Method for establishing biomarker reference intervals based on transcriptome sequencing gene expression data

Abstract

The application discloses a method for formulating a biomarker reference interval based on transcriptome sequencing gene expression data, which comprises the following steps: constructing a transcriptome sequencing gene expression dataset of a healthy group and a patient group; screening out low-expression genes in the healthy group and the patient group; detecting and removing outlier samples in the dataset of the healthy group and the patient group based on an IQR method, and filling the outliers; extracting counts values of corresponding genes according to gene names shared in the transcriptome sequencing gene expression dataset of the healthy group and the patient group; normalizing gene length and sequencing depth, and converting the extracted counts values of the genes into TPM values; determining a reference interval based on an SMR algorithm; determining the trend of each gene; and determining a target biomarker and a corresponding reference interval. The application can well match the skew distribution characteristics of transcriptome sequencing gene expression data, and divide a reference interval of biomarker gene expression.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological data reference interval determination, and particularly relates to a method for determining a biomarker reference interval based on transcriptome sequencing gene expression data. BACKGROUND

[0002] In the biomedical field, multiple indicators often need to be detected to evaluate the physiological condition of a biological individual. At this time, a reference interval is often needed to help doctors or researchers determine whether the sample detection value is abnormal.

[0003] Currently, two methods for determining a reference interval are as follows:

[0004] 1. The lower limit of P5 and the upper limit of P95 are determined to represent the 95% data distribution range.

[0005] 2. The reference limits of the 2.5% and 97.5% positions are determined by the percentile method to determine the 95% reference interval, or the lower limit of P5 or the upper limit of P95 is used on one side to determine the reference interval.

[0006] Method 1 requires the data to meet the normal distribution; and the reference interval determined by method 2 may not be the most concentrated interval of the sample. For the skew distribution of the transcriptome sequencing gene expression data, neither of the above two methods can be well applied to the division of the reference interval. SUMMARY

[0007] In order to overcome the defects and deficiencies in the prior art, the present application provides a method for determining a biomarker reference interval based on transcriptome sequencing gene expression data. The present application can determine the reference interval of the biomarker gene expression based on the skew distribution characteristics of the transcriptome sequencing gene expression data, and the interval is the most concentrated distribution interval of the 95% healthy sample gene expression.

[0008] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0009] The present application provides a method for determining a biomarker reference interval based on transcriptome sequencing gene expression data, comprising the following steps:

[0010] Obtaining transcriptome sequencing data to construct a transcriptome sequencing gene expression dataset of a healthy group and a patient group;

[0011] Setting a screening condition for low-expression genes, and screening the transcriptome sequencing gene expression dataset of the healthy group and the patient group based on the set screening condition;

[0012] Detecting and removing outlier samples in the screened transcriptome sequencing gene expression dataset of the healthy group and the patient group based on the IQR method, and filling the outliers.​

[0013] According to the gene name common to the transcriptome sequencing gene expression data set of the health group and the patient group, the count value of the corresponding gene is extracted;

[0014] The counts value of the extracted gene is converted into TPM value by normalizing the gene length and the sequencing depth;

[0015] The interval with the most concentrated distribution of the TPM value of 95% sample size of the health group transcriptome sequencing gene expression data set is obtained as the reference interval based on the SMR algorithm;

[0016] The trend of each gene is determined based on the TPM value of the patient group transcriptome sequencing gene expression data set;

[0017] The target biomarker and the corresponding reference interval are determined based on the gene trend result.

[0018] As a preferred technical solution, the transcriptome sequencing gene expression data set of the health group and the patient group is screened based on the set screening condition, specifically including:

[0019] The proportion of the counts value less than the set value is calculated for each gene, and the expression value corresponding to the gene with the proportion greater than the set proportion value is excluded.

[0020] As a preferred technical solution, the outlier samples in the screened health group and patient group transcriptome sequencing gene expression data set are detected and removed based on the IQR method, specifically including:

[0021] For each gene remaining in the screened health group and patient group, data outside the [Q1-1.5IQR, Q3+1.5IQR] interval is excluded, wherein Q1 is the 25th percentile, Q3 is the 75th percentile, and IQR=Q3-Q1.

[0022] As a preferred technical solution, the outlier point is filled, specifically including:

[0023] The average value of the transcriptome sequencing gene expression after removing the outlier samples is calculated, and the average value is filled in the outlier point.

