic relationships. Univ. Kans., Sci. Bull. 38, 1409–1438 (1958).
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Items 201–300 of 1,001
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Mou, L., Wang, T. B., Chen, Y., Luo, Z., Wang, X., & Pu, Z. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1554876 · Cited by 15
Caron, D. P., Specht, W. L., Chen, D., et al. (2023) · bioRxiv · doi:10.1101/2023.07.06.547944 · Cited by 20
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Smith, N. P., Yan, Y., Pan, Y., Williams, J. B., Manakongtreecheep, K., Pant, S. M., Zhao, J., Tian, T., Pan, T., Stingley, C. A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.29.560006 · Cited by 12
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Prabaharan, E., Armant, D. R., & Drewlo, S. (2025) · Systems Biology in Reproductive Medicine · doi:10.1080/19396368.2025.2533992 · Cited by 12
Naas, J., Balmaña, M., Holcik, L., Novatchkova, M., Dobnikar, L., Krausgruber, T., Ladstätter, S., Bock, C., von Haeseler, A., Esk, C., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116168 · Cited by 14
Decoding Human Biology and Disease Using Single-Cell Omics Technologies
Shi, Q., Chen, X., & Zhang, Z. (2023) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.06.003 · Cited by 11
Chen, P., Wang, Y., & Zhou, B. (2024) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2024.111918 · Cited by 10
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Moody, J., Kouno, T., Kojima, M., Koya, I., Leon, J., Suzuki, A., Hasegawa, A., Akiyama, T., Akiyama, N., Amagai, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.13.566791 · Cited by 9
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Wang, H., Leskovec, J., & Regev, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.02.587824 · Cited by 10
Basal cell of origin resolves neuroendocrine–tuft lineage plasticity in cancerPreprint
Ireland, A. S., Hawgood, S. B., Xie, D. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.13.623500 · Cited by 10
A single-cell cytokine dictionary of human peripheral bloodPreprint
Theis, F. J., Oesinghaus, L., Becker, S., Vornholz, L., Papalexi, E., Pangallo, J., Moinfar, A., Liu, J., Fleur, A. L., Shulman, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.12.693897 · Cited by 29
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Khan, S. A., Martinez‐de‐Morentin, X., Alsabbagh, A. R., Maillo, A., Lagani, V., Gómez-Cabrero, D., Lehmann, R., & Tegner, J. N. (2025) · Nature Methods · doi:10.1038/s41592-025-02918-6 · Cited by 17
Interpretation, extrapolation and perturbation of single cells
Dimitrov, D., Schrod, S., Rohbeck, M., & Stegle, O. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-025-00920-4 · Cited by 26
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Jiménez-Gracia, L., Maspero, D., Aguilar-Fernández, S., Craighero, F., Boulougouri, M., Ruiz, M., Marchese, D., Caratù, G., Liñares-Blanco, J., Berasategi, M., et al. (2026) · Nature Medicine · doi:10.1038/s41591-025-04126-3 · Cited by 23
Dietrich, A., Merotto, L., Pelz, K., Eder, B., Zackl, C., Reinisch, K., Edenhofer, F., Marini, F., Sturm, G., List, M., et al. (2026) · Genome biology · doi:10.1186/s13059-026-03955-w · Cited by 30
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Kim, I. S. (2023) · Cells · doi:10.3390/cells13010027 · Cited by 12
Lee, Y., Xu, Y., Gao, P., & Chen, J. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168543 · Cited by 9
Single-Cell Informatics for Tumor Microenvironment and Immunotherapy
Tian, J., Bai, X., & Quek, C. Y. J. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25084485 · Cited by 12
Filippi, J., Casti, P., Antonelli, G., Murdocca, M., Mencattini, A., Corsi, F., D’Orazio, M., Pecora, A., Luca, M. D., Curci, G., et al. (2024) · Small Methods · doi:10.1002/smtd.202300923 · Cited by 9
