KR20180036622A - Apparatus and method for integrated analysis of gene expression omnibus's gene expression data - Google Patents

Abstract

The present invention relates to an apparatus for integrated analysis processing of gene expression data and a method thereof. The apparatus for integrated analysis processing according to one embodiment of the present invention comprises: a gene expression omnibus (GEO) data processing portion for collecting GEO data, and classifying the collected GEO data into phenotype data, gene expression value data, and gene expression data including a GEO platform (GPL); a phenotype data conversion portion for standardizing the expression data based on a feature word of the phenotype data; a gene expression data conversion portion for normalizing the gene expression value data included in the gene expression data; a data group generating portion for classifying the standardized phenotype data and the normalized gene expression data into a plurality of data groups; and a data analysis portion for analyzing the classified data groups and outputting analysis results.

Description

[0001] APPARATUS AND METHOD FOR INTEGRATED ANALYSIS OF GENE EXPRESSION OMNIBUS'S GENE EXPRESSION DATA [0002]

TECHNICAL FIELD [0001] The present invention relates to an apparatus and method for integrated analysis processing of gene expression data, and more particularly, to a technical idea of integrating gene expression data and providing the same to a user.

GEO (Gene Expression Omnibus) is the most famous database for storing and managing data generated through research in bioinformatics.

However, since GEO data stored in the GEO homepage stores data without examination process, the structure of stored data differs for each data. Therefore, when the GEO data is directly downloaded by the researcher, it requires an excessive effort to analyze the data.

In addition, when using the site for GEO analysis, it is difficult to analyze the data according to the nature of the data stored by each researcher, and since only one set can be analyzed, various data can not be analyzed at once, It has the limitation that it is possible to analyze only the data that is specified.

Korean Patent Publication No. 10-2016-0045617 "Method and Apparatus for Integrated Analysis of miRNA and mRNA Expression Data"

An object of the present invention is to provide a technique capable of easily acquiring result data for analyzing genome expression data by generating integrated GEO data only with a list of GEOs.

It is also an object of the present invention to provide a technique capable of improving diversity of data utilization by providing integrated analysis with TCGA data.

The integrated analysis processing apparatus according to an embodiment of the present invention collects GEO (Gene Expression Omnibus) data, converts the collected GEO data into expression data, gene expression value data, and gene expression data including GPL (GEO Platform) A genotype data conversion unit for normalizing gene expression value data included in the gene expression type data, a genotype expression data conversion unit for normalizing the gene expression value data included in the gene expression type data, A data group generation unit for classifying standardized phenotype data and normalized gene expression type data into a plurality of data groups, and a data analysis unit for analyzing the divided data groups and outputting analysis results.

According to one aspect, the GEO data processing unit collects GEO data based on the GEO series number, and in the collected GEO data, the phenotype data includes at least one of the patient's age, gender, tissue location and tissue status, The gene expression value data includes the expression amount of the mRNA, and the GPL (GEO Platform) can convert the probe of gene expression value data into a biological symbol.

According to one aspect, the phenotype data conversion unit can perform standardization for separating a feature word from a value in a phenotype data, and converting the phenotype data into a formalized data form based on separated key words and values.

According to one aspect, the phenotype data conversion unit includes at least one of a representative key word-similarity database, a representative value-similarity value database, and a conversion script database, wherein the phenotype database includes a phenotype database storing data to be referred to for standardizing the phenotype data .

According to one aspect of the present invention, the gene expression type data conversion unit converts the name of the probe included in the gene expression data into the name of the gene that is used more than a preset threshold among the gene names having a biologically common meaning with the detector name , And normalize each gene expression value data corresponding to the changed detector name.

According to one aspect, TCGA (The Cancer Genome Atlas) data is collected, and a virtual GSE number (GEO Series Number) is given to the collected TCGA data to generate at least one of the form of expression data and the form of gene expression data And a TCGA data converting unit for converting the data into a form of a TCGA data.

According to one aspect, the phenotype data conversion unit receives the TCGA data of the phenotype data type, integrates the TCGA data of the phenotype data type and the phenotype data received by the GEO data processing unit, and can standardize the integrated phenotype data.

