CN116525004A - Single cell expression pattern difference evaluation method, medium and device based on two groups of comparison - Google Patents
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Abstract
The invention discloses a single cell expression pattern difference evaluation method, medium and equipment based on two groups of comparison, and relates to the technical field of biological information analysis. The method comprises the following steps: s101, combining single cell transcriptome expression profiles of a control group and a treatment group; s102, grouping all cells in the combined single cell transcriptome expression profile; s103, identifying cell types of each cell population; s104, screening out cell types existing in a control group and a treatment group simultaneously, and extracting expression profiles of the cell types; s105, calculating a differential enrichment score of the cell type; and S106, ranking the cell types from large to small according to the difference enrichment scores. The invention can evaluate the exact index of the difference size and the difference enrichment degree of the single cell expression spectrum under two groups of comparison, and provides a basis for scientifically selecting the subsequent research direction.
Description
Technical Field
The invention relates to the technical field of biological information analysis, in particular to a single cell expression pattern difference evaluation method, medium and device based on two groups of comparison.
Background
Single cell level sequencing techniques such as single cell transcriptome, single cell ATAC histology (high throughput analysis of single cell chromatin transposase accessibility), single cell epigenetic histology and the like can obtain RNA and chromatin information of thousands or tens of thousands of genes in a single cell, and comprehensively display the gene expression difference between each cell. The high-flux single-cell sequencing platform (such as a related platform of 10X Genomics) can realize high-flux cell sorting and capturing by utilizing technologies such as micro-flow control, oil drop wrapping, barcode labeling and the like, can separate and label hundreds or even tens of thousands of cells at one time, can obtain information such as transcriptome or chromatin, site methylation and the like of each cell after processing such as amplification, sequencing and the like, and has the advantages of high cell flux, low library construction cost, high efficiency and the like. The technology can be used for analyzing the expression, chromatin or methylation characteristics of different cell types by combining with the characteristic of a marker signal (marker gene) or a cell type marking algorithm (such as SingleR) of different cell types, and further can be used for researching the aspects of biological development, disease development, immune change and the like.
Analysis of single cell histology data typically involves the following steps: filtering low quality cells, identifying cell types, initially analyzing the overall characteristics or expression patterns of each cell type, selecting cell types for in-depth analysis, and performing personalized analysis (e.g., pathway enrichment analysis, predicting differentiation trajectories, transcription factor activity, cellular communication, etc.) for the target cell type. The last step of personalized analysis, theoretically, can be performed using a virtually unlimited number of existing software, and therefore typically takes the most manpower, effort and time in this step. The former step of selecting a cell type is the basis of personalized analysis, namely, selecting a key and proper cell type, then the subsequent personalized analysis can mine important and meaningful information, and if the cell type is selected improperly, a great deal of time and effort can be spent, but only valuable or low-value information is mined.
Currently, researchers select single cell data with both comparison and control groups (i.e., both comparison) for subsequent study directions, typically based on existing biological knowledge, rather than on the characteristics and differences of the data itself. This may lead to many potentially valuable research directions being missed. Whether there is a difference between the two groups on different cell types, the magnitude of the difference, the significance of the difference, are indicative of the extent to which the treatment or experimental conditions have an effect on the expression pattern of the different cells. However, due to the huge data volume and cell volume of single-cell histology and complex data conditions, the prior art is difficult to directly and accurately evaluate the difference of cell expression patterns.
Disclosure of Invention
In order to solve at least one technical problem in the background art, the invention aims to provide a single cell expression pattern difference evaluation method, medium and equipment based on two groups of comparison, which can evaluate the exact index of the difference size and the difference enrichment degree of single cell expression patterns under the two groups of comparison and provide basis for scientifically selecting the subsequent research direction.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a single cell expression pattern difference assessment method based on two sets of comparisons, comprising the steps of:
s101, combining single cell transcriptome expression profiles of a control group and a treatment group;
s102, grouping all cells in the combined single cell transcriptome expression profile;
s103, identifying cell types of each cell population;
s104, screening out cell types existing in a control group and a treatment group simultaneously, and extracting expression profiles of the cell types;
s105, calculating a differential enrichment score of the cell type;
and S106, ranking the cell types from large to small according to the difference enrichment scores.
Further, the method for calculating the differential enrichment score is as follows:
s1051, calculating the characteristic expression profile distance of a control group and a treatment group in the cell type;
s1052, the cell groups of the cell types are disordered, the cell numbers of the cell types are randomly recombined into two groups according to the original group number, and the characteristic expression spectrum distances of the cell types are calculated respectively;
s1053, repeating the step S1052 for a plurality of times to obtain a plurality of characteristic expression spectrum distances to form natural difference zero distribution of the expression spectrum;
s1054, calculating the ratio of the characteristic expression spectrum distance obtained in S1051 to the average value of the natural difference zero distribution of the expression spectrum, namely the difference enrichment score.
