This portfolio contains my notebooks about machine and deep learning algorithms applied on single-cell ATAC-seq data. This data was downloaded from NeuroIPS 2021. There are described below:
scATAC-seq 🧬: Feature Importance with TabNet (see it in action at Kaggle)
In this notebook, i used TabNet, a kind of transformer useful for tabular data. As many now, expressión data is just a matrix. This matrix is contained on my adata object, adata.X. In resume, i used the mentioned matrix and adata.obs["nucleosome_signal"] as my labels, i split it, trained my model and calculate feature importance as you can see below:
scATAC-seq 🧬: EpiScanpy & PeakVI (see it in action at Kaggle)
Here i used for the first time EpiScanpy and PeakVi. The can be used so easy on this kind of data.
