Patent Publication Number: US-2022237471-A1

Title: Cell state transition features from single cell data

Description:
BACKGROUND 
     The present disclosure relates in general to computing systems and methods, and more specifically, to cognitive computing systems and methods configured to identify cell state transitions using machine learning techniques on single cell data. 
     Medicine resistance can be a limiting factor in curing various diseases, such as cancer. In some examples, medicine development applications for developing medicines can use bulk sequencing data (e.g., data representing bulk of cells) analysis to develop medicines and treatments for diseases. However, bulk sequencing analysis may not characterize individual cell states, leading to failures in determining when and how cells change state. Cell state transition can allow characterization of cell state populations, including outlier states that may represent phenotypes such as medicine resistance or signs of relapse, for residual disease monitoring, new medicine screens, or patient-personalized treatments. 
     SUMMARY 
     In some examples, a method for training a machine learning model is generally described. The method can include transforming, by a processor, a set of single cell data in a first dimensional space into a set of projection data in a second dimensional space having a dimensionality lower than or equal to a dimensionality of the first dimensional space. The method can further include producing, by the processor, a cover of the set of projection data. The cover can include a plurality of sets, and a union of the sets can include the entirety of the set of projection data. The method can further include determining, by the processor, a plurality of transition paths among the plurality of sets. A transition path can represent a transition from one cell state to another cell state. The method can further include translating, by the processor, the plurality of transition paths from the second dimensional space onto the set of single cell data in the first dimensional space. The method can further include extracting, by the processor, a plurality of features from the transition paths translated onto the first dimensional space. The method can further include generating, by the processor, a set of training data using the extracted features. The method can further include training, by the processor, a machine learning model using the set of training data. The machine learning model can be trained for classifying transitions between different cell states. 
     In some examples, a system for training a machine learning model is generally described. The system can include a memory configured to store a set of instructions. The system can further include a processor configured to be in communication with the memory. The processor can be configured to execute the set of instructions to transform a set of single cell data in a first dimensional space data into a set of projection data in a second dimensional space having a dimensionality lower than or equal to a dimensionality of the first dimensional space. The processor can be further configured to execute the set of instructions to produce a cover of the set of projection data. The cover can include a plurality of sets, and a union of the sets include the entirety of the set of projection data. The processor can be further configured to execute the set of instructions to determine a plurality of transition paths among the plurality of sets. A transition path can represent a transition from one cell state to another cell state. The processor can be further configured to execute the set of instructions to translate the plurality of transition paths from the second dimensional space onto the set of single cell data in the first dimensional space. The processor can be further configured to execute the set of instructions to extract a plurality of features from the transition paths translated onto the first dimensional space. The processor can be further configured to execute the set of instructions to generate a set of training data using the extracted features. The processor can be further configured to execute the set of instructions to train a machine learning model using the set of training data, wherein the machine learning model is being trained for classifying transitions between different cell states. 
     In some examples, a computer program product for training a machine learning model is generally described. The computer program product may include a computer readable storage medium having program instructions embodied therewith. The program instructions may be executable by a processor to cause the processor to perform one or more methods described herein. 
     Further features as well as the structure and operation of various embodiments are described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram showing an example system that can implement cell state transition features from single cell data in one embodiment. 
         FIG. 2A  is a diagram showing an example implementation of the example system of  FIG. 1  to generate a cover for a set of projection data in one embodiment. 
         FIG. 2B  is a diagram showing an example cover that can be generated by an implementation of the example system of  FIG. 1  in one embodiment. 
         FIG. 3A  is a diagram showing an example implementation of the example system of  FIG. 1  to identify transition paths in one embodiment. 
         FIG. 3B  is a diagram showing an example set of transition paths that can be identified by an implementation of the example system of  FIG. 1  in one embodiment. 
         FIG. 4  is a diagram showing an example implementation of the example system of  FIG. 1  to translate a plurality of transition paths to an original dimensional space in one embodiment. 
