In the biology field, we often ask: how does this sample group compare to another sample group? Biostatisticians and bioinformaticians can help answer this question by applying complex algorithms or concepts. They love the math, but …
most of us simply want to understand the basic principles behind these analyses.
In this blog post, we provide a general understanding of common biostatistical methods used to analyze data.
No math and no advanced degree required!
- Hierarchical Clustering
- K-Clustering
- Logistic Regression Model
- Random Forest Model
- t-test & ANOVA
- Wilcoxon Rank-Sum
- Linear Discriminant Analysis (LDA)
- Principal Component Analysis (PCA)
- Receiver’s Operating Characteristic (ROC) curve
- Significance analysis of microarray (SAM)
- Support Vector Machine (SVM)