It is a challenging task to integrate scRNA-seq and scATAC-seq data obtained from different batches. Existing methods tend to use a pre-defined gene activity matrix to convert the scATAC-seq data into scRNA-seq data. The pre-defined gene activity matrix is often of low quality and does not...
Moreover, the high dropout rates in scRNA-seq and scATAC-seq data can impact the performance of cell clustering. RESULTS. We propose a cell clustering model based on a masked autoencoder, scDRMAE. Utilizing a masking mechanism, scDRMAE effectively learns the relationships between different ...
Across modalities (e.g., scRNAseq and spatial transcriptomics data, scMethylation, or scATAC-seq) Once multiple datasets are integrated, the package provides functionality for further data exploration, analysis, and visualization. Users can:
Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides ...
(This article belongs to the Special IssueThe Intersection of Multi-Omics Data and Machine Learning in Medicine) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased su...
Across modalities (e.g., scRNAseq and spatial transcriptomics data, scMethylation, or scATAC-seq) Once multiple datasets are integrated, the package provides functionality for further data exploration, analysis, and visualization. Users can: Identify clusters Find significant shared (and dataset-specific...