METHODS AND APPARATUS RELATED TO GATE BOUNDARIES WITHIN A DATA SPACE

• US 20100014741A1
• Filed: 07/10/2009
• Published: 01/21/2010
• Est. Priority Date: 07/10/2008
• Status: Abandoned Application

• Current Assignee: Nodality Inc.
• Original Assignee: Nodality Inc.
• Inventors: Banville, Steven C., Putta, Santosh K., Spellmeyer, David C.
• Application Number:
  US12/501,295
• Publication Number:
  US 20100014741A1
• Time in Patent Office: Days
• US Class Current: 382/133
• CPC Class Codes: G06F 18/40 Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts

Abstract

In one embodiment, one or more processor-readable media storing code representing instructions that when executed by one or more processors cause the one or more processors to receive a set of parameter values defining a boundary within a data space associated with a dataset. The dataset can represent signaling related to a test substance. A first metric can be defined based on a first portion of the dataset associated with a first region defined by the boundary. A second metric can be defined based on a second portion of the dataset associated with a second region defined by the boundary after the boundary is modified. The second region can be different than the first region.

Claims

1. One or more processor-readable media storing code representing instructions that when executed by one or more processors cause the one or more processors to:

receive a set of parameter values defining a boundary within a data space associated with a dataset, the dataset representing signaling related to a test substance;

define a first metric based on a first portion of the dataset associated with a first region defined by the boundary;

modify the boundary; and

define a second metric based on a second portion of the dataset associated with a second region defined by the boundary after the boundary is modified, the second region being different than the first region.

2. The one or more processor-readable media of claim 1, wherein the boundary is modified based on a random number.

3. The one or more processor-readable media of claim 1, wherein the boundary includes a vertex, the modifying of the boundary includes modifying the vertex of the boundary.

4. The one or more processor-readable media of claim 1, wherein the modifying of the boundary includes scaling the boundary along an axis.

5. The one or more processor-readable media of claim 1, further storing code representing instructions that when executed by the one or more processors cause the one or more processors to:

define an axis of the boundary based on an Eigenvector, the boundary being modified along the axis based on a random number.

6. The one or more processor-readable media of claim 1, further storing code representing instructions that when executed by the one or more processors cause the one or more processors to:

define an axis based on an orientation of the dataset within the data space, the modifying of the boundary is based on the axis.

7. The one or more processor-readable media of claim 1, wherein the boundary includes a vertex,

the one or more processor-readable media further storing code representing instructions that when executed by the one or more processors cause the one or more processors to:

define an axis based on a shape mathematically fitted to the boundary, the modifying of the boundary includes modifying the vertex of the boundary based on the axis.

8. The one or more processor-readable media of claim 1, wherein the modifying of the boundary is performed based on a user preference.

9. The one or more processor-readable media of claim 1, wherein the first portion of the dataset includes a plurality of datapoints outside of the data space.

10. The one or more processor-readable media of claim 1, wherein the boundary is a first boundary, the dataset is defined based on a second boundary within the data space, the first boundary being logically related to the second boundary.

11. The one or more processor-readable media of claim 1, wherein the boundary is a first boundary, the first region is based on a combination of the first boundary and a second boundary,

the one or more processor-readable media further storing code representing instructions that when executed by the one or more processors cause the one or more processors to:

modify the second boundary, the second region is based on a combination of the second boundary after the second boundary is modified and the first boundary after the first boundary is modified.

12. The one or more processor-readable media of claim 1, wherein the boundary includes a line segment.

13. The one or more processor-readable media of claim 1, wherein the boundary includes a hyperplane.

14. The one or more processor-readable media of claim 1, further storing code representing instructions that when executed by the one or more processors cause the one or more processors to:

identify the modified boundary as a boundary template.

15. One or more processor-readable media storing code representing instructions that when executed by one or more processors cause the one or more processors to:

receive a set of parameter values defining a first boundary within a data space associated with a dataset, the first boundary defining a region inside the first boundary, the dataset representing signaling related to a test substance;

receive a set of parameter values defining a second boundary circumscribing the first boundary, the second boundary being within the data space; and

define a plurality of metrics based on a portion of the dataset included in the region and based on a plurality of relationships between a plurality of perturbations of the second boundary and a portion of the dataset outside of the region.

16. The one or more processor-readable media of claim 15, further storing code representing instructions that when executed by the one or more processors cause the one or more processors to:

receive a set of parameter values defining a third boundary circumscribing the second boundary, each perturbation from the plurality of perturbations of the second boundary being within a region between the first boundary and the third boundary.

17. The one or more processor-readable media of claim 15, wherein the region is a first region,

further storing code representing instructions that when executed by the one or more processors cause the one or more processors to:

receive a set of parameter values defining a third boundary circumscribing the second boundary, the portion of the dataset is within a second region between the second boundary and the third boundary.

18. The one or more processor-readable media of claim 15, wherein the region is a first region,

further storing code representing instructions that when executed by the one or more processors cause the one or more processors to:

receive a set of parameter values defining a third boundary circumscribing the second boundary, each perturbation from the plurality of perturbations is defined by a vertex located within a second region between the second boundary and the third boundary.

19. One or more processor-readable media storing code representing instructions that when executed by one or more processors cause the one or more processors to:

receive a set of parameter values defining a multi-dimensional boundary within a data space associated with a dataset, the multi-dimensional boundary having more than two dimensions, the dataset representing signaling related to a test substance;

define a metric based on a relationship between the multi-dimensional boundary and the dataset; and

modify at least one of the dimensions of the multi-dimensional boundary.

20. The one or more processor-readable media of claim 19, wherein the metric is defined based on a parameter value included in the dataset and outside of the data space.

21. The one or more processor-readable media of claim 19, further storing code representing instructions that when executed by the one or more processors cause the one or more processors to:

define a second metric based on a relationship between the dataset and the multi-dimensional boundary after the multi-dimensional boundary is modified.

22. One or more processor-readable media storing code representing instructions that when executed by one or more processors cause the one or more processors to:

receive a set of parameter values defining a boundary within a data space;

define a first sensitivity value representing a sensitivity of a metric to changes in a relationship between the boundary and at least a portion of a first dataset associated with the data space; and

define a second sensitivity value representing a sensitivity of the metric to changes in a relationship between the boundary and at least a portion of a second dataset associated with the data space, the first sensitivity value and the second sensitivity value being calculated based on a plurality of perturbations of the boundary.

23. The one or more processor-readable media of claim 22, wherein the relationship between the boundary and the portion of the first dataset is a spatial relationship.

24. The one or more processor-readable media of claim 22, wherein the data space has more than two dimensions.

25. The one or more processor-readable media of claim 22, wherein each perturbation from the plurality of perturbations of the boundary is a scaled perturbation of the boundary.

26. The one or more processor-readable media of claim 22, wherein each perturbation from the plurality of perturbations of the boundary is based on combination of a first axis and a second axis non-parallel to the first axis.

27. The one or more processor-readable media of claim 22, wherein at least one of the first dataset or the second dataset represents signaling related to a test substance.

28. The one or more processor-readable media of claim 22, wherein the first dataset represents signaling from an experiment related to a first sample, the second dataset represents signaling from an experiment related to a second sample mutually exclusive from the first sample.