Single cell sequencing conservative expression gene and differential expression gene calculation tool

Abstract

The invention discloses a single cell sequencing conservative expression gene and differential expression gene calculation tool, and relates to the technical field of single cell sequencing. The method comprises the following calculation steps: step 1, identifying conservative expression genes among different clusters in a single-cell RNA sequencing data set through an scCEGsfunction, and calculating and generating a list; and 2, identifying differential expression genes between two conditions in the single-cell RNA sequencing data set through an scDEGsfunction, and calculating and generating a list. Compared with a common analysis tool at present, the method can automatically calculate conservative expression genes and differential expression genes among the cell populations and generate a list, automatically identify and reject the cell populations with the cell number being less than 3, and avoid frequent manual setting, so that errors are effectively reduced, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of single-cell sequencing, and in particular to a calculation tool for single-cell sequencing of conservatively expressed genes and differentially expressed genes. Background Art

[0002] At present, FindConservedMarkers is widely used in the field of single-cell sequencing result analysis to calculate and obtain conservatively expressed genes between groups of samples. This tool requires manual setting and calculation of each group of cells; FindMarkers is used to manually set and calculate differentially expressed genes between different cell groups between groups, and it cannot automatically identify or automatically skip cell groups with less than 3 cells. If there are many types of cell clusters, manual input errors are likely to occur, and the work efficiency is low.

[0003] Therefore, we proposed a computational tool for single-cell sequencing of conserved and differentially expressed genes to solve the above problems.

[0004] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0005] The purpose of the present invention is to provide a computational tool for single-cell sequencing of conservatively expressed genes and differentially expressed genes to solve the problems raised by the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a single cell sequencing conservatively expressed genes and differentially expressed genes calculation tool, comprising the following calculation steps:

[0007] Step 1: Use the `scCEGs` function to identify, calculate and generate a list of conserved expression genes between different clusters in the single-cell RNA sequencing dataset;

[0008] Step 2: Use the `scDEGs` function to identify, calculate, and generate a list of differentially expressed genes between two conditions in the single-cell RNA sequencing dataset.

[0009] Preferably, the specific process of step 1 is:

[0010] Get Cluster: retrieve unique cluster identifiers from a Seurat object;

[0011] Initialize the result list: create an empty list to store the conservative markers of each cluster;

[0012] Iterate through each cluster: For each cluster:

[0013] - Find conserved markers: Use `Seurat::FindConservedMarkers` to identify genes that are conserved in the specified grouping variables;

[0014] -Add metadata: add cluster identifier and gene name to each result;

[0015] -Store results: Store the results of each cluster in a list;

[0016] Standardize columns: ensure that all data frames in the list have the same columns, and fill missing columns with `NA`;

[0017] Merge results: merge all standardized data frames into one data frame;

[0018] Returns: The function returns a data frame of conserved labels across clusters.

[0019] Preferably, the specific process of step 2 is:

[0020] Validate input: check if the provided object is a Seurat object;

[0021] Initialize the result list: create an empty list to store the differentially expressed genes of each cluster;

[0022] Get Cluster: retrieve unique cluster identifiers from a Seurat object;

[0023] Initialize progress bar: set up the progress bar to track the processing process;

[0024] Iterate through each cluster: For each cluster:

[0025] -Subset cells: extract cells belonging to the current cluster;

[0026] - Check cell counts: make sure there are sufficient numbers of cells for both conditions;

[0027] - Find markers: If the conditions are met, use `Seurat::FindMarkers` to perform differential expression analysis;

[0028] -Add metadata: add cluster identifier and gene name to each result;

[0029] -Store results: Store the results of each cluster in a list;

[0030] Merge results: Merge all cluster-specific results into a single data frame;

[0031] Rearrange columns: Adjust the order of columns to put `CellType` and `gene` at the front;

[0032] Returns: The function returns a data frame of differentially expressed genes across clusters.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] Compared with currently commonly used analysis tools, the present invention can automatically calculate and list the conservatively expressed genes and differentially expressed genes between various cell populations, and automatically identify and eliminate cell populations with less than 3 cells, avoiding frequent manual settings, thereby effectively reducing errors and improving calculation efficiency.

[0035] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the calculation process of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] A calculation tool for conservatively expressed genes and differentially expressed genes in single-cell sequencing, comprising the following calculation steps:

[0039] Step 1: Use the `scCEGs` function to identify, calculate and generate a list of conserved expression genes between different clusters in the single-cell RNA sequencing dataset;

[0040] parameter:

[0041] `seu_obj`: Seurat object containing single-cell RNA sequencing data.

[0042] `ident.1`: The identity of the cluster to be analyzed.

[0043] `grouping.var`: Data variable used for grouping when looking for conservative markers.

