Monkeying Around: Automatically Analyzing Malaria Infections in Rhesus Macaques
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The document outlines a framework for automatically analyzing malaria infections in rhesus macaques, focusing on the need for automation due to high-dimensional data and time efficiency. It discusses various techniques such as data normalization, curve fitting, regression modeling, and clustering to reveal relationships among clinical parameters and severity phenotypes. The ultimate goal is to create a comprehensive method that allows non-experts to draw insights from malaria data and apply the framework to future experiments.
Monkeying Around: Automatically Analyzing Malaria Infections in Rhesus Macaques
- 1. Monkeying Around: Automatically Analyzing Malaria Infections in Rhesus Macaques Lindsay Hexter
- 2. Motivation ★ Why Malaria? ○ 3.2 billion people at risk globally ★ Why begin at E04? ○ To automate analysis in Joyner 2016, foundation for other experiments ○ Compare Human vs. Machine Analysis
- 3. Motivation: Why Automation? ★ High-dimensional data ★ Precision of data analysis ★ Time: can re-apply same analysis to new data ★ Discover something new ★ Non-experts can still make conclusions about the data
- 4. Purpose ★ Automatic analysis of E04 ○ Come up with a framework to study small datasets (differently shaped) ★ Apply this framework to other experiments ○ Generalizable to other parasite strains? How and why do the results change?
- 5. Overview of E04 Experiment ★ P. cynomolgi || P. vivax ★ Rhesus macaques ⇒ humans ★ Provide data for better understanding of the infection = better treatments!
- 6. Dataset ★ 5 monkeys: 2 non-severe, 1 severe, 1 very severe, 1 lethal ★ Clinical parameters taken daily over the course of the experiment - looked at specifically blood-related parameters
- 7. Techniques: Data preprocessing / Normalization ★ Scaling, missing data? ★ Normalize data to equalize computations for distance metrics Unit vector Min-max normalization
- 8. Normalization reduced important variation ★ Used to reduce noise, but in this case, removed important malaria phase information
- 9. Techniques: Iterations on Curve-fitting ★ Two ways of thinking: ○ Guess x # of Gaussians based on x # peaks, then divide that window into x chunks for fitting (e.g. 3 peaks, so divide window evenly and fit to those 3 ranges) - original logic to code an “n_gaussians” function ○ Guess x # Gaussians based on x # peaks, then specifically divide windows based on peak ranges instead - code a fitting function over the whole interval ★ Parameter search space?
- 10. Techniques: Iterations on Curve-fitting ★ Two ways of thinking: ○ Guess x # of Gaussians based on x # peaks, then divide that window into x chunks for fitting (e.g. 3 peaks, so divide window evenly and fit to those 3 ranges) - original logic to code an “n_gaussians” function ○ Guess x # Gaussians based on x # peaks, then specifically divide windows based on peak ranges instead - code a fitting function over the whole interval ★ Two approaches: minimizing residual between fit and data vs. relying on built-in function ○ Curve-fitting based on reduction of user-defined loss function: scipy minimize, scipy leastsq, scipy fmin_slsqp ○ Curve-fitting based on built-in loss function: scipy curve_fit
- 11. Techniques: Iterations on Curve-fitting
- 12. ★ Fitting window? Peakutils, scipy peak-finding... Result: Curve fitting / peak finding My peak function - includes plateaux! Peakutils results in poor fit
- 13. Data is cleaned, scaled and normalized; mathematical representation via concatenated Gaussian functions; now onto analysis...
