Droplet scRNA-seq is not zero-inflated

Svensson, Valentine

doi:10.1101/582064

Cited by 143 publications

(226 citation statements)

References 35 publications

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“…However, 98.3% of ERCC spike-ins and 94% of the control RNA under study are retrieved as NB. These results corroborates the hypothesis from [7] that droplet-based ERCC spike-ins measurements are not zero-inflated and even may extend it to the control RNAs part of these datasets.…”

Section: Application To Detecting Zero-inflation In Real Datasetssupporting

confidence: 90%

“…However, for all cell types, we find that the fraction of predicted ZINB genes decreases with the version of 10X Chromium, potentially showing additional technical factors in zero-inflation. This may lead to a less straightforward conclusion than in [7] where droplet-based zero-inflation is stated as only biological.…”

Section: Application To Detecting Zero-inflation In Real Datasetsmentioning

confidence: 94%

“…Negative control (ERCC spike-in and RNA) We apply AutoZI to four droplet-based negative control datasets, spanning a wide range of experimental protocols (10x Chromium v1 [14], inDrops [15] and GemCode [16]) and based on ERCC spike-ins and control RNAs. Such datasets do not capture any biological process and the ERCCs were shown not to be zero inflated in [7]. To investigate whether AutoZI decision-making mechanism can reproduce similar results, we selected ERCC spike-ins and added the 100 most expressed control RNAs in each dataset for joint analysis.…”

Section: Application To Detecting Zero-inflation In Real Datasetsmentioning

confidence: 99%

“…A recent study [7] however questioned the universality of zero-inflation added to NB models for scRNA-seq data. This analysis was primarily based on datasets of "negative controls" (e.g., ERCC spike-ins) -namely exogenous transcripts that are constitutively expressed, thus reducing the contribution of biological factors as a cause for zero measurements.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

Clivio

Lopez

Regier

et al. 2019

Preprint

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In single-cell RNA sequencing data, biological processes or technical factors may induce an overabundance of zero measurements. Existing probabilistic approaches to interpreting these data either model all genes as zero-inflated, or none. But the overabundance of zeros might be gene-specific. Hence, we propose the AutoZI model, which, for each gene, places a spike-and-slab prior on a mixture assignment between a negative binomial (NB) component and a zero-inflated negative binomial (ZINB) component. We approximate the posterior distribution under this model using variational inference, and employ Bayesian decision theory to decide whether each gene is zero-inflated. On simulated data, AutoZI outperforms the alternatives. On negative control data, AutoZI retrieves predictions consistent to a previous study on ERCC spike-ins and recovers similar results on control RNAs. Applied to several datasets and instances of the 10x Chromium protocol, AutoZI allows both biological and technical interpretations of zero-inflation. Finally, AutoZI's decisions on mouse embyronic stem-cells suggest that zero-inflation might be due to transcriptional bursting.

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Section: Application To Detecting Zero-inflation In Real Datasetssupporting

confidence: 90%

Section: Application To Detecting Zero-inflation In Real Datasetsmentioning

confidence: 94%

Section: Application To Detecting Zero-inflation In Real Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

Clivio

Lopez

Regier

et al. 2019

Preprint

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“…Here we show that empirical statistics on gene detection rate can in fact provide accurate estimates of gene-cell type specificity. This is consistent with the recent observation that droplet scRNA-Seq data is not zero-inflated [26], indicating that expression "drop-outs" tend to be biologically driven rather than technically driven. Gene detection rate correlates with gene expression level, often following a sigmoid-like curve [3,4], regardless of the scRNA-Seq protocol used (Supplementary Figure S1).…”

Section: Discussionsupporting

confidence: 91%

genesorteR: Feature Ranking in Clustered Single Cell Data

Ibrahim

Kramann²

2019

Preprint

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Marker genes identified in single cell experiments are expected to be highly specific to a certain cell type and highly expressed in that cell type. Detecting a gene by differential expression analysis does not necessarily satisfy those two conditions and is typically computationally expensive for large cell numbers.Here we present genesorteR, an R package that ranks features in single cell data in a manner consistent with the expected definition of marker genes in experimental biology research. We benchmark genesorteR using various data sets and show that it is distinctly more accurate in large single cell data sets compared to other methods. genesorteR is orders of magnitude faster than current implementations of differential expression analysis methods, can operate on data containing millions of cells and is applicable to both single cell RNA-Seq and single cell ATAC-Seq data.genesorteR is available at https://github.com/mahmoudibrahim/genesorteR.

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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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Droplet scRNA-seq is not zero-inflated

Cited by 143 publications

References 35 publications

Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

genesorteR: Feature Ranking in Clustered Single Cell Data

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

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