A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes
By Romain Lopez, Jeffrey C. Regier, Michael Cole, Michael I. Jordan, Nir Yosef. 2017
7 citations recorded by the source corpus.
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing. In the model, each cell has a low-dimensional latent representation. Additional latent variables account for technical effects that may erroneously set some observations of gene expression levels to zero. Conditional distributions are specified by neural networks, giving the proposed model enough flexibility to fit the data well. We use variational inference and stochastic optimization to approximate the posterior distribution. The inference procedure scales to over one million cells, whereas competing algorithms do not. Even for smaller datasets, for several tasks, the proposed procedure outperforms state-of-the-art methods like ZIFA and ZINB-WaVE. We also extend our framework to take into account batch effects and other confounding factors and propose a natural Bayesian hypothesis framework for differential expression that outperforms tradition DESeq2.
Subfields: Artificial Intelligence, Cancer Research, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yibo Yang
- Paris Perdikaris
- Naoko Koyano-Nakagawa
- Daniel J. Garry
- Il-Youp Kwak
- Wuming Gong
- G. Em Karniadakis
- Maizar Raissi
In Molecular Biology
Citations in the corpus
- Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems (2019, background): “…from the design optimization of light emitting diodes [26], to the design of new molecules [27], to the calibration of cosmological surveys [28], to RNA sequencing[29],toanalyzingcancergeneexpressions[30]— all involving the approximation of very high-dimensional probability densities.”
- DrImpute: imputing dropout events in single cell RNA sequencing data (2017, methodology): “Zero Inflated Factor Analysis (ZIFA) was the first specific tool designed for factorizing and visualizing scRNA-seq data [31], followed by a few recent methods [39, 40].”