SCIP: A scalable, reproducible, and open-source pipeline for morphological profiling image cytometry and microscopy data.
Cytometry A
; 2024 Oct 01.
Article
en En
| MEDLINE
| ID: mdl-39351999
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Cytometry A
Año:
2024
Tipo del documento:
Article
País de afiliación:
Bélgica
Pais de publicación:
Estados Unidos