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

methods of unsupervised machine learning (learning to) has demonstrated its usefulness in a single cell mRNA noisy data-sequencing (scRNA-seq), where models generalize well, although a zero-inflation of the data. A class of neural networks, namely autoencoders, has been useful for denoising a single cell data, imputed values ​​and dimensions reduction.Here missing, we present a striking feature with the potential to greatly improve the usability of autoencoders:

With special training, autoencoder not only able to generalize on data, but also to tease apart biologically meaningful modules, which we found encoded in the representation of the network layer. Our models can, from the data scRNA-seq, Delineate biologically meaningful modules that regulate dataset, as well as the information given to the active module in every single cell. Importantly, most of these modules can be explained by the known biological function, such as that provided by Hallmark genes sets.We found that training was adjusted from autoencoder allows for deconvolute biology module embedded in the data, without assumptions. By comparison with the gene signatures of canonical pathways we see that the modules are directly interpreted.

The scope of the present invention has important implications, as it allows to elaborate on specific effects of cell behind the driver. Compared to the other dimension reduction method, or model to be followed for classification, our approach has benefits both deal with both the nature of the zero-increase of scRNA-seq, and validate that the model captures the relevant information, to establish the relationship between the input and translate data. In perspective, our model in combination with classifying this method is able to provide information about a given single cell subtypes belong to, as well as the biological functions that determine membership.

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

This study evaluated nitrogen removal methods Novel shortcut using a mixture consortium of microalgae, enriched ammonia oxidizing bacteria (AOB) and methanol utilizing denitrifier (MUD) in a photo-sequencing batch reactor (PSBR) for treating ammonium-rich wastewater (ARWW).

Alternating periods of light and darkness followed to obtain complete elimination of biological nitrogen (BNR) without external aeration and with the addition of methanol as a sole carbon source, respectively. The results showed that the effect of NH4 + is oxidized to NO 2 by AOB during the light period at a rate of 8:09 mg NH4 + -N L-1h-1

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