Fig. 1: Flowchart delineating the steps using binarized scRNA-seq data for cell clustering and integrated analysis with scATAC-seq data.
From: Facilitate integrated analysis of single cell multiomic data by binarizing gene expression values
In standard workflow, scRNA-seq raw count matrix is used for PCA, clustering and Uniform Manifold Approximation and Projection (UMAP) visualization, while scATAC-seq raw count matrix is used for TF-IDF/SVD processing, clustering, and UMAP visualization. In our proposed approach, scRNA-seq is binarized and contatenated with the raw scATAC-seq data and the contatenated data matrix is then used for TF-IDF/LSI processing and UMAP visualization, following the standard scATAC-seq procedure.
