Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts

Ntranos, Vasilis; Kamath, Govinda M.; Zhang, Jesse M.; Pachter, Lior; Tse, David

doi:10.1186/s13059-016-0970-8

Cited by 109 publications

(95 citation statements)

References 54 publications

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“…Pseudoalignment divides (under reasonable assumptions 121 ) reads into equivalence classes, each consisting of the set of transcripts the read could have originated from. By counting the number of reads a cell has in each of the classes (transcript-compatibility counts, TCC), one obtains a high-dimensional representation of the cells with features other than explicitly quantified gene expression 122 . While this feature space is not as biologically interpretable as gene expression space, it can produce 122 a similar cell clustering while sidestepping the time-consuming quantification task and avoiding the need to define a statistical model for read generation.…”

Section: Revealing the Vectors Of Cellular Identitymentioning

confidence: 99%

“…By counting the number of reads a cell has in each of the classes (transcript-compatibility counts, TCC), one obtains a high-dimensional representation of the cells with features other than explicitly quantified gene expression 122 . While this feature space is not as biologically interpretable as gene expression space, it can produce 122 a similar cell clustering while sidestepping the time-consuming quantification task and avoiding the need to define a statistical model for read generation. Therefore, this approach reverses the order of quantification and clustering: one first clusters cells in the TCC space, and then quantifies gene expression only from representative cells in each cluster, or pooled data from the entire cluster, to assign a biological interpretation to the clusters.…”

Section: Revealing the Vectors Of Cellular Identitymentioning

confidence: 99%

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Revealing the vectors of cellular identity with single-cell genomics

2016

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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 type and of the ways in which they are 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 a taxonomy of 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.

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Section: Revealing the Vectors Of Cellular Identitymentioning

confidence: 99%

Section: Revealing the Vectors Of Cellular Identitymentioning

confidence: 99%

Revealing the vectors of cellular identity with single-cell genomics

2016

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“…For gene expression quantification some examples include; STAR, RSEM, the Tuxedo Suite, and Kallisto [25–29]. These approaches map sequencing reads to a reference genome, a transcriptome index or perform de novo assembly without a reference genome to allow for expression quantification.…”

Section: Data Processingmentioning

confidence: 99%

“…We illustrate a simple example using Kallisto, which quantifies expression quickly due to its use of transcriptome psuedoalignment [25,29]. The output from Kallisto will provide gene expression in transcripts per million (TPM) which is normalized for library depth.…”

Section: Figurementioning

confidence: 99%

Single-Cell Genomics: Approaches and Utility in Immunology

Neu

Tang

Wilson

et al. 2017

Trends in Immunology

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Single cell genomics offers powerful tools for studying lymphocytes, which make it possible to observe rare and intermediate cell states that cannot be resolved at the population-level. Advances in computer science and single cell sequencing technology have created a data-driven revolution in immunology. The challenge for immunologists is to harness computing and turn an avalanche of quantitative data into meaningful discovery of immunological principles, predictive models, and strategies for therapeutics. Here, we review the current literature on computational analysis of single cell RNA-seq data and discuss underlying assumptions, methods, and applications in immunology, and highlight important directions for future research.

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“…DTU testing on equivalence classes is fast and alleviates shortcomings in directly estimating transcript abundances before statistical testing. Indeed, performing analysis directly on equivalent classes has been proposed previously in the context of fast clustering single-cell RNA-seq data 7 .…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate differential transcript usage by testing equivalence class counts

Cmero

Davidson

Oshlack

2018

Preprint

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RNA sequencing has enabled high-throughput and fine-grained quantitative analyses of the transcriptome. While differential gene expression is the most widely used application of this technology, RNA-seq data also has the resolution to infer differential transcript usage (DTU), which can elucidate the role of different transcript isoforms between experimental conditions, cell types or tissues. DTU has typically been inferred from exon-count data, which has issues with assigning reads unambiguously to counting bins, and requires alignment of reads to the genome. Recently, approaches have emerged that use transcript quantifications estimates directly for DTU. Transcript counts can be inferred from 'pseudo' or lightweight aligners, which are significantly faster than traditional genome alignment. However, recent evaluations show lower sensitivity in DTU analysis. Transcript abundances are estimated from equivalence classes (ECs), which determine the transcripts that any given read is compatible with. Here we propose performing DTU testing directly on equivalence class read counts. We evaluate this approach on simulated human and drosophila data, as well as on a real dataset through subset testing. We find that ECs counts have similar sensitivity and false discovery rates as exon-level counts but can be generated in a fraction of the time through the use of pseudo-aligners. We posit that equivalent class counts is a natural unit on which to perform many types of analysis.

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Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts

Cited by 109 publications

References 54 publications

Revealing the vectors of cellular identity with single-cell genomics

Revealing the vectors of cellular identity with single-cell genomics

Single-Cell Genomics: Approaches and Utility in Immunology

Fast and accurate differential transcript usage by testing equivalence class counts

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