SpatialQC: automated quality control for spatial transcriptome data.

Autor:	Mao G; Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210000, China.; Co-innovation Center of Neuroregeneration, Nantong University, Nantong 226000, China., Yang Y; Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210000, China., Luo Z; Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210000, China.; Co-innovation Center of Neuroregeneration, Nantong University, Nantong 226000, China.; Center of Reproductive Medicine, Fujian Maternity and Child Health Hospital, Fuzhou 350000, China.; Shenzhen Research Institute, Southeast University, Shenzhen, China., Lin C; Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing 210000, China.; Co-innovation Center of Neuroregeneration, Nantong University, Nantong 226000, China.; Center of Reproductive Medicine, Fujian Maternity and Child Health Hospital, Fuzhou 350000, China.; Shenzhen Research Institute, Southeast University, Shenzhen, China., Xie P; School of Biological Science & Medical Engineering, Southeast University, Nanjing 518000, China.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2024 Aug 02; Vol. 40 (8).
DOI:	10.1093/bioinformatics/btae458
Abstrakt:	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). (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
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