Computer device for detecting an optimal candidate compound and methods thereof
The invention relates to a method for a computer device, for detecting an optimal candidate compound based on a plurality of samples comprising a cell line and one or more biomarkers, and a plate map configuration, wherein the plate map configuration is providing locations of samples comprising cell lines exposed to one or more biomarkers and different concentrations of a candidate compound forming at least one concentration gradient, the candidate compound being comprised in a plurality of can-didate compounds, said method comprising generating ( 310 ) phenotypic profiles of each concentration gradient of each of the plurality of candidate compounds at a plurality of successive points in time to form a plurality of compound profiles, wherein generating phenotypic profiles comprises the steps obtaining ( 312 ) image data depicting each sample comprised in the concentration gradient, generating ( 314 ) a class-label and a class for each cell of the samples based on the image data, detecting ( 320 ) the optimal candidate compound by evaluating a comparison criterion on the plurality of compound profiles. Furthermore, the invention also relates to corre-sponding computer device, a computer program, and a computer program product.
1. A computer-implemented method for generating class-labels and/or classes for cells comprising:
obtaining image data associated with a plurality of samples of a cell line treated with a biomarker and subjected to experimental perturbation;
segmenting, by a processor, cell objects from the obtained image data;
generating a set of class-labels corresponding to each of the cell objects;
running an unsupervised clustering algorithm on the processor to classify each of the cell objects with a corresponding one of the set of class-labels;
displaying at least one cell object and the respective class-label;
receiving data indicative of an operation on at least one class-label; and
performing the operation on the class-label by the processor based on the received data.
2. The method of claim 1 , wherein experimental perturbation comprises at least one of: inhibiting enzyme activities in cells, promoting enzyme activities in cells, genetic modification of cells, or applying a candidate compound to cells.
3. The method of claim 2 wherein applying a candidate compound comprises applying different concentrations of the candidate compound to the cells forming a concentration gradient.
4. The method of claim 1 , wherein the operation comprises one of add class, delete class, split class, and merge class.
5. The method of claim 1 , wherein displaying at least one cell object and the respective class-label comprises displaying an exemplary cell object and the respective class-label and/or class comprises:
calculating a confidence measure of how well each cell object represents the class-label; and
selecting the exemplary cell object as the cell object with the highest confidence measure.
6. The method of claim 1 wherein the class-label is indicative of cell health status or cell cycle status.
7. The method of claim 1 , further comprising generating phenotypic profiles of a concentration gradient for a candidate compound at each of a plurality of successive points in time to form a compound profile.
8. The method of claim 7 , further comprising forming a first collection of cytometric parameter sets by generating a cytometric parameter set for each cell object based on the image data.
9. The method of claim 1 , further comprising storing at a memory a phenotypic classification model based upon the class-labels, wherein the phenotypic classification model is configured to map a cytometric parameter set to a class, based on a parameter similarity function.
10. The method of claim 9 , wherein the parameter similarity function is a multi-dimensional correlation function configured to operate over two or more cytometric parameter sets.
11. The method of claim 10 , wherein the parameter similarity function is a machine learning algorithm selected from the group consisting of: self-organizing maps, auto -encoders, Ward Clustering, K-Means Clustering, and t-SNE Dimensionality Reduction.
12. The method of claim 1 , further comprising:
detecting an optimal candidate compound selected from one or more reference compound profiles and based on the experimental perturbation; and
calculating a multi-dimensional differential value for each of the one or more reference compound profiles based on the experimental perturbation.
13. A system comprising:
a computer-readable medium storing instructions that, when executed by a processor:
obtain, by the processor, image data associated with a plurality of samples of a cell line treated with a biomarker and subjected to experimental perturbation;
segment cell objects from the obtained image data;
generate a set of class-labels corresponding to each of the cell objects; and
run an unsupervised clustering algorithm on the processor to classify each of the cell objects with a corresponding one of the set of class-labels;
render for display at least one cell object and respective class-label from the obtained image data, wherein the rendered displayed at least one cell objects are segmented based upon the classification from the unsupervised clustering algorithm;
receive data indicative of an operation on at least one class-label; and
perform the operation on the class-label by the processor based on the received data.
14. The system of claim 13 , wherein the operation comprises one of add class, delete class, split class, and merge class.
15. The system of claim 13 , wherein displaying at least one cell object and the respective class-label comprises displaying an exemplary cell object and the respective class-label and/or class comprises:
calculating a confidence measure of how well each cell object represents the class-label; and
selecting the exemplary cell object as the cell object with the highest confidence measure.
16. The system of claim 13 , wherein the class-label is indicative of cell health status or cell cycle status.
17. The system of claim 13 , wherein the processor further generates phenotypic profiles of a concentration gradient for a candidate compound at each of a plurality of successive points in time to form a compound profile.
18. The system of claim 17 , wherein the processor further forms a first collection of cytometric parameter sets by generating a cytometric parameter set for each cell object based on the image data.
19. The system of claim 13 , further comprising storing at the computer -readable medium a phenotypic classification model based upon the class-labels, wherein the phenotypic classification model is configured to map a cytometric parameter set to a class, based on a parameter similarity function.
20. The system of claim 13 , wherein the processor is further configured to:
detect an optimal candidate compound selected from one or more reference compound profiles and based on the experimental perturbation; and
calculate a multi-dimensional differential value for each of the one or more reference compound profiles based on the experimental perturbation.