Back to Search
Start Over
Cell morphology-based machine learning models for human cell state classification
- Source :
- NPJ Systems Biology and Applications, npj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-9 (2021)
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.
- Subjects :
- 0301 basic medicine
QH301-705.5
Computer science
Machine learning
computer.software_genre
Cell morphology
Article
General Biochemistry, Genetics and Molecular Biology
Flow cytometry
Machine Learning
03 medical and health sciences
chemistry.chemical_compound
0302 clinical medicine
Text mining
Drug Discovery
Classifier (linguistics)
medicine
Humans
Propidium iodide
Biology (General)
Cell Size
medicine.diagnostic_test
business.industry
Applied Mathematics
Flow Cytometry
Computer Science Applications
Random forest
Support vector machine
030104 developmental biology
chemistry
030220 oncology & carcinogenesis
Modeling and Simulation
Multilayer perceptron
Computer modelling
Neural Networks, Computer
Artificial intelligence
business
Biomedical engineering
computer
Propidium
Subjects
Details
- ISSN :
- 20567189
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- npj Systems Biology and Applications
- Accession number :
- edsair.doi.dedup.....b4f1739f673ac14e05b5658871e469a5