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Conde‐López, C., Marripati, D., Elkabets, M., Heß, J., & Kurth, I. (2024) · Cancers · doi:10.3390/cancers16193265 · Cited by 9
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Moreno‐Gonzalez, M., Sierra, I., & Kind, J. (2024) · International Journal of Cancer · doi:10.1002/ijc.35307 · Cited by 11
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Wang, Q., Zhu, H., Deng, L., Xu, S., Xie, W., Li, M., Wang, R., Tie, L., Zhan, L., & Yu, G. (2025) · Small Methods · doi:10.1002/smtd.202401107 · Cited by 12
Li, S., Liu, J., Peyton, M., Lazaro, O., McCabe, S., Huang, X., Liu, Y., Shi, Z., Zhang, Z., Walker, B. A., et al. (2025) · Cancers · doi:10.3390/cancers17040653 · Cited by 11
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Nachtigall, P. G., Hamilton, B. R., Kazandjian, T. D., Stincone, P., Petras, D., Casewell, N. R., & Undheim, E. A. B. (2025) · Genome biology · doi:10.1186/s13059-025-03602-w · Cited by 11
Torres-Narvaez, E. S., González, D., Artunduaga-Alvarado, J. A., & Ortega‐Recalde, O. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1584178 · Cited by 16
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Wang, Y., Li, J., Zha, H., Liu, S., Huang, D., Fu, L., & Liu, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf339 · Cited by 9
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Wolfram-Schauerte, M., Vogel, T., Tuoken, H., Savitski, M. F., Simon, E., & Nieselt, K. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf388 · Cited by 11
hadge: a comprehensive pipeline for donor deconvolution in single-cell studies
Curion, F., Wu, X., Heumos, L., André, M. M. G., Halle, L., Ozols, M., Grant‐Peters, M., Rich‐Griffin, C., Yeung, H., Dendrou, C. A., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03249-z · Cited by 10
Self-citationCombining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samplesPreprint
Baghdassarian, H., Dimitrov, D., Armingol, E., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.28.538731 · Cited by 9
Self-citationInterpretable Inflammation Landscape of Circulating Immune cellsPreprint
Jiménez-Gracia, L., Maspero, D., Aguilar-Fernández, S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.28.568839 · Cited by 9
Self-citationSingmiR: a single-cell miRNA alignment and analysis tool
Engel, A., Rishik, S., Hirsch, P., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae225 · Cited by 8
Marx, V. (2024) · Nature Methods · doi:10.1038/s41592-024-02263-0 · Cited by 9
HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling
Otoničar, J., Lazareva, O., Mallm, J., Simovic-Lorenz, M., Philippos, G., Sant, P., Parekh, U., Hammann, L., Li, A., Yildiz, U., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03450-0 · Cited by 7
Biases in machine-learning models of human single-cell data
Willem, T., Shitov, V. A., Luecken, M. D., Kilbertus, N., Bauer, S., Piraud, M., Buyx, A., & Theis, F. J. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01619-8 · Cited by 12
Self-citationKontiza, A., von Gerichten, J., Spick, M. P., Fraser, E., Costa, C., Saunders, K. D. G., Whetton, A. D., Newman, C. F., & Bailey, M. J. (2025) · The Analyst · doi:10.1039/d5an00037h · Cited by 11
Schaefer, N. K., Pavlovic, B. J., Schmitz, M. T., & Pollen, A. A. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101275 · Cited by 9
Allometry of cell types in planarians by single-cell transcriptomics
Emili, E., Pérez-Posada, A., Vanni, V., Salamanca-Díaz, D. A., Ródriguez-Fernández, D., Christodoulou, M. D., & Solana, J. (2025) · Science Advances · doi:10.1126/sciadv.adm7042 · Cited by 19
A single-cell transposable element atlas of human cell identity
Reyes-Gopar, H., Marston, J. L., Singh, B., Greenig, M., Lin, J., Ostrowski, M., Randall, K. N., Sandoval‐Motta, S., Dopkins, N., Lawrence, E., et al. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101086 · Cited by 14
