Capturing single-cell heterogeneity via data fusion improves image-based profiling
A challenge with single-cell resolution methods is that cell heterogeneity should be captured while allowing for comparisons between populations. Here the authors fuse information from the dispersion profiles with the average profiles at the level of profiles’ similarity matrices for single cell ima...
Enregistré dans:
Auteurs principaux: | Mohammad H. Rohban, Hamdah S. Abbasi, Shantanu Singh, Anne E. Carpenter |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2019
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/1960879fb520478bb38c90d85f45b3ae |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Compressive spectral image fusion via a single aperture high throughput imaging system
par: Hoover Rueda-Chacon, et autres
Publié: (2021) - International journal of image and data fusion
-
Resolving cell state in iPSC-derived human neural samples with multiplexed fluorescence imaging
par: Martin L. Tomov, et autres
Publié: (2021) -
A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources
par: Kashinath Shafiza Ariffin, et autres
Publié: (2021) -
Molecular profiling of single organelles for quantitative analysis of cellular heterogeneity
par: Andrey N. Kuzmin, et autres
Publié: (2017)