Further, the method for solving the characteristic expression spectrum distance is as follows:
separating the control group and the treatment group from the original expression spectrum to form two expression spectrums, and measuring the average value of the gene expression of each gene respectively to obtain characteristic expression spectrums of the control group and the treatment group; and calculating the distances of the characteristic expression profiles of the control group and the treatment group to obtain the characteristic expression profile distance.
Further, the distance is a euclidean distance or a manhattan distance.
Further, in S102, the cells are normalized and/or PCA reduced in dimension prior to being clustered.
Further, in S103, identifying the cell type by using a marker gene having cell type specificity, and identifying the cell population with high expression of the marker gene as the corresponding cell type; alternatively, identification of each cell type was performed using SingleR software.
A computer storage medium having stored thereon a computer program, characterized in that the program when executed by a processor implements a single cell expression pattern difference assessment method based on two sets of comparisons as described above.
A terminal device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a single cell expression pattern difference assessment method based on two sets of comparisons as described above when executing the computer program.
Compared with the prior art, the invention has the beneficial effects that:
the invention calculates the characteristic expression spectrum distance of the random group by repeatedly carrying out random grouping on the cells of the same type, generates natural difference distribution, compares the distance between the characteristic expression spectrums of the original group with the natural difference distribution to obtain the difference score of each cell type, and obtains the evaluation result of the difference amplitude of the expression mode of each cell type after sequencing according to the size. Providing basis for scientific selection of the subsequent research direction.
Drawings
FIG. 1 is an overall flow chart of an embodiment of the present invention.
FIG. 2 is a flow chart of the differential enrichment score calculation according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
referring to fig. 1, the embodiment provides a single cell expression pattern difference evaluation method based on two sets of comparison, which includes the following steps:
s101, combining single cell transcriptome expression profiles of a control group and a treatment group; as shown in table 1 below:
table 1: pooled expression profiles
In the above table, samp1 to Samp5 represent sample names; c1 to C14001 represent cells; gene1 to Gene3 represent genes; the numbers of the body part represent the gene expression level.
S102, grouping all cells in the combined single cell transcriptome expression profile by using the SEurat software; and meanwhile, normalization and dimension reduction can be performed, wherein the normalization method selects 'lognormal', and the screening method of the hypervariable genes uses 'vst'.
S103, identifying cell types of each cell population; there are two general methods of identification, one is to identify using marker genes with cell type specificity (such as CD4 and CD8 genes of human T cells), and identify cell populations highly expressing marker genes as corresponding cell types; the other is the use of cell identification function software (e.g., singleR, scCATCH) for each cell population. The results of the assay are shown in table 2 below:
table 2: cell identification results
In the above table, type1 through Type3 are identified cell types.
S104, counting the number of groups of each cell type, screening out the cell types existing in the control group and the treatment group simultaneously, and extracting the expression profile of the cell types;
if there is a cell Type1 in both the control and treatment groups, the cell Type is selected, and if there is a Type3 in the treatment group, the cell Type is selected, the cell Type is also selected, and if there is a Type2 in the treatment group, the cell Type is ignored.
For each cell type selected, the number of cell types in common is denoted n and the name T n And extracting the corresponding expression profile M n . As shown in table 3 below:
table 3: co-cell type expression profiling
S105, calculating a differential enrichment score of the cell type to measure the differential enrichment degree under the cell type.
Specifically, as shown in fig. 2, the method for calculating the differential enrichment score is as follows:
s1051, calculating the characteristic expression profile distance of the control group and the treatment group in the cell type.
The solving method of the characteristic expression spectrum distance is as follows: separating the control group and the treatment group from the original expression spectrum to form two expression spectrums, and measuring the average value of the gene expression of each gene respectively to obtain characteristic expression spectrums of the control group and the treatment group; and calculating the distances of the characteristic expression profiles of the control group and the treatment group to obtain the characteristic expression profile distance.
Taking cell Type1 as an example: first, the expression profile of the control group and the treatment group was obtained from M 1 Two expression profiles were formed, each of which was averaged over the gene expression for each gene, i.e., two sets of characteristic expression profiles, as shown in table 4 below:
table 4: analysis of the expression profiles of the groups in cell Type1 and calculation of the characteristic expression profile
Calculating to obtain Euclidean distance E between two characteristic expression spectrums 1 (Manhattan distance may be used instead), i.e., the characteristic expression profile distance.
S1052, the cell group of the cell type is disturbed, the cell number of the cell type is randomly recombined into two disordered groups according to the original group, and the characteristic expression profiles of the two disordered groups are respectively calculated; as shown in table 5 below:
table 5: recalculating characteristic expression profiling after disarranged grouping in T1 cell types
The characteristic expression profile distance was determined from the characteristic expression profile obtained in Table 5.
