CoChat
Sign Up
Cited by 632Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisGene expression and cancer classification

Revealing the vectors of cellular identity with single-cell genomics

Open access
Wagner, Allon, Regev, Aviv, Yosef, Nir·2016·Nature Biotechnology
DOI 10.1038/nbt.3711PMC PMC5465644
Open this paper in CoChat — free account

Abstract

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

Integrity signals

Registry

Crossref · 10.1038/nbt.3711

Cites

219 of 219 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 226 cited sources checked · 0 supported · 0 flagged

  • Findingcitation

    Pseudoalignment reduces quantification time by two orders of magnitude without compromising accuracy.

    “by replacing read alignment with pseudoalignment, i.e., by only identifying the transcripts from which a read could have originated without determining the exact sequence alignments between them, the time required to perform quantification (assigning expression values to genes) is decreased by two orders of magnitude without compromising accuracy.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [121] Tsafrir, D. et al. Sorting points into neighborhoods (SPIN): data analysis and visualization by ordering distance matrices. Bioinformatics 21, 2301–2308 (2005). source

      Not checked in the free run.

  • Findingcitation

    Several studies inferred individual cells' cell cycle phase from scRNA-seq using transcriptional signatures of discrete phases from earlier bulk profiling.

    “Several studies inferred the cell cycle phase of individual cells from single-cell RNA-seq data30,35,97,99, relying on transcriptional signatures of discrete phases from earlier bulk profiling experiments146.”
    Quote in the paperNot a substantial claim0 of 5 sources checked
    • cites [30] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source

      Not checked in the free run.

    • cites [35] Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016). source

      Not checked in the free run.

    • cites [97] Antebi, Y.E. et al. Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates. PLoS Biol. 11, e1001616 (2013). source

      Not checked in the free run.

    • cites [99] Patel, A.P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014). source

      Not checked in the free run.

    • cites [146] Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269–276 (2015). source

      Not checked in the free run.

  • Findingcitation

    True biological hybrid cells may be observed even when a cell population partitions into discrete types.

    “even when a cell population partitions into discrete types, hybrid cells—single cells that are a mix of two or more types—may be observed95,97”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [95] Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015). source

      Not checked in the free run.

    • cites [97] Antebi, Y.E. et al. Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates. PLoS Biol. 11, e1001616 (2013). source

      Not checked in the free run.

  • Findingcitation

    In leukemic bone marrow, t-SNE embedding of mass cytometry profiles showed some markers in distinct clusters and others in a continuous gradient.

    “In leukemic bone marrow105, t-SNE108 embedding of mass cytometry profiles showed that while some markers were expressed in distinct clusters, others formed a continuous gradient.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [105] Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013). source

      Not checked in the free run.

    • cites [108] Chu, L.-F. et al. Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm. Genome Biol. 17, 173 (2016). source

      Not checked in the free run.

  • Findingcitation

    Ergodic rate analysis accurately inferred cell cycle trajectories from single-cell measurements of fixed steady-state populations.

    “In pioneering work based on a few markers, ergodic rate analysis (ERA) was used to accurately infer trajectories along the cell cycle from single-cell measurements of fixed steady-state populations136.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [136] Gupta, P.B. et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146, 633–644 (2011). source

      Not checked in the free run.

  • May combine two claims. 53
  • Classified as probably not a substantial claim. 28

  • May combine two claims.

    Findingcitation

    In in vitro differentiated TH17 lymphocytes, scRNA-seq did not reveal distinct subtypes, but the first principal component correlated with the cells' ability to trigger an autoimmune response.

    “scRNA-seq profiles obtained from T helper 17 (TH17) lymphocytes differentiated in vitro did not reveal distinct subtypes, but their first principal component was highly correlated with a cell’s ability to trigger an autoimmune response (N.Y., A.R. and colleagues1).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [1] Gaublomme, J.T. et al. Single-cell genomics unveils critical regulators of Th17 Cell pathogenicity. Cell 163, 1400–1412 (2015). source

      Not checked in the free run.

  • May combine two claims.

    Findingcitation

    Single-cell profiles provide near-continuous sampling and show high conservation across cell types and species.

    “single-cell profiles provide a near-continuous sampling and show high conservation across cell types and species30,97,99”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [30] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source

      Not checked in the free run.

    • cites [97] Antebi, Y.E. et al. Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates. PLoS Biol. 11, e1001616 (2013). source

      Not checked in the free run.

    • cites [99] Patel, A.P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014). source

      Not checked in the free run.

  • May combine two claims.

    Findingcitation

    The TCC feature space can produce cell clustering similar to gene expression space while avoiding quantification and a statistical model for read generation.

    “While this feature space is not as biologically interpretable as gene expression space, it can produce122 a similar cell clustering while sidestepping the time-consuming quantification task and avoiding the need to define a statistical model for read generation.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [122] Madeira, S.C. & Oliveira, A.L. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans. Comput. Biol. Bioinformatics 1, 24–45 (2004). source

      Not checked in the free run.

  • May combine two claims.

    Findingcitation

    In TH2 differentiation, scLVM highlighted correlated genes enriched in TH2 differentiation and distinguished two subpopulations that were not detectable when cell cycle covariates were present.

    “Applied to T helper 2 (TH2) cell differentiation, scLVM helped highlight a set of correlated genes enriched in TH2 cell differentiation and distinguish two subpopulations—neither of which is detectable when the cell cycle covariates are present101.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [101] Pollen, A.A. et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    A cell's identity is formed by the instantaneous intersection of multiple factors, including its position in a cell type taxonomy, progress of time-dependent processes, response to local signals, and spatial location and neighborhood.