         FIG. 5  is a diagram showing another example implementation of the example system of  FIG. 1  in one embodiment. 
         FIG. 6  is a flow diagram illustrating a process of implementing cell state transition features from single cell data in one embodiment. 
         FIG. 7  illustrates a schematic of an example computer or processing system relating to cell state transition features from single cell data in one embodiment. 
         FIG. 8  depicts a cloud computing environment according to an embodiment of the present invention. 
         FIG. 9  depicts abstraction model layers according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     In an example, single cell sequencing data can provide resolution of the cellular state of a biological sample. Single cell sequencing data analysis can involve data in relatively high dimensionality space, and can typically involve relatively large amounts of individual gene information, causing difficulty in identifying potentially small fraction of cells whose state may be in transition from one cell state to another cell state. 
     The methods and systems described herein can use single cell data to identify all cell state populations among the single cell data, and the underlying features that support transitions from one cell state to another cell state. The methods and systems described herein can transform the single cell data to a lower dimensional space (or another space of equal dimensionality) that preserves the feature space (e.g., the original dimensional space of the single cell data). In the lower dimensional space, transition paths between cell states can be identified by constructing trajectory using topological data analysis (TDA) techniques to establish relationships between cell states, in particular at the edges between nodes as well as within heterogeneous nodes. The identified transition paths can be mapped back to the original dimensional space, and features can be extracted from the transition paths mapped to the original dimensional space. These extracted features can be used to derive new medicine resistant cell line lines for medicine screens through knockout or knockup assays. For example, the extracted features can be used as training data to train machine learning models (e.g., classification models, neural network, deep learning models, etc.) that can classify single cell data into cell state transitions, where these cell state transitions can indicate changes in cellular phenotypes. The cell state transitions outputted by these machine learning models can be used to derive hyper-personalized patient therapies through development of targeted treatments, such as clustered regularly interspaced short palindromic repeats (CRISPR) and antibody vectors. For example, Characterize extracted features for implicated biological pathways 
       FIG. 1  is a diagram showing an example system that can implement cell state transition features from single cell data in one embodiment. The system  100  can include a data source  102 , a processor  110 , and a memory  112 . The processor  110  can be, for example, a processing unit (e.g., a central processing unit or microprocessor, a processor core, and/or other types of processing unit) of a computer device (e.g., desktop computer, server-class computer, laptop computer, and/or other types of computing devices). The processor  110  can be configured to be in communication with the data source  102  and the memory  112 . In some examples, the data source  102 , the processor  110 , and the memory  112  can be part of the same computer device. In some examples, the data source  102 , the processor  110 , and the memory  112  can be located in different or separate devices and configured to be in communication through a network (e.g., the Internet, a cellular network, and/or other types of wired or wireless network). For example, the data source  102  can be located in a first geographical location, and the processor  110  and memory  112  can be located a different geographical location, where the data source  102 , the processor  110 , and the memory  112  can be part of a cloud computing network. 
     The memory  112  can be, for example, a memory device including volatile and/or non-volatile memory elements. The memory  112  can be configured to store a set of instructions  113 . The set of instructions  113  can include, for example, source code and/or executable code. The set of instructions  113  can be executable by the processor  110  to perform one or more task to implement the system  100 . In some examples, the set of instructions  113  can be program modules including, but not limited to, routines, programs, objects, components, logic, data structures, and other types of program modules. 
     The data source  102  can store a set of single cell data (or dataset) of a plurality of subjects. The single cell data stored in the data source can be categorized into different cell states, such as different stages of diseases including, but not limited to, various types of cancer and/or other types of diseases. The processor  110  can be configured to receive or obtain one or more subsets of the set of single cell data stored in the data source  102 . For example, in the example shown in  FIG. 1 , the processor  110  can obtain single cell data  104  from the data source  102 . The single cell data  104  can be, for example, single cell deoxyribonucleic acid (DNA) sequencing data, single cell ribonucleic acid (RNA) sequencing data, and/or other types of single cell data of an individual subject. 
