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PUPAID:一种用于多通道免疫荧光数据全面且半自动处理和分析的 R + ImageJ 流水线。

PUPAID: A R + ImageJ pipeline for thorough and semi-automated processing and analysis of multi-channel immunofluorescence data.

机构信息

Immunology-Immunopathology-Immunotherapy (i3) Laboratory, INSERM UMR-S 959, Sorbonne Université, Paris, France.

Biotherapy Unit (CIC-BTi), Inflammation-Immunopathology-Biotherapy Department (DHU i2B), Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France.

出版信息

PLoS One. 2024 Sep 19;19(9):e0308970. doi: 10.1371/journal.pone.0308970. eCollection 2024.

Abstract

PUPAID is a workflow written in R + ImageJ languages which is dedicated to the semi-automated processing and analysis of multi-channel immunofluorescence data. The workflow is designed to extract fluorescence signals within automatically-segmented cells, defined here as Areas of Interest (AOI), on whole multi-layer slides (or eventually cropped sections of them), defined here as Regions of Interest (ROI), in a simple and understandable yet thorough manner. The included (but facultative) R Shiny-based interactive application makes PUPAID also suitable for scientists who are not fluent with R programming. Furthermore, we show that PUPAID identifies significantly more cells, especially in high-density regions, as compared to already published state-of-the-art methods such as StarDist or Cellpose. For extended possibilities and downstream compatibility, single cell information is exported as FCS files (the standardized file format for single cell-based cytometry data) in order to be openable using any third-party cytometry analysis software or any analysis workflow which takes FCS files as input.

摘要

PUPAID 是一个用 R 和 ImageJ 语言编写的工作流程,专门用于对多通道免疫荧光数据进行半自动处理和分析。该工作流程旨在以简单、易懂但全面的方式,从自动分割的细胞(这里定义为感兴趣区域,AOI)中提取荧光信号,这些细胞位于整个多层幻灯片(或最终裁剪的部分)上(这里定义为感兴趣区域,ROI)。包含的(但可选的)基于 R Shiny 的交互式应用程序使 PUPAID 也适合不熟悉 R 编程的科学家。此外,我们表明,与已经发表的最先进的方法(如 StarDist 或 Cellpose)相比,PUPAID 可以识别出更多的细胞,尤其是在高密度区域。为了实现更广泛的可能性和下游兼容性,单细胞信息以 FCS 文件(基于单细胞的细胞仪数据的标准化文件格式)的形式导出,以便可以使用任何第三方细胞仪分析软件或任何以 FCS 文件作为输入的分析工作流程打开。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7717/11412663/5d48bc674ba4/pone.0308970.g001.jpg

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