/ML_DL_on_scRNA

A portfolio to prove that i can model scRNA-seq data with ML and DL algorithms

Primary LanguageJupyter Notebook

Description of this portfolio

This portfolio contains my notebooks about machine and deep learning algorithms applied on single-cell RNA-seq data. This data was downloaded from NeuroIPS 2021. These notebooks are described below:

Linearly decoded VAE (LDVAE) is a generative model similar to PCA which can be used similarly as scVI, however, due that it has linear functions instead of neural networks it's more easy to interpret its ouput. In this notebook i used this model to get differential expression analysis.
ldvae

SCMER (single-cell manifold-preserving feature selection) was used in notebook to get a compact version of the full dataset. I also do some visualizations and i have to admit that is too easy to use with Scanpy, once you have your data in form of adata object.

scmer

After some practice with SCMER i decided to use it to get a matrix that i could handle to train graph neural networks and only 13GB RAM. The GNN used is scGAE, a recently published model on Nature. scGAE performs clustering on its latent space and it can be used for downstram analysis.

scgae

Contact: hiramcoria@gmail.com