     In an example embodiment, the single cell data  104  can be a matrix, such as a gene expression matrix. For example, the single cell data  104  can be a matrix, where the matrix&#39;s elements can be count values, where rows of the matrix can be associated with a plurality of gene identifiers (ID), and columns of the matrix can be associated with a plurality of cell identifiers (e.g., cellular barcodes). In some examples, the single cell data  104  can be a transpose of the matrix such that the rows can be associated with the cell identifiers and the columns can be associated with the gene identifiers. The elements of the matrix can be represented as a plurality of data points  108  in a first dimensional space  106 . The processor  110  can be configured to transform the single cell data  104  from the first dimensional space  106  into a set of projection data in a second dimensional space that has a lower dimensionality, or an equivalent dimensionality, when compared to the dimensionality of the first image dimensional space  106 . 
     The processor  110  can be configured to determine a plurality of transition paths using the projection data in the second dimensional space, and translate the determined transition paths back to the first dimensional space  106 . The processor  110  can be configured to identify a set of data points  132  from the translated transition paths  130  in the first dimensional space  106 . The identified set of data points  132  can be, for example, data points that are positioned on the translated transition paths  130  in the first dimensional space  106 . The processor  110  can be configured to generate the set of training data  140  using the extracted set of data points  132 . The processor  110  can be configured to train a machine learning model  150  using the training data  140 . The machine learning model  150  can be, for example, a classification model, a neural network, a deep learning model, and/or other types of machine learning models. By using data points positioned along a plurality of transition paths in the original dimensional space (e.g., first dimensional space  106 ) of the single cell data  104 , the machine learning model  150  can be trained to learn how individual cells transition from one cell state to another cell state in the original dimensional space. 
       FIG. 2A  is a diagram showing an example implementation of the example system of  FIG. 1  to generate a cover for a set of projection data in one embodiment. In the example shown in  FIG. 2A , the processor  110  can be configured to execute dimensionality reduction techniques on the single cell data  104  to transform the single cell data  104  into a set of projection data  204 . The projection data  204  can be represented as a set of projection points  208  in the second in a second dimensional space  206 . The second dimensional space  206  can have a lower dimensionality, or an equivalent dimensionality, when compared to the first image dimensional space  106 . The transformation of the single cell data  104  in the first dimensional space  106  into the projection data  204  in the second dimensional space can preserve a feature space (e.g., first image dimensional space  106 ) of the original input data (e.g., single cell data  104 ). Further, as a result of the transformation, every projection point among the set of projection points  208  can be transformed from exactly one data point among the set of data point  108 . 
     By way of example, the set of instructions  113  stored in the memory  112  can be implemented by the processor  110  to implement various machine learning algorithms and techniques that can perform the transformation from the single cell data  104  in the first dimensional space  106  into the projection data  204  in the second dimensional space  206 . For example, the set of instructions  113  can include executable code that can be implemented by the processor  110  to transform the single cell data  104  into the projection data  204  using random forest, principal component analysis (PCA), multidimensional scaling (MDS), linear discriminant analysis (LDA), and/or other types of supervised or unsupervised machine learning algorithms or techniques that can be used for dimensionality reduction. A choice of the machine learning algorithm being used for the transformation can depend on the dimensionality of the first dimensional space  106  and the second dimensional space  206 . 
     In response to transforming the single cell data  104  into the projection data  204 , the processor  110  can identify all distinct cell state populations among the projection data  204 . In an example, to identify all distinct cell state populations, the processor  110  can perform set covering on the projection data  204  to produce a set of subsets that can have non-trivial intersections between its elements. In some examples, the set covering can be achieved by first performing a clustering technique (e.g., partitioning the set of projection data  204  to obtain a set of subsets that are disjoint), and then expand the clusters to cause some of the clusters to overlap, resulting in a cover of the set of projection data  204 . In the example shown in  FIG. 2A , the processor  110  can apply a clustering technique to cluster the set of projection data  204  into a plurality of clusters  221 ,  222 ,  223 ,  224 . Note that the clusters  221 ,  222 ,  223 ,  224  shown in  FIG. 2A  may not overlap with one another. 
