Description:	Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine and deep learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine and deep learning approaches to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-picked features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand selected features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches. Additional information about individual files is in the accompanying CSV file (item_metadata.csv). This dataset was originally deposited in the Simon Fraser University institutional repository.
Authors:	Khater, Ismail M.; Simon Fraser University; 0000-0001-7827-7745
Keywords:	Point clouds Super-resolution Nanoscopy Single molecule localization microscopy (SMLM) Cluster analysis Machine learning Biological structures Molecular complexes
Field of Research:	Computer and information sciences > Bioinformatics > Molecular and atomic computing
Publication Date:	2019-06-19
Publisher:	Federated Research Data Repository / dépôt fédéré de données de recherche
URI:	https://doi.org/10.25314/e55f116a-a0a3-4504-a274-377803ea0ea0
Related Identifiers:	This dataset is cited by https://doi.org/10.1038/s41598-018-27216-4 This dataset is a supplement to https://doi.org/10.1038/s41598-019-46174-z This dataset is a supplement to https://doi.org/10.1101/495382 This dataset is a supplement to https://doi.org/10.1101/526327 This dataset is a supplement to https://doi.org/10.1093/bioinformatics/btz113 This dataset is a supplement to https://doi.org/10.1371/journal.pone.0211659
Appears in Collections:	SFU Research Data