According to one aspect, the gene expression type data conversion unit receives TCGA data in the form of gene expression data, integrates TCGA data in the form of gene expression data and gene expression data received in the GEO data processing unit, The gene expression value data included in the data can be normalized.

According to one aspect, the data group generation unit may utilize a statistical technique for calculating a P value (p-value) in order to divide the data group into a plurality of data groups.

The integrated analysis processing method according to an embodiment of the present invention collects GEO (Gene Expression Omnibus) data in a GEO data processing unit and converts the collected GEO data into expression data, gene expression value data, and GPL (GEO Platform) A step of standardizing the expression data based on a feature word of the expression data in a phenotype data conversion unit, a step of standardizing the expression data in a gene expression type data conversion unit, Value data, and a step of dividing the normalized expression data and the normalized gene expression type data into a plurality of data groups in the data group generation unit, and analyzing the data group separated in the data analysis unit and outputting the analysis result .

According to one aspect, the step of distinguishing between phenotype data and gene expression data collects GEO data based on the GSE number (GEO Series Number), and in the collected GEO data, the phenotype data includes the patient's age, sex, , The gene expression value data includes the expression amount of mRNA, and the GPL (GEO Platform) can convert a probe of gene expression value data into a biological symbol.

According to one aspect, the TCGA data conversion unit collects the Cancer Genome Atlas (TCGA) data, gives a virtual GSE number (GEO Series Number) to the collected TCGA data, and stores TCGA data in the form of expression data and gene expression data Into a form of at least one of the following.

According to one aspect, the step of standardizing the phenotype data includes receiving the TCGA data of the phenotype data type, integrating the TCGA data of the phenotype data type and the phenotype data received by the GEO data processing section, and standardizing the integrated phenotype data.

According to one aspect, the step of normalizing gene expression value data includes receiving TCGA data in the form of gene expression data, integrating TCGA data in the form of gene expression data and gene expression data received in the GEO data processing section, The gene expression value data included in the gene expression type data can be normalized.

According to one aspect, the step of dividing into a plurality of data groups may utilize a statistical method of calculating a P value (p-value) to divide the data into a plurality of data groups.

According to one embodiment, integrated GEO data can be generated with only a list of GEOs to easily obtain result data for analysis of genomic expression data.

Also, according to one embodiment, the diversity of data utilization can be improved by providing an integrated analysis with TCGA data.

1 is a diagram showing an integrated analysis processing apparatus according to an embodiment of the present invention.
2 is a diagram showing a GEO data processing unit of an integrated analysis processing apparatus according to an embodiment of the present invention.
3 is a diagram showing a phenotype data conversion unit of an integrated analysis processing apparatus according to an embodiment of the present invention.
4 is a diagram showing a gene expression type data conversion unit of an integrated analysis processing apparatus according to an embodiment of the present invention.
5A and 5B are diagrams for explaining an embodiment of normalizing gene expression type data in a gene expression type data conversion unit according to an embodiment of the present invention.
6 is a diagram for explaining an embodiment for converting a name of a detector of gene expression type data in a gene expression type data conversion unit according to an embodiment of the present invention.
FIGS. 7A to 7C are diagrams for explaining an embodiment of dividing data groups in the data group generation unit of the integrated analysis processing apparatus according to an embodiment of the present invention.
8A and 8B are diagrams for explaining an embodiment for deriving an analysis result in a data analysis unit of an integrated analysis processing apparatus according to an embodiment of the present invention.
9 is a diagram showing an integrated analysis processing apparatus according to another embodiment of the present invention.
FIG. 10 is a diagram showing a method of integrated analysis processing according to an embodiment of the present invention.
11 is a diagram showing an integrated analysis processing method according to another embodiment of the present invention.

It is to be understood that the specific structural or functional descriptions of embodiments of the present invention disclosed herein are presented for the purpose of describing embodiments only in accordance with the concepts of the present invention, May be embodied in various forms and are not limited to the embodiments described herein.

Embodiments in accordance with the concepts of the present invention are capable of various modifications and may take various forms, so that the embodiments are illustrated in the drawings and described in detail herein. However, it is not intended to limit the embodiments according to the concepts of the present invention to the specific disclosure forms, but includes changes, equivalents, or alternatives falling within the spirit and scope of the present invention.

