Methods of analyzing cells

Abstract

A single-cell transition signature is accessed that represents differential cellular-component expression between a first and altered cell state. The altered state occurs through cellular transition from the first to altered cell state. The transition signature comprises an identification of a plurality of cellular-components and, for each such cellular-component, a corresponding first significance score quantifying an association between a change in expression of the respective cellular-component and a change in cell state between the first and altered cell state. The transition signature is compared to a perturbation signature representing differential cellular-component expression between unperturbed cells and perturbed cells exposed to the perturbation. The perturbation signature comprises, for each respective cellular-component, a corresponding second significance score quantifying an association between (i) a change in expression of the cellular-components between the unperturbed and perturbed cells and (ii) a change in cell state between the unperturbed and perturbed cells.

What is claimed is: 
     
         1 . A method for predicting whether a perturbation will affect a cellular transition, the method comprising:
 at a computer system comprising a memory and one or more processors:   accessing, in electronic form, a single-cell transition signature representing a measure of differential cellular-component expression between a first cell state and an altered cell state, wherein the altered cell state occurs through the cellular transition from the first cell state to the altered cell state, and wherein the single-cell transition signature comprises an identification of a plurality of cellular-components and, for each respective cellular-component in the plurality of cellular-components, a corresponding first significance score that quantifies an association between a change in expression of the respective cellular-component and a change in cell state between the first cell state and the altered cell state;   accessing, in electronic form, a perturbation signature representing a measure of differential cellular-component expression between a plurality of unperturbed cells and a plurality of perturbed cells exposed to the perturbation, wherein the perturbation signature comprises an identification of all or a portion of the plurality of cellular-components and, for each respective cellular-component in the all or the portion of the plurality of cellular-components, a corresponding second significance score that quantifies an association between (i) a change in expression of the respective cellular-component between the plurality of unperturbed cells and the plurality of perturbed cells and (ii) a change in cell state between the plurality of unperturbed cells and the plurality of perturbed cells; and   comparing the single-cell transition signature and the perturbation signature thereby determining whether the perturbation will affect the cellular transition.   
     
     
         2 . The method of  claim 1 , wherein accessing the single-cell transition signature comprises:
 determining the single-cell transition signature based on (i) a first plurality of first single-cell cellular-component expression datasets, and (ii) a second plurality of second single-cell cellular-component expression datasets, wherein:   each respective first single-cell cellular-component expression dataset in the first plurality of first single-cell cellular-component expression datasets is obtained from a corresponding single cell of a first plurality of cells in the first cell state, and   each respective second single-cell cellular-component expression dataset in the second plurality of second single-cell cellularity component expression datasets is obtained from a corresponding single cell of a second plurality of cells in the altered cell state.   
     
     
         3 . The method of  claim 2 , wherein:
 each respective dataset of the first plurality of single-cell cellular-component expression datasets comprises a corresponding cellular-component vector, in a first plurality of cellular-component vectors,   each respective dataset of the second plurality of single-cell cellular-component expression datasets comprises a corresponding cellular-component vector, in a second plurality of cellular-component vectors,   each respective cellular-component vector in the first and second plurality of cellular-component vectors comprises a plurality of elements, each respective element in the respective cellular-component vector associated with a corresponding cellular-component in the plurality of cellular-components and including a corresponding value that represents a quantity of the corresponding cellular-component for the corresponding single cell that is represented by the respective dataset of the first and second pluralities of single-cell cellular-component expression datasets.   
     
     
         4 . The method of  claim 3 , further comprising:
 performing dimensionality reduction on the first and/or the second plurality of single-cell cellular-component expression datasets to generate a plurality of dimension reduction components;   applying, for each respective cellular-component vector in the first and second plurality of cellular-component vectors, the plurality of dimension reduction components to the respective cellular-component vector to form a corresponding dimension reduction vector that includes a dimension reduction component value for each respective dimension reduction component in the plurality of dimension reduction components, thereby forming a corresponding first and second plurality of dimension reduction vectors; and   performing clustering to generate a set of clusters C j , each cluster comprising a plurality of points corresponding to a subset of the first and second plurality of dimension reduction vectors;   identifying the first plurality of cells from a first cluster of the set of clusters C j ; and   identifying the second plurality of cells from a second cluster of the set of clusters C j , the method optionally further comprising performing manifold learning with the corresponding first and second plurality of dimension reduction vectors to identify a relative cell state of each cell with respect to each other cell in the first and second plurality of cells.   
     
