SpatialQC: automated quality control for spatial transcriptome data-Reference-Cited by-同舟云学术

SpatialQC: automated quality control for spatial transcriptome data

Published:2024-07-25 Issue:8 Volume:40 Page:
ISSN:1367-4811
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Mao Guangyao¹²^ORCID,Yang Yi¹^ORCID,Luo Zhuojuan¹²³⁴^ORCID,Lin Chengqi¹²³⁴,Xie Peng⁵^ORCID

Affiliation:

1. Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University , Nanjing 210000, China

2. Co-innovation Center of Neuroregeneration, Nantong University , Nantong 226000, China

3. Center of Reproductive Medicine, Fujian Maternity and Child Health Hospital , Fuzhou 350000, China

4. Shenzhen Research Institute, Southeast University , Shenzhen, China

5. School of Biological Science & Medical Engineering, Southeast University , Nanjing 518000, China

Abstract

Abstract Summary The advent of spatial transcriptomics has revolutionized our understanding of the spatial heterogeneity in tissues, providing unprecedented insights into the cellular and molecular mechanisms underlying biological processes. Although quality control (QC) critical for downstream data analyses, there is currently a lack of specialized tools for one-stop spatial transcriptome QC. Here, we introduce SpatialQC, a one-stop QC pipeline, which generates comprehensive QC reports and produces clean data in an interactive fashion. SpatialQC is widely applicable to spatial transcriptomic techniques. Availability and implementation source code and user manuals are available via https://github.com/mgy520/spatialQC, and deposited on Zenodo (https://doi.org/10.5281/zenodo.12634669).

Funder

National Key R&D Program of China

Publisher

Oxford University Press (OUP)

Link

https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae458/58648863/btae458.pdf

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