# US 2017/0286597 A1 · Methods and systems for visualizing gene expression data

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- Number: US 2017/0286597 A1
- Document ID: US20170286597A1
- Status: Published application
- Kind: A1
- Application number: 15/507,275
- Filed: 2015-08-17
- Published: 2017-10-05
- Earliest claimed: 2014-09-05
- Assignees: Koninklijke Philips N.V.
- Applicants: Koninklijke Philips N.V.
- Inventors: ALEXANDER RYAN MANKOVICH; NEVENKA DIMITROVA; VARTIKA AGRAWAL; NILANJANA BANERJEE
- Web page: https://patentgrep.com/patent/US20170286597A1

## CPC

- G16B 45/00: ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
- C12Q 1/68: involving nucleic acids
- G01N 33/48: Biological material, e.g. blood, urine (G01N33/02, G01N33/26, G01N33/44, G01N33/46 take precedence); Haemocytometers (counting blood corpuscules distributed over a surface by scanning the surface G06M11/02)
- G01N 33/50: Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing (measuring or testing processes involving enzymes or microorganisms, compositions or test papers therefor; processes for forming such compositions, condition responsive control in microbiological or enzymological processes C12Q)
- G16B 25/00: ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
- G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR

## Abstract

Methods and systems for visualizing gene expression data in a way that permits the comparison of different patient groups to facilitate medical applications, including cancer diagnostics and treatment planning, particularly breast cancer. The method organises gene expression data for at least one patient into a plurality of windows of a specified size, calculates an average RSEM score for all of the genes in each window and presents the average RSEM scores in a two-dimensional array, wherein one axis organises the windows by patient and the other axis organises the windows by sequence.

## Claims

### Claim 1

A method, in a data processing system comprising a processor, a user interface and a memory, for visualization and analysis of gene expression data, the method comprising:
receiving, in the data processing system from a database, a digital file comprising one or more sets of long range gene expression data for one or more patients;
loading said long range gene expression data into an analytical tool comprising said user interface and said memory, wherein said user interface is configured to receive commands from and to provide feedback to an operator;
defining, by said operator, the size of at least one window for evaluating said long range gene expression data, wherein said window size is measured by sequence size in kb, and inputting said defined window size into said analytical tool;
converting, by said analytical tool, the long range gene expression data for at least one patient into a series of concatenated windows that are the size of said defined windows;
identifying, by said processor, each gene that is contained within each of said windows;
generating a sample-specific gene abundance score for each of said genes using transcript abundance estimation software stored in said processor;
calculating, by said processor, an average gene abundance score for all of the genes identified in each of said windows; and
presenting the average gene abundance scores for at least some of the windows in a two-dimensional array, wherein one axis of the array organizes the windows by patient and the other axis of the array organizes the windows by gene sequence.

### Claim 2 (depends on 1)

The method of claim 1, wherein the transcript abundance estimation software is RSEM software.

### Claim 3 (depends on 2)

The method of claim 2, wherein the averaging of the RSEM scores for all of the genes within a window is performed for each window-sized sequence in the long range gene expression data in series or in parallel.

### Claim 4 (depends on 2)

The method of claim 2, wherein the averaged RSEM scores for all of the windows are presented together in the form of a chromosome-wide long range expression pattern.

### Claim 5 (depends on 2)

The method of claim 2, wherein a minimum level of variance is specified by the operator through the user interface, and the variance for each window-sized sequence is calculated.

### Claim 6 (depends on 2)

The method of claim 2, further comprising he step of filtering out window-sized sequences of low variance.

### Claim 7 (depends on 5)

The method of claim 5, wherein the series of concatenated windows for each patient are displayed together in an array to be evaluated by the operator.

### Claim 8 (depends on 7)

The method of claim 7, wherein the series of concatenated windows for each patient are clustered to form the array.

### Claim 9 (depends on 2)

The method of claim 2, wherein the defined window size is 23 kb or 100 kb.

### Claim 10

A non-transitory computer-readable storage medium tangibly encoded with computer readable instructions, that when executed by a processor associated with a computing device, performs a method for visualizing and analyzing gene expression data, the method comprising:
receiving from a database, a digital file comprising one or more sets of long range gene expression data for one or more patients;
loading said long range gene expression data into an analytical tool comprising a user interface and data storage, wherein said user interface is configured to receive commands from and to provide feedback to an operator;
in response to receiving from the operator of said user interface, instructions defining the size of at least one window for evaluating said long range gene expression data, wherein said window size is measured by sequence size in kb, inputting said defined window size into said analytical tool;
converting the long range gene expression data for at least one patient into a series of concatenated windows that are the size of said defined windows;
identifying, by said processor, each gene that is contained within each of said windows;
generating a sample-specific gene abundance score for each of said genes using transcript abundance estimation software stored in said processor;
calculating, by said processor, an average gene abundance score for all of the genes identified in each of said windows; and
presenting the average gene abundance scores for at least some of the windows in a two-dimensional array, wherein one axis of the array organizes the windows by patient and the other axis of the array organizes the windows by gene sequence.

### Claim 11 (depends on 10)

The method of claim 10, wherein the transcript abundance estimation software is RSEM software.

### Claim 12 (depends on 11)

The method of claim 11, wherein the averaging of the RSEM scores for all of the genes within a window is performed for each window-sized sequence in the long range gene expression data in series or in parallel.

### Claim 13 (depends on 11)

The method of claim 11, wherein the averaged RSEM scores for all of the windows are presented together in the form of a chromosome-wide long range expression pattern.

### Claim 14 (depends on 11)

The method of claim 11, wherein a minimum level of variance is specified by the operator through the user interface, and the variance for each window-sized sequence is calculated.

### Claim 15 (depends on 14)

The method of claim 14, wherein the series of concatenated windows for each patient are displayed together in an array to be evaluated by the operator.

### Claim 16 (depends on 11)

The method of claim 11, further comprising he step of filtering out window-sized sequences of low variance.

### Claim 17 (depends on 16)

The method of claim 16, wherein the series of concatenated windows for each patient are clustered to form the array.

### Claim 18 (depends on 11)

The method of claim 11, wherein the defined window size is 23 kb or 100 kb.

### Claim 19

A system for visualizing and analyzing gene expression profile data, comprising;
one or more non-transitory computer-readable storage devices tangibly encoded with computer readable instructions, that when executed by a processor associated with a computing device, performs a method comprising:
receiving from a database, a digital file comprising one or more sets of long range gene expression data for one or more patients;
loading said long range gene expression data into an analytical tool comprising a user interface and data storage, wherein said user interface is configured to receive commands from and to provide feedback to an operator;
in response to receiving from the operator of said user interface, instructions defining the size of at least one window for evaluating said long range gene expression data, wherein said window size is measured by sequence size in kb, inputting said defined window size into said analytical tool;
converting the long range gene expression data for at least one patient into a series of concatenated windows that are the size of said defined windows;
identifying, by said processor, each gene that is contained within each of said windows;
generating a sample-specific gene abundance score for each of said genes using transcript abundance estimation software stored in said processor; calculating, by said processor, an average gene abundance score for all of the genes identified in each of said windows; and
presenting the average gene abundance scores for at least some of the windows in a two-dimensional array, wherein one axis of the array organizes the windows by patient and the other axis of the array organizes the windows by gene sequence.