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Related Experiment Video

Updated: Oct 25, 2025

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Published on: May 9, 2017

Validation and tuning of in situ transcriptomics image processing workflows with crowdsourced annotations.

Jenny M Vo-Phamhi¹, Kevin A Yamauchi¹, Rafael Gómez-Sjöberg¹

¹Chan Zuckerberg Biohub, San Francisco, California, United States of America.

Plos Computational Biology

|August 9, 2021

Summary

This summary is machine-generated.

This study introduces an open-source toolkit for in situ transcriptomics image analysis. It leverages crowdsourced annotations as ground truth to evaluate and tune automated spot-calling algorithms, offering a viable alternative to expert annotations.

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Area of Science:

Spatial transcriptomics
Bioimage analysis
Computational biology

Background:

In situ transcriptomics methods reveal spatial organization of biological processes.
Automated image analysis algorithms require parameter tuning for optimal performance.
Ground truth datasets are crucial for evaluating and tuning these algorithms.

Purpose of the Study:

To present a novel open-source toolkit for in situ transcriptomics image analysis.
To incorporate crowdsourced annotations as a source of ground truth.
To provide tools for preparing images, performing quality control, and tuning/evaluating spot-calling algorithms.

Main Methods:

Development of a modular, flexible pipeline for in situ transcriptomics image analysis.
Integration of tools for image preparation, crowdsourcing annotation, and quality control.
Utilizing crowdsourced annotations (e.g., from Amazon Mechanical Turk) and expert annotations for ground truth generation.

Main Results:

Demonstrated the utility of crowdsourced annotations for validating and tuning spot-calling algorithms.
Established rules for annotation quality control by studying worker sensitivity to spot characteristics.
Confirmed that consensus crowdsourced annotations are a viable substitute for expert-generated ground truth.

Conclusions:

The developed toolkit offers a scalable and cost-effective approach to generating ground truth for in situ transcriptomics analysis.
Crowdsourced annotations provide a reliable alternative to expert annotations for algorithm evaluation and parameter tuning.
This framework facilitates the advancement of automated image analysis in spatial transcriptomics.