The terms first, second, or the like may be used to describe various elements, but the elements should not be limited by the terms. The terms may be named for the purpose of distinguishing one element from another, for example without departing from the scope of the right according to the concept of the present invention, the first element being referred to as the second element, Similarly, the second component may also be referred to as the first component.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Expressions that describe the relationship between components, for example, "between" and "immediately" or "directly adjacent to" should be interpreted as well.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ", or" having ", and the like, are used to specify one or more of the features, numbers, steps, operations, elements, But do not preclude the presence or addition of steps, operations, elements, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the meaning of the context in the relevant art and, unless explicitly defined herein, are to be interpreted as ideal or overly formal Do not.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these embodiments. Like reference symbols in the drawings denote like elements.

1 is a diagram showing an integrated analysis processing apparatus according to an embodiment of the present invention.

Referring to FIG. 1, the integrated analysis apparatus 100 according to an embodiment divides GEO data into expression data and gene expression data. Then, the expression data can be standardized and the gene expression data can be normalized.

In addition, the integrated analysis apparatus 100 according to an exemplary embodiment may classify the standardized phenotype data and the normalized gene expression type data into a plurality of data groups.

For this, a GEO data processing unit 110, a phenotype data conversion unit 120, a gene expression type data conversion unit 130, a data group generation unit 140, and a data analysis unit 150 may be included.

First, the GEO data processing unit 110 according to an embodiment collects GEO (Gene Expression Omnibus) data and converts the collected GEO data into expression data, a gene expression value data, and a gene expression type including a GPL (GEO Platform) Data.

According to one aspect, the GEO data processing unit 110 can collect GEO data based on the GSE number (GEO Series Number).

For example, the GEO data processing unit 110 can download data from the GEO data server based on the GEO list based on the entered GSE number when the researcher inputs the GSE number.

According to one aspect, in the collected GEO data, the phenotype data may include at least one of the patient's age, sex, tissue location, and tissue status. Also, in the collected GEO data, the gene expression value data may include an expression amount of the mRNA, and the GPL (GEO Platform) can convert the probe of the gene expression value data into a biological symbol.

The GEO data processing unit 110 shown in FIG. 1 will be described in more detail with reference to FIG.

The phenotype data conversion unit 120 according to one embodiment can standardize the expression type data based on the keyword of the expression type data.

According to one aspect, the phenotype data conversion unit 120 separates a feature word and a value from the phenotype data, and performs a standardization for converting the expression data into a form of a formalized data based on the separated key words and values can do.

According to one aspect, the phenotype data conversion unit 120 may include a phenotype database storing data to be referred to for standardizing the phenotype data. In addition, the phenotype database may include at least one of a representative key word-similarity database, a representative value-similarity value database, and a conversion script database.

The phenotype data conversion unit 120 shown in FIG. 1 will be described in more detail with reference to FIG.

The gene expression type data conversion unit 130 according to one embodiment can normalize the gene expression value data included in the gene expression type data.

According to one aspect of the present invention, the gene expression type data conversion unit 130 converts the name of the detectors included in the gene expression type data to a gene name having a biologically common meaning, .

In addition, the gene expression type data conversion unit 130 may normalize the gene expression value data corresponding to the changed detector name.

For example, the gene expression type data conversion unit 130 may convert the name of the detector included in the gene expression type data based on one gene name most commonly used among the gene names having a biologically common meaning .

That is, the name of the detectors contained in the gene expression data can be unified into the most frequently used gene names until recently.

In addition, the gene expression type data conversion unit 130 may normalize the gene expression value data corresponding to the changed detector name.

The gene expression type data conversion unit 130 shown in FIG. 1 will be described in more detail with reference to FIG. 4 through FIG.

The data group generation unit 140 may classify the normalized expression data and the normalized gene expression type data into a plurality of data groups.

According to one aspect, the data group generating unit 140 may utilize a statistical technique for calculating a P value (p-value) to divide the data into a plurality of data groups.

