Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder

requirement

numpy>=1.14.2 pandas>=0.22.0 scipy>=0.19.1 scikit-learn>=0.19.1 torch>=1.0.0 tqdm>=4.28.1 matplotlib>=3.0.2 seaborn>=0.9.0

Installation

scIVA neural network is implemented in Pytorch framework.

Input

• either a count matrix file:
  • row is peak and column is barcode, in txtformat

Output

Output will be saved in the output folder including:

• feature.txt: latent feature representations of each cell used for clustering or visualization
• embryo.eps: visualization of each cell
• ** The effect of clustering** normalized mutual information (NMI), adjusted rand index (ARI) , completeness (COM), and homogeneity (HOM)

Useful options

• save results in a specific folder: [-o] or [--outdir]
• modify the initial learning rate, default is 0.00001: [--lr]
• change iterations by watching the convergence of loss, default is 20000: [-i] or [--max_iter]
• run with scRNA-seq dataset: [--log_transform]

Help

Look for more usage of SCALE

scIVA.py --help 

Use functions in SCALE packages.

import sciva
from sciva import *
from sciva.plot import *
from sciva.utils import *

Data availability

Datasets were obtained from the Hemberg group (https://hemberg-lab.github.io/scRNA.seq.datasets/)