     
         5 . The method of  claim 1 , wherein the plurality of unperturbed cells are control cells that have not been exposed to the perturbation, or wherein the unperturbed cells are an average taken over unrelated perturbed cells that have been exposed to the perturbation. 
     
     
         6 . The method of  claim 1 , the method further comprising:
 pruning the single-cell transition signature and the perturbation signature to limit the plurality of cellular-components to transcription factors, optionally measured at the RNA level.   
     
     
         7 . The method of  claim 2 , wherein the determining the single-cell transition signature comprises:
 determining a difference in cellular-component quantities across the plurality of cellular-components between (i) the first plurality of first single-cell cellular-component expression datasets and the second plurality of second single-cell cellular-component expression datasets using one of a difference of means test, a Wilcoxon rank-sum test, a t-test, a logistic regression, and a generalized linear model.   
     
     
         8 . The method of  claim 1 , wherein the measure of differential cellular-component expression quantifies a difference in cellular-component quantities between (i) a third plurality of third single-cell cellular-component expression datasets and (ii) a fourth plurality of fourth single-cell cellular-component expression datasets using one of a Wilcoxon rank-sum test, a t-test, a logistic regression, and a generalized linear model, wherein:
 each respective third single-cell cellular-component expression dataset in the third plurality of third single-cell cellular-component expression datasets is obtained from a corresponding single cell of in the plurality of unperturbed cells, and   each respective fourth single-cell cellular-component expression dataset in the fourth plurality of fourth single-cell cellularity component expression datasets is obtained from a corresponding single cell of a fourth plurality of cells in the plurality of perturbed cells exposed to the perturbation.   
     
     
         9 . The method of  claim 1 , further comprising:
 filtering the single-cell transition signature and the perturbation signature to reduce a number of cellular-components included in the single-cell transition signature and the perturbation signature,   optionally wherein the filtering the single-cell transition signature and the perturbation signature comprises reducing the number of cellular-components included in the single-cell transition signature and the perturbation signature according to a threshold p-value or according to a threshold number of cellular-components.   
     
     
         10 . The method of  claim 1 , wherein determining the corresponding second significance score for a respective cellular-component comprises:
 replacing the significance score for the respective cellular-component with a corresponding matching score for the respective cellular-component, for each respective cellular-component in the plurality of cellular-components;   combining the matching scores for the plurality of cellular-components to generate a matching score for the perturbation; and   determining whether the perturbation is associated with the transition of cells between the first cell state and the altered cell state based on the matching score for the respective perturbation,   optionally wherein the corresponding matching score comprise discrete or continuous score.   
     
     
         11 . The method of  claim 10 , wherein replacing the significance score comprises:
 replacing the significance score with a first score if the cellular-component quantity from the single-cell transition signature for the respective cellular-component and the cellular-component quantity from the perturbation signature for the respective cellular-component are both up-regulated;   replacing the significance score with a second score if the cellular-component quantity from the single-cell transition signature for the respective cellular-component is up-regulated and the cellular-component quantity from the perturbation signature for the respective cellular-component is down-regulated; and   replacing the significance score with a third score if the cellular-component quantity from the perturbation signature for the respective cellular-component is not significantly up-regulated or down-regulated.   
     
     
         12 . The method of  claim 10 , wherein replacing the significance score comprises:
 replacing the significance score with a first score if the cellular-component quantity from the single-cell transition signature for the respective cellular-component and the cellular-component quantity from the perturbation signature for the cellular-component are both down-regulated;   replacing the significance score with a second score if the cellular-component quantity from the single-cell transition signature for the respective cellular-component is down-regulated and the cellular-component quantity from the perturbation signature for the cellular-component is up-regulated; and   replacing the significance score with a third score if the cellular-component quantity from the perturbation signature for the cellular-component is not significantly up-regulated or down-regulated.   
     
     
         13 . The method of  claim 1 , wherein the plurality of cellular-components comprise a plurality of genes, optionally measured at the RNA level. 
     