[0044] The specific process is:

[0045] Get Cluster: retrieve unique cluster identifiers from a Seurat object;

[0046] Initialize the result list: create an empty list to store the conservative markers of each cluster;

[0047] Iterate through each cluster: For each cluster:

[0048] - Find conserved markers: Use `Seurat::FindConservedMarkers` to identify genes that are conserved in the specified grouping variables;

[0049] -Add metadata: add cluster identifier and gene name to each result;

[0050] -Store results: Store the results of each cluster in a list;

[0051] Standardize columns: ensure that all data frames in the list have the same columns, and fill missing columns with `NA`;

[0052] Merge results: merge all standardized data frames into one data frame;

[0053] Returns: The function returns a data frame of conserved labels across clusters.

[0054] Seurat: used to find conserved markers.

[0055] dplyr: for data manipulation, such as `mutate`, `select`, and `bind_rows`

[0056] Step 2: Use the `scDEGs` function to identify, calculate, and generate a list of differentially expressed genes between two conditions in the single-cell RNA sequencing dataset.

[0057] parameter:

[0058] `seu_obj`: Seurat object containing single-cell RNA sequencing data.

[0059] `ident.1`: The ident of the first condition to be compared.

[0060] `ident.2`: The identifier of the second condition to be compared.

[0061] `group.by`: Grouping variable for differential expression analysis.

[0062] `only.pos`: Logical flag indicating whether to return only positive marks.

[0063] `min.pct`: Minimum percentage of cells expressing the gene.

[0064] `logfc.threshold`: Minimum log fold-change threshold.

[0065] The specific process is:

[0066] Validate input: check if the provided object is a Seurat object;

[0067] Initialize the result list: create an empty list to store the differentially expressed genes of each cluster;

[0068] Get Cluster: retrieve unique cluster identifiers from a Seurat object;

[0069] Initialize progress bar: set up the progress bar to track the processing process;

[0070] Iterate through each cluster: For each cluster:

[0071] -Subset cells: extract cells belonging to the current cluster;

[0072] - Check cell counts: make sure there are sufficient numbers of cells for both conditions;

[0073] - Find markers: If the conditions are met, use `Seurat::FindMarkers` to perform differential expression analysis;

[0074] -Add metadata: add cluster identifier and gene name to each result;

[0075] -Store results: Store the results of each cluster in a list;

[0076] Merge results: Merge all cluster-specific results into a single data frame;

[0077] Rearrange columns: Adjust the order of columns to put `CellType` and `gene` at the front;

[0078] Returns: The function returns a data frame of differentially expressed genes across clusters.

[0079] Seurat: used for differential expression analysis.

[0080] dplyr: for data manipulation such as `bind_rows` and `relocate`.

[0081] progress: Used to display a progress bar during processing.

[0082] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0083] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by hardware associated with program instructions, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps in the method embodiment.

[0084] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0085] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A computational tool for single-cell sequencing of conservatively expressed genes and differentially expressed genes, characterized in that: The calculation steps include: Step 1: Use the `scCEGs` function to identify, calculate and generate a list of conserved expression genes between different clusters in the single-cell RNA sequencing dataset; Step 2: Use the `scDEGs` function to identify, calculate, and generate a list of differentially expressed genes between two conditions in the single-cell RNA sequencing dataset.

2. A single cell sequencing conservatively expressed gene and differentially expressed gene calculation tool according to claim 1, characterized in that: The specific process of step 1 is as follows: Get Cluster: retrieve unique cluster identifiers from a Seurat object; Initialize the result list: create an empty list to store the conservative markers of each cluster; Iterate through each cluster: For each cluster: - Find conserved markers: Use `Seurat::FindConservedMarkers` to identify genes that are conserved in the specified grouping variables; -Add metadata: add cluster identifier and gene name to each result; -Store results: Store the results of each cluster in a list; Standardize columns: ensure that all data frames in the list have the same columns, and fill missing columns with `NA`; Merge results: merge all standardized data frames into one data frame; Returns: The function returns a data frame of conserved labels across clusters.

3. A single cell sequencing conservatively expressed gene and differentially expressed gene calculation tool according to claim 1, characterized in that: The specific process of step 2 is as follows: Validate input: check if the provided object is a Seurat object; Initialize the result list: create an empty list to store the differentially expressed genes of each cluster; Get Cluster: retrieve unique cluster identifiers from a Seurat object; Initialize progress bar: set up the progress bar to track the processing process; Iterate through each cluster: For each cluster: -Subset cells: extract cells belonging to the current cluster; - Check cell counts: make sure there are sufficient numbers of cells for both conditions; - Find markers: If the conditions are met, use `Seurat::FindMarkers` to perform differential expression analysis; -Add metadata: add cluster identifier and gene name to each result; -Store results: Store the results of each cluster in a list; Merge results: Merge all cluster-specific results into a single data frame; Rearrange columns: Adjust the order of columns to put `CellType` and `gene` at the front; Returns: The function returns a data frame of differentially expressed genes across clusters.

Images