[0024] As a preferred technical solution, the interval with the most concentrated distribution of the TPM value of 95% sample size of the health group transcriptome sequencing gene expression data set is obtained as the reference interval based on the SMR algorithm, and the calculation formula of the lower limit and the upper limit of the reference interval in the SMR algorithm is represented as:

[0025] (L i , U i ) = {(min(T ik ), max(T ik))|min(ΔT ik )}

[0026] ΔT ik =max(T ik )-min(T ik )

[0027] Among them, T ik represents the kth subset of the set of TPM values ​​of all samples of the i-th gene in the healthy group, X ij represents the TPM value of the jth sample of the i-th gene in the healthy group, j∈[1,n], n represents the total number of samples in the healthy group, and the subset T ik The number of elements k∈[1,a], a is the number of combinations card() indicates the number of elements in the collection. Indicates that among n samples, 95% n samples are rounded down and ΔT is taken. ik Represents subset T ik The difference between the maximum and minimum values, L i Indicates the lower limit of the reference interval, U i Indicates the upper limit of the reference interval;

[0028] The healthy group was obtained by SMR algorithm The interval where the TPM values ​​of samples are most concentrated is [L i , U i ] as the reference interval.

[0029] As a preferred technical solution, the trend of each gene is determined based on the TPM value of the transcriptome sequencing gene expression dataset of the patient group, specifically including:

[0030] Each sample in the patient group is scored:

[0031]

[0032] Among them, Y ih represents the score of the hth sample of the i-th gene in the patient group, S ih represents the TPM value of the hth sample of the i-th gene in the patient group;

[0033] Determine the trend for each gene:

[0034]

[0035] Among them, A i is the trend result of gene i, and N is the total number of samples in the patient group.

[0036] The application also provides a system for determining a biomarker reference interval based on transcriptome sequencing gene expression quantity data, comprising: a data set construction module, a gene screening module, an outlier data removal module, an outlier point filling module, a gene counts value extraction module, a TPM value conversion module, a reference interval construction module, a gene trend calculation module, and a result output module.

[0037] The data set construction module is used to obtain transcriptome sequencing data and construct transcriptome sequencing gene expression quantity data sets of a healthy group and a patient group.

[0038] The gene screening module is used to set a screening condition for low-expression genes, and screen the transcriptome sequencing gene expression quantity data sets of the healthy group and the patient group based on the set screening condition.

[0039] The outlier data removal module is used to detect and remove outlier samples in the screened transcriptome sequencing gene expression quantity data sets of the healthy group and the patient group based on an IQR method.

[0040] The outlier point filling module is used to fill the outlier points.

[0041] The gene counts value extraction module is used to extract counts values of corresponding genes according to gene names common in the transcriptome sequencing gene expression quantity data sets of the healthy group and the patient group.

[0042] The TPM value conversion module is used to normalize gene length and sequencing depth, and convert the extracted counts values of the genes into TPM values.

[0043] The reference interval construction module is used to obtain, based on an SMR algorithm, an interval with the most concentrated distribution of TPM values of 95% sample quantity of the transcriptome sequencing gene expression quantity data set of the healthy group as a reference interval.

[0044] The gene trend calculation module is used to determine the trend of each gene based on the TPM values of the transcriptome sequencing gene expression quantity data set of the patient group.

[0045] The result output module is used to determine a target biomarker and a corresponding reference interval based on the gene trend result.

[0046] The application also provides a computer readable storage medium storing a program, which, when executed by a processor, implements the method for determining a biomarker reference interval based on transcriptome sequencing gene expression quantity data.

[0047] The present invention also provides a computer device comprising a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the method for formulating a biomarker reference interval based on transcriptome sequencing gene expression data as described above is implemented.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] The present invention adopts the innovative SMR (search for the minimum range) algorithm to divide the skewed distribution data of transcriptome sequencing data into reference intervals for biomarker gene expression. This interval is the interval where the gene expression levels of 95% of healthy samples are most concentrated. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Schematic diagram of the process of the present invention for establishing biomarker reference intervals based on transcriptome sequencing gene expression data. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] Example 1

[0053] like Figure 1 As shown, this embodiment provides a method for formulating a biomarker reference interval based on transcriptome sequencing gene expression data. After obtaining the transcriptome sequencing gene expression data set of the healthy group / patient group, the two groups of data are preprocessed by screening low expression, removing and filling outliers; the counts values ​​of the corresponding genes in the two groups are extracted according to the common gene names of the healthy group and the patient group; the gene length and sequencing depth are standardized to obtain the TPM value; the TPM value reference interval is delineated by the SMR algorithm; the gene trend is determined by the proportion of the number of patient group samples within the reference interval; the target biomarker is screened according to the trend and the final reference interval is obtained.