The operation of dividing a data group in the data group generator 140 shown in FIG. 1 will be described in more detail with reference to FIGS. 7A through 7C.

The data analysis unit 150 according to an embodiment may analyze the divided data group and output the analysis result.

The operation of outputting the analysis result by the data analysis unit 150 shown in FIG. 1 will be described in more detail with reference to FIGS. 8A to 8B.

That is, using the present invention, it is possible to easily acquire the result data for analysis of the genome expression data by generating the integrated GEO data with only the GEO list.

2 is a diagram showing a GEO data processing unit of an integrated analysis processing apparatus according to an embodiment of the present invention.

Referring to FIG. 2, the GEO data processing unit 200 according to an exemplary embodiment collects GEO data and divides the collected GEO data into expression data and gene expression data.

For this, a data collecting unit 210 and a data gathering unit 220 may be included.

According to one aspect, the data collection unit 210 can collect GEO data based on the GSE number.

For example, the data collector 210 may generate a GEO list based on a GSE number when a researcher inputs a GSE number acquired through a GEO homepage or a research paper through a text file or a console during research.

Thereafter, the data collecting unit 210 can directly download data from the GEO data server based on the generated GEO list.

In addition, if an Internet connection error or a server error that may occur when data is directly downloaded from the GEO data server occurs, the data collection unit 210 may perform a redo routine to reconnect and download again in the future to avoid errors .

According to one aspect, the data organizing unit 220 can divide the collected GEO data into expression data, gene expression value data, and gene expression type data including a GPL (GEO Platform).

According to one aspect, the phenotype data includes at least one of age, sex, tissue location and tissue state of the patient, and the gene expression value data may include an expression amount of mRNA. In addition, the GPL (GEO Platform) can convert probes of gene expression value data into biological symbols.

For example, the data organizing unit 220 may store the separated expression data and the gene expression data in a separate storage device.

3 is a diagram showing a phenotype data conversion unit of an integrated analysis processing apparatus according to an embodiment of the present invention.

Referring to FIG. 3, the phenotype data conversion unit 300 according to one embodiment can standardize expression type data based on a keyword (Feature Word).

For this, the key word separating unit 310, the key word integrating unit 320, and the expression database 330 may be included.

According to one aspect, the keyword separator 310 can separate the keyword from the keyword in the expression data received from the GEO data processor of FIG.

More specifically, Table 1 below shows the expression data received from the GEO data processing unit of FIG. 1 by the keyword separating unit 310.

[Table 1]

According to one aspect of the present invention, the key word separator 310 can easily separate key words and values from data tables having different key word and value expression methods for each data as shown in Table 1.

For example, if the column of the phenotype data does not match, the key word separation unit 310 separates the relation of 'key word: value' into cell units instead of column units, and then finds key words and re-arranges the columns can do.

In particular, in the non-standardized phenotype data, the keyword separating unit 310 can find a plurality of symbols for classifying the relation of 'key word: value' to find the column.

In the case of the expression data assigned to the other columns, the keyword separator 310 can determine that the expression data is stored when the expression "key word: value" is confirmed.

That is, the keyword separator 310 can organize the expression data based on the keyword as shown in the following Table 2 through the above-described examples.

[Table 2]

According to one aspect, the key word integrating unit 320 can perform standardization for converting the expression data into the form of the standardized data based on the separated key words and values.

According to one aspect, the keyword integration unit 320 may refer to the data stored in the expression database 330 to standardize the expression data.

According to one aspect, the phenotype database 330 may include at least one of a representative key word-similarity database, a representative value-similarity value database, and a conversion script database.

Table 3 below shows an example of a representative key word-similarity database, Table 4 shows an example of a representative value-similarity database, and Table 5 shows an example of a conversion script database.

[Table 3]

[Table 4]

[Table 5]

More specifically, the key word integrating unit 320 can convert the separated key words into a form having the same mutual meaning by referring to the representative key word-similarity database in Table 3 stored in the expression database 330. [

Also, the keyword integration unit 320 may convert the separated values into the same format with reference to the representative value-similarity database of Table 4 stored in the phenotype database 330.