     By way of example, the set of instructions  113  stored in the memory  112  can be implemented by the processor  110  to implement various supervised or unsupervised cluster models (or clustering algorithms or techniques) to cluster the projection points  208 . Such cluster models can include, but not limited to, centroid models (e.g., k-means clustering), density models (e.g., density-based spatial clustering of applications with noise (DBSCAN)), group models, and/or other types of cluster models that can be used to cluster the projection points  208 . A choice of the clustering algorithm or cluster model being used by the processor  110  can depend on various properties of the projection data  204  and the second dimensional space  206 . 
     The processor  110  can expand the coverage areas of the clusters  221 ,  222 ,  223 ,  224  in the second dimensional space  206  to produce a cover  230  for the set of projection points  208  among the projection data  204 . For example, the clusters  221 ,  222 ,  223 ,  224  can be expanded to sets  231 ,  232 ,  233 ,  234 , respectively. The cover  230  of the set of projection points  208  can be a collection of sets whose union includes the set of projection points  208  as a subset. In the example shown in  FIG. 2A , the cover  230  can include a plurality of sets  231 ,  232 ,  233 ,  234 , where a set of a cover can be a set of elements or a partition including elements of the cover. A union of the sets  231 ,  232 ,  233 ,  234  can include an entire set of projection points  208 . Further, some of the sets among the sets  231 ,  232 ,  233 ,  234  can overlap one another. For example, the set  231  can overlap with the sets  232 ,  233 , the set  232  can overlap with the sets  231 ,  234 , the set  233  can overlap with the sets  231 ,  234 , and the set  234  can overlap with the sets  232 ,  233 . 
     In an example, outlier points, such as a set of outlier points  235  may not be within any cluster, but the expansion of the cluster  222  to the set  232  allow the outlier points  235  to be included in the set  232 . In some examples, the processor  110  can expand the clusters until all outlier points (e.g., points that do not belong to any cluster) are within a set, such that the cover can include all projection points  208 . The expansion of the clusters  221 ,  222 ,  223 ,  224  to produce the cover  230  can allow outlier points to be categorized into cell populations as well. 
     Each set among the plurality of sets  231 ,  232 ,  233 ,  234 , can represent a cell state. In the example shown in  FIG. 2A , as a result of generating the cover  230  having the sets  231 ,  232 ,  233 ,  234 , four distinct cell state populations represented by sets  231 ,  232 ,  233 ,  234  are identified. Although the cover  230  shown in  FIG. 2A  has four sets, it will be apparent to a person of ordinary skill in the art that the processor  110  can generate a cover having an arbitrary number of sets. For example, if the single cell data  104  includes four types of data points (e.g., data points  108 ) corresponding to four cell states, then there can be at least four cell state populations among the projection data  204  resulting in at least four sets. Further, each cell state population among the projection points  208  can be scattered across the second dimensional space  206  such that more than one non-overlapping sets can be formed for each cell state population. For example, another cover  240  is shown in  FIG. 2B , where four distinct cell states or cell state population, labeled as A, B, C, D, are identified by constructing the cover  240 . Using cell state A as an example, the cell state A has two sets of distinct population and thus, there can be two non-overlapping sets for the cell state A in the cover  240 . 
     The processor  110  can be further configured to generate data relating to the cover  230  and the sets that formed the cover  230 , and store the generated data in the memory  112 . These data can include, for example, the number of projection points in each set, the sizes of the sets, the location of the sets in the second dimensional space  206 , the locations of each projection point in each set, various properties of the sets such as locations of centroids of the sets, and/or other information of the sets of the cover  230 . 
