SCIP : a scalable, reproducible, and open-source pipeline for morphological profiling image cytometry and microscopy data
- Author
- Maxim Lippeveld (UGent) , Daniel Peralta (UGent) , Andrew Filby and Yvan Saeys (UGent)
- Organization
- Project
- Abstract
- Imaging flow cytometry (IFC) provides single-cell imaging data at a high acquisition rate. It is increasingly used in image-based profiling experiments consisting of hundreds of thousands of multi-channel images of cells. Currently available software solutions for processing microscopy data can provide good results in downstream analysis, but are limited in efficiency and scalability, and often ill-adapted to IFC data. In this work, we propose Scalable Cytometry Image Processing (SCIP), a Python software that efficiently processes images from IFC and standard microscopy datasets. We also propose a file format for efficiently storing IFC data. We showcase our contributions on two large-scale microscopy and one IFC datasets, all of which are publicly available. Our results show that SCIP can extract the same kind of information as other tools, in a much shorter time and in a more scalable manner.
- Keywords
- data analysis, distributed computing, feature extraction, imaging flow cytometry, machine learning
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01J96NJTG4C7D4KMTRW7168R4Q
- MLA
- Lippeveld, Maxim, et al. “SCIP : A Scalable, Reproducible, and Open-Source Pipeline for Morphological Profiling Image Cytometry and Microscopy Data.” CYTOMETRY PART A, vol. 105, no. 11, 2024, pp. 816–28, doi:10.1002/cyto.a.24896.
- APA
- Lippeveld, M., Peralta, D., Filby, A., & Saeys, Y. (2024). SCIP : a scalable, reproducible, and open-source pipeline for morphological profiling image cytometry and microscopy data. CYTOMETRY PART A, 105(11), 816–828. https://doi.org/10.1002/cyto.a.24896
- Chicago author-date
- Lippeveld, Maxim, Daniel Peralta, Andrew Filby, and Yvan Saeys. 2024. “SCIP : A Scalable, Reproducible, and Open-Source Pipeline for Morphological Profiling Image Cytometry and Microscopy Data.” CYTOMETRY PART A 105 (11): 816–28. https://doi.org/10.1002/cyto.a.24896.
- Chicago author-date (all authors)
- Lippeveld, Maxim, Daniel Peralta, Andrew Filby, and Yvan Saeys. 2024. “SCIP : A Scalable, Reproducible, and Open-Source Pipeline for Morphological Profiling Image Cytometry and Microscopy Data.” CYTOMETRY PART A 105 (11): 816–828. doi:10.1002/cyto.a.24896.
- Vancouver
- 1.Lippeveld M, Peralta D, Filby A, Saeys Y. SCIP : a scalable, reproducible, and open-source pipeline for morphological profiling image cytometry and microscopy data. CYTOMETRY PART A. 2024;105(11):816–28.
- IEEE
- [1]M. Lippeveld, D. Peralta, A. Filby, and Y. Saeys, “SCIP : a scalable, reproducible, and open-source pipeline for morphological profiling image cytometry and microscopy data,” CYTOMETRY PART A, vol. 105, no. 11, pp. 816–828, 2024.
@article{01J96NJTG4C7D4KMTRW7168R4Q,
abstract = {{Imaging flow cytometry (IFC) provides single-cell imaging data at a high acquisition rate. It is increasingly used in image-based profiling experiments consisting of hundreds of thousands of multi-channel images of cells. Currently available software solutions for processing microscopy data can provide good results in downstream analysis, but are limited in efficiency and scalability, and often ill-adapted to IFC data. In this work, we propose Scalable Cytometry Image Processing (SCIP), a Python software that efficiently processes images from IFC and standard microscopy datasets. We also propose a file format for efficiently storing IFC data. We showcase our contributions on two large-scale microscopy and one IFC datasets, all of which are publicly available. Our results show that SCIP can extract the same kind of information as other tools, in a much shorter time and in a more scalable manner.}},
author = {{Lippeveld, Maxim and Peralta, Daniel and Filby, Andrew and Saeys, Yvan}},
issn = {{1552-4922}},
journal = {{CYTOMETRY PART A}},
keywords = {{data analysis,distributed computing,feature extraction,imaging flow cytometry,machine learning}},
language = {{eng}},
number = {{11}},
pages = {{816--828}},
title = {{SCIP : a scalable, reproducible, and open-source pipeline for morphological profiling image cytometry and microscopy data}},
url = {{http://doi.org/10.1002/cyto.a.24896}},
volume = {{105}},
year = {{2024}},
}
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