    “A cell’s identity, which is reflected in its molecular profile, is formed by the instantaneous intersection of multiple factors. These include its position in a taxonomy of cell types, the progress of multiple time-dependent processes that take place simultaneously, its response to signals from its local environment, and the precise location and neighborhood in which it resides”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: A cell's identity is reflected in its molecular profile.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Computational analysis methods for single-cell RNA sequencing have started to uncover multiple simultaneous aspects of cellular identity, ranging from discrete cell-type taxonomies to continuous dynamic transitions and spatial positions.

    “Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell’s identity, from a taxonomy of discrete cell types to continuous dynamic transitions and spatial locations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The scale and precision of single-cell studies has continued to increase, reaching tens of thousands of cells for massively parallel scRNA-seq and millions of cells for mass cytometry protein measurements.

    “The scale and precision of such studies has continued to increase, reaching tens of thousands of cells for massively parallel scRNA-seq30,31 and millions of cells for mass cytometry signature protein measurements32–34.”
    Quote in the paperNot a substantial claim0 of 5 sources checked
    • cites [30] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source

      Not checked in the free run.

    • cites [31] Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015). source

      Not checked in the free run.

    • cites [32] Krishnaswamy, S. et al. Systems biology. Conditional density-based analysis of T cell signaling in single-cell data. Science 346, 1250689 (2014). source

      Not checked in the free run.

    • cites [33] Sen, N. et al. Single-cell mass cytometry analysis of human tonsil T cell remodeling by varicella zoster virus. Cell Reports 8, 633–645 (2014). source

      Not checked in the free run.

    • cites [34] Levine, J.H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015). source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    Single-cell data pose analytical challenges including experimental design and power analysis, preprocessing to distinguish biological from technical variation, inferring key aspects of a cell atlas, and deriving molecular mechanisms from cell-to-cell variation.

    “The analytical challenges include (1) designing experiments and performing power analysis (e.g., how many cells do we need to profile for a given task? At what depth?); (2) preprocessing to distinguish biological from technical variation, especially false-negative gene detections (dropouts); (3) inferring the key aspects of a cell atlas, from discrete sub-types to continuous spatiotemporal order”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisenone given

    Some technical variation is common to both scRNA-seq and bulk RNA-seq, whereas zero inflation due to false negatives, overamplification, and cell doublets are specific to technical variation between single-cell profiles.

    “Some technical variation (Fig. 3) is common to both scRNA-seq and bulk (population) RNA-seq, whereas several other factors— including zero inflation due to false negatives, overamplification, and cell doublets—are specific to the technical variation between single-cell profiles.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    Common technical challenges in single-cell data include noise, sparsity, false negatives, ever-increasing scale, partial dependencies between facets of cell identity, and the need for accessible visualizations.

    “common technical challenges, such as noise, sparsity, and false negatives; ever-increasing scale, which defies many traditional implementations of basic tasks in genomics; partial dependencies between the multiple facets of a cell’s identity (its type, state, position, etc.), such that variation in one biological dimension may be a confounder for another; and the need for accessible and interpret”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    False negatives can be erroneously interpreted as allele-specific expression or as evidence for a wild-type homozygote rather than a genetic variant.

    “For example, false negatives can be erroneously interpreted as allele-specific expression38,75 or as evidence for a wild-type homozygote rather than a genetic variant (or mutation)77.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [38] Kim, J.K., Kolodziejczyk, A.A., Ilicic, T., Teichmann, S.A. & Marioni, J.C. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat. Commun. 6, 8687 (2015). source

      Not checked in the free run.

    • cites [75] Grün, D. & van Oudenaarden, A. Design and analysis of single-cell sequencing experiments. Cell 163, 799–810 (2015). source

      Not checked in the free run.

    • cites [77] Reinius, B. et al. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq. Nat. Genet. http://dx.doi.org/10.1038/ng.3678 (2016). source

      Not checked in the free run.

  • May combine two claims.

    Premisereasoning

    Dropout events manifest as bimodality of gene expression with one mode at zero, and in many cases this bimodality is dominated by false negatives.

    “Across a set of cells, this manifests as bimodality of gene expression (with one mode at zero). While in some cases such bimodality is due to stochastic (‘bursty’) gene expression40 or more stable variation between cell types and states3, in many others it is dominated by false negatives70,85.”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [40] Raj, A., Peskin, C.S., Tranchina, D., Vargas, D.Y. & Tyagi, S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309 (2006). source

      Not checked in the free run.

    • cites [3] Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013). source

      Not checked in the free run.

    • cites [70] Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011). source

      Not checked in the free run.

    • cites [85] Shiroguchi, K., Jia, T.Z., Sims, P.A. & Xie, X.S. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proc. Natl. Acad. Sci. USA 109, 1347–1352 (2012). source

      Not checked in the free run.

  • May combine two claims.

    Premisenone given

    Current RMT protocols are limited to end-counting and cannot provide full-length transcript information.

    “Finally, while RMTs are a compelling method, current protocols are limited to ‘end-counting’ and cannot provide full-length transcript information.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Factors defining cell identity may be discrete categories, continuous spectra, or combinations of discrete and continuous categories.

    “These factors may not only be the discrete categories that are often assumed when classifying cells into major types4,30,94 but may also represent a continuous spectrum1, or a combination of discrete and continuous categories36,95–97,227.”
    Quote in the paperSubstantial claim0 of 9 sources checked
    • cites [4] Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015). source

      Not checked in the free run.

    • cites [30] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source

      Not checked in the free run.

    • cites [94] DeTomaso, D. & Yosef, N. FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data. BMC Bioinformatics 17, 315 (2016). source

      Not checked in the free run.

    • cites [1] Gaublomme, J.T. et al. Single-cell genomics unveils critical regulators of Th17 Cell pathogenicity. Cell 163, 1400–1412 (2015). source

      Not checked in the free run.