       FIG. 3A  is a diagram showing additional details of the example system of  FIG. 1  in one embodiment. In response to identifying the distinct cell state populations in the projection data  204 , the processor  110  can identify all transition paths between the distinct cell state populations. To identify the transition paths, the processor  110  can perform trajectory analysis using techniques such as topological data analysis (TDA). To identify the transition paths among the distinct cell populations, the processor  110  can construct a simplicial complex  310  from the cover  230 . The simplicial complex  310  can be a standard simplicial complex or a weighted simplicial complex of the cover  230 . In an example, a weighted simplicial complex can be chosen over a non-weighted simplicial complex in response to an amount of overlap between cover sets satisfying specific criteria (e.g., satisfying an optimization of a particular model). The amount of overlap can be used to determine the weights for their respective edges. For example, larger overlaps in a given dataset can indicate more gradual transitions across cell types, whereas smaller overlaps can indicate sharper transitions. The amount of overlap can be modulated with features recovered from transition vectors or paths. The simplicial complex  310  can be constructed using an 1-skeleton of a nerve of the cover  230  (e.g., vertices corresponding to sets in the cover, and edges corresponding to intersections of two sets in the cover). For example, an edge can connect the centroids of the sets  231 ,  232 , and another edge can connect the centroids of the sets  231  and  233 . Note that no edge is connecting the sets  231  and  234  because the sets  231  and  234  do not overlap one another. 
     In the example shown in  FIG. 3A , the simplicial complex  310  can include vertices or nodes  301 ,  302 ,  303 ,  304  representing the sets  231 ,  232 ,  233 ,  234 , respectively; and can include edges  311 ,  312 ,  313 ,  314  representing the 1-skeleton of the nerve of the cover  230 . In some examples, the nodes  301 ,  302 ,  303 ,  304  can represent the centroids of the sets  231 ,  232 ,  233 ,  234 , respectively. In some examples, the centroids of clusters represented by the nodes  301 ,  302 ,  303 ,  304  can be projection points among the projection points  208  (see  FIG. 2A ) in the second dimensional space  206 . The edges  311 ,  312 ,  313 ,  314  can be identified by the processor  110  as a plurality of transition paths between different cell states (e.g., between different clusters representing different cell states). Another example simplicial complex  320  having additional nodes representing additional clusters is shown in  FIG. 3B . In the example shown in  FIG. 3B , the simplicial complex  320  can include more than four nodes, and the nodes among the simplicial complex  320  can represent an arbitrary number of cell states, such as a number of cell states represented by the original input single cell data  104 . 
       FIG. 4  is a diagram showing an example implementation of the example system of  FIG. 1  to translate a plurality transition paths to an original dimensional space in one embodiment. The processor  110  can be configured to translate the edges, or the plurality of transition paths,  311 ,  312 ,  313 ,  314  from the second dimensional space  206  onto the set of single cell data  104  in the first dimensional space  106 . In an example, the processor  110  can map the nodes  301 ,  302 ,  303 ,  304  (or centroids of the sets  231 ,  232 ,  233 ,  234  in  FIG. 2A ) to their corresponding data point in the first dimensional space  106 . In the example shown in  FIG. 4 , the nodes  301 ,  302 ,  303 ,  304  are mapped to data points  401 ,  402 ,  403 ,  404 , respectively, where the data points  401 ,  402 ,  403 ,  404  can be among the data points  108  (see  FIG. 1 ) in the first dimensional space  106 . The data points  401 ,  402 ,  403 ,  404  in the first dimensional space  106  can represent the same cell state as their corresponding nodes in the second dimensional space  206 . For example, the data point  401  in the first dimensional space  106  and the node  301  in the second dimensional space  206  can represent the same cell state. 
