.png){kind=icon}
.png){kind=icon}
Return
Computational approaches for high-throughput single-cell data analysis
.png){kind=icon}
OA
AI
DOI：10.1111/febs.14613.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
During the past decade, the number of novel technologies to interrogate biological systems at the single-cell level has skyrocketed. Numerous approaches for measuring the proteome, genome, transcriptome and epigenome at the single-cell level have been pioneered, using a variety of technologies. All these methods have one thing in common: they generate large and high-dimensional datasets that require advanced computational modelling tools to highlight and interpret interesting patterns in these data, potentially leading to novel biological insights and hypotheses. In this work, we provide an overview of the computational approaches used to interpret various types of single-cell data in an automated and unbiased way.
Keywords:
bioinformatics
computational tools
proteome
single cell
transcriptome
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.