Outline of AbStat section pre 11-8-13

2015-01-13

++ Outline of Ab Stat section pre 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?

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

Results
-Bart's mouse infection data
-Human disease data
--330K wafer data (20, 22, 25, and 46)
--LLNL 10K dataset
-Alzheimers data
-Human vaccination data
-Bart's dog lymphoma data
-Mouse cancer progression data
-Human time course data
-Mouse young and old
-Human young and old
-Spiking antibody into sera (mouse and human)
-Monoclonal affinity data
-Two monoclonals mixed
-Analysis of Important Peptides
-Rank of Measures
-Range of Measures

Discussion
-Optimization of Java program
-Discussion of Mechanism
-Potential to combine ABSTAT into single number
-Use
--Diagnosis
--Constant monitoring
--Collect and analyze later
--Metric for improving performance and correlation with lifestyles
-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)
--other data
-Other Measures?
--Discussion of Kolmogorov complexity and meaningful patterns
-Prediction about immune system training