Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data.

ABSTRACT:

Background

Unsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural networks, namely autoencoders, has been useful for denoising of single cell data, imputation of missing values and dimensionality reduction.

Results

Here, we present a striking feature with the potential to greatly increase the usability of autoencoders: With specialized training, the autoencoder is not only able to generalize over the data, but also to tease apart biologically meaningful modules, which we found encoded in the representation layer of the network. Our model can, from scRNA-seq data, delineate biological meaningful modules

SUBMITTER: Kinalis S 

PROVIDER: S-EPMC6615267 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature