Eleven grand challenges in single-cell data science

Laehnemann, David; Köster, Johannes; Szczurek, Ewa; McCarthy, Davis J.; Hicks, Stephanie; Robinson, Mark D.; Vallejos, Catalina A.; Campbell, Kieran R; Beerenwinkel, Niko; Mahfouz, Ahmed; Pinello, Luca; Skums, Pavel; Stamatakis, Alexandros; Attolini, Camille Stephan‐Otto; Aparicio, Samuel; Baaijens, Jasmijn A.; Balvert, Marleen; Barbanson, Buys de; Cappuccio, Antonio; Corleone, Giacomo; Dutilh, Bas E.; Florescu, Maria; Guryev, Victor; Holmer, Rens; Jahn, Katharina; Lobo, Thamar Jessurun; Keizer, Emma M; Khatri, Indu; Kiełbasa, Szymon M.; Korbel, Jan; Kozlov, Alexey M; Kuo, Tzu Hao; Lelieveldt, Boudewijn P. F.; Măndoiu, Ion; Marioni, John C.; Marschall, Tobias; Mölder, Felix; Niknejad, Amir; Rączkowski, Łukasz; Reinders, Marcel J. T.; Ridder, Jeroen de; Saliba, Antoine Emmanuel; Somarakis, Antonios; Stegle, Oliver; Theis, Fabian J.; Yang, Huan; Zelikovsky, Alexander; McHardy, Alice C.; Raphael, Benjamin J.; Shah, Sohrab P.; Schönhuth, Alexander

doi:10.1186/s13059-020-1926-6

Cited by 994 publications

(985 citation statements)

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“…They could e.g. prove useful in models for phylogenetic reconstruction of the lineage relationship of sequenced single cells 29,48,49 , while keeping them computationally tractable 13 .…”

Section: /28mentioning

confidence: 99%

“…In general, the use of a fixed empirical model for MDA allelic bias does not seem to impede ProSolo's performance in alternative allele calling compared to the other tools, but has a noticeable effect on genotyping (Supplement, Section S 2.6) and might be responsible for slight imprecisions when controlling for very small false discovery rates (Supplement Section S 2.4). When future datasets are generated based on improved MDA protocols 13 , these effects might be exacerbated.…”

Section: /28mentioning

confidence: 99%

“…In cancer development, this variation can be used to trace the cellular ancestry of tumour subclones and metastases [8][9][10][11] , and to characterise the evolutionary dynamics of cancer progression 12,13 . In the long run, methods that account for the dynamics of mutational signatures in cellular evolution will improve diagnosis, treatment and prognosis of diseases for which somatic alterations are a key factor.…”

Section: Introductionmentioning

confidence: 99%

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ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data

Laehnemann

Köster

Fischer

et al. 2020

Preprint

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1Obtaining accurate mutational profiles from single cell DNA is essential for the analysis of genomic cell-to-cell heterogeneity at the finest level of resolution. However, sequencing libraries suitable for genotyping require whole genome amplification, which introduces allelic bias and copy errors. As a result, single cell DNA sequencing data violates the assumptions of variant callers developed for bulk sequencing, which when applied to single cells generate significant numbers of false positives and false negatives. Only dedicated models accounting for amplification bias and errors will be able to provide more accurate calls.We present ProSolo, a probabilistic model for calling single nucleotide variants from multiple displacement amplified single cell DNA sequencing data. It introduces a mechanistically motivated empirical model of amplification bias that improves the quantification of genotyping uncertainty. To account for amplification errors, it jointly models the single cell sample with a bulk sequencing sample from the same cell population-also enabling a biologically relevant imputation of missing genotypes for the single cell.Through these innovations, ProSolo achieves substantially higher performance in calling and genotyping single nucleotide variants in single cells in comparison to all state-of-the-art tools. Moreover, ProSolo implements the first approach to control the false discovery rate reliably and flexibly; not only for single nucleotide variant calls, but also for artefacts of single cell methodology that one may wish to identify, such as allele dropout. ProSolo's model is implemented into a flexible framework, encouraging extensions. The source code and usage instructions are available at: https://github.com/prosolo/prosolo ADO -allele dropout, alt -alternative, err -error, het -heterozygous, hom -homozygous, ref -reference. 6/28 18/28

