Multiple-channel bias removal methods with little dependence on population size

• US 20060142948A1
• Filed: 12/23/2004
• Published: 06/29/2006
• Est. Priority Date: 12/23/2004
• Status: Abandoned Application

• Current Assignee: Agilent Technologies, Inc.
• Original Assignee: Agilent Technologies, Inc.
• Inventors: Minor, James M.
• Application Number:
  US11/021,948
• Publication Number:
  US 20060142948A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Abstract

Methods, systems and computer readable media for removing labeling-bias factors affecting data from two or more data sources after single source biasing factors have been removed to the extent possible. Respective data points from the data sources are considered in combination to generate a population of data points. The population of data points is subdivided into portions of the overall population and, for each portion, the data points are sorted within that portion, relative to values of all other data values in that portion. A function is then generated for each portion from the sorted data points for that portion. For each portion, a value representative of highest population density of data points within that portion is identified. The identified values are fitted to a predetermined curve, and values of all data points are adjusted relative to the fitted values.

Claims

1. A method of removing labeling-bias factors affecting data from two or more data sources, said method comprising the steps of:

subdividing combined respective data points from the two or more data sources into portions of the population of combined data points;

for each portion, sorting the data points that are members of that portion according to relative values of the data points that are members;

for each portion, generating a function from the sorted data points belonging to that portion;

for each function, identifying a value representative of highest population density of data points within the respective portion;

fitting the values representative of highest population densities within the portions, respectively to a predetermined curve; and

adjusting values of all data points relative to the fitted values.

2. The method of claim 1, further comprising plotting said data points from two or more data sources against one another, respectively.

3. The method of claim 2, wherein two data sources are considered and said plotting comprises plotting data values from a first of said sources against respective data values from a second of said sources.

4. The method of claim 1, wherein said data sources are microarray channels and said data points are representative of signal values from said channels.

5. The method of claim 1, wherein said subdividing comprises binning the population into portions of the population.

6. The method of claim 1, further comprising displaying the adjusted values of data points.

7. The method of claim 1, wherein said portions are created by passing a moving window over the population of combined data points.

8. The method of claim 1, wherein data points within each of said portions are not all mutually exclusive of one another.

9. The method of claim 1, wherein said function comprises a cumulative distribution function.

10. The method of claim 1, wherein, for each function, the value representative of highest population density of data points within the respective portion is identified by identifying an inflection point of the function.

11. The method of claim 9, wherein, for each cumulative distribution function, the value representative of highest population density of data points within the respective portion is identified by identifying an inflection point of the cumulative distribution function.

12. The method of claim 11, wherein each said inflection point is identified using a convolution filter based on a fast Fourier transform.

13. The method of claim 1, further comprising:

plotting respective data points from first and second of the data sources against one another; and

repositioning an entire population of said plotted respective data points so that highest population density values over the entire population approximate a predetermined curve.

14. The method of claim 13, wherein the predetermined curve is a straight line coincident with one of two axes according to which said respective data points were plotted.

15. The method of claim 13, wherein the population is repositioned by applying principal components analysis to rotate the population relative to the predetermined curve.

16. The method of claim 13, wherein the population is repositioned by applying regression analysis to project the population relative to the predetermined curve.

17. The method of claim 13, further comprising repositioning the population again, after said adjusting values of all data points relative to the fitted values, by repositioning inversely to said repositioning to approximate the predetermined curve.

26. The method of claim 13, further comprising repositioning the population again, after said adjusting values of all signal values relative to the fitted values, by repositioning inversely to said repositioning to approximate the predetermined curve.

18. A method of dye-normalizing array data obtained from at least two channels, said method comprising the steps of:

subdividing a combined population of signal values from the at least two channels into portions of the overall combined population of signal values;

for each portion, sorting the signal values that are members of that portion according to relative values of the signal values that are members;

for each portion, generating a function from the sorted signal values belonging to that portion;

for each function, identifying a value representative of highest population density of signal values within the portion;

fitting the values representative of highest population densities within the portions, respectively to a predetermined curve; and

adjusting values of all signal values relative to the fitted values based upon the adjustments made to fit the fitted values.

19. The method of claim 18, wherein said identifying comprises identifying the mode of each said function.

20. The method of claim 18, further comprising plotting said respective signal values against one another.

21. The method of claim 18, wherein said function comprises a cumulative distribution function.

22. The method of claim 20, further comprising repositioning an entire population of said plotted respective signal values so that highest population density values over the entire population approximate a predetermined curve.

23. The method of claim 22, wherein the predetermined curve is a straight line coincident with one of two axes according to which said respective signal values were plotted.

24. The method of claim 22, wherein the population is repositioned by applying principal components analysis to rotate the population relative to the predetermined curve.

25. The method of claim 22, wherein the population is repositioned by applying regression analysis to project the population relative to the predetermined curve.

27. A system for removing labeling-bias factors affecting data from two or more data sources, said system comprising:

a feature for considering respective data points from said sources in combination;

a feature for subdividing the combined respective data points into portions of the overall population of combined data points;

for each portion, a feature for sorting the data points that are members of that portion according to relative values of the data points that are members;

for each portion, a feature for generating a function from the sorted data points belonging to that portion;

for each function, a feature for identifying a value representative of highest population density of data points within the respective portion;

a feature for fitting the values representative of highest population densities within the portions, respectively to a predetermined curve; and

a feature for adjusting values of all data points relative to the fitted values.

28. The system of claim 27, further comprising a feature for plotting said respective data points from said data sources against one another.

29. The system of claim 28, further comprising a feature for repositioning an entire population of said plotted respective data points so that highest population density values over the entire population approximate a predetermined curve.

30. The system of claim 29, further comprising a feature for repositioning the population again, after adjusting said values of all data points relative to the fitted values, by repositioning inversely to said repositioning to approximate the predetermined curve.

31. A computer readable medium carrying one or more sequences of instructions for removing labeling-bias factors affecting data from two or more data sources, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:

subdividing combined respective data points into portions of the overall population of combined data points;

for each portion, sorting the data points that are members of that portion according to relative values of the data points that are members;

for each portion, generating a function from the sorted data points belonging to that portion;

for each function, identifying a value representative of highest population density of data points within the respective portion;

fitting the values representative of highest population densities within the portions, respectively to a predetermined curve; and

adjusting values of all data points relative to the fitted values.