单细胞数据整合方法 | Comprehensive Integration of Single-Cell Data

Comprehensive Integration of Single-Cell Data

实在是没想到，这篇seurat的V3里面的整合方法居然发在了Cell主刊。

果然：大佬+前沿领域=无限可能

可以看到bioRxiv上是November 02, 2018发布的，然后Cell主刊June 06, 2019正式发表。

方法的创意应该在2017年底就有了，那时候我才刚来做single cell。

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters.

As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function.

Here, we develop a strategy to “anchor” diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.

After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations.

Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns.

Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

what's my problem?

标签：cell,datasets,seq,scRNA,Integration,Cell,Data,across
来源： https://www.cnblogs.com/leezx/p/11244731.html