[0054] The transcriptome counts data in this example are affected by gene length and sequencing depth and are not suitable for comparison between samples. Therefore, after normalizing gene length and sequencing depth, the TPM value is used to define the reference interval. The specific steps include:

[0055] S1: Acquire transcriptome sequencing data, which is to obtain transcriptome sequencing gene expression datasets of healthy individuals and patients from public databases or clinical samples;

[0056] S2: Screen out low expression genes, the specific steps are:

[0057] Transcriptome sequencing will obtain a large number of short fragments, called reads. Counts is the number of reads aligned to each gene position on the reference genome. Reflects the original expression value of the gene in the sequencing sample. For each gene, the proportion of counts values less than the set value is calculated respectively, and the set value in this embodiment is preferably 10. The expression values of those genes with a proportion greater than the set proportion value are screened out, and the set proportion value in this embodiment is preferably 50%. The above operations are performed on the health group and patient group data respectively. Such low expression genes are not easy to detect and are not suitable for use as biomarkers. And screening out low expression genes can make subsequent calculation more efficient.

[0058] S3: Detect and remove outliers in the data set based on the IQR method, the specific steps are:

[0059] After screening out low expression genes, for each gene remaining in the health group / patient group, samples outside the interval [Q1-1.5IQR, Q3+1.5IQR] are screened out respectively. Quartile data: Q1 (25% quantile), Q3 (75% quantile), IQR (Q3-Q1), outliers may cause bias in subsequent calculations, so they need to be detected and removed.

[0060] S4: Fill in the outliers, the specific steps are:

[0061] After screening out outliers in the health group / patient group, the average value of the remaining sample gene expression is calculated, and the obtained average value is filled in the outlier. After screening out outliers in step S3, missing values are generated, and the number of samples is reduced. In order to keep the number of samples consistent for each gene, the average value is used to fill in the missing values.

[0062] S5: Extract the counts values of the corresponding genes in the two groups according to the gene names common to the health group and the patient group;

[0063] S6: Standardize the gene length and sequencing depth, and convert the gene counts values extracted in step S5 to TPM values. Transcriptome counts data is affected by gene length and sequencing depth, and is not suitable for comparison between samples, so after standardizing the gene length and sequencing depth, TPM values are obtained. The subsequent calculation of the reference interval and the screening of the target biomarker are all calculated using TPM values.

[0064] S7: Determine the reference interval using the SMR (search for the minimum range) algorithm, the lower limit L i of the reference interval and the upper limit U i The calculation formula is as follows:

[0065] (L i , U i )={(min(T ik ), max(T ik ))|min(ΔT ik )}

[0066] ΔT ik =max(T ik )-min(T ik )

[0067] Among them, T ik represents the kth subset of the set of TPM values ​​of all samples of the i-th gene in the healthy group, X ij represents the TPM value of the jth sample of the i-th gene in the healthy group, i∈[1,m], j∈[1,n], m represents the number of genes in the sample, and the number of genes in each sample is equal, the subset T ik The number of elements k∈[1,a], a is the number of combinations card() indicates the number of elements in the collection. Indicates that among n samples, 95% n samples are rounded down and ΔT is taken. ik Represents subset T ik The difference between the maximum and minimum values;

[0068] The healthy group can be obtained by SMR algorithm The interval where the TPM values ​​of samples are most concentrated is [L i , U i ] as the reference interval.

[0069] S8: Determine the trend of each gene. The specific formula is:

[0070]

[0071] Among them, Y ih is the score of the hth sample of the i-th gene in the patient group, S ih represents the TPM value of the hth sample of the i-th gene in the patient group, i∈[1,m], h∈[1,N];

[0072]

[0073] Among them, A i is the trend result of gene i, N represents the total number of samples in the patient group;

[0074] S9: Determine the target biomarker and the corresponding reference interval, A iWhen the value is 1, it indicates that the patient group has more than 95% N samples of TPM values outside the reference interval, and it is known from step S7 that the healthy group has samples of TPM values within the reference interval, and the TPM value distribution of the two groups is obviously distinguished, indicating that the gene can be used as a biomarker, and the reference range of the biomarker TPM value determined in step S7 is [L i , U i ].