- 14. Analysis Roadmap: Goal + Technique Relationship among clinical parameters Regression modeling Representation of monkeys in vector space Residual matrices Automatic grouping of clinical parameters in this vector space Clustering Minimizing biological noise to increase similarity among monkeys of similar phenotype Bayesian optimization
- 15. Analyses we can find automatically Joyner et al. 2016 Automated analyses # Reticulocytes - possible indicator of lethal phenotype ✅ Anemic phenotype worsens with severity ✅ Relationship between hemoglobin and: parasitemia kinetics, mean corpuscular volume (red blood cell size) ✅ Role of thrombocytopenia (platelet deficiency) not well understood ✅ + insight? Lower parasitemia in non-severe phenotype ✅
- 16. Techniques: Regression ★ Ridge method★ Combined model: Stochastic Gradient Descent Regressor- weights for each monkey as measure of predictor significance
- 17. Results: Regression ★ Coefficients for both non-severe monkeys are much more similar, in comparison to the other monkeys ★ Suggests some ‘normal’ phenotype vs. sick = anomaly
- 18. Results: Regression ★ # Reticulocytes largest positive coefficient for lethal phenotype - possible indicator ★ MCV - red blood cell size - as another distinguishing factor? # Reticulocytes - possible indicator of lethal phenotype ✅ Anemic phenotype worsens with severity ✅
- 19. Results: Regression ★ Hgb shown to have the same relationship among two non-severe monkeys and among the other group ★ Hgb is negative as compared to positive in non-severe monkeys ★ Hgb unrelated to mcv, as found in paper Relationship between hgb and: parasitemia, mcv ✅
- 20. Results: Combined Regression ★ Possible representation of non-severe phenotype with low regression weights
- 21. Results: Phased Regression ★ Did not improve mean squared error over whole interval - peak-finding worked well for Gaussian fitting, but not finding phases automatically ★ Also because of evaluation pre-shifting, some of the phases may not have aligned (resulting in larger errors for combined models, shown in table) Target monkey Sum of MSEs of all phases RIc14 (non-severe) 34.288 RSb14 (non-severe) 29.817
- 22. Techniques: Clustering ★ Clustering, e.g. kmeans (tried Gaussian means, agglomerative hierarchical, spectral, and birch methods) ★ Evaluation via Silhouette Score b = avg dissimilarity with nearest neighboring cluster a = avg similarity within own cluster
- 23. Techniques: Residual Matrices- representing Monkeys in Vector Space ★ Construct residual matrices where each clinical parameter is characterized by the residual between two monkeys m1 vs. m2 ... m4 vs. m5 gran lymph ... wbc Monkey pairwise residuals / sign match ⇒ Clinicalparameter⇒
- 24. Techniques: Bayesian Optimization ★ Bayesian Optimization ○ Motivation: derive insights from very complex functions ○ E.g. 7^20 * 7^20 = !!!!!! ■ extremely computationally heavy ○ Optimality of guessed result based on loss function (in our case, residual between two monkeys)
- 25. RSb14 (non-severe) and RIc14 (non-severe)
- 26. Results: Residual Matrices + Shifting ★ Shifting helped elucidate the trend between non-severe monkeys Reduced residual from ~ 57 to ~ 12 Lower parasitemia in non-severe phenotype ✅
- 27. Results: Residual Matrices + Shifting ★ Shifting helped elucidate the trend between non-severe monkeys Relationship between hemoglobin and: parasitemia kinetics ✅ Pre-shifting Post-shifting
- 28. Results: Residual Matrices + Shifting ★ Monocytes - role in adaptive immune system, so important in first phase? (prognostic of long-term survival?)
- 29. Results: Clustering + Shifting Parasites / uL clustered with monocytes and reticulocytes, as previously mentioned, post-shifting (up to day 23) # Reticulocytes - possible indicator of lethal phenotype ✅
- 30. Results: Clustering + Shifting ★ Parameters clustered together over all days, k = 4: how are they related? Granulocytes, lymphocytes, monocytes, platelets, # reticulocytes, reticulocytes concentration, white blood cell total count Parasites / uL Red blood cells / volume, hemoglobin, mean corpuscular hemoglobin conc, mean corpuscular hemoglobin, red blood cell volume, mean platelet volume, total red blood cell count, red blood cell distribution width % reticulocytes (proportional to total red blood cell count) Immune response? Red blood cell-related parameters?
- 31. Results: Normalization in clustering - tradeoff Role of thrombocytopenia (platelet deficiency) not well understood ✅ + insight?
- 32. Can we apply this methodology to NEW experiments?
- 33. E03: P. coatneyi Hackeri (i.e. different parasite) ★ Different representation in vector space ★ Reticulocytes even further from the other clinical data (more significant in this parasite?) ★ More clusters = white blood cells / red blood cells again separated
- 34. E23: Iterative P. cynomolgi, new monkeys ★ Similar representation in vector space as E04 ★ Thus - a way to characterize the malaria parasite?
- 35. Conclusions & Contributions & Future Work! ★ Comprehensive framework to analyze malaria experiments - automatically characterizing relationships among monkeys (severity phenotype?), among clinical parameters (which are similarly important?), and malaria parasite ○ Such that non-experts can analyze data ○ Applicable to other experiments ○ Reduce TIME spent studying these results and provide more precise analysis ★ Future ○ Expand existing framework with new methods ○ Application of FULL framework to other experiments - build a more generalized model ○ More in-depth consideration of biological profiles ○ More comprehensive data storage to help automate entire process from start to finish (especially Bayesian optimization) ★ I’ve learned a lot!
- 36. Thank you ... ★ Dr. Galinski and co. for running the experiments! ★ Thesis committee members - Dr. Eisen, Dr. Fossati, Dr. Prinz ★ My friends for being here!! ★ Dr. Choi for guiding me through the process and pushing me throughout my CS career!
- 37. Questions?
- 38. E23 combined model weights? Monkey Coefficient RBg14 0.456473 ROh14 0.431664 RAd14 0.085915 RJn13 0.080195 ROc14 0.010533 RIb13 0.044158