According to one aspect, the conversion script database of Table 5 stored in the phenotype database 330 includes a conversion function for a value expression that can infer the keyword, and a function for converting a text into a function to convert the value .

That is, the phenotype data conversion unit 300 may integrate the expression type data by performing standardization for converting the expression type data into the form of a regularized data having the same meaning.

4 is a diagram showing a gene expression type data conversion unit of an integrated analysis processing apparatus according to an embodiment of the present invention.

Referring to FIG. 4, the gene expression type data conversion unit 400 according to one embodiment can normalize the gene expression value data included in the gene expression type data.

For this, the gene expression type data conversion unit 400 may include a data ID conversion unit 410 and a data normalization unit 420.

According to one aspect, the data ID conversion unit 410 converts the name of a probe included in the gene expression type data received from the GEO data processing unit of FIG. 1 into a predetermined threshold value among gene names having a biologically common meaning It can be changed based on the gene name being used above.

Table 6 below shows an example of gene expression information data.

[Table 6]

More specifically, as shown in Table 6, the gene expression type data is configured in the form of a matrix, each row means a detector name, and each column indicates information that can identify each case.

According to one aspect, the data normalization unit 420 can normalize the gene expression value data corresponding to the changed detector name in the data ID conversion unit 410.

In other words, the data normalization unit 420 may perform a normalization process of converting the gene expression type data so that the gene expression type data can be used as comparable integrated data.

The operation of normalizing the gene expression type data in the gene expression type data conversion unit 400 shown in FIG. 4 will be described in more detail with reference to FIGS. 5A to 5B.

That is, the gene expression type data conversion unit 400 can integrate the gene expression type data by unifying the name of the detectors of the gene expression type data with a gene name having a biologically common meaning and normalizing the gene expression value data have.

5A and 5B are diagrams for explaining an embodiment of normalizing gene expression type data in a gene expression type data conversion unit according to an embodiment of the present invention.

In the drawings of FIGS. 5A and 5B, row (X-axis) represents each experiment, and column (Y-axis) represents gene expression value data.

Referring to FIGS. 5A and 5B, as shown in reference numeral 510, since gene expression value data in each experiment have different average values and expression sensitivities, discrepancies exist between gene expression type data in each experiment .

Therefore, the gene expression type data conversion unit may perform at least one of the methods of averaging the gene expression value data, equalizing the first quartile and the third quartile of each case, and using a separate normalizer, Lt; RTI ID = 0.0 > genomic < / RTI > expression data can be normalized.

6 is a diagram for explaining an embodiment for converting a name of a detector of gene expression type data in a gene expression type data conversion unit according to an embodiment of the present invention.

Referring to FIG. 6, the gene expression type data conversion unit may use a conversion module to convert a detector name.

For example, the gene expression type data conversion unit may utilize the result data of Ailun (http://ailun.stanford.edu) corresponding to the gene name of BioMart module or gene expression type data for use in the conversion module.

More specifically, in order to utilize the matching information between the gene name and the gene provided in the BioMart module, the gene expression type data conversion unit can extract the convertible table by inquiring based on the name and value of the detector included in the GPL attribute table have.

That is, the gene expression type data conversion unit converts the gene name of the gene expression type data stored primarily by using the module 610 as shown in reference numeral 610 by using a module called HGNC Helper, You can use the latest gene names as they are.

FIGS. 7A to 7C are diagrams for explaining an embodiment of dividing data groups in the data group generation unit of the integrated analysis processing apparatus according to an embodiment of the present invention.

Referring to FIG. 7, the data group generation unit may receive normalized expression data and normalized gene expression type data from the phenotype data conversion unit and the gene expression type data conversion unit of FIG.

In addition, the data group generation unit may divide the received data into a plurality of data groups, such as 710, for analysis of the received data.

According to one aspect of the present invention, the data group generation unit can classify data that can identify a data group into continuous data, categorical data, and survival analysis data.

Also, the data group generator can use categorical variables or continuous variables as independent variables for classification and categorical, continuous or survival analysis variables as dependent variables.

Preferably, the data group generation unit may classify the data groups using a statistical technique corresponding to each dependent variable as shown in Table 7 below.