[0075] The present application adopts the SMR algorithm, which can divide the reference interval of the biomarker gene expression amount for the skew distribution data of the transcriptome sequencing data. The interval is the interval in which 95% of the health sample gene expression amounts are most concentrated, and can well match the skew distribution characteristics of the gene expression amount in the transcriptome sequencing data.

[0076] Embodiment 2

[0077] The present embodiment provides a system for formulating a biomarker reference interval based on transcriptome sequencing gene expression data, comprising: a data set construction module, a gene screening module, an outlier data removal module, an outlier point filling module, a gene counts value extraction module, a TPM value conversion module, a reference interval construction module, a gene trend calculation module, and a result output module.

[0078] In the present embodiment, the data set construction module is used to obtain transcriptome sequencing data and construct a transcriptome sequencing gene expression data set of the healthy group and the patient group.

[0079] In the present embodiment, the gene screening module is used to set a screening condition for low expression genes, and the transcriptome sequencing gene expression data set of the healthy group and the patient group is screened based on the set screening condition.

[0080] In the present embodiment, the outlier data removal module is used to detect and remove outlier samples in the screened transcriptome sequencing gene expression data set of the healthy group and the patient group based on the IQR method.

[0081] In the present embodiment, the outlier point filling module is used to fill the outlier points.

[0082] In the present embodiment, the gene counts value extraction module is used to extract the counts value of the corresponding gene according to the gene name common to the transcriptome sequencing gene expression data set of the healthy group and the patient group.

[0083] In the present embodiment, the TPM value conversion module is used to normalize the gene length and sequencing depth, and convert the extracted counts value of the gene into a TPM value.

[0084] In the embodiment, the reference interval construction module is configured to obtain, based on the SMR algorithm, an interval in which the TPM values of 95% of the sample quantity of the healthy group transcriptome sequencing gene expression quantity dataset are most concentrated as the reference interval;

[0085] In the embodiment, the gene trend calculation module is configured to determine the trend of each gene based on the TPM values of the transcriptome sequencing gene expression quantity dataset of the patient group.

[0086] In the embodiment, the result output module is configured to determine the target biomarker and the corresponding reference interval based on the gene trend result.

[0087] Embodiment 3

[0088] The embodiment provides a storage medium, which can be a ROM, a RAM, a magnetic disk, an optical disk or the like storage medium. The storage medium stores one or more programs. When the programs are executed by a processor, the method for determining a biomarker reference interval based on transcriptome sequencing gene expression quantity data in the embodiment 1 is implemented.

[0089] Embodiment 4

[0090] The embodiment provides a computing device, which can be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer or other terminal device with a display function. The computing device comprises a processor and a memory. The memory stores one or more programs. When the processor executes the programs stored in the memory, the method for determining a biomarker reference interval based on transcriptome sequencing gene expression quantity data in the embodiment 1 is implemented.

[0091] The above embodiments are the preferred embodiments of the present application, but the embodiments of the present application are not limited to the above embodiments. Any changes, modifications, substitutions, combinations and simplifications made without departing from the spirit and principle of the present application shall be equivalent replacement modes and shall be included in the protection scope of the present application.

Claims

1. A method for establishing biomarker reference intervals based on transcriptome sequencing gene expression data, characterized in that: The steps include: Obtain transcriptome sequencing data and construct transcriptome sequencing gene expression datasets for healthy and patient groups; Set screening conditions for low-expression genes, and screen the transcriptome sequencing gene expression datasets of the healthy group and the patient group based on the set screening conditions; Based on the IQR method, outlier samples in the transcriptome sequencing gene expression dataset of the screened healthy group and patient group were detected and removed, and outlier points were filled; Extract the count values ​​of the corresponding genes based on the common gene names in the transcriptome sequencing gene expression datasets of the healthy group and the patient group; Gene length and sequencing depth were standardized, and the counts of the extracted genes were converted into TPM values; Based on the SMR algorithm, the interval with the most concentrated TPM values ​​of 95% of the sample size of the healthy group transcriptome sequencing gene expression data set was obtained as the reference interval; The trend of each gene was determined based on the TPM value of the transcriptome sequencing gene expression dataset of the patient group; Target biomarkers and corresponding reference intervals were determined based on the gene trend results.

2. The method for establishing biomarker reference intervals based on transcriptome sequencing gene expression data according to claim 1, characterized in that: The transcriptome sequencing gene expression datasets of the healthy group and the patient group were screened based on the set screening conditions, including: For each gene, the proportion of counts values ​​less than the set value is calculated, and the expression values ​​corresponding to genes with a proportion greater than the set proportion are screened out.