     In the example shown in  FIG. 4 , the transition paths  311 ,  312 ,  313 ,  314  can be translated into a plurality of translated transition paths  411 ,  412 ,  413 ,  414 , respectively. In an example, the translated transition paths  411 ,  412 ,  413 ,  414  can be generated by the processor  110  by connecting the data points  401 ,  402 ,  403 ,  404  in the first dimensional space  106 . In an example, the processor  110  can determine whether to connect two data points in the first dimensional space based on a presence or absence of transition paths in the second dimensional space. For example, the processor  110  can connect the data points  401  and  402  to generate the translated transition path  411  in the first dimensional path  106  based on a presence of the transition path  311  connecting the nodes  301  and  302  in the second dimensional space  206 . In another example, the processor  110  can determine an absence of a transition path between the nodes  301  and  304  in the second dimensional space and, in response, determine that the data points  401  and  404  shall not be connected in the first dimensional space  106 . 
     The translated transition paths, and the data points lying on these translated transition paths, in the first dimensional space  106  can represent a trajectory or path indicating how cells transition from one cell state to another cell state. For example, if the data point  401  represents a cell state A, and the data point  402  represents a cell state B, then data points along the translated transition path  411  can represent a trajectory or path indicating how a cell can transition from the cell state A to the cell state B. In response to generating the translated transition paths in the first dimensional space  106 , the processor  110  can extract a set of data points (e.g., set of data points  132  in  FIG. 1 ), from the data points  108 , that lies along the generated translated transition paths in the first dimensional space  106 . In some examples, the processor  110  can extract a set of data points (e.g., set of data points  132  in  FIG. 1 ), from the data points  108 , that lies within a distance from the translated transition paths in the first dimensional space  106 . In the example shown in  FIG. 4 , the processor  110  can extract different sets of data points  421 ,  422 ,  423 ,  424  that lies on the translated transition paths  413 ,  411 ,  412 ,  414 , respectively. The processor  110  can generate the set of training data  140  using the extracted features or data points  421 ,  422 ,  423 ,  424 . The processor  110  can train a machine learning model  150  (see  FIG. 1 ) using the set of training data  140 , where the trained machine learning model  150  can be run by the processor  110  for classifying transitions between different cell states. In some examples, the processor  110  can extract a set of data points, from the data points  108 , that has particular characteristics. For example, the processor  110  can extract data points belonging to one or more particular cell states (e.g., particular stages of a disease) that lie along the translated transition paths in the first dimensional space  106 . 
     In an example embodiment, the processor  110  can train the machine learning model  150  using a supervised learning approach, by generating the training data  140  to include a plurality of input-output pair data. In an example, such input-output pair data can use the single cell data in the data source  102  as the input, and characteristics such as cell transitions and disease stages of the single cell data as the output. In another example embodiment, the processor  110  can train the machine learning model  150  using an unsupervised learning approach, by generating the training data  140  to include input data but no output data. In an example, the unsupervised training data can include the single cell data in the data source  102 , but exclude characteristics such as cell transitions and disease stages corresponding to the single cell data. 
       FIG. 5  is a diagram showing another example implementation of the example system of  FIG. 1  in one embodiment. In an example, the processor  110  can run the trained machine learning model  150  to classify a set of test data  502 . The machine learning model  150  can output a result  504  indicating a classification result of the test data  502 . In an example, the test data  502  can be a set of single cell data of an individual subject, and the machine learning model  150  can be a classification model that can classify the test data  502  into a cell state transition. For example, the result  504  can indicate that the individual subject having the single cell data represented by the test data  502  has an X likelihood of being in a transition from a disease stage B to stage C. In another example, the result  504  can indicate that the individual subject having the single cell data represented by the test data  502  has a Y likelihood of being in a transition from current cell state to medicine resistance state. In some examples, the system  100  can further include a screen or a display that can output a user interface, where the user interface can show a visual output indicating the result  504 . 
