Broad outline for AbStat Section 11-8-13

2015-01-13

++ Broad outline for Ab Stat Section 11-8-13

-Introduction
--Fundamental Question
When sera antibodies react with an array of random peptides, each peptide will exhibit a different level of binding quantified as a peptide intensity.  The distribution of peptide intensities will depend on the number of different antibodies and their affinities and avidities for various peptide sequences.  The number of antibodies, the affinities of the antibodies, and the avidities of the antibodies will depend on the state of the organism: healthy or disease and young or old.  Can the information acquired from the peptide intensity distribution resulting from sera antibodies reacting with an array of random sequence peptides be used to distinguish healthy or disease states?  Can this information be compressed into a single quantified variable?

--Description of platform (reference Rebecca's papers)

--Correlation of antibody affinity (and avidity?) with disease and age
---Papers
----Effects of age on antibody affinity maturation.
-----http://www.ncbi.nlm.nih.gov/pubmed/12653658
----Antibody quality in old age.
-----http://www.ncbi.nlm.nih.gov/pubmed/16608408
----Effect of age on humoral immunity, selection of the B---cell repertoire and B---cell development.
-----http://www.ncbi.nlm.nih.gov/pubmed/9476670
---Increase of autoimmune disease with age

--Single number measures
---characteristics of single number measures: entropy, kurtosis, skew, etc.
---Paper: Entropy Measures Quantify Global Splicing Disorders in Cancer
---min and max of entropy, normalized entropy, entropy doesn't change with normalized values
---How is physics entropy, information entropy, and peptide intensity entropy related?

--Use for diagnosis and monitoring

-Materials and Methods
--Description of array production
--Description of array procedures
--Description of mathematical measures
--Description of Java program
---Optimization of Java program
--Description of Methods of analysis
---box and dotplots
---Student's T test
---SVM classification algorithm
---Classification tree
---Heirarchical clustering and heatmaps
---Software used: Custom Java program, Deducer, JMP, Excel

-Results
--AbStat Changes with Artificial Antibody Experiments (Are there changes in the AbStat measurement with artificial antibody experiments?)
---Spiking antibody into sera (mouse and human)
---Monoclonal affinity data
---Two monoclonals mixed
--Mouse vaccines and infections (Are there changes in the AbStat measurement with artificial mouse vaccines and infections?)
---Bart's mouse infection data (three different experiments)
----mouse infection data
----survival records
----some mic with good vaccine and some mice with partial protection
--Human vaccination data (Are there changes in the AbStat measurement with human vaccination data?)
---Human time course data
--Reduction in antibody repertoire complexity with lymphoma (Are there changes in the AbStat measurement during the course of a lymphoma which reduces the complexity of the antibody repertoire?  During the course of a B cell lymphoma one particular antibody in the repertoire becomes dominant as one B cell proliferates uncontrollably and produces large amounts of the antibody)
---Bart's dog lymphoma data
--Mouse cancer progression data (Are there changes in the AbStat measurement as mice develop cancer?)
--Human disease data (Can the AbStat measurements distinguish between humans with and without disease on different platforms?)
---330K wafer data (20, 22, 25, and 46)
---LLNL 10K dataset
--Alzheimers data  (Can the AbStat measurement distinguish between aged humans with alzheimers and aged human controls?)
--Changes with Age (Can the AbStat measurement be used to distinguish between young and aged organisms?)
---Mouse young and old
---Human young and old
--Rank of Measures (Which metrics of the AbStat measures provide the most information about healthy and disease states?)
--Range of Measures (What are typical ranges for the various AbStat measures in healthy and disease states?)
--Analysis of Important Peptides (What is the characteristic of the peptides which contribute the most to the entropy measurement of AbStat?)

-Discussion
--AbStat Changes with Artificial Antibody Experiments
---Discussion of Mechanism
--Mouse vaccines and infections
--Human vaccination data
--Reduction in antibody repertoire complexity with lymphoma
--Mouse cancer progression data
--Human disease data
--Alzheimers data
--Changes with Age
---use for adjusting baseline to baseline of normals
---quantifying attempts to mitigate aging process
--Rank of Measures
--Range of measures
--Analysis of important peptides
--Potential to combine ABSTAT into single number
--Use
---Diagnosis
---Constant monitoring
---Collect and analyze later
---Metric for improving performance and correlation with lifestyles
---Displaying results to public
--Potential of ABSTAT with specific peptides for different diseases
--Prediction about old and young vaccine results
--Applying same analysis to other datasets
---brain scan data (distribution of numbers for number of connections each neuron has; sanity and insanity)
---RNA expression data (expression levels of all of the different transcripts)
---prosperity of a city
---other data
--Other Measures?
---Discussion of Kolmogorov complexity and meaningful patterns
--Prediction about immune system training

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