Characterization and Sorting for Particle Analyzers

Abstract

Non-parametric transforms such as t-distributed stochastic neighbor embedding (tSNE) are used to analyze multi-parametric data such as data derived from flow cytometry or other particle analysis systems and methods. These transforms may be included for dimensionality reduction and identification of subpopulations (e.g., gating). By nature, non-parametric transforms cannot transform new observations without training a new transformation based on the entire dataset including the new observations. The features described parameterize non-parametric transforms using a neural network thereby allowing a small training dataset to be transformed using non-parametric techniques. The training dataset may then be used to generate an accurate parametric model for assessing additional events in a manner consistent with the initial events.

1 .- 55 . (canceled) 
     
     
         56 . A system comprising:
 a light source configured to irradiate a sample comprising particles in a flow stream;   a light detection system comprising a photodetector configured to generate data signals from light detected from the particles in the flow stream; and   a processor comprising memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to:   apply a non-parametric transformation to a first set of data signals from the light detection system to generate a first transformed data set;   generate a second transformed data set from the first transformed data set; and   generate a transformation model using the second transformed data set; and   apply the transformation model to a second set of data signals from the light detection system.   
     
     
         57 . The system of  claim 56 , wherein the non-parametric transformation comprises t-distributed stochastic neighbor embedding (tSNE). 
     
     
         58 . The system of  claim 56 , wherein the first set of data signals from the light detection system comprises data from 10,000 detected cells or less from the flow cytometer. 
     
     
         59 . The system of  claim 56 , wherein the processor comprises memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to generate the second transformed data set by adding a noise component. 
     
     
         60 . The system according to  claim 59 , wherein the processor comprises memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to generate the second transformed data set by adding a noise component to one or more of the first set of data signals from the light detection system, the first transformed data set and a combination thereof. 
     
     
         61 . The system according to  claim 60 , wherein the processor comprises memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to generate the second transformed data set by adding a noise component to a data pair that comprises a raw data point and transformed data point. 
     
     
         62 . The system according to  claim 61 , wherein the raw data point and the transformed data point are selected by random selection. 
     
     
         63 . The system according to  claim 62 , wherein the raw data point and the transformed data point are selected by a weighted random selection. 
     
     
         64 . The system according to  claim 63 , wherein the random selection is weighted by one or more of density and cell population. 
     
     
         65 . The system of  claim 56 , wherein the noise component is based on a probability distribution. 
     
     
         66 . The system according to  claim 65 , wherein the probability distribution is selected from the group consisting of uniform, Gaussian and Poisson. 
     
     
         67 . The system of  claim 56 , wherein the transformation model is a dynamic algorithm. 
     
     
         68 . The system of  claim 67 , wherein the dynamic algorithm is a machine learning algorithm. 
     
     
         69 . The system of  claim 56 , wherein the transformation model is applied to data signals from the light detection system in real time. 
     
     
         70 . The system of  claim 56 , wherein the processor comprises memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to classify particles of the sample in the flow stream. 
     
     
         71 . The system according to  claim 70 , wherein the processor comprises memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to generate a sort decision based on the particle classification. 
     
     
         72 . The system of  claim 56 , further comprising a cell sorter. 
     
     
         73 . The system according to  claim 72 , wherein the cell sorter comprises a droplet deflector. 
     
     
         74 . The system of  claim 56 , further comprising an integrated circuit device. 
     
     
         75 . The system according to  claim 74 , wherein the integrated circuit device is a field programmable gate array (FPGA). 
     
     
         76 - 97 . (canceled)