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Related Concept Videos

Quality Control01:05

Quality Control

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Levels of Use of a GIS01:29

Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Manipulation and Analysis01:21

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Spotsweeper-py: spatially-aware quality control metrics for spatial omics data in the Python ecosystem.

Xingyi Chen1, Michael Totty2, Stephanie C Hicks2,3,4,5,6,7

  • 1Department of Applied Math and Statistics, Johns Hopkins University, Baltimore, MD, USA.

Biorxiv : the Preprint Server for Biology
|December 22, 2025
PubMed
Summary
This summary is machine-generated.

SpotSweeper-py offers spatially-aware quality control for spatial transcriptomics data in Python. This tool enhances data reliability by identifying local artifacts without removing biologically relevant tissue regions.

Keywords:
Pythonquality controlscversespatially-resolved transcriptomics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Spatially-resolved transcriptomics (SRT) generates complex datasets.
  • Global quality control (QC) metrics can inaccurately remove biological signals or miss localized artifacts in SRT data.
  • Existing spatially-aware QC tools are limited to the R programming language, hindering integration with the Python/scverse ecosystem.

Purpose of the Study:

  • To introduce SpotSweeper-py, a Python package providing neighborhood-aware QC metrics for SRT data.
  • To enable seamless integration of advanced local QC within the Python/scverse environment.
  • To improve the accuracy and reliability of SRT data analysis by reducing false positives and preserving tissue architecture.

Main Methods:

  • Development of SpotSweeper-py, a Python package implementing neighborhood-aware z-scores for QC metrics.
  • Computation of z-scores for total counts, log total counts, detected genes, and mitochondrial percentage.
  • Demonstration of SpotSweeper-py performance on 10x Genomics Visium and VisiumHD datasets.
  • Inclusion of plotting utilities for outlier visualization.

Main Results:

  • SpotSweeper-py effectively computes local, spatially-aware QC metrics.
  • The package integrates smoothly with the Python/scverse ecosystem.
  • It successfully reduces false positives from global QC while preserving tissue-specific structures.
  • Performance was validated on public Visium and VisiumHD datasets.

Conclusions:

  • SpotSweeper-py provides a robust, Python-based solution for local QC in SRT analysis.
  • This tool enhances the reliability of SRT data processing pipelines.
  • It makes advanced, spatially-aware QC accessible to a wider range of researchers.