Akıncılar, S. C., Fidan, K., Kumar, N., Ng, Q. F., Majee, P., Wu, L., Han, D. J., Chan, C. H. T., Chua, J. Y. H., Idzham, K., et al. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01706-w · Cited by 8
Bai, D., Mo, S., Zhang, R., Luo, Y., Gao, J., Yang, J. P., Wu, Q., Rahmani, H., Amariuta, T., Grotjahn, D., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69102-y · Cited by 15
Ramirez, J. J., Hardin, E. J., Sakers, K., Kim, J., Ortiz, C. K. C., Savage, J. T., Hanamsagar, R., Block, C. L., Singh, S. K., Bilbo, S. D., et al. (2026) · Neuron · doi:10.1016/j.neuron.2025.12.028 · Cited by 14
Beyond single cells: microfluidics empowering multiomics analysis
Tian, T., Lin, S., & Yang, C. J. (2023) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-023-05028-4 · Cited by 8
Sampson, M. M., Morgan, R. K., Sloan, S. A., & Bakulski, K. M. (2024) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2024.100464 · Cited by 12
Neres, D. F., & Wright, R. C. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103151 · Cited by 9
Metadata
- Authors
- Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., Single-cell Best Practices Consortium, Aliee, H., Ansari, M., Badia-i-Mompel, P., Büttner, M., Dann, E., Dimitrov, D., Dony, L., Frishberg, A., He, D., Hediyeh-zadeh, S., Hetzel, L., Ibarra, I. L., Jones, M. G., Lotfollahi, M., Martens, L. D., Müller, C. L., Nitzan, M., Ostner, J., Palla, G., Patro, R., Piran, Z., Ramírez-Suástegui, C., Saez-Rodriguez, J., Sarkar, H., Schubert, B., Sikkema, L., Srivastava, A., Tanevski, J., Virshup, I., Weiler, P., Schiller, H. B., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Heidelberg University (DE)
- University of Bonn (DE)
- Wellcome Sanger Institute (GB)
- University of Maryland, College Park (US)
- Stanford Medicine (US)
- Hebrew University of Jerusalem (IL)
- La Jolla Institute for Immunology (US)
- Harvard University (US)
- New York Genome Center (US)
- Corresponding author
- Theis, Fabian J.
- ORCID
- Heumos, Lukas
- Schaar, Anna C.
- Lance, Christopher
- Litinetskaya, Anastasia
- Drost, Felix
- Zappia, Luke
- Lücken, Malte D.
- Strobl, Daniel C.
- Henao, Juan
- Curion, Fabiola
- Aliee, Hananeh
- Ansari, Meshal
- Badia-i-Mompel, Pau
- Büttner, Maren
- Dann, Emma
- Dimitrov, Daniel
- Dony, Leander
- Frishberg, Amit
- He, Dongze
- Hediyeh-zadeh, Soroor
- Hetzel, Leon
- Ibarra, Ignacio L.
- Jones, Matthew G.
- Lotfollahi, Mohammad
- Martens, Laura D.
- Müller, Christian L.
- Nitzan, Mor
- Ostner, Johannes
- Palla, Giovanni
- Patro, Rob
- Piran, Zoe
- Ramírez-Suástegui, Ciro
- Sarkar, Hirak
- Schubert, Benjamin
- Sikkema, Lisa
- Srivastava, Avi
- Tanevski, Jovan
- Virshup, Isaac
- Weiler, Philipp
- Schiller, Herbert B.
- Theis, Fabian J.
- Venue
- Nature Reviews Genetics
- Published
- 2023-03-31
- Volume
- 24
- Issue
- 8
- Pages
- 550-572
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC10066026
- OpenAlex
- W4362521490
- Cited by
- 1,051
- References
- 269
- Citations by year
- 2026: 295 · 2025: 367 · 2024: 296 · 2023: 90
- FWCI
- 28.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1763680, 1750472)
- Joachim Herz Stiftung
- Wellcome Trust (108413 /A/15/D)
- Deutsche Forschungsgemeinschaft (452881907, SFB-TRR 338/1, TRR355, TP Z02, SFB-TRR 338, 490846870)
- Bundesministerium für Bildung und Forschung (01IS18053A)
- Helmholtz Artificial Intelligence Cooperation Unit (ZT-I-PF-5-01)
- National Institutes of Health
- National Human Genome Research Institute (R01HG009937)
- National Cancer Institute (K99CA267677)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- single-cell multi-omics, multimodal data integration, single-cell transcriptomics, chromatin accessibility, surface protein expression, adaptive immune receptor repertoire
- MeSH terms
- Gene Expression Profiling, Proteomics, Single-Cell Analysis
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 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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