S1053, repeating the step S1052 for a plurality of times, preferably 1000 times, to obtain 1000 characteristic expression profile distances; the 1000 characteristic expression profile distances are taken as the natural difference zero distribution Z of the expression profile of the cell Type1 1 。
S1054, calculating the characteristic expression spectrum distance E obtained in S1051 1 Natural difference zero profile Z from the expression profile 1 Average value u of (2) 1 Ratio S of (2) 1 S, i.e 1 ＝E 1 /u 1 I.e. the difference enrichment score. Other cell types are similarly considered to obtain a plurality of differential enrichment scores S n To evaluate the magnitude of the change in expression pattern between the control group and the comparison group.
S106, enriching the cell types according to the difference enrichment score S n Ranking from large to small, as a result of evaluation of differential enrichment degree of expression patterns of each cell type under two sets of comparison, and ranking of potential research value, differential enrichment score S n The larger the potential representing the corresponding cell typeThe higher the research value.
Embodiment two:
based on the first embodiment, the second embodiment performs reliability screening on the cell Type before ranking in S106, specifically, includes the following steps (taking cell Type1 as an example):
s1061, expressing the characteristic spectrum distance E 1 And the natural difference zero profile Z of the expression profile of the corresponding cell type 1 In comparison, the recorded expression profile is greater than the characteristic expression profile distance E in the natural difference zero distribution 1 Number of values N 1 ；
S1062, calculating the natural difference zero distribution Z of the number in the expression profile 1 The product of the ratio of the cell type and the total number n of the cell type is used as the p value after the difference correction of the cell type, namely p 1 ＝N 1 Y is n, wherein Y represents the natural differential zero profile Z of the expression profile 1 The number of mid-feature expression profile distances; the p-value is used to measure the reliability of the difference, and the smaller the p-value, the more significant the difference.
S1063, excluding cell types with p-value greater than the threshold G (typically set to 0.05), ranking of remaining cell types as ranking of the expression pattern difference size and potential research value size of the control group and the comparison group at each cell type.
Embodiment III:
a computer storage medium having a computer program stored thereon, wherein the program when executed by a processor implements the single cell expression pattern difference evaluation method based on two sets of comparisons as described in embodiment one or embodiment two.
Embodiment four:
a terminal device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the single cell expression pattern difference evaluation method based on two sets of comparisons as described in embodiment one or embodiment two when executing the computer program.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (8)
1. A single cell expression pattern difference assessment method based on two sets of comparisons, comprising the steps of:
s101, combining single cell transcriptome expression profiles of a control group and a treatment group;
s102, grouping all cells in the combined single cell transcriptome expression profile;
s103, identifying cell types of each cell population;
s104, screening out cell types existing in a control group and a treatment group simultaneously, and extracting expression profiles of the cell types;
s105, calculating a differential enrichment score of the cell type;
and S106, ranking the cell types from large to small according to the difference enrichment scores.
2. The method for evaluating the difference of expression patterns of single cells based on two sets of comparison according to claim 1, wherein the method for calculating the difference enrichment score is as follows:
s1051, calculating the characteristic expression profile distance of a control group and a treatment group in the cell type;
s1052, the cell groups of the cell types are disordered, the cell numbers of the cell types are randomly recombined into two groups according to the original group number, and the characteristic expression spectrum distances of the cell types are calculated respectively;
s1053, repeating the step S1052 for a plurality of times to obtain a plurality of characteristic expression spectrum distances to form natural difference zero distribution of the expression spectrum;
s1054, calculating the ratio of the characteristic expression spectrum distance obtained in S1051 to the average value of the natural difference zero distribution of the expression spectrum, namely the difference enrichment score.
3. The method for evaluating the difference of single cell expression patterns based on two sets of comparison according to claim 1, wherein the method for solving the characteristic expression profile distance is as follows:
separating the control group and the treatment group from the original expression spectrum to form two expression spectrums, and measuring the average value of the gene expression of each gene respectively to obtain characteristic expression spectrums of the control group and the treatment group; and calculating the distances of the characteristic expression profiles of the control group and the treatment group to obtain the characteristic expression profile distance.
4. The method for evaluating the difference of single-cell expression patterns based on two sets of comparison according to claim 3, wherein the distance is a euclidean distance or a manhattan distance.
5. The method according to claim 1, wherein in S102, the cells are normalized and/or PCA reduced in dimension before being clustered.
6. The method according to claim 1, wherein in S103, the cell type is identified by using a marker gene having cell type specificity, and the cell population with high expression of the marker gene is identified as the corresponding cell type; alternatively, identification of each cell type was performed using SingleR software.
7. A computer storage medium having stored thereon a computer program, which when executed by a processor implements the two-set comparison-based single cell expression pattern difference assessment method according to any one of claims 1 to 6.
8. A terminal device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the two sets of comparison based single cell expression pattern difference assessment method according to any one of claims 1 to 6 when executing the computer program.
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