    • cites [36] Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016). source

      Not checked in the free run.

    • cites [95] Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015). source

      Not checked in the free run.

    • cites [96] Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011). source

      Not checked in the free run.

    • cites [97] Antebi, Y.E. et al. Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates. PLoS Biol. 11, e1001616 (2013). source

      Not checked in the free run.

    • cites [227]

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    The extent of technical variation can be affected by biological differences, undermining definitions of quality and limiting the ability to remove technical variation.

    “In some cases, the extent of technical variation is affected by biological differences, undermining definitions of quality and limiting our ability to remove technical variation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    Through deconvolution of complex samples, one may infer their cellular milieu and characterize rare, functionally important, and unknown cell types they contain.

    “Through deconvolution of complex samples30, such as tumor biopsies35, one may infer their cellular milieu, and characterize the rare5, functionally important1, and unknown36 cell types they contain.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [30] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source

      Not checked in the free run.

    • cites [35] Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016). source

      Not checked in the free run.

    • cites [5] Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Imperfections in capture, conversion, and amplification of RNA in individual cells lead to both false negatives and false positives for individual transcripts, which may confound understanding of biological variation.

    “Imperfections in the capture, conversion, and amplification77,78 of the minute quantities of RNA in an individual cell into a cDNA library lead to challenges in regard to both false negatives (expressed but undetected transcripts) and false positives (transcripts with inflated expression levels) for individual transcripts, which may confound the understanding of biological variation.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [77] Reinius, B. et al. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq. Nat. Genet. http://dx.doi.org/10.1038/ng.3678 (2016). source

      Not checked in the free run.

    • cites [78] Jaitin, D.A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014). source

      Not checked in the free run.

  • May combine two claims.

    Premisereasoning

    Massive whole-transcriptome amplification amplifies existing noise and can introduce additional noise, such as false-positive detections due to DNA polymerase errors at an early PCR cycle.

    “Massive whole-transcriptome amplification (WTA) inevitably amplifies any source of noise already present in the data and can introduce additional sources of noise, such as false-positive detections due to DNA polymerase errors occurring at an early PCR amplification cycle (Fig. 3c).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Doublets in scRNA-seq can manifest as high-quality cells by many measures and lead to erroneous biological conclusions, such as false detection of 'hybrids' that are actually mixtures of RNA from two or more cells.

    “These can manifest as high-quality cells by many measures (more transcripts, more complex libraries) and lead to erroneous biological conclusions, such as false detection of ‘hybrids’—cells that seem to be an intermediate type between two distinct cell populations but are in fact a mixture of RNA from two or more cells (A.R. and colleagues30).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [30] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The number of undetected transcripts is correlated with batch effects and other metrics of library quality, and weakly expressed transcripts are more frequently undetected.

    “Indeed, the number of undetected transcripts is correlated to batch effects60 and other metrics of library quality, and transcripts that are weakly expressed when detected are also more frequently undetected1.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [60] Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013). source

      Not checked in the free run.

    • cites [1] Gaublomme, J.T. et al. Single-cell genomics unveils critical regulators of Th17 Cell pathogenicity. Cell 163, 1400–1412 (2015). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Cells in a given spatial position may be more quiescent, complicating inference of whether variation should be attributed to cell cycle or spatial position.

    “For instance, cells in a given position may be more quiescent compared to other positions, which complicates the inference of the biological variation that should be attributed to cell cycle versus spatial position101.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [101] Pollen, A.A. et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    In pathological settings, especially cancer, hybrid states may reflect transitions or an intrinsic abnormal mixture of functional modules.

    “In pathological settings, especially cancer97,130,131, such hybrid states may reflect either transitions (below) or the intrinsic abnormal mixture of different functional modules (N.Y., A.R. and colleagues130).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [97] Antebi, Y.E. et al. Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates. PLoS Biol. 11, e1001616 (2013). source

      Not checked in the free run.

    • cites [130] Fruchterman, T.M.J. & Reingold, E.M. Graph drawing by force-directed placement. Softw. Pract. Exper. 21, 1129–1164 (1991). source

      Not checked in the free run.

    • cites [131] Shoval, O. et al. Evolutionary trade-offs, Pareto optimality, and the geometry of phenotype space. Science 336, 1157–1160 (2012). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Sources of biological variation that determine cell identity can be dependent, which complicates their identification and interpretation and poses a problem of statistical identifiability.

    “However, unlike basis vectors in algebra, they can in fact be dependent, which further complicates their identification and interpretation, and poses a problem of statistical identifiability.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationnone given

    Solutions to many of the challenges in cell type classification are only beginning to emerge.

    “Solutions to many of these challenges are only beginning to emerge.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Distance metrics become unstable in high-dimensional space, which obstructs clustering there.

    “Clustering in high-dimensional space is obstructed by the instability of distance metrics in high dimension103 (which is a facet of the ‘curse of dimensionality’).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [103] Lande-Diner, L., Stewart-Ornstein, J., Weitz, C.J. & Lahav, G. Single-cell analysis of circadian dynamics in tissue explants. Mol. Biol. Cell 26, 3940–3945 (2015). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Classification of cells into discrete types from single-cell profiles is a problem of unsupervised clustering in high dimensions.

    “Classification of cells into discrete types from single-cell profiles is a problem of unsupervised clustering in high dimensions.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisereasoning

    Each dynamic process is typically reflected in a cell's molecular profile, so single-cell RNA or protein analysis can position a cell along a temporal trajectory.

    “Each dynamic process is typically reflected in the cell’s molecular profile, such that single-cell analysis of RNA or protein can position a cell in a temporal trajectory.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Some facets of cellular identity are transient in time and space, and temporal processes may progress along trajectories, oscillate, or be influenced by spatial position.