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“…They could e.g. prove useful in models for phylogenetic reconstruction of the lineage relationship of sequenced single cells 29,48,49 , while keeping them computationally tractable 13 .…”

Section: /28mentioning

confidence: 99%

Section: /28mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data

Laehnemann

Köster

Fischer

et al. 2020

Preprint

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“…However, biases in the transcriptome pool may result from the initial amplification, as some cDNA evade amplification/mRNA capture 6 . These biases contribute to technical dropout, wherein uneven and pseudo-random detection of medium-and low-expressed genes significantly occludes scRNAseq results and has been recognized as a key concern for single-cell experiments 7,8,9 . While a number of computational approaches have been designed to help overcome dropout through imputation, such methods have difficulty in recovering true gene-gene co-expression relationships and consequent co-expression networks 10,11 .…”

Section: Main (2261/2000)mentioning

confidence: 99%

scSTATseq: Diminishing Technical Dropout Enables Core Transcriptome Recovery and Comprehensive Single-cell Trajectory Mapping

Zheng¹,

Tang

Qiu³

et al. 2020

Preprint

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157/150)The advent of single-cell RNA sequencing has provided illuminating information on complex systems. However, large numbers of genes tend to be scarcely detected in common scRNAseq approaches due to technical dropout. Although bioinformatics approaches have been developed to approximate true expression profiles, assess the dropout events on single-cell transcriptomes is still consequently challenging. In this report, we present a new plate-based method for scRNAseq that relies on Tn5 transposase to tagment cDNA following second strand synthesis. By utilizing pre-amplification tagmentation step, scSTATseq libraries are insulated against technical dropout, allowing for detailed analysis of gene-gene co-expression relationships and mapping of pathway trajectories. The entire scSTATseq library construction workflow can be completed in 7 hours, and recover transcriptome information on up to 8,000 protein-coding genes. Investigation of osteoclast differentiation using this workflow allowed us to identify novel markers of interest such as Rab15. Overall, scSTATseq is an efficient and economical method for scRNAseq that compares favorably with existing workflows.

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“…Single cell differential gene expression analysis typically seeks to identify genes whose expression levels are markedly different between sets of cells of different cell types [14][15][16]. Here, we focus on the identification of cell type specific DEGs between sets of samples from different experimental conditions or genotypes, each measured at the single cell level, which has been identified as one of the grand challenges for single cell data analysis [17]. Expression quantitative trait locus (eQTL) [18][19][20][21] analysis is a special case of differential gene expression analysis where gene expression is combined with genetic information.…”

Section: Introductionmentioning

confidence: 99%

Design and power analysis for multi-sample single cell genomics experiments

Schmid

Cruceanu

Böttcher

et al. 2020

Preprint

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Background:The identification of genes associated with specific experimental conditions, genotypes or phenotypes through differential expression analysis has long been the cornerstone of transcriptomic analysis. Single cell RNA-seq is revolutionizing transcriptomics and is enabling interindividual differential gene expression analysis and identification of genetic variants associated with gene expression, so called expression quantitative trait loci at cell-type resolution. Current methods for power analysis and guidance of experimental design either do not account for the specific characteristics of single cell data or are not suitable to model interindividual comparisons. Results: Here we present a statistical framework for experimental design and power analysis of single cell differential gene expression between groups of individuals and expression quantitative trait locus analysis. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within individual cell types. Power analysis is based on data driven priors from literature or pilot experiments across a wide range of application scenarios and single cell RNA-seq platforms. Using these priors we show that, for a fixed budget, the number of cells per individual is the major determinant of power. Conclusion: Our model is general and allows for systematic comparison of alternative experimental designs and can thus be used to guide experimental design to optimize power. For a wide range of applications, shallow sequencing of high numbers of cells per individual leads to higher overall power than deep sequencing of fewer cells. The model is implemented as an R package scPower.

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Eleven grand challenges in single-cell data science

Cited by 994 publications

References 386 publications

ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data

ProSolo: Accurate Variant Calling from Single Cell DNA Sequencing Data

scSTATseq: Diminishing Technical Dropout Enables Core Transcriptome Recovery and Comprehensive Single-cell Trajectory Mapping

Design and power analysis for multi-sample single cell genomics experiments

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