Bayesian inference of gene expression states from single-cell RNA-seq data

Breda, Jérémie; Zavolan, Mihaela; Nimwegen, Erik van

doi:10.1038/s41587-021-00875-x

Cited by 56 publications

(86 citation statements)

References 54 publications

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“…A parallel publication [ 50 ] suggested a Bayesian procedure named Sanity for estimating expression strength underlying the observed UMI counts, based on Poisson likelihood and Bayesian shrinkage. Importantly, Pearson residuals are not aiming at estimating the underlying expression strength; rather, they quantify how strongly each observed UMI count deviates from the null model of constant expression across cells.…”

Section: Discussionmentioning

confidence: 99%

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Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

2021

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Background Standard preprocessing of single-cell RNA-seq UMI data includes normalization by sequencing depth to remove this technical variability, and nonlinear transformation to stabilize the variance across genes with different expression levels. Instead, two recent papers propose to use statistical count models for these tasks: Hafemeister and Satija (Genome Biol 20:296, 2019) recommend using Pearson residuals from negative binomial regression, while Townes et al. (Genome Biol 20:295, 2019) recommend fitting a generalized PCA model. Here, we investigate the connection between these approaches theoretically and empirically, and compare their effects on downstream processing. Results We show that the model of Hafemeister and Satija produces noisy parameter estimates because it is overspecified, which is why the original paper employs post hoc smoothing. When specified more parsimoniously, it has a simple analytic solution equivalent to the rank-one Poisson GLM-PCA of Townes et al. Further, our analysis indicates that per-gene overdispersion estimates in Hafemeister and Satija are biased, and that the data are in fact consistent with the overdispersion parameter being independent of gene expression. We then use negative control data without biological variability to estimate the technical overdispersion of UMI counts, and find that across several different experimental protocols, the data are close to Poisson and suggest very moderate overdispersion. Finally, we perform a benchmark to compare the performance of Pearson residuals, variance-stabilizing transformations, and GLM-PCA on scRNA-seq datasets with known ground truth. Conclusions We demonstrate that analytic Pearson residuals strongly outperform other methods for identifying biologically variable genes, and capture more of the biologically meaningful variation when used for dimensionality reduction.

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Section: Discussionmentioning

confidence: 99%

“…the variance of Pearson residuals grows linearly with μ . This makes sense because for higher-expressed genes there is more statistical certainty about over-Poisson variability, but at the same time highlights that Pearson residuals do not aim to estimate the underlying (log-)expression, unlike, e.g., Sanity [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

2021

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“…However, the reported sequencing depth is typically over-inflated compared to the true nucleotide capture probability of the experiments leading to an inflated estimate of the total likelihood. This issue has been well discussed in single cell RNA sequencing (see for example 47 ). One approach to solve this in the context of the microbiome to obtain technical repeats which can in turn allow us to estimate the true technical noise.…”

Section: Discussionmentioning

confidence: 98%

EMBED: Essential Microbiome Dynamics, a dimensionality reduction approach for longitudinal microbiome studies

Shahin

Dixit

2022

Preprint

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Dimensionality reduction can offer unique insights into high dimensional microbiome dynamics by leveraging collective abundance fluctuations of multiple bacteria driven by similar ecological perturbations. However, methods providing lower-dimensional representations of microbiome dynamics both at the community and individual taxa level are not currently available. To that end, we present EMBED: Essential MicroBiomE Dynamics, a probabilistic non-linear tensor factorization approach. Similar to normal mode analysis in structural biophysics, EMBED infers ecological normal modes (ECNs), which represent the unique orthogonal modes capturing the collective behavior of microbial communities. A very small number of ECNs can accurately approximate microbiome dynamics across multiple data sets. Inferred ECNs reflect specific ecological behaviors, providing natural templates along which the dynamics of individual bacteria may be partitioned. Moreover, the multi-subject treatment in EMBED systematically identifies subject-specific and universal abundance dynamics that are not detected by traditional approaches. Collectively, these results highlight the utility of EMBED as a versatile dimensionality reduction tool for studies of microbiome dynamics.

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“…The key underlying assumption is that the observed mRNA counts are the combined result of the inherent biological variability between cells and of the sampling process due to RNA capture and sequencing [11, 20]. Therefore, the probability of observing a specific expression profile in a cell c , from which M c transcripts have been sequenced, is given by where represents the true frequency of the mRNA i in cell c .…”

Section: Methodsmentioning

confidence: 99%

Emergent Statistical Laws in Single-Cell Transcriptomic Data

Lazzardi

Valle

Mazzolini

et al. 2021

Preprint

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Large scale data on single-cell gene expression have the potential to unravel the specific transcriptional programs of different cell types. The structure of these expression datasets suggests a similarity with several other complex systems that can be analogously described through the statistics of their basic building blocks. Transcriptomes of single cells are collections of messenger RNA abundances transcribed from a common set of genes just as books are different collections of words from a shared vocabulary, genomes of different species are specific compositions of genes belonging to evolutionary families, and ecological niches can be described by their species abundances. Following this analogy, we identify several emergent statistical laws in single-cell transcriptomic data closely similar to regularities found in linguistics, ecology or genomics. A simple mathematical framework can be used to analyze the relations between different laws and the possible mechanisms behind their ubiquity. Importantly, treatable statistical models can be useful tools in transcriptomics to disentangle the actual biological variability from general statistical effects present in most component systems and from the consequences of the sampling process inherent to the experimental technique.

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Bayesian inference of gene expression states from single-cell RNA-seq data

Cited by 56 publications

References 54 publications

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

EMBED: Essential Microbiome Dynamics, a dimensionality reduction approach for longitudinal microbiome studies

Emergent Statistical Laws in Single-Cell Transcriptomic Data

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