| Title: |
scVAE: Variational auto-encoders for single-cell gene expression data |
| Authors: |
Grønbech, Christopher Heje; Vording, Maximillian Fornitz; Timshel, Pascal; Sønderby, Casper Kaae; Pers, Tune Hannes; Winther, Ole |
| Source: |
Grønbech , C H , Vording , M F , Timshel , P , Sønderby , C K , Pers , T H & Winther , O 2020 , ' scVAE: Variational auto-encoders for single-cell gene expression data ' , Bioinformatics , vol. 36 , no. 16 , pp. 4415–4422 . https://doi.org/10.1093/bioinformatics/btaa293 |
| Publication Year: |
2020 |
| Collection: |
Technical University of Denmark: DTU Orbit / Danmarks Tekniske Universitet |
| Description: |
Models for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual data-processing steps, each with their own set of hyperparameter choices. With deep generative models one can work directly with count data, make likelihood-based model comparison, learn a latent representation of the cells and capture more of the variability in different cell populations. We propose a novel method based on variational auto-encoders (VAEs) for analysis of single-cell RNA sequencing (scRNA-seq) data. It avoids data preprocessing by using raw count data as input and can robustly estimate the expected gene expression levels and a latent representation for each cell. We tested several count likelihood functions and a variant of the VAE that has a priori clustering in the latent space. We show for several scRNA-seq data sets that our method outperforms recently proposed scRNA-seq methods in clustering cells and that the resulting clusters reflect cell types. Our method, called scVAE, is implemented in Python using the TensorFlow machine-learning library, and it is freely available at https://github.com/scvae/scvae. Supplementary data are available at Bioinformatics online. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| DOI: |
10.1093/bioinformatics/btaa293 |
| Availability: |
https://orbit.dtu.dk/en/publications/b55e7688-585c-4f1d-80dd-de0f029cbd88; https://doi.org/10.1093/bioinformatics/btaa293; https://backend.orbit.dtu.dk/ws/files/216210029/btaa293.pdf |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.E1B65F23 |
| Database: |
BASE |