     
         14 . The method of  claim 2 , wherein each single-cell cellular-component expression dataset in the first plurality of first single-cell cellular-component expression datasets and the second plurality of second single-cell cellular-component expression datasets is generated using a method selected from the group consisting of: single-cell ribonucleic acid (RNA) sequencing (scRNA-seq), scTag-seq, single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), CyTOF/SCoP, E-MS/Abseq, miRNA-seq, CITE-seq, and any combinations thereof, as well as summaries of the same, including combinations, such as linear combinations, representing activated pathways in the single-cell cellular-component expression datasets. 
     
     
         15 . The method of  claim 1 , the method further comprising:
 identifying the perturbation as one that promotes the altered cell state based on the comparing, or   identifying the perturbation as one that inhibits the altered cell state based on the comparing.   
     
     
         16 . The method of  claim 1 , wherein the cell transition signature and the perturbation signature are generated using different types of cellular-components. 
     
     
         17 . The method of  claim 1 , wherein the cell transition signature and the perturbation signature are generated using the same types of cellular-components. 
     
     
         18 . The method of  claim 1 , wherein
 the accessing, in electronic form, is performed for each respective signature in a plurality of perturbations, thereby obtaining a plurality of perturbation signatures   the comparing compares the single-cell transition signature and the perturbation signature to each respective signature in a plurality of perturbation signatures, thereby   determining a subset of the plurality of perturbations that are associated with the transition of cells between the first cell state and the altered cell state.   
     
     
         19 . A computer system, comprising one or more processors and memory, the memory storing instructions for performing a method for predicting whether a perturbation will affect a cellular transition, the method comprising:
 accessing, in electronic form, a single-cell transition signature representing a measure of differential cellular component expression between a first cell state and an altered cell state, wherein the altered cell state occurs through the cellular transition from the first cell state to the altered cell state, and wherein the single-cell transition signature comprises an identification of a plurality of cellular-components and, for each respective cellular-component in the plurality of cellular-components, a corresponding first significance score that quantifies an association between a change in expression of the respective cellular-component and a change in cell state between the first cell state and the altered cell state;   accessing, in electronic form, a perturbation signature representing a measure of differential cellular-component expression between a plurality of unperturbed cells and a plurality of perturbed cells exposed to the perturbation, wherein the perturbation signature comprises an identification of all or a portion of the plurality of cellular-components and, for each respective cellular-component in the all or the portion of the plurality of cellular-components, a corresponding second significance score that quantifies an associated between (i) a change in expression of the respective cellular-component between the plurality of unperturbed cells and the plurality of perturbed cells and (ii) a change in cell state between the plurality of unperturbed cells and the plurality of perturbed cells; and   comparing the single-cell transition signature and the perturbation signature thereby determining whether the perturbation will affect the cellular transition.   
     
     
         20 . A non-transitory computer-readable medium storing one or more computer programs, executable by a computer, for predicting whether a perturbation will affect a cellular transition, the computer comprising one or more processors and a memory, the one or more computer programs collectively encoding computer executable instructions that perform a method comprising:
 accessing, in electronic form, a single-cell transition signature representing a measure of differential cellular component expression between a first cell state and an altered cell state, wherein the altered cell state occurs through the cellular transition from the first cell state to the altered cell state, and wherein the single-cell transition signature comprises an identification of a plurality of cellular-components and, for each respective cellular-component in the plurality of cellular-components, a corresponding first significance score that quantifies an association between a change in expression of the respective cellular-component and a change in cell state between the first cell state and the altered cell state;   accessing, in electronic form, a perturbation signature representing a measure of differential cellular-component expression between a plurality of unperturbed cells and a plurality of perturbed cells exposed to the perturbation, wherein the perturbation signature comprises an identification of all or a portion of the plurality of cellular-components and, for each respective cellular-component in the all or the portion of the plurality of cellular-components, a corresponding second significance score that quantifies an associated between (i) a change in expression of the respective cellular-component between the plurality of unperturbed cells and the plurality of perturbed cells and (ii) a change in cell state between the plurality of unperturbed cells and the plurality of perturbed cells; and   comparing the single-cell transition signature and the perturbation signature thereby determining whether the perturbation will affect the cellular transition.