     Further, the processor  110  can retrain the machine learning model  150  using the test data  502  and the result  504 . For example, the processor  110  can update the training data  140  by adding the test data  502  and the result  504  to the training data  140  to generate an updated set of training data  510 . The processor  110  can perform retrain the machine learning model  150  by training another machine learning model  520  using the updated training data  510 , where the machine learning model  520  can be a updated or refined version of the machine learning model  150 . In some examples, the processor  110  can send the result  504  (or results from the updated machine learning model  520 , and subsequent retrained models) to another device  530 . The device  530  can be a device running another application that can use the result  504 . For example, the device  530  can be running an application relating to development of medicines, such as new resistance cell lines for medicine screens, development of agents targeting transition genes and pathways indicated by the result  504 , antibody development, CRISPR gene editing, medicine repurposing, and/or other types of medicine development applications. In some examples, the device  530  can develop new medicine resistant cell lines by knocking out or up features (e.g., extracted data points  132 ) and pathways (e.g., translated transition paths  413 ,  411 ,  412 ,  414 ) among the result  504 . For example, genes can be knocked out or up (e.g., making the gene inoperative) using techniques such as CRISPR, similarly pathways can be knocked out or up with the same approach on key regulators of the pathway. The classification results from the machine learning model  150  can indicate which genes and/or pathways may be contributing to particular cell state changes. Thus, the genes and pathways indicated by the classification result can be knocked out or up from the original dataset (e.g., single cell data  104 ). The resulting data set can be used to train new machine learning models that can indicate and confirm effects of the knocked out or knocked up genes and pathways on cell state changes among the original dataset. Other types of assay of cell or model viability used in medicine development can also be performed using the remaining dataset after knocking out or up genes and pathways. 
     In an example, the system  100  can be implemented provide analytical results for medicine development application and systems. For example, an application for medicine resistance in melanoma can utilize the classification results from the machine learning model  150  to determine whether a subject is experiencing relapse after successful treatment in the primary disease. Single cell RNA data of a plurality of cells having, for example, four distinct transcriptional states according to MITF activity (e.g., MITF being a positive biomarker for medicine resistance) can be used to train the machine learning model  150  to identify BRAF mutant melanoma cancer cells. This trained machine learning model  150  can identify the features that are associated with cell state change, such as cells moving from zero MITF activity to higher order of activity (e.g., phase  3 ). Further, the training of the machine learning model  150  can provide additional topological data, such as the simplicial complex shown in  FIG. 3B , that can be used for identifying distinct clusters of cells that can be reflective of not only MITF activity but also potentially new cell classes through learning of new cell labels. Furthermore, the machine learning model  150  can classify how cells can be moving from one cell state to another cell state, and capture cells that are potentially in transition to a higher medicine resistance state. By returning to the original feature space (e.g., first dimensional space  106 ), the system can identify genes and pathways that may be associated with these potential transitions. Thus, appropriate medicine targeting of these genes and pathways using new targeted agents, such as CRISPR or antibodies, can be therapeutic options to prevent relapse. 
       FIG. 6  is a flow diagram illustrating a process  600  to implement cell state transition features from single cell data in one embodiment. The process  600  can include one or more operations, actions, or functions as illustrated by one or more of blocks  602 ,  604 ,  606 ,  608 ,  610 ,  612 , and/or  614 . Although illustrated as discrete blocks, various blocks can be divided into additional blocks, combined into fewer blocks, eliminated, or performed in parallel, depending on the desired implementation. 
     The process  600  can being at block  602 . At block  602 , a processor can transform a set of single cell data in a first dimensional space into a set of projection data in a second dimensional space. The second dimensional space can have a dimensionality lower than or equal to a dimensionality of the first dimensional space. In some examples, the set of single cell data can be one of a single cell deoxyribonucleic acid (DNA) sequencing data and a single cell ribonucleic acid (RNA) sequencing data. 
     The process  600  can proceed from block  602  to block  604 . At block  604 , the processor can produce a cover of the set of projection data. The cover can include a plurality of sets, and a union of the sets can include the entirety of the set of projection data. In some examples, the processor can cluster the projection data into a plurality of clusters. The processor can expand the plurality of clusters into the plurality of sets to produce the cover. 