    “First, in contexts such as development and physiology, some facets of cellular identity are transient in time and space. Temporal processes may, for example, progress along one or more trajectories (e.g., differentiation); oscillate continuously between cellular states (e.g., the cell cycle98,99 or circadian rhythm100); or be influenced by the physical position and neighborhood of the cell71,93.”
    Quote in the paperSubstantial claim0 of 5 sources checked
    • cites [98] Korem, Y. et al. Geometry of the gene expression space of individual cells. PLOS Comput. Biol. 11, e1004224 (2015). source

      Not checked in the free run.

    • cites [99] Patel, A.P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014). source

      Not checked in the free run.

    • cites [100] Gokce, O. et al. Cellular taxonomy of the mouse striatum as revealed by single-cell RNA-Seq. Cell Reports 16, 1126–1137 (2016). source

      Not checked in the free run.

    • cites [71] Marinov, G.K. et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014). source

      Not checked in the free run.

    • cites [93] Buettner, F., Moignard, V., Göttgens, B. & Theis, F.J. Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data. Bioinformatics 30, 1867–1875 (2014). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationnone given

    Reduced-dimensionality data are less noisy than high-dimensional data but lose some biological variance.

    “Importantly, the reduced dimensionality data are less noisy than the high-dimensional data but lose some of the biological variance.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    SPADE down-sampling makes rare cell types as prevalent as abundant types, increasing the chance that rare types form their own clusters rather than becoming outliers, at the cost of lower signal-to-noise.

    “This increases the probability that rare cell types will form their own clusters rather than becoming outliers to clusters of more abundant types, albeit at the cost of a decreased biological signal-to-noise ratio.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [94] DeTomaso, D. & Yosef, N. FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data. BMC Bioinformatics 17, 315 (2016). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Some dynamic processes, such as cell cycle and differentiation, may not be largely independent.

    “It is often assumed (implicitly or explicitly) that some of these processes are largely independent, but this may not be the case (e.g., cell cycle and differentiation).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisereasoning

    Key challenges in classifying cells into discrete types include adapting methods to increasing scale, ensuring reproducibility, finding proper granularity and hierarchies, distilling markers, matching legacy knowledge, and visualizing/sharing/comparing classifications.

    “The key, inter-related challenges include (1) adapting methods to the exponentially increasing scale of single-cell data; (2) ensuring that the resulting classification is reproducible across experiments and platforms; (3) finding the proper granularity and detecting hierarchies of types and subtypes where they exist, especially when cell type frequency varies by multiple orders of magnitude from”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Bulk genomic assays of a continuous dynamic process require breaking the process into discrete time points and synchronizing the cell population.

    “Studying a continuous dynamic process through bulk genomic assays requires to artificially break and measure the process at discrete time points, and also to synchronize the population of cells.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [134] Sheftel, H., Shoval, O., Mayo, A. & Alon, U. The geometry of the Pareto front in biological phenotype space. Ecol. Evol. 3, 1471–1483 (2013). source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Cellular identity and function are governed by elaborate regulatory circuits, many of which act through control of RNA expression.

    “Cellular identity and function are governed by elaborate regulatory circuits, many of which act through control of RNA expression166,167.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [166] Chen, K.H., Boettiger, A.N., Moffitt, J.R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015). source

      Not checked in the free run.

    • cites [167] Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Deciphering circuitry typically relies on combining observations across diverse samples with manipulation of one or more regulators to observe their effect.

    “Deciphering circuitry typically relies on combining observations across diverse samples with manipulation of one or more regulators to observe their effect.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The cell cycle often has an enormous impact on cellular gene expression, and this impact can extend to other processes and be affected by them.

    “The cell cycle often has an enormous impact on cellular gene expression146,149,150 (although some87 have challenged this view), but this impact can extend to other processes, and in turn be affected by them.”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [146] Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269–276 (2015). source

      Not checked in the free run.

    • cites [149] Marco, E. et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl. Acad. Sci. USA 111, E5643–E5650 (2014). source

      Not checked in the free run.

    • cites [150] Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B. Stat. Methodol. 63, 411–423 (2001). source

      Not checked in the free run.

    • cites [87] Kharchenko, P.V., Silberstein, L. & Scadden, D.T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014). source

      Not checked in the free run.

  • May combine two claims.

    Premisenone given

    Spatial information for a limited number of landmark genes can be readily obtained by traditional methods, and extensive catalogs exist for many model systems.

    “spatial information can be readily obtained for a limited number of ‘landmark’ genes by traditional experimental methods such as in situ hybridization and histochemistry; indeed, for many model systems there are extensive catalogs of such information.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Canonical structure is likely in most normal tissues (e.g., brain structures) and physiological processes (e.g., embryogenesis).

    “While this is a likely situation in most normal tissues (e.g., brain structures71) and physiological processes (e.g., embryogenesis93)”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [71] Marinov, G.K. et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014). source

      Not checked in the free run.

    • cites [93] Buettner, F., Moignard, V., Göttgens, B. & Theis, F.J. Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data. Bioinformatics 30, 1867–1875 (2014). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The number of required landmarks is related to spatial complexity, introducing the notion of power to detect spatial patterns.

    “The number of required landmarks is related to this spatial complexity, introducing the notion of power to detect spatial patterns93.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [93] Buettner, F., Moignard, V., Göttgens, B. & Theis, F.J. Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data. Bioinformatics 30, 1867–1875 (2014). source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationcitation

    STAT2 and IRF7 were predicted to control the module because variation in their protein expression levels was expected to propagate downstream and create variation in their targets' mRNA expression, as well as in their own mRNA expression through autoregulation.

