2021.03.23 Introduce the select_var_feature from Episcanpy to filter peaks
2021.01.14 Update to compatible with h5ad file and scanpy
SCALE neural network is implemented in Pytorch framework.
Running SCALE on CUDA is recommended if available.
pip install scale
git clone git://github.com/jsxlei/SCALE.git
cd SCALE
python setup.py install
Installation only requires a few minutes.
- h5ad file
- count matrix file:
- row is peak and column is barcode, in txt / tsv (sep="\t") or csv (sep=",") format
- mtx folder contains three files:
- count file: count in mtx format, filename contains key word "count" / "matrix"
- peak file: 1-column of peaks chr_start_end, filename contains key word "peak"
- barcode file: 1-column of barcodes, filename contains key word "barcode"
SCALE.py -d [input]
if cluster number k is known:
SCALE.py -d [input] -k [k]
Output will be saved in the output folder including:
- model.pt: saved model to reproduce results cooperated with option --pretrain
- adata.h5ad: saved data including Leiden cluster assignment, latent feature matrix and UMAP results.
- umap.pdf: visualization of 2d UMAP embeddings of each cell
Get binary imputed data in adata.h5ad file using scanpy adata.obsm['binary'] with option --binary (recommended for saving storage)
SCALE.py -d [input] --binary
or get numerical imputed data in adata.h5ad file using scanpy adata.obsm['imputed'] with option --impute
SCALE.py -d [input] --impute
- save results in a specific folder: [-o] or [--outdir]
- embed feature by tSNE or UMAP: [--embed] tSNE/UMAP
- filter low quality cells by valid peaks number, default 100: [--min_peaks]
- filter low quality peaks by valid cells number, default 10: [--min_cells]
- modify the initial learning rate, default is 0.002: [--lr]
- change iterations by watching the convergence of loss, default is 30000: [-i] or [--max_iter]
- change random seed for parameter initialization, default is 18: [--seed]
- binarize the imputation values: [--binary]
Look for more usage of SCALE
SCALE.py --help
Use functions in SCALE packages.
import scale
from scale import *
from scale.plot import *
from scale.utils import *
Tutorial Forebrain Run SCALE on dense matrix Forebrain dataset (k=8, 2088 cells)