Results 271 to 280 of about 183,994 (309)

scRNA-seq for Microcephaly Research [III]: Computational Analysis of scRNA-seq Data

2022
Single-cell transcriptomic analysis (scRNA-seq) can enable researchers to explore the gene expression patterns of thousands of individual cells simultaneously. Processing the complex data generated by scRNA-seq requires specialized computational tools.
Benjamin, Babcock, Daniel, Malawsky
openaire   +2 more sources

Integration of scATAC-Seq with scRNA-Seq Data

2022
Single-cell studies are enabling our understanding of the molecular processes of normal cell development and the onset of several pathologies. For instance, single-cell RNA sequencing (scRNA-Seq) measures the transcriptome-wide gene expression at a single-cell resolution, allowing for studying the heterogeneity among the cells of the same population ...
Berest, Ivan, Tangherloni, Andrea
openaire   +3 more sources

The triumphs and limitations of computational methods for scRNA-seq

Nature Methods, 2021
The rapid progress of protocols for sequencing single-cell transcriptomes over the past decade has been accompanied by equally impressive advances in the computational methods for analysis of such data. As capacity and accuracy of the experimental techniques grew, the emerging algorithm developments revealed increasingly complex facets of the ...
openaire   +2 more sources

Mouse heart scRNA-seq

2023
Mouse heart scRNA-seq data of different develop ...
openaire   +1 more source

Computational Methods for scRNA-seq Analysis at Cell Level

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019
The remarkable progress in single-cell RNA sequencing (scRNA-seq) in recent years has led to the heat of great development of computational methods and tools for scRNA-seq data analysis. They have seen wide applications in data preprocessing, discovering differential expression, cell grouping, and trajectory inference, etc.
Tinghao Zhu, Jinfei Zhou, Le Zhang 0004, Yang Cao   +3 more
openaire   +1 more source

scRNA-seq of Nereis

scRNA-seq of ...
openaire   +1 more source

Identifying Damage-Related Features in scRNA-seq Data

Quality control (QC) is fundamental in single-cell RNA sequencing (scRNA-seq) data analysis pipelines to ensure data reliability. A critical QC step involves identifying damaged cells using quality metrics like the percentage of mitochondrial genes or the total number of reads.
Giovanni Marteletto, Bruno G. Galuzzi, Chiara Damiani   +2 more
openaire   +2 more sources