    “These factors were predicted to control the module because variation in their protein expression levels was expected to propagate downstream and create variation in their targets’ mRNA expression, as well in as their own mRNA expression through autoregulation.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [3] Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Protocols that lyse entire cells measure total mRNA, and consequently they mix the fluctuating nuclear RNA and the more stable cytoplasmic RNA and combine transcriptional and post-transcriptional effects in a single measurement.

    “Second, protocols that lyse entire cells measure total mRNA, and consequently they mix the fluctuating nuclear RNA and the more stable cytoplasmic RNA177,178 and combine transcriptional and post-transcriptional effects in a single measurement.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [177] Xue, Z. et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 500, 593–597 (2013). source

      Not checked in the free run.

    • cites [178] Munsky, B., Neuert, G. & van Oudenaarden, A. Using gene expression noise to understand gene regulation. Science 336, 183–187 (2012). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    A related strategy to identify co-regulated genes is by comparing their expression between two conditions, each of which is represented by multiple individual cells from multiple samples.

    “A related strategy to identify co-regulated genes is by comparing their expression between two conditions, each of which is represented by multiple individual cells from multiple samples.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    Experimental and computational biologists are still learning the characteristics of single-cell data and the proper ways to accommodate them, while at the same time these very characteristics change rapidly as experimental technologies advance.

    “Experimental and computational biologists are still learning the characteristics of single-cell data and the proper ways to accommodate them, while at the same time these very characteristics change rapidly as experimental technologies advance.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    Single-cell genomics has opened a new frontier for understanding biological systems at multiple levels, including cell types and states, underlying molecular circuits, and tissue organization.

    “Single-cell genomics has opened a new frontier for understanding biological systems at multiple levels, including cell types and states, underlying molecular circuits, and tissue organization.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Functionally related genomic elements, such as all the sites bound by the same transcription factor, would co-vary within the same cell, providing a higher-order aggregate signal that allows one to group cells by their shared inferred state, as well as to determine the extent of variation in the activity of the associated factors between cells.

    “To overcome this intrinsic sparseness, several studies26,28 have reasoned that functionally related genomic elements, such as all the sites bound by the same transcription factor, would co-vary within the same cell (analogous to the observation above on transcript co-regulation to infer circuits), providing a higher-order aggregate signal that allows one to group cells by their shared inferred”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [26] Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015). source

      Not checked in the free run.

    • cites [28] Buenrostro, J.D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    Pioneering work over the past few years has provided an initial toolbox of algorithms.

    “Pioneering work over the past few years has provided an initial toolbox of algorithms.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Using the latest protocols for massively parallel scRNA-seq and mass cytometry, a single laboratory can now readily collect tens of thousands to hundreds of thousands of single-cell RNA-seq profiles and tens of millions of single-cell protein profiles.

    “Using the latest protocols for massively parallel scRNA-seq30,31 and mass cytometry32–34, a single laboratory can now readily collect tens of thousands to hundreds of thousands of single-cell RNA-seq profiles and tens of millions of single-cell protein profiles.”
    Quote in the paperSubstantial claim0 of 5 sources checked
    • cites [30] Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). source

      Not checked in the free run.

    • cites [31] Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015). source

      Not checked in the free run.

    • cites [32] Krishnaswamy, S. et al. Systems biology. Conditional density-based analysis of T cell signaling in single-cell data. Science 346, 1250689 (2014). source

      Not checked in the free run.

    • cites [33] Sen, N. et al. Single-cell mass cytometry analysis of human tonsil T cell remodeling by varicella zoster virus. Cell Reports 8, 633–645 (2014). source

      Not checked in the free run.

    • cites [34] Levine, J.H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015). source

      Not checked in the free run.

  • May combine two claims.

    Premisereasoning

    Single-cell epigenomic data are very sparse: a single cell contains only one copy of each allele molecule, and owing to the size of the genome and the limited efficiency of current single-cell epigenetics protocols, only a small subset of loci is measured in any given cell.

    “However, single-cell epigenomic data are very sparse: a single cell contains only one copy of each allele molecule, and owing to the size of the genome and the limited efficiency of current single-cell epigenetics protocols, only a small subset of loci is measured in any given cell.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Similar approaches have been used in other biological settings, and enhanced resolution, for example from pseudotemporal ordering, has helped recover such dependencies.

    “Similar approaches have been used in other biological settings1, and enhanced resolution, for example from pseudotemporal ordering, has helped recover such dependencies135,137.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [1] Gaublomme, J.T. et al. Single-cell genomics unveils critical regulators of Th17 Cell pathogenicity. Cell 163, 1400–1412 (2015). source

      Not checked in the free run.

    • cites [135] Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296–309 (2011). source

      Not checked in the free run.

    • cites [137] Wagner, F. GO-PCA: an unsupervised method to explore gene expression data using prior knowledge. PLoS One 10, e0143196 (2015). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Transcript abundance is affected not only by allele-extrinsic factors, but also by allele-intrinsic stochasticity, resulting in its divergence across cells exposed to the same extrinsic triggers.

    “First, transcript abundance is affected not only by allele-extrinsic factors, but also by allele-intrinsic stochasticity, resulting in its divergence across cells exposed to the same extrinsic triggers42,43 (see above and Fig. 2).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [42] Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008). source

      Not checked in the free run.

    • cites [43] Swain, P.S., Elowitz, M.B. & Siggia, E.D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci. USA 99, 12795–12800 (2002). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The statistically strongest correlations are often indicative of underlying regulatory circuits.

    “While the statistically strongest correlations are often indicative of underlying regulatory circuits (N.Y., A.R. and colleagues1)”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [1] Gaublomme, J.T. et al. Single-cell genomics unveils critical regulators of Th17 Cell pathogenicity. Cell 163, 1400–1412 (2015). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Some methods adapt well-established tools previously used in bulk genomics to the particular challenges of noise and scale in single-cell data, whereas others are designed specifically for single-cell data.

