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)