3. The method for establishing biomarker reference intervals based on transcriptome sequencing gene expression data according to claim 1, characterized in that: Based on the IQR method, outlier samples in the transcriptome sequencing gene expression datasets of the screened healthy group and patient group were detected and removed, including: For each gene remaining in the healthy group and patient group after screening, the data outside the interval of [Q1-1.5IQR, Q3+1.5IQR] were screened out, where Q1 is the 25% quantile, Q3 is the 75% quantile, and IQR=Q3-Q1.

4. The method for establishing biomarker reference intervals based on transcriptome sequencing gene expression data according to claim 1, characterized in that: The filling of outliers specifically includes: The average expression level of transcriptome sequencing genes after removing outlier samples was calculated, and the average value was filled in the outlier points.

5. The method for establishing biomarker reference intervals based on transcriptome sequencing gene expression data according to claim 1, characterized in that: Based on the SMR algorithm, the interval with the most concentrated distribution of TPM values ​​of 95% of the sample size of the healthy group transcriptome sequencing gene expression data set was obtained as the reference interval. The calculation formulas for the lower and upper limits of the reference interval in the SMR algorithm are expressed as follows: (L i ,U i )={(min(T ik ),max(T ik ))|min(ΔT ik )} ΔT ik =max(T ik )-min(T ik ) Among them, T ik represents the kth subset of the set of TPM values ​​of all samples of the i-th gene in the healthy group, X ij represents the TPM value of the jth sample of the i-th gene in the healthy group, j∈[1,n], n represents the total number of samples in the healthy group, and the subset T ik The number of elements k∈[1,a], a is the number of combinations card() indicates the number of elements in the collection. Indicates that among n samples, 95% n samples are rounded down and ΔT is taken. ik Represents subset T ik The difference between the maximum and minimum values, L i Indicates the lower limit of the reference interval, U i Indicates the upper limit of the reference interval; The healthy group was obtained by SMR algorithm The interval where the TPM values ​​of samples are most concentrated is [L i , U i ] as the reference interval.

6. The method for establishing biomarker reference intervals based on transcriptome sequencing gene expression data according to claim 1, characterized in that: The trend of each gene was determined based on the TPM value of the transcriptome sequencing gene expression dataset of the patient group, including: Each sample in the patient group is scored: Among them, Y ih represents the score of the hth sample of the i-th gene in the patient group, S ih represents the TPM value of the hth sample of the i-th gene in the patient group; Determine the trend for each gene: Among them, A i is the trend result of gene i, and N is the total number of samples in the patient group.

7. A system for establishing biomarker reference intervals based on transcriptome sequencing gene expression data, characterized in that: include: Dataset construction module, gene screening module, outlier data removal module, outlier filling module, gene count value extraction module, TPM value conversion module, reference interval construction module, gene trend calculation module, result output module; The dataset construction module is used to obtain transcriptome sequencing data and construct transcriptome sequencing gene expression datasets for the healthy group and the patient group; The gene screening module is used to set screening conditions for low-expression genes, and screen the transcriptome sequencing gene expression data sets of the healthy group and the patient group based on the set screening conditions; The outlier data removal module is used to detect and remove outlier samples in the transcriptome sequencing gene expression data set of the screened healthy group and patient group based on the IQR method; The outlier filling module is used to fill the outliers; The gene counts value extraction module is used to extract the count values ​​of corresponding genes based on the common gene names in the transcriptome sequencing gene expression data sets of the healthy group and the patient group; The TPM value conversion module is used to standardize gene length and sequencing depth, and convert the count values ​​of the extracted genes into TPM values; The reference interval construction module is used to obtain the interval with the most concentrated distribution of TPM values ​​of 95% of the sample size of the healthy group transcriptome sequencing gene expression data set based on the SMR algorithm as the reference interval; The gene trend calculation module is used to determine the trend of each gene based on the TPM value of the transcriptome sequencing gene expression data set of the patient group; The result output module is used to determine the target biomarker and the corresponding reference interval based on the gene trend results.

8. A computer-readable storage medium storing a program, characterized in that: When the program is executed by a processor, the method for formulating a biomarker reference interval based on transcriptome sequencing gene expression data as described in any one of claims 1 to 6 is implemented.

9. A computer device comprising a processor and a memory for storing a program executable by the processor, characterized in that When the processor executes the program stored in the memory, it implements the method for formulating biomarker reference intervals based on transcriptome sequencing gene expression data as described in any one of claims 1 to 6.

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