    “Some of these methods adapt well-established tools previously used in bulk genomics to the particular challenges of noise and scale in single-cell data1,44,89,90,105, whereas others are designed specifically for single-cell data71,93,135,137.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [1] Gaublomme, J.T. et al. Single-cell genomics unveils critical regulators of Th17 Cell pathogenicity. Cell 163, 1400–1412 (2015). source

      Not checked in the free run.

    • cites [44] Ilicic, T. et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17, 29 (2016). source

      Not checked in the free run.

    • cites [89] McDavid, A. et al. Modeling bi-modality improves characterization of cell cycle on gene expression in single cells. PLOS Comput. Biol. 10, e1003696 (2014). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    It is a challenge to effectively visualize such magnitudes of data.

    “It is also a challenge to effectively visualize such magnitudes of data”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    More recent studies have begun to determine regulatory circuit topology beyond pairwise interaction.

    “More recent studies have begun to determine regulatory circuit topology beyond pairwise interaction.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [141] Kafri, R. et al. Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle. Nature 494, 480–483 (2013). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Low capture ratios coupled to massive amplification would lead to epihaplotypes, similarly to falsely allelic scRNA-seq, where a gene could be erroneously classified as monoallelically expressed because of dropouts, and to allelic dropouts in single-cell DNA-seq due to which heterozygous variants could be falsely classified as homozygous.

    “Low capture ratios coupled to massive amplification would also lead to epihaplotypes, similarly to falsely allelic scRNA-seq, where a gene could be erroneously classified as monoallelically expressed because of dropouts38,75, and to allelic dropouts77 in single-cell DNA-seq due to which heterozygous variants could be falsely classified as homozygous.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [38] Kim, J.K., Kolodziejczyk, A.A., Ilicic, T., Teichmann, S.A. & Marioni, J.C. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat. Commun. 6, 8687 (2015). source

      Not checked in the free run.

    • cites [75] Grün, D. & van Oudenaarden, A. Design and analysis of single-cell sequencing experiments. Cell 163, 799–810 (2015). source

      Not checked in the free run.

    • cites [77] Reinius, B. et al. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq. Nat. Genet. http://dx.doi.org/10.1038/ng.3678 (2016). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationcitation

    The increase in scale presents computational opportunities, allowing benefitting from 'big data' approaches, most notably deep learning, developed in fields such as image and text analysis, in which data sets tend to have many more samples than were available in genomics thus far.

    “On the other hand, the increase in scale presents computational opportunities. It allows benefitting from ‘big data’ approaches, most notably deep learning193, developed in fields such as image and text analysis, in which data sets tend to have many more samples than were available in genomics thus far.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [193] Cedar, H. & Bergman, Y. Linking DNA methylation and histone modification: patterns and paradigms. Nat. Rev. Genet. 10, 295–304 (2009). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Data at this scale present difficulties in basic processing (e.g., calculating a covariation matrix; clustering) and in assessing the statistical significance and robustness of results.

    “Data at this scale present difficulties in basic processing192 (e.g., calculating a covariation matrix; clustering) and in assessing the statistical significance and robustness of results.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [192] Clark, S.J., Lee, H.J., Smallwood, S.A., Kelsey, G. & Reik, W. Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol. 17, 72 (2016). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    scRNA-seq makes it possible to study the mechanisms underlying allele-intrinsic variation by examining differences in other moments of the distribution (e.g., variance), which can shed light on the kinetics of the transcription process.

    “On the other hand, scRNA-seq does make it possible to study the mechanisms underlying allele-intrinsic variation by examining differences in other moments of the distribution (e.g., variance), which can shed light on the kinetics of the transcription process168,171,172.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [168] Rockhill, R.L., Euler, T. & Masland, R.H. Spatial order within but not between types of retinal neurons. Proc. Natl. Acad. Sci. USA 97, 2303–2307 (2000). source

      Not checked in the free run.

    • cites [171] Durruthy-Durruthy, R. et al. Reconstruction of the mouse otocyst and early neuroblast lineage at single-cell resolution. Cell 157, 964–978 (2014). source

      Not checked in the free run.

    • cites [172] Durruthy-Durruthy, R., Gottlieb, A. & Heller, S. 3D computational reconstruction of tissues with hollow spherical morphologies using single-cell gene expression data. Nat. Protoc. 10, 459–474 (2015). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The framework of expression quantitative trait loci (eQTLs) can be re-cast in single-cell analysis, where every cell is now an individual, and dependencies between single-cell genome variants and transcriptome variation allow inferring causality from the former to the latter.

    “For example, the framework of expression quantitative trait loci (eQTLs215) can be re-cast in single-cell analysis168, where every cell is now an individual, and dependencies between single-cell genome variants and transcriptome variation allow inferring causality from the former to the latter.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [215] Weirather, J.L. et al. Characterization of fusion genes and the significantly expressed fusion isoforms in breast cancer by hybrid sequencing. Nucleic Acids Res. 43, e116–e116 (2015). source

      Not checked in the free run.

    • cites [168] Rockhill, R.L., Euler, T. & Masland, R.H. Spatial order within but not between types of retinal neurons. Proc. Natl. Acad. Sci. USA 97, 2303–2307 (2000). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Genetic variation between cells, whether in normal or pathological contexts, requires specific computational methods.

    “Another prominent domain that requires specific computational methods is genetic variation between cells, whether in normal or pathological contexts.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    These methods have been used to decipher pathological evolution in tumors and somatic mosaicism in healthy tissues and in non-cancerous disease.

    “These methods have been used to decipher pathological evolution in tumors11,15,195–197 and somatic mosaicism198 in healthy tissues and in non-cancerous disease199–203.”
    Quote in the paperNot a substantial claim0 of 5 sources checked
    • cites [11] Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011). source

      Not checked in the free run.