     The process  600  can proceed from block  604  to block  606 . At block  606 , the processor can determine a plurality of transition paths among the plurality of sets. A transition path can represent a transition from one cell state to another cell state. In some examples, the processor can construct a simplicial complex from the plurality of sets. The processor can identify a plurality of nerves of the simplicial complex as the transition paths. A transition path can connect a pair of centroids of a pair of sets. 
     The process  600  can proceed from block  606  to block  608 . At block  608 , the processor can translate the plurality of transition paths from the second dimensional space onto the set of single cell data in the first dimensional space. The process  600  can proceed from block  608  to block  610 . At block  610 , the processor can extract a plurality of features from the transition paths translated onto the first dimensional space. 
     The process  600  can proceed from block  610  to block  612 . At block  612 , the processor can generate a set of training data using the extracted features. In some examples, the processor can extract a set of data points among the set of single cell data. The extracted set of data points can be data points on the transition paths translated onto the first dimensional space. In some examples, the processor can extract at least one data point having a particular characteristic. 
     The process  600  can proceed from block  612  to block  614 . At block  614 , the processor can train a machine learning model using the set of training data. The machine learning model can be trained for classifying transitions between different cell states. In some examples, the machine learning model can be trained for classifying transitions between different cell states including a transition from a current state of a plurality of cells to a medicine resistance state. In some examples, the machine learning model can be trained for classifying transitions between different cell states into a plurality of cell classes. 
     In some examples, the processor can receive new single cell data. The processor can run the machine learning model to classify the new single cell data into a particular cell state. The processor can retrain the machine learning model using the new single cell data and the particular cell state. 
       FIG. 7  illustrates a schematic of an example computer or processing system that may implement cell state transition features from single cell data in one embodiment of the present disclosure. The computer system is only one example of a suitable processing system and is not intended to suggest any limitation as to the scope of use or functionality of embodiments of the methodology described herein. The processing system shown may be operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the processing system shown in  FIG. 7  may include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, supercomputers, and distributed cloud computing environments that include any of the above systems or devices, and the like. 
     The computer system may be described in the general context of computer system executable instructions, such as program modules, being implemented by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. The computer system may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. 
     The components of computer system may include, but are not limited to, one or more processors or processing units  12 , a system memory  16 , and a bus  14  that couples various system components including system memory  16  to processor  12 . The processor  12  may include a module  30  (e.g., machine learning module  30 ) that performs the methods described herein. The module  30  may be programmed into the integrated circuits of the processor  12 , or loaded from memory  16 , storage device  18 , or network  24  or combinations thereof. 
     Bus  14  may represent one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. 
     Computer system may include a variety of computer system readable media. Such media may be any available media that is accessible by computer system, and it may include both volatile and non-volatile media, removable and non-removable media. 
     System memory  16  can include computer system readable media in the form of volatile memory, such as random access memory (RAM) and/or cache memory or others. Computer system may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  18  can be provided for reading from and writing to a non-removable, non-volatile magnetic media (e.g., a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus  14  by one or more data media interfaces. 
     Computer system may also communicate with one or more external devices  26  such as a keyboard, a pointing device, a display  28 , etc.; one or more devices that enable a user to interact with computer system; and/or any devices (e.g., network card, modem, etc.) that enable computer system to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces  20 . 
     Still yet, computer system can communicate with one or more networks  24  such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter  22 . As depicted, network adapter  22  communicates with the other components of computer system via bus  14 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system. Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by a computing device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be implemented substantially concurrently, or the blocks may sometimes be implemented in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
       FIG. 8  depicts a cloud computing environment according to an embodiment of the present invention. It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as Follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as Follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as Follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG. 8 , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG. 8  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
       FIG. 9  depicts abstraction model layers according to an embodiment of the present invention. Referring now to  FIG. 9 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 8 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 9  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and cell state transition identification application  96 . 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements, if any, in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.