Methods and systems for data analysis

• US 20060015291A1
• Filed: 06/20/2005
• Published: 01/19/2006
• Est. Priority Date: 10/18/2002
• Status: Abandoned Application

• Current Assignee: Stanford University
• Original Assignee: Leland Stanford Junior University (Stanford University)
• Inventors: Parks, David R., Moore, Wayne A.
• Application Number:
  US11/157,468
• Publication Number:
  US 20060015291A1
• Time in Patent Office: Days
• US Class Current: 702/179
• CPC Class Codes: G01N 15/1459 the analysis being performed on a sample stream
• CPC Class Codes: G01N 2015/1402 Data analysis by thresholding or gating operations performed on the acquired signals or stored data
• CPC Class Codes: G01N 2015/1488 Methods for deciding
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/10 Signal processing, e.g. from mass spectrometry [MS] or from PCR
• CPC Class Codes: G16B 45/00 ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis

Abstract

The present invention provides methods of analyzing and/or displaying data. In one aspect, the invention provides methods for visualizing or displaying high dynamic range data obtained from flow cytometry analyses. Related systems and computer programs products are also provided.

Claims

1. A method of analyzing data, said method comprising:

scaling raw data using at least one scaling function that provides substantially linear transformations for data values proximal to zero and substantially logarithmic transformations for other data values to generate scaled data; and

,

using said scaled data to identify portions of said raw data of interest.

2. The method of claim 1, wherein said raw data comprises high dynamic range data.

3. The method of claim 1, wherein said scaling and/or said using comprise using a computer.

4. The method of claim 1, wherein said scaling function transforms negative raw data values.

5. The method of claim 1, wherein a transition from linear to logarithmic scaling in said scaled data is substantially smooth.

6. The method of claim 1, wherein the second derivative of said scaling function is zero for a corresponding raw data value of zero.

7. The method of claim 1, wherein said scaling function comprises one or more optimization functions for viewing different raw data sets.

8. The method of claim 1, wherein said scaling function is substantially symmetrical proximal to a raw data value of zero.

9. The method of claim 1, wherein said raw data is derived through fluorescence compensation.

10. The method of claim 1, wherein said scaling comprises specifying at least one preliminary parameter such that other variables are constrained by one or more criteria of said scaling function, thereby defining at least one single variable transformation.

11. The method of claim 10, wherein said single variable transformation comprises a family of related transformations.

12. The method of claim 1, wherein said using comprises inputting said scaled data into at least one data analysis algorithm to identify said portions of said raw data of interest.

13. The method of claim 12, wherein said data analysis algorithm comprises automated data analysis software.

14. The method of claim 1, wherein said using comprises displaying said scaled data for a human viewer.

15. The method of claim 14, wherein said scaled data is displayed on a coordinate grid and said scaling function primarily depends on data in a single data dimension, thereby assuring that said coordinate grid is substantially rectilinear.

16. The method of claim 14, wherein display values increase in size more than corresponding display variables in linear regions of said scaled data as a family-generating variable is adjusted to increase a range of linearity.

17. The method of claim 14, wherein said scaling function comprises at least one generalized hyperbolic sin e function.

18. The method of claim 17, wherein said generalized hyperbolic sin e function is a form of V=Z(10^n/m−

1−

G²(10<sup>−

n/mG</sup>−

1)), where V is a data value to be displayed at channel position n in a plot of said scaled data, m is the asymptotic channels per decade, and G is linearization strength.

19. The method of claim 17, wherein said generalized hyperbolic sin e function is a form of V=a(e^x−

p²e<sup>−

px</sup>+p²1), where V is a data value to be plotted at display position x in a plot, a is a scaling factor, and p is linearization strength.

20. The method of claim 17, wherein said generalized hyperbolic sin e function is a form of S(x;

a, b, c, d, So)=ae^bx−

ce<sup>−

dx</sup>−

So, for positive x and for negative x, a reflection of said positive x in a form of Sref(x;

a, b, c, d, So)=(x/absx) S(absx;

a, b, c, d, So), where absx is the absolute value of variable x.

21. A computer program product comprising a computer readable medium having one or more logic instructions for scaling raw data using at least one scaling function that provides substantially linear transformations for data values proximal to zero and substantially logarithmic transformations for other data values to generate scaled data.

22. The computer program product of claim 21, wherein said computer readable medium comprises one or more of:

a CD-ROM, a floppy disk, a tape, a flash memory device or component, a system memory device or component, a hard drive, or a data signal embodied in a carrier wave.

23. A system for analyzing data, comprising:

(a) at least one detector; and

,

(b) at least one computer operably connected to said detector, said computer having system software comprising one or more logic instructions for;

receiving raw data from said detector in said computer; and

scaling said raw data using at least one scaling function that provides substantially linear transformations for data values proximal to zero and substantially logarithmic transformations for other data values to generate scaled data.

24. The system of claim 23, wherein said system software further comprises one or more logic instructions for displaying said scaled data for a human viewer.

25. The system of claim 23, wherein said system software further comprises one or more logic instructions for analyzing said scaled data to identify portions of said raw data of interest.