    • cites [15] Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014). source

      Not checked in the free run.

    • cites [195] Dixon, J.R. et al. Chromatin architecture reorganization during stem cell differentiation. Nature 518, 331–336 (2015). source

      Not checked in the free run.

    • cites [196] Landan, G. et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat. Genet. 44, 1207–1214 (2012). source

      Not checked in the free run.

    • cites [197] Shipony, Z. et al. Dynamic and static maintenance of epigenetic memory in pluripotent and somatic cells. Nature 513, 115–119 (2014). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    A physical description is illuminating but insufficient to understand the operation of a circuit and how it creates a biological phenotype.

    “Just as for intracellular molecular circuits, a physical description is illuminating but insufficient to understand the operation of a circuit and how it creates a biological phenotype.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Even without new experimental methods, computational techniques can help relate distinct data types across single cells, as was effectively shown for relating spatially resolved in situ hybridization data to scRNA-seq to derive a spatial mapping of cells to positions.

    “Even without new experimental methods, computational techniques can help relate distinct data types across single cells, as was effectively shown for relating spatially resolved in situ hybridization data to scRNA-seq to derive a spatial mapping of cells to positions71,93.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [71] Marinov, G.K. et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014). source

      Not checked in the free run.

    • cites [93] Buettner, F., Moignard, V., Göttgens, B. & Theis, F.J. Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data. Bioinformatics 30, 1867–1875 (2014). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    This can boost the power of eQTL detection and analysis of factors that affect expression variance and temporal dynamics.

    “This can boost the power of eQTL detection168 and analysis of factors that affect expression variance and temporal dynamics.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [168] Rockhill, R.L., Euler, T. & Masland, R.H. Spatial order within but not between types of retinal neurons. Proc. Natl. Acad. Sci. USA 97, 2303–2307 (2000). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Integrating different molecular profiles at the single-cell level, either by direct experimental measurement or by computational inference, has exciting potential.

    “These possibilities highlight the exciting potential of integrating different molecular profiles at the single-cell level, either by direct experimental measurement or by computational inference.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Important strides have been made in single-cell DNA sequencing as well as in analysis of copy-number variations (CNVs) and point mutations from single-cell DNA data.

    “While our focus has been on single-cell transcriptomics, important strides have also been made in single-cell DNA sequencing77,194 as well as in analysis of copy-number variations (CNVs)11 and point mutations15,195 from single-cell DNA data.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [77] Reinius, B. et al. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq. Nat. Genet. http://dx.doi.org/10.1038/ng.3678 (2016). source

      Not checked in the free run.

    • cites [194] Zhou, V.W., Goren, A. & Bernstein, B.E. Charting histone modifications and the functional organization of mammalian genomes. Nat. Rev. Genet. 12, 7–18 (2011). source

      Not checked in the free run.

    • cites [11] Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011). source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Multiple-omic protocols will not only allow results to be corroborated by distinct data types but also elucidate how information flows in biological circuits from DNA to RNA to protein.

    “Multiple-omic protocols will not only allow results to be corroborated by distinct data types (e.g., CNV calling from DNA-seq11 and scRNA-seq data97) but also elucidate how information flows in biological circuits from DNA to RNA to protein.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [11] Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011). source

      Not checked in the free run.

    • cites [97] Antebi, Y.E. et al. Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates. PLoS Biol. 11, e1001616 (2013). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Certain data types require distinct computational strategies.

    “However, certain data types require distinct computational strategies.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Premisenone given

    Data sparsity currently characterizes all single-cell assays, and it is particularly challenging in epigenomic profiles.

    “For instance, while data sparsity currently characterizes all single-cell assays, it is particularly challenging in epigenomic profiles, as discussed above.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Early successes include parallel measurement of the transcriptome and either the genome (DR-seq and G&T-seq), the methylome (scM&T-seq, an adaptation of G&T-seq), or the proteome.

    “Early successes include parallel measurement of the transcriptome and either the genome (DR-seq209 and G&T-seq210), the methylome (scM&T-seq211, an adaptation of G&T-seq), or the proteome212–214.”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [209] McConnell, M.J. et al. Mosaic copy number variation in human neurons. Science 342, 632–637 (2013). source

      Not checked in the free run.

    • cites [210] Gole, J. et al. Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells. Nat. Biotechnol. 31, 1126–1132 (2013). source

      Not checked in the free run.

    • cites [211] Knouse, K.A., Wu, J., Whittaker, C.A. & Amon, A. Single cell sequencing reveals low levels of aneuploidy across mammalian tissues. Proc. Natl. Acad. Sci. USA 111, 13409–13414 (2014). source

      Not checked in the free run.

70 of 70

  • The authors state this as an assumption.

    Spike-in controls are unaffected by biological covariates.

    “are assumed to be unaffected by the biological covariates”

  • The authors state this as an assumption.

    The amount of pooled starting material is high enough to make uneven stochastic splitting of transcripts between libraries unlikely.

    “assuming that the amount of pooled starting material is high enough to make uneven stochastic splitting of transcripts between the libraries unlikely”

  • The authors state this as an assumption.

    Negative control genes are uniformly expressed by all cells in the sample.

    “negative control genes that are assumed a priori to be uniformly expressed by all the cells in the sample”

  • The paper relies on this without support.

    A cell's identity is reflected in its molecular profile.

    “A cell’s identity, which is reflected in its molecular profile”

  • The paper relies on this without support.

    Single-cell genomics has made it possible to create a comprehensive atlas of human cells.

    “Single-cell genomics has now made it possible to create a comprehensive atlas of human cells.”

  • The paper relies on this without support.

