Methods for analysis of biological dataset profiles

1 . A method of determining the functional homology between two agents, the method comprising:

deriving a biological dataset profile comprising output from 2 or more parameters, from an experimental system for a test agent;

generating a prediction envelope from a control biological dataset profile, which prediction envelope provides upper and lower limits for experimental variation;

wherein a test agent profile is considered to be different than the control if at least one parameter value of the profile exceeds the prediction envelope limits that correspond to a predefined level of significance.

2 . The method according to claim 1 , wherein said test agent is a genetic agent.

3 . The method according to claim 1 , wherein said agent is a chemical or biological agent.

4 . The method according to claim 1 , wherein said biological dataset profile comprises readouts from multiple cellular parameters resulting from exposure of cells to biological factors in the absence or presence of a test agent.

5 . The method according to claim 4 , wherein said system comprises a plurality of samples of a single cell type or types in a common biologically relevant context; comprising at least one control in the absence of the test agent.

6 . The method according to claim 5 , wherein a plurality of systems are concatenated for simultaneous analysis.

7 . The method according to claim 6 , further comprising the step of displaying relationships between two or more agents after non-supervised hierarchical clustering.

8 . The method according to claim 1 , wherein said biological dataset profile from an experimental system for a test agent; and said control biological dataset profile are normalized by the method comprising:

obtaining a mean value for each parameter;

dividing the mean parameter value by the mean parameter value from a negative control sample to generate a ratio;

transforming said ratio.

9 . The method according to claim 8 , wherein said control prediction envelope is non-centered, and generated by the method comprising:

creating a 1-standard deviation envelope around the profile of the combined means for each measured values for parameters;

moving the envelope lines in a parallel fashion outwards until a predetermined number of control profiles are completely contained within the envelope lines; and a user specified number has at least one of the measured parameters outside the envelope lines.

10 . The method according to claim 8 , wherein said control prediction envelope is centered, and generated by the method comprising:

determining the mean from two control point estimates;

subtracting the mean from the two control point estimates to center the points;

combining the points from all parameters of a system to obtain centered profiles.

11 . The method according to claim 10 , wherein said control prediction envelope further comprises a third control curve.

12 . The method according to claim 10 , wherein said control prediction envelope is centered, and generated by the method comprising:

calculating a covariance matrix of a set of centered profile

forming a quadratic form of profile vector and the covariance matrix to obtain a single numerical value that represents the distance of each control profile from the center of all control profiles.

13 . The method according to claim 8 , wherein normalized test agent profiles are used to generate a trusted profile, the method comprising:

obtaining an initial trusted profile by averaging N datasets of profiles from N experiments;

classifying X number of datasets that utilize the same experimental system, but which have not been included in the averaging process to generate the initial trusted profile;

plotting the classification error;

establishing a value for N that minimizes classification error;

generating a trusted profile using said value of N that minimizes classification error.

14 . The method according to claim 8 , further comprising the step of determining the false discovery rate, by the method comprising:

generating a set of null distributions of dissimilarity values.

15 . The method according to claim 14 , wherein said generating a set of null distributions comprises:

permuting the values of each profile for all available profiles;

calculating the pairwise correlation coefficients for all profiles;

calculating the probability density function of the correlation coefficients for this permutation; and repeating the procedure for N times; and

using N null distributions to calculate a measure of the count of correlation coefficient values whose values exceed the value obtained from the experimentally observed distribution for given significance level.

16 . The method according to claim 7 , wherein a Pearson correlation is employed as the clustering metric.

17 . The method according to claim 16 , wherein multidimensional scaling is applied in one, two or three dimensions.

18 . The method according to claim 17 , wherein a combination of multidimensional scaling and pivoting is used to move high correlations toward the diagonal

19 . The method according to claim 18 , wherein the results of said multidimensional scaling and pivoting are displayed as a network.

20 . The method according to claim 19 , wherein the display of information further comprises other classification schemes to aid in analysis.

21 . The method according to claim 20 , wherein additional information is conveyed by the use of multiple visualization windows.

22 . The method according to claim 21 , wherein additional information is conveyed by the use of stereo visualization.

23 . The method according to claim 19 , where the field of view of said display is restricted to a portion of the complete set, and where distances are optimized for those points currently visualized.

24 . A system for the determining the functional homology between two agents, the system comprising:

a data processor comprising software for determination of functional homology between two agents by the algorithm comprising:

deriving a biological dataset profile from an experimental system for a test agent;

generating a prediction envelope from a control biological dataset profile, which prediction envelope provides upper and lower limits for experimental variation;

wherein a test agent profile is considered to be different than the control if at least one parameter value of the profile exceeds the prediction envelope limits that correspond to a predefined level of significance.