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

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

doi:10.1101/036863

Cited by 22 publications

(33 citation statements)

References 54 publications

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“…As previously proposed in [11], the equivalence class [15,[19][20][21][22][23][24] that gives rise to a fragment encodes some positional information, by means of encoding the set of transcripts to which the fragment is mapped. This can be used to avoid over-collapsing UMI tags that are likely to result from different molecules by considering UMIs as distinct per equivalence class.…”

Section: Mapping Reads and Umi De-duplicationmentioning

confidence: 99%

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Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

Srivastava

Malik

Smith

et al. 2018

Preprint

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We introduce alevin, an efficient pipeline for gene quantification from dscRNA-seq (droplet-based single-cell RNA-seq) data. Alevin is an end-to-end quantification pipeline that starts from sample-demultiplexed FASTQ files and generates gene-level counts for two popular droplet-based sequencing protocols (drop-seq [1], and 10x-chromium [2]). Importantly, alevin handles all processing internally, avoiding reliance on external pipeline programs, and the need to write large intermediate files to disk. Alevin adopts efficient algorithms for cellular-barcode whitelist generation, cellular-barcode correction, lightweight per-cell UMI deduplication and quantification. This integrated solution allows alevin to process data much faster (typically ∼ 10 times faster) than other approaches, while also working within a reasonable memory budget. This enables full, end-to-end analysis for single-cell human experiment consisting of ∼ 4500 cells with 335 Million reads with 13G of RAM and 8 threads (of an Intel Xeon E5-2699 v4 CPU) in 27 minutes.

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Section: Mapping Reads and Umi De-duplicationmentioning

confidence: 99%

“…Second, and more importantly, the output of the pipelines is not of the same basic form. While alevin, by default, produces a gene-level count matrix, the kb-pipe method produces transcript compatibility counts [11] (i.e. equivalence class counts) as output.…”

Section: Comparing Alevin To Existing Approachesmentioning

confidence: 99%

Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

Srivastava

Malik

Smith

et al. 2018

Preprint

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“…formance is nontrivial. While there has been some work examining the dependence of principal components analysis on read depth [4,5], the methods used do not extend quantitatively to other multivariate analyses.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq

Svensson

Beltrame

Pachter

2019

Preprint

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The allocation of a sequencing budget when designing single cell RNA-seq experiments requires consideration of the tradeoff between number of cells sequenced and the read depth per cell. One approach to the problem is to perform a power analysis for a univariate objective such as differential expression. However, many of the goals of single-cell analysis requires consideration of the multivariate structure of gene expression, such as clustering. We introduce an approach to quantifying the impact of sequencing depth and cell number on the estimation of a multivariate generative model for gene expression that is based on error analysis in the framework of a variational autoencoder. We find that at shallow depths, the marginal benefit of deeper sequencing per cell significantly outweighs the benefit of increased cell numbers. Above about 15,000 reads per cell the benefit of increased sequencing depth is minor. Code for the workflow reproducing the results of the paper is available at https://github.com/pachterlab/SBP_2019/.

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“…Modern advances in single-cell technologies can cheaply generate genomic profiles of millions of individual cells [1,2]. Depending on the type of assay, these profiles can describe cell features such as RNA expression, transcript compatability counts [3], epigenetic features [4], or nuclear RNA expression [5]. Because the cell types of individual cells often cannot be known prior to the computational step, a key step in single-cell computational pipelines [6,7,8,9,10] is clustering: organizing individual cells into biologically meaningful populations.…”

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confidence: 99%

Valid post-clustering differential analysis for single-cell RNA-Seq

Zhang

Kamath

Tse

2018

Preprint

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Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (di↵erential expression analysis). Stateof-the-art pipelines perform di↵erential analysis after clustering on the same dataset. We observe that because clustering forces separation, reusing the same dataset generates artificially low p-values and hence false discoveries. In this work, we introduce a valid postclustering di↵erential analysis framework which corrects for this problem. We provide software at https://github.com/jessemzhang/tn_test. ⇤ Corresponding Author.

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

Cited by 22 publications

References 54 publications

Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

Alevin efficiently estimates accurate gene abundances from dscRNA-seq data

Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq

Valid post-clustering differential analysis for single-cell RNA-Seq

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