    PCA produces a deterministic and interpretable projection of genomic profiles into a lower dimension and is highly scalable.

    “principal-component analysis (PCA) produces a deterministic and interpretable projection of the genomic profiles into the lower dimension, and is highly scalable.”

  • The paper relies on this without support.

    Cell proportions between common and rare cell types in the same tissue differ by orders of magnitude, for example bone marrow hematopoietic stem cells are present at less than 1 in 100,000 while neutrophils exceed 50%.

    “the orders of magnitude of difference in cell proportions between a common and rare cell type in the same tissue (e.g., in the bone marrow, hematopoietic stem cells) are present at <1:100,000; neutrophils are >50%”

  • The paper relies on this without support.

    Pathological tissues such as tumors are idiosyncratic; each tissue is unique.

    “it is not the case in pathological tissues, such as tumors, where each tissue is idiosyncratic.”

  • The paper relies on this without support.

    The probability that a gene will drop out, once conditioned on its expression, is independent from biological gene-to-gene covariation.

    “because the probability that a gene will drop out, once conditioned on its expression, is independent from biological gene-to-gene covariation.”
9 of 9

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?

  • CROP-seq includes the gRNA in a poly-A mRNA transcript, which is sequenced with the rest of the mRNA, simplifying the screening of larger libraries of gRNAs.

    “In contrast, CROP-seq includes the gRNA in a poly-A mRNA transcript, which is sequenced with the rest of the mRNA, simplifying the screening of larger libraries of gRNAs [93].”

    From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [93]

  • Current single-cell RNA-seq protocols need fresh tissue to make single-cell suspensions, which limits clinical, archived, and hard-to-dissociate samples.

    “However, current protocols require preparation of single cell suspension from fresh tissue, a major roadblock in many applications, including handling of clinical samples, archived materials, and tissues that cannot be readily dissociated.”

    From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [1]

  • Single-cell transcriptome methods have discovered new cell types and functional cell states, which motivates tools for single-cell chromatin regulation.

    “In parallel, methods to measure the transcriptomes of single cells have been used to discover new cell types11 and new functional cell states12,13, and provide additional motivation for the development of tools to measure chromatin regulation at single-cell resolution14.”

    From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [13]

  • There are several axes of variation that determine cellular identity.

    “While it may be tempting to assume that the clusters detected in single‐cell data represent cell types, there are several axes of variation that determine cellular identity (Wagner et al, 2016; Clevers et al, 2017).”

    From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Wagner et al, 2016

  • Subclustering can allow identification of cell states within cell-type clusters, but may also lead to patterns that arise only from noise.

    “Such subclustering can allow the user to identify cell states within cell‐type clusters (Wagner et al, 2016), but may also lead to patterns that arise only from noise in the data.”

    From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Wagner et al, 2016

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.

  • Oscope clustering target changes from cells to genes

    Minor

    The Oscope description says it clusters cells using errors calculated for gene pairs, but the next sentence says each resulting cluster consists of genes. These are different clustering targets.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 8

  1. Hicks, S.C., Teng, M. & Irizarry, R.A. On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data. bioRxiv (2015).
  2. Beyer, K.S., Goldstein, J., Ramakrishnan, R. & Shaft, U. When is “nearest neighbor” meaningful? in Proceedings of the 7th International Conference on Database Theory (ICDT'99) (eds. Beeri, C. & Buneman, P.) 217–235 (Springer, 1999).
  3. van der Maaten, L. & Hinton, G.E. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
  4. van der Maaten, L. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014).
  5. Maaten, L. Learning a parametric embedding by preserving local structure. in Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09) (eds. Dyk, D.V & Welling, M.) 384–391 (2009).
  6. Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon, USA (eds. Simoudis, E., Han, J. & Fayyad, U.) 226–231 (AAAI Press, 1996).
  7. Lawrence, N. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Mach. Learn. Res. 6, 1783–1816 (2005).
  8. Risso, D. et al. Power gain: how normalization affects reproducibility and biological insight of RNA-seq studies in neuroscience [v1; not peer reviewed]. F1000Research ISCB Comm J. 4, 411 (2015).

Cited by

Sorted by citations

Authors
Wagner, A., Regev, A., & Yosef, N.
Affiliations
  • University of California, Berkeley (US)
  • Broad Institute (US)
  • Ragon Institute of MGH, MIT and Harvard (US)
Corresponding author
Regev, Aviv
ORCID
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2016-11
Volume
34
Issue
11
Pages
1145-1160
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
DOI
10.1038/nbt.3711
PMC
PMC5465644
OpenAlex
W2555892463
Cited by
632
References
219
Citations by year
2026: 43 · 2025: 55 · 2024: 52 · 2023: 53 · 2022: 57 · 2021: 61 · 2020: 84 · 2019: 91 · 2018: 79 · 2017: 57
FWCI
6.88
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
  • PMC
Funders
  • Howard Hughes Medical Institute
  • Broad Institute (P30-CA14051)
  • Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology (P30-CA14051)
  • National Institutes of Health (5rm1hg006193-10)
  • Klarman Cell Observatory, Broad Institute (1U24CA180922, P30-CA14051)
  • National Institute of Mental Health (U01MH105979, U01MH105960)
  • National Human Genome Research Institute (RM1‐HG006193, P50HG006193)
  • National Cancer Institute (U24CA180922, CA14051, P30CA014051, P30-CA-14051)
  • National Institute of Allergy and Infectious Diseases (P30-CA14051, U24AI118672)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
cell identity, single-cell RNA sequencing, cellular heterogeneity, technical noise
MeSH terms
Animals, Cell Physiological Phenomena, Computer Simulation, Gene Expression Regulation, Humans, Models, Biological, Signal Transduction, Proteome, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-05.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-05.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review