[Table 7]

More specifically, the data group generator presents a cut-off value for sequential or continuous variables so as to have an optimal P value (p-value) in a dependent variable that is a continuous or survival data type, In the case of a type variable, a group with the smallest P value can be presented.

For example, the data group generating unit may include ESR1 among the estrogen receptor, one of the important markers of breast cancer, as an independent variable, and the results of recurrence, which is a continuous variable, age and survival analysis parameter, are indicated as reference numerals 720 to 730 And the data group can be divided into two groups based on this.

8A and 8B are diagrams for explaining an embodiment for deriving an analysis result in a data analysis unit of an integrated analysis processing apparatus according to an embodiment of the present invention.

8A and 8B, the data analyzer may analyze the data group classified by the data group generator of FIG. 1 and output the analysis result.

More specifically, the data analyzer displays a key value of the phenotype data in a row (X-axis) and a P value (Y-axis) as data and statistical significance P-value) can be provided to the researcher.

In addition, the data analysis unit may provide an analysis result through a heat map capable of finding an important gene or macroscopically viewing the data, as shown in reference numeral 820.

In addition, the data analysis unit may provide a plurality of analysis results at once by using a plurality of independent variables and dependent variables in the data group generation unit of FIG.

9 is a diagram showing an integrated analysis processing apparatus according to another embodiment of the present invention.

Among the contents described with reference to FIG. 9, the description of the integrated analysis processing apparatus according to one embodiment will not be repeated.

9, the integrated analysis apparatus 900 according to another embodiment divides GEO data into expression data and gene expression type data, and classifies the TCGA data into at least one of the type of expression data and the type of gene expression data .

In addition, the expression data can be standardized, the gene expression type data can be normalized, and the standardized expression data and the normalized gene expression type data can be divided into a plurality of data groups.

To this end, a GEO data processing unit 910, a TCGA data conversion unit 920, a phenotype data conversion unit 930, a gene expression type data conversion unit 940, a data group generation unit 950 and a data analysis unit 960, . ≪ / RTI >

The GEO data processing unit 910 according to another embodiment collects GEO (Gene Expression Omnibus) data and converts the collected GEO data into gene expression data including expression data, gene expression value data, and GPL (GEO Platform) .

According to one aspect, the TCGA data converter 920 collects TCGA (Cancer Genome Atlas) data, and assigns a virtual GSE number (GEO Series Number) to the collected TCGA data, And can be converted into at least one of the types of the expression type data.

The phenotype data conversion unit 930 according to another embodiment may standardize expression type data based on the keyword of the expression type data.

According to one aspect, the phenotype data conversion unit 930 receives the TCGA data of the phenotype data type, and can integrate the TCGA data of the phenotype data type and the phenotype data received by the GEO data processing unit 910. In addition, the phenotype data conversion unit 930 can standardize the integrated expression data.

The gene expression type data converter 940 according to another embodiment may normalize the gene expression value data included in the gene expression type data.

According to one aspect, the gene expression type data conversion unit 940 receives TCGA data in the form of gene expression type data, and can integrate TCGA data in the form of gene expression type data and gene expression type data received in the GEO data processing unit . In addition, the gene expression type data converting unit 940 can normalize the gene expression value data included in the integrated gene expression type data.

The data group generation unit 950 according to another embodiment divides the standardized expression data and the normalized gene expression type data into a plurality of data groups.

The data analysis unit 960 according to another embodiment analyzes the divided data group and outputs the analysis result.

That is, by using the present invention, the GEO data and the TCGA data can be integrated and analyzed, thereby improving diversity of data utilization.

FIG. 10 is a diagram showing a method of integrated analysis processing according to an embodiment of the present invention.

The integrated analysis processing method according to the embodiment shown in FIG. 10 can be performed by the integrated analysis processing apparatus according to an embodiment.

Therefore, among the contents described with reference to FIG. 10, the description of the integrated analysis processing apparatus according to the embodiment will be omitted from the description overlapping with the contents described with reference to FIG. 1 through FIG. 8B.

Referring to FIG. 10, in step 1010, the integrated analysis processing method according to an embodiment collects GEO (Gene Expression Omnibus) data from a GEO data processing unit and outputs the collected GEO data to the expression data, the gene expression value data, and the GPL GEO Platform).

