Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model

Townes, F. William; Hicks, Stephanie C.; Aryee, Martin J.; Irizarry, Rafael A.

doi:10.1101/574574

Cited by 140 publications

(296 citation statements)

References 51 publications

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“…If the scale parameter were also held fixed, the QUMI target distribution would be identical for every cell and would predict a constant zero fraction across cells. But this is discordant with the fact that UMI count data exhibit variation in the zero fraction across cells [6]. Since the varying zero fractions in read counts exactly match the zero fractions in underlying UMI counts, it would be inappropriate to alter these correct expression values by normalizing to a global target distribution.…”

Section: Quantile Normalization Of Read Counts To Quasi-umismentioning

confidence: 99%

“…This is a result of cell-to-cell differences in capture and RT efficiency, which has nothing to do with underlying biology. For UMI counts, systematic variation introduced by these technical components can be addressed by using multinomial models [6]. However, for read counts, such models are precluded by the additional multiplicative distortions of PCR.…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the large number of zeros in scRNA-seq, log transformation of normalized counts requires a pseudocount, which introduces substantial bias [10]. The resulting distributions can be far from Gaussian, even for UMI count data [6]. In contrast, the census counts method transforms read counts and attempts to match the underlying UMI distribution based on the key observation that the mode of the nonzero UMI count distribution is typically one.…”

Section: Introductionmentioning

confidence: 99%

“…Rather than matching a normal distribution, this approach needs only to remove PCR bias to be effective. The resulting census counts can be analyzed as if they were UMI counts by methods specifically developed for UMI data [11,6]. Census count normalization relies on a complex mechanistic model of scRNA-seq biochemistry and applies a linear transformation [1].…”

Section: Introductionmentioning

confidence: 99%

“…The most widely implemented version of quantile normalization generates the target distribution by averaging over empirical distributions from the data [12]. In the case of scRNA-seq, however, we know that if we could remove PCR duplicates from read counts we would obtain UMI counts, which have a markedly different distribution from any of the empirical read count distributions [6]. We therefore use the characteristics of UMI counts as a guide to construct the QUMI target distribution such that it will approximate a true UMI count distribution.…”

Section: Introductionmentioning

confidence: 99%

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Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

Townes

Irizarry

2019

Preprint

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Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than alternatives such as census counts. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNAseq datasets.

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Section: Quantile Normalization Of Read Counts To Quasi-umismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

Townes

Irizarry

2019

Preprint

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TWO‐SIGMA: A novel two‐component single cell model‐based association method for single‐cell RNA‐seq data

Buren

Weng

et al. 2020

Genetic Epidemiology

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In this paper, we develop TWO-SIGMA, a TWO-component SInGle cell Modelbased Association method for differential expression (DE) analyses in single-cell RNA-seq (scRNA-seq) data. The first component models the probability of "drop-out" with a mixed-effects logistic regression model and the second component models the (conditional) mean expression with a mixed-effects negative binomial regression model. TWO-SIGMA is extremely flexible in that it: (i) does not require a log-transformation of the outcome, (ii) allows for overdispersed and zero-inflated counts, (iii) accommodates a correlation structure between cells from the same individual via random effect terms, (iv) can analyze unbalanced designs (in which the number of cells does not need to be identical for all samples), (v) can control for additional sample-level and cell-level covariates including batch effects, (vi) provides interpretable effect size estimates, and (vii) enables general tests of DE beyond two-group comparisons. To our knowledge, TWO-SIGMA is the only method for analyzing scRNA-seq data that can simultaneously accomplish each of these features. Simulations studies show that TWO-SIGMA outperforms alternative regression-based approaches in both type-I error control and power enhancement when the data contains even moderate within-sample correlation. A real data analysis using pancreas islet single-cells exhibits the flexibility of TWO-SIGMA and demonstrates that incorrectly failing to include random effect terms can have dramatic impacts on scientific Di Wu contributed equally to this study.

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Differential expression of single‐cell RNA‐seq data using Tweedie models

Mallick

Chatterjee

Chowdhury

et al. 2022

Statistics in Medicine

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The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform-and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate-and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control.Himel Mallick and Suvo Chatterjee contributed equally to this article.

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Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model

Cited by 140 publications

References 51 publications

Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

TWO‐SIGMA: A novel two‐component single cell model‐based association method for single‐cell RNA‐seq data

Differential expression of single‐cell RNA‐seq data using Tweedie models

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