1. Data Mining Techniques For Correlating Phenotypic Expressions With Genomic and Medical Characteristics
This work has been supported by DTC, IBM and NSF grant and Computational resources for this work were provided by the Minnesota Supercomputing Institute.
Acknowledgements
References
e-coords:
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Rohit Gupta, Blayne Field, Michael Steinbach, Vipin Kumar, Rich Mushlin*, Fred Kulack+ Department of Computer Science and Engineering, University of Minnesota (200 Union Street SE, Minneapolis MN USA)
*IBM T. J Watson Research Center, +IBM Rochester
Obtaining genomic information is increasingly affordable
Single Nucleotide Polymorphisms (SNPs) offer the potential to tests for disease or susceptibility for disease
Electronic medical records (EMRs) are becoming increasingly common
Automated analysis of patient information is now possible
This revolution in genetic and medical potentially leads to Personalized medicine, i.e., using detailed genomic and medical information about a person for the detection, treatment, or prevention of disease
Given: A patient data set that records
Phenotypic Expression (Disease)
Genetic characteristics
Medical characteristics
Objective: Finding patterns combining medical and genetic characteristics that best defines the phenotypic expression under study
Challenges:
High dimensionality and low sample size
Combinatorial explosion
Noise
Non-linear interactions
Various association analysis algorithms have been applied to find connections between genetic characteristics (SNPs) and disease
Techniques for finding closed itemsets have proven effective for finding SNP patterns in synthetic data
Algorithms exist for finding ETIs have shown promise, but the evaluation is not complete
Computational demands of the algorithms are high
Odds Ratio and P-value are found to be the best indicator of real patterns for synthetic SNP data. They are also found to be highly correlated to other similarity measures
Project Motivation
Problem Formulation
INTRODUCTION
METHODS
RESULTS AND DISCUSSIONS
Data Set
Genetic data (SNPs)
Simulated SNP data using known models has been used for this study. Approximately, 2000 cases and 6000 control records have been generated
Real SNP data for Parkinson’s and Myeloma disease.
Conclusions
Cases
Controls
Row Margins
With Pattern a b
Nwith
Without Pattern c d
Nwithout
Column Margins
Ncases
Ncontrols
Ntotal
a, b, c, and d are the number of cases with the pattern, controls with the pattern, cases without the pattern, and controls without the pattern, respectively.
Evaluation Measures
There are many different figures of merit (FOM), i.e. functions of a, b, c, d, that can be used to characterize the table
We use odds ratio (OR), and P-value (P)
OR quantifies how different are cases and controls for a specific pattern
P quantifies the significance of the difference reflected by OR
Odds Ratio, OR = a*d / b*c
P is the probability of a table (shown above) with the same fixed margins having a higher (or same) OR
Probability distribution, p, as a function of odds ratio, OR, for Ntotal = 1000 and several sets of margins (Full range of points is shown). The margins in the legend are in the order Ncases, Ncontrols, Nwith, Nwithout
Association Analysis
Patients Genetic Information (SNPs) as Binary Matrix and disease (Yes/No) as Class Label.
Data Mining-based association analysis is applied to find patterns that capture the connections between SNPs and disease
Frequent closed itemsets capture SNP patterns where all SNPs must be present
Error-tolerant itemsets (ETIs) capture more general SNP patterns, where not all SNPs need to occur in all patients defining the pattern
Existing techniques includes statistical association analysis, Logistic Regression, Multifactor Dimensionality Reduction, CART, Random Forests, etc
Based on the disease variable, patients are categorized as cases or controls.
First, we find patterns (closed itemsets or ETIs) in cases and then check for their presence in control patients. Odds Ratio (OR) and P-value metrics (as described below) are used to evaluate the identified patterns
 = 1/4. In other words, each transaction needs to have 3/4 (75%) of the items
{i1, i2, i3, i4} and {i5, i6, i7, i8} are both ETIs with a support of 4
Figures of Merit for 2 x 2 table
Itemset
Odds Ratio
-log10(pvalue)
aa1 aa2 aa3 aa4
5.442
5.452
Aa1 aa2 aa4 Aa8
1.661
3.935
aa1 Aa2 aa3 AA5 AA6
3.002
3.770
Aa1 aa2 AA5 AA6 AA7 Aa8
3.845
3.739
aa1 aa2 AA7 Aa8
1.934
3.661
aa1 aa2 aa3 AA5
2.844
3.541
aa1 aa3 AA5 AA6
1.965
3.503
aa2 aa3 AA5 Aa7 Aa8
2.177
3.448
aa2 aa3 AA5 Aa7
1.682
3.421
aa1 aa3
2.486
3.414
Find strong patterns in cases
Evaluate strength of patterns in controls
Rank all the patterns using OR and p-value to obtain final results