According to one aspect, in step 1010, the integrated analysis processing method according to an embodiment may collect GEO data based on a GSE number (GEO Series Number).

According to one aspect, in the collected GEO data, the phenotype data may include at least one of the patient's age, sex, tissue location, and tissue status. Also, in the collected GEO data, the gene expression value data may include an expression amount of the mRNA, and the GPL (GEO Platform) can convert the probe of the gene expression value data into a biological symbol.

In step 1020, the integrated analysis processing method according to an exemplary embodiment may standardize the expression data based on the keyword of the expression data in the expression data conversion unit.

In step 1030, the integrated analysis processing method according to an exemplary embodiment may normalize the gene expression value data included in the gene expression type data in the gene expression type data conversion unit.

In step 1040, the integrated analysis processing method according to an exemplary embodiment may divide the normalized expression data and the normalized gene expression data into a plurality of data groups in the data group generation unit.

According to one aspect, in step 1040, the integrated analysis processing method according to an exemplary embodiment may utilize a statistical method of calculating a P value (p-value) to divide into a plurality of data groups.

In step 1050, the integrated analysis processing method according to an embodiment can analyze the data group classified by the data analysis unit and output the analysis result.

11 is a diagram showing an integrated analysis processing method according to another embodiment of the present invention.

The integrated analysis processing method according to another embodiment shown in FIG. 11 can be performed by the integrated analysis processing apparatus according to another embodiment.

Therefore, among the contents described with reference to FIG. 11, the description overlapping with the above-described contents will be omitted.

11, in step 1110, the integrated analysis processing method according to an exemplary embodiment collects GEO (Gene Expression Omnibus) data from a GEO data processing unit, and compares the collected GEO data with expression data, gene expression value data, and GPL GEO Platform). The data can be classified into gene expression data.

According to one aspect, in the integrated analysis processing method according to an exemplary embodiment, the TCGA data conversion unit collects the Cancer Genome Atlas (TCGA) data and assigns a virtual GSE number (GEO Series Number) to the collected TCGA data TCGA data can be converted into at least one of the form of the phenotype data and the form of the gene expression type data.

In step 1130, the integrated analysis processing method according to an exemplary embodiment may standardize the expression data based on the keyword of the expression data in the expression data conversion unit.

According to one aspect, in step 1130, the integrated analysis processing method according to an embodiment receives TCGA data of the phenotype data type, integrates the TCGA data of the phenotype data type and the phenotype data received by the GEO data processing unit, Can be standardized.

In step 1140, the integrated analysis processing method according to an exemplary embodiment may normalize the gene expression value data included in the gene expression type data in the gene expression type data conversion unit.

According to one aspect, in step 1140, the integrated analysis processing method according to an exemplary embodiment receives TCGA data in the form of gene expression data, integrates TCGA data in the form of gene expression data, and gene expression data received in the GEO data processing unit And gene expression value data included in the integrated gene expression data can be normalized.

In step 1150, the integrated analysis processing method according to an exemplary embodiment may divide the normalized expression data and the normalized gene expression data into a plurality of data groups in the data group generation unit.

In step 1160, the integrated analysis processing method according to an embodiment can analyze the data group classified by the data analysis unit and output the analysis result.

As a result, using the present invention, integrated GEO data can be generated with only a list of GEOs to easily obtain the resultant data for analysis of genome expression data.

In addition, by providing analysis data in which TCGA data and GEO data are integrated, diversity of data utilization can be improved.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

100: Integrated analysis apparatus 110: GEO data processing section
120: phenotype data conversion unit 130: gene expression type data conversion unit
140: Data group generation unit 150: Data analysis unit

Claims (13)

