As we delve deeper into the vast oceans of data generated by mass cytometry, machine learning emerges as our trusty vessel, helping us navigate these complex waters and extract meaningful insights. Let’s embark on an exploration of how machine learning is revolutionizing mass cytometry data analysis.
Supervised and Unsupervised Learning Approaches
Machine learning in cytometry broadly falls into two categories: supervised and unsupervised learning.
Supervised Learning: In supervised learning, we train models on labeled data to predict outcomes or classify new, unseen data.
- Random Forests:
- Example: Aghaeepour et al. (2017) used random forests for automated cell population identification in their paper “An immune clock of human pregnancy” (Science Immunology, 2(15), eaan2946).
- Advantages: Random forests are robust to outliers and handle high-dimensional data well. They can capture complex, non-linear relationships in the data and provide measures of feature importance.
- Drawbacks: They can be computationally intensive for very large datasets and may overfit if not properly tuned. The model’s decision-making process can also be less interpretable compared to simpler methods.
- Support Vector Machines (SVM):
- Example: Greenplate et al. (2019) used SVM, among other machine learning methods, to analyze mass cytometry data and predict immunotherapy response in cancer patients in their paper “Computational immune monitoring reveals abnormal double-negative T cells present across human tumor types” (Cancer Immunology Research, 7(1), 86-99).
- Advantages: SVMs are effective in high-dimensional spaces, versatile through the use of different kernel functions, and work well when there’s a clear margin of separation between classes.
- Drawbacks: They can be sensitive to feature scaling, may perform poorly on highly imbalanced datasets, and can be computationally intensive for large-scale problems. Additionally, the choice of kernel and parameter tuning can significantly affect performance.
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In the great debate of scientific method, I found myself as the unsupervised clustering enthusiast, the data explorer without a map. While some scientists clung to their research questions like life rafts, I was busy tossing those rafts overboard and diving headfirst into the sea of unbiased discovery. For me, true science was about letting the data speak for itself, free from the shackles of our preconceived notions: so I was ready to fight any scientist asking me "But what is your research question?". CyTOF became my trusty submarine in this vast ocean of cellular data. With unsupervised clustering as my periscope, I was ready to spot patterns that no hypothesis-driven research would ever dream of. Some called it madness. I called it love at first cluster. Because in the end, isn't the most exciting question in science simply, "I wonder what we'll find?" And with CyTOF and unsupervised clustering, the answer was always, "Something unexpected!"
