Deep Generative Models for Detecting Differential Expression in Single Cells

Xing

et al. 2021

Preprint

Self Cite

Probabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.

Section: Resultsmentioning

confidence: 99%

scvi-tools: a library for deep probabilistic analysis of single-cell omics data

Gayoso

Xing

et al. 2021

Preprint

Self Cite

“…As γ a result, we must use a frequentist test for the differential expression. A more principled approach, and a subject for future work, is to apply variational inference and exploit the uncertainty for Bayesian differential expression [55,56].…”

Section: Discussionmentioning

confidence: 99%

Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation

Keren‐Shaul

et al. 2021

Preprint

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The function of mammalian cells is largely influenced by their tissue microenvironment. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function by enabling a transcriptome-wide evaluation of gene expression in situ. A critical limitation of the current technologies, however, is that their resolution is limited to niches (spots) of sizes well beyond that of a single cell, thus providing measurements for cell aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single-cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyses are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states that cannot be clearly demarcated but reflects important differences in the way cells function and interact with their surroundings. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi-resolution analysis for spatial transcriptomics that explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods and that it can estimate gene expression by every cell type inside every spot. We then introduce an automated pipeline that uses DestVI for analysis of single tissue slices and comparison between tissues. We apply this pipeline to study the immune crosstalk within lymph nodes to infection and explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a high resolution and accurate spatial characterization of the cellular organization of these tissues, and that it is capable of identifying important cell-type-specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software package in the scvi-tools codebase (https://scvi-tools.org).

“…Overall, the Bayes factors inferred by totalVI for the RNA data were highly correlated with those produced by scVI ( Supplementary Figure S11a), which has been independently evaluated [47]; therefore, we focused on evaluating the protein DE test. For the protein DE test, we focused on testing for accurate detection of true positive and negative cases of DE and reproducibility across datasets.…”

Section: Differential Expressionmentioning

confidence: 99%

“…totalVI can leverage its estimates of uncertainty from a single model fit to detect differentially expressed features between sets of cells while controlling for noise and other modeled technical biases. In a test between two sets of cells, totalVI estimates the posterior odds that any feature is differentially expressed (using Bayes factors [13,46,47]; Methods 4.3). Here, the Bayesian equivalent of a null hypothesis for a particular feature is that the log fold change (LFC) of expression between the two sets is contained within a small interval centered at zero.…”

Section: Differential Expressionmentioning

confidence: 99%

“…Differential expression With a single model fit, totalVI can detect differentially expressed features between sets of cells, i.e., the model does not need to be retrained for every test. Here we use the Bayesian framework of [47] to detect differential expression (DE) of genes and proteins. Let…”

Section: Posterior Predictive Distributions Linked To Downstream Tasksmentioning

confidence: 99%

See 1 more Smart Citation

Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells

Gayoso

Steier²,

et al. 2020

Preprint

Self Cite

The paired measurement of RNA and surface protein abundance in single cells with CITE-seq is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, each data modality exhibits unique technical biases, making it challenging to conduct a joint analysis and combine these two views into a unified representation of cell state. Here we present Total Variational Inference (totalVI), a framework for the joint probabilistic analysis of paired RNA and protein data from single cells. totalVI probabilistically represents the data as a composite of biological and technical factors such as limited sensitivity of the RNA data, background in the protein data, and batch effects. To evaluate totalVI, we performed CITE-seq on immune cells from murine spleen and lymph nodes with biological replicates and with different antibody panels measuring over 100 surface proteins. With this dataset, we demonstrate that totalVI provides a cohesive solution for common analysis tasks like the integration of datasets with matched or unmatched protein panels, dimensionality reduction, clustering, evaluation of correlations between molecules, and differential expression testing. totalVI enables scalable, end-to-end analysis of paired RNA and protein data from single cells and is available as open-source software.