A GEO data processing unit for collecting GEO (Gene Expression Omnibus) data and dividing the collected GEO data into expression data, gene expression value data, and gene expression type data including a GPL (GEO Platform);
A phenotype data conversion unit for standardizing the expression data based on a keyword of the expression data;
A gene expression type data conversion unit for normalizing the gene expression value data included in the gene expression type data;
A data group generation unit for classifying the normalized phenotype data and the normalized gene expression type data into a plurality of data groups,
A data analysis unit for analyzing the divided data group and outputting the analysis result,
And an integrated analysis processing unit. The method according to claim 1,
The GEO data processing unit
Wherein the GEO data is collected based on a GSE number (GEO Series Number), and the phenotype data in the collected GEO data includes at least one of an age, a sex, a tissue location and a tissue state of the patient, mRNA, and the GPL (GEO Platform) converts the probe of the gene expression value data into a biological symbol
Integrated analysis processing device. 3. The method of claim 2,
The expression data conversion unit
Separating the feature word and value from the expression data, and performing the standardization for converting the expression data to a form of a standardized data based on the separated key words and values
Integrated analysis processing device. The method of claim 3,
The expression data conversion unit
And a phenotype database in which data to be referred to in normalizing the expression data is stored,
Wherein the phenotype database includes at least one of a representative key word-similarity database, a representative value-similarity value database, and a conversion script database
Integrated analysis processing device. The method according to claim 1,
Wherein the gene expression type data conversion unit
Changing a name of a probe included in the gene expression type data based on a gene name that is used above a predetermined threshold value among gene names having a biologically common meaning with the detector name, Normalization of each gene expression value data corresponding to the changed detector name
Integrated analysis processing device. The method according to claim 1,
The TCGA data is collected by applying a virtual GSE number (GEO Series Number) to the collected TCGA data to generate at least one of the type of the phenotype data and the type of the gene expression data Into a form of a TCGA data converter
Integrated analysis processing device. The method according to claim 6,
The expression data conversion unit
Receiving TCGA data of the phenotype data type, integrating the TCGA data of the phenotype data type and the phenotype data received by the GEO data processing unit, standardizing the integrated phenotype data,
Wherein the gene expression type data conversion unit
Wherein the TCGA data in the form of gene expression data is integrated with the TCGA data in the form of gene expression data and the gene expression data received in the GEO data processing unit, To normalize the value data
Integrated analysis processing device. The method of claim 1, wherein
The data group generation unit
And a statistical technique for calculating a P value (p-value) is used to divide the data group into the plurality of data groups
Integrated analysis processing device. Collecting GEO (Gene Expression Omnibus) data from the GEO data processor, and separating the collected GEO data into expression data, gene expression value data, and gene expression type data including a GPL (GEO Platform);
Standardizing the expression data based on a feature word of the expression data in the expression data conversion unit;
A step of normalizing the gene expression value data included in the gene expression type data in the gene expression type data conversion unit;
Dividing the normalized expression data and the normalized gene expression type data into a plurality of data groups in a data group generation unit
Analyzing the divided data group in the data analysis unit and outputting the analysis result
Wherein the integrated analysis processing method comprises: 10. The method of claim 9,
The step of dividing the expression data into gene expression type data
Wherein the GEO data is collected based on a GSE number (GEO Series Number), and the phenotype data in the collected GEO data includes at least one of an age, a sex, a tissue location and a tissue state of the patient, mRNA, and the GPL (GEO Platform) converts the probe of the gene expression value data into a biological symbol
Integrated analysis processing method. The method of claim 10, wherein
The TCGA data conversion unit collects the Cancer Genome Atlas (TCGA) data, gives a virtual GSE number (GEO Series Number) to the collected TCGA data, and stores the TCGA data in the form of the phenotype data and the gene expression data Into a form of at least one of < RTI ID = 0.0 >
Integrated analysis processing method. 12. The method of claim 11,
The step of standardizing the expression data
Receiving TCGA data of the phenotype data type, integrating the TCGA data of the phenotype data type and the phenotype data received by the GEO data processing unit, standardizing the integrated phenotype data,
The step of normalizing the gene expression value data
Wherein the TCGA data in the form of gene expression data is integrated with the TCGA data in the form of gene expression data and the gene expression data received in the GEO data processing unit, To normalize the value data
Integrated analysis processing method. 10. The method of claim 9,
The step of dividing into the plurality of data groups
And a statistical technique for calculating a P value (p-value) is used to divide the data group